diff --git a/Paris_tree_analysis.ipynb b/Paris_tree_analysis.ipynb deleted file mode 100644 index 173c88b..0000000 --- a/Paris_tree_analysis.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells":[{"cell_type":"markdown","id":"307bea88-e08c-4787-9d3a-047272601ef1","metadata":{"id":"307bea88-e08c-4787-9d3a-047272601ef1"},"source":["# Exploring and Analyzing Paris trees data"]},{"cell_type":"markdown","id":"39449094-4efa-4627-b19c-6aa521c7a7e8","metadata":{"id":"39449094-4efa-4627-b19c-6aa521c7a7e8"},"source":["Taken from https://s3-eu-west-1.amazonaws.com/static.oc-static.com/prod/courses/files/AI+Engineer/Project+2+Participez+%C3%A0+un+concours+sur+la+Smart+City/p2-arbres-fr.csv, coming from https://opendata.paris.fr/explore/dataset/les-arbres/map/?dataChart=eyJxdWVyaWVzIjpbeyJjb25maWciOnsiZGF0YXNldCI6Imxlcy1hcmJyZXMiLCJvcHRpb25zIjp7fX0sImNoYXJ0cyI6W3siYWxpZ25Nb250aCI6dHJ1ZSwidHlwZSI6ImNvbHVtbiIsImZ1bmMiOiJBVkciLCJ5QXhpcyI6ImlkYmFzZSIsInNjaWVudGlmaWNEaXNwbGF5Ijp0cnVlLCJjb2xvciI6IiMwMDMzNjYifV0sInhBeGlzIjoidHlwZWVtcGxhY2VtZW50IiwibWF4cG9pbnRzIjo1MCwic29ydCI6IiJ9XSwidGltZXNjYWxlIjoiIiwiZGlzcGxheUxlZ2VuZCI6dHJ1ZSwiYWxpZ25Nb250aCI6dHJ1ZX0%3D&disjunctive.typeemplacement&disjunctive.arrondissement&disjunctive.libellefrancais&disjunctive.genre&disjunctive.espece&disjunctive.varieteoucultivar&disjunctive.stadedeveloppement&disjunctive.remarquable&location=12,48.85984,2.31341&basemap=jawg.streets"]},{"cell_type":"markdown","id":"ceeb296c-0ca5-43d6-b5fb-a4620de1fc75","metadata":{"id":"ceeb296c-0ca5-43d6-b5fb-a4620de1fc75"},"source":["## 1) Opening csv file"]},{"cell_type":"code","execution_count":1,"id":"rA8K5-0Cmf33","metadata":{"id":"rA8K5-0Cmf33","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1737062546472,"user_tz":-60,"elapsed":2641,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"b849ecc7-fe2f-4c96-cd73-fcd37b003220"},"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n","Working directory: /content/drive/MyDrive/Colab_Notebooks/Project_2\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')\n","import os\n","os.listdir()\n","os.chdir(\"/content/drive/MyDrive/Colab_Notebooks/Project_2\")\n","print(f'Working directory: {os.getcwd()}')"]},{"cell_type":"code","execution_count":2,"id":"b4458a0a-853f-4a87-bb46-93ac6a9ba08b","metadata":{"id":"b4458a0a-853f-4a87-bb46-93ac6a9ba08b","executionInfo":{"status":"ok","timestamp":1737062547662,"user_tz":-60,"elapsed":1192,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["import pandas as pd\n","pd.set_option('display.float_format', '{:.2f}'.format) # don't want scientific notation. Floats are enough\n","\n","csv_path = \"./p2-trees-fr.csv\"\n","df = pd.read_csv(csv_path, sep=';')"]},{"cell_type":"markdown","id":"9de7fc9e-a309-46ac-a4f8-74effb1ca249","metadata":{"id":"9de7fc9e-a309-46ac-a4f8-74effb1ca249"},"source":["## 2) Naive Analysis"]},{"cell_type":"markdown","id":"d555a028-62b6-47ae-841b-13f45533ad3c","metadata":{"id":"d555a028-62b6-47ae-841b-13f45533ad3c"},"source":["### Use pandas basic methods"]},{"cell_type":"code","execution_count":3,"id":"5f0dc948-1c0f-4273-b8d5-7b9d0fc80f23","metadata":{"id":"5f0dc948-1c0f-4273-b8d5-7b9d0fc80f23","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1737062548670,"user_tz":-60,"elapsed":1013,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"49cc84d0-f342-4904-b7fd-6fd0ed44f038"},"outputs":[{"output_type":"stream","name":"stdout","text":["Size in MB:\n"]},{"output_type":"execute_result","data":{"text/plain":["145.955268"]},"metadata":{},"execution_count":3}],"source":["import sys\n","print('Size in MB:')\n","sys.getsizeof(df)/(10**6)"]},{"cell_type":"code","execution_count":4,"id":"a302ef86-7553-4b38-8734-b0ae5896c584","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a302ef86-7553-4b38-8734-b0ae5896c584","scrolled":true,"executionInfo":{"status":"ok","timestamp":1737062548671,"user_tz":-60,"elapsed":8,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"c072acc6-50e3-42ac-d905-a1369a0a6be6"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","RangeIndex: 200137 entries, 0 to 200136\n","Data columns (total 18 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 id 200137 non-null int64 \n"," 1 type_emplacement 200137 non-null object \n"," 2 domanialite 200136 non-null object \n"," 3 arrondissement 200137 non-null object \n"," 4 complement_addresse 30902 non-null object \n"," 5 numero 0 non-null float64\n"," 6 lieu 200137 non-null object \n"," 7 id_emplacement 200137 non-null object \n"," 8 libelle_francais 198640 non-null object \n"," 9 genre 200121 non-null object \n"," 10 espece 198385 non-null object \n"," 11 variete 36777 non-null object \n"," 12 circonference_cm 200137 non-null int64 \n"," 13 hauteur_m 200137 non-null int64 \n"," 14 stade_developpement 132932 non-null object \n"," 15 remarquable 137039 non-null float64\n"," 16 geo_point_2d_a 200137 non-null float64\n"," 17 geo_point_2d_b 200137 non-null float64\n","dtypes: float64(4), int64(3), object(11)\n","memory usage: 27.5+ MB\n"]}],"source":["df.info() # display count of columns, lines and columns names"]},{"cell_type":"code","execution_count":5,"id":"f24becfa-7e0f-45a0-82b9-40e211922f5c","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":270},"id":"f24becfa-7e0f-45a0-82b9-40e211922f5c","scrolled":true,"executionInfo":{"status":"ok","timestamp":1737062548671,"user_tz":-60,"elapsed":6,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"ab0be1d5-2314-4f87-d4bf-a44da82aab5d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" id type_emplacement domanialite arrondissement complement_addresse \\\n","0 99874 Arbre Jardin PARIS 7E ARRDT NaN \n","1 99875 Arbre Jardin PARIS 7E ARRDT NaN \n","\n"," numero lieu id_emplacement \\\n","0 NaN MAIRIE DU 7E 116 RUE DE GRENELLE PARIS 7E 19 \n","1 NaN MAIRIE DU 7E 116 RUE DE GRENELLE PARIS 7E 20 \n","\n"," libelle_francais genre espece variete circonference_cm \\\n","0 Marronnier Aesculus hippocastanum NaN 20 \n","1 If Taxus baccata NaN 65 \n","\n"," hauteur_m stade_developpement remarquable geo_point_2d_a geo_point_2d_b \n","0 5 NaN 0.00 48.86 2.32 \n","1 8 A NaN 48.86 2.32 "],"text/html":["\n","
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idtype_emplacementdomanialitearrondissementcomplement_addressenumerolieuid_emplacementlibelle_francaisgenreespecevarietecirconference_cmhauteur_mstade_developpementremarquablegeo_point_2d_ageo_point_2d_b
099874ArbreJardinPARIS 7E ARRDTNaNNaNMAIRIE DU 7E 116 RUE DE GRENELLE PARIS 7E19MarronnierAesculushippocastanumNaN205NaN0.0048.862.32
199875ArbreJardinPARIS 7E ARRDTNaNNaNMAIRIE DU 7E 116 RUE DE GRENELLE PARIS 7E20IfTaxusbaccataNaN658ANaN48.862.32
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"df"}},"metadata":{},"execution_count":5}],"source":["df.head(2) # display 2 first lines"]},{"cell_type":"code","execution_count":6,"id":"5543e915-5701-43ed-81dc-c1dd54715e95","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"id":"5543e915-5701-43ed-81dc-c1dd54715e95","scrolled":true,"executionInfo":{"status":"ok","timestamp":1737062549223,"user_tz":-60,"elapsed":556,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"d6ad12a6-0590-4b03-c28e-f355a87f86dd"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" id numero circonference_cm hauteur_m remarquable \\\n","count 200137.00 0.00 200137.00 200137.00 137039.00 \n","mean 387202.68 NaN 83.38 13.11 0.00 \n","std 545603.24 NaN 673.19 1971.22 0.04 \n","min 99874.00 NaN 0.00 0.00 0.00 \n","25% 155927.00 NaN 30.00 5.00 0.00 \n","50% 221078.00 NaN 70.00 8.00 0.00 \n","75% 274102.00 NaN 115.00 12.00 0.00 \n","max 2024745.00 NaN 250255.00 881818.00 1.00 \n","\n"," geo_point_2d_a geo_point_2d_b \n","count 200137.00 200137.00 \n","mean 48.85 2.35 \n","std 0.03 0.05 \n","min 48.74 2.21 \n","25% 48.84 2.31 \n","50% 48.85 2.35 \n","75% 48.88 2.39 \n","max 48.91 2.47 "],"text/html":["\n","
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idnumerocirconference_cmhauteur_mremarquablegeo_point_2d_ageo_point_2d_b
count200137.000.00200137.00200137.00137039.00200137.00200137.00
mean387202.68NaN83.3813.110.0048.852.35
std545603.24NaN673.191971.220.040.030.05
min99874.00NaN0.000.000.0048.742.21
25%155927.00NaN30.005.000.0048.842.31
50%221078.00NaN70.008.000.0048.852.35
75%274102.00NaN115.0012.000.0048.882.39
max2024745.00NaN250255.00881818.001.0048.912.47
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","summary":"{\n \"name\": \"## > 250000 cm as circumference, > 800000 m as height\",\n \"rows\": 8,\n \"fields\": [\n {\n \"column\": \"id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 636490.3531323484,\n \"min\": 99874.0,\n \"max\": 2024745.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 387202.68230761925,\n 221078.0,\n 200137.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"numero\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 0.0,\n \"max\": 0.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"circonference_cm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 105028.38312832116,\n \"min\": 0.0,\n \"max\": 250255.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 83.38047937163043\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hauteur_m\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 309559.4466281352,\n \"min\": 0.0,\n \"max\": 881818.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 13.110509301128728\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"remarquable\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 48450.55066982106,\n \"min\": 0.0,\n \"max\": 137039.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 0.0013426834696692183\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"geo_point_2d_a\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 70744.31300132743,\n \"min\": 0.0302341454886933,\n \"max\": 200137.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 48.85449078901268\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"geo_point_2d_b\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 70758.40152298885,\n \"min\": 0.05121963963079915,\n \"max\": 200137.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 2.3482078955462966\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":6}],"source":["df.describe()# general statistics only on number types variables (int and floats)\n","\n","# problems:\n","## 0 as min value for circumference, height\n","## > 250000 cm as circumference, > 800000 m as height"]},{"cell_type":"code","execution_count":86,"id":"5c2225ff-7a7c-421c-9bde-1dc02e2e3742","metadata":{"id":"5c2225ff-7a7c-421c-9bde-1dc02e2e3742","executionInfo":{"status":"ok","timestamp":1737062771291,"user_tz":-60,"elapsed":423,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["from data_analysis_functions import plot_univariate_variable_analysis, plot_bivariate_variable_analysis, compute_bivariate_stats_indicators"]},{"cell_type":"code","source":["type(df[df['espece'].notna()]['espece'].values[0]) == str"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"la1Q1bZ0-B-L","executionInfo":{"status":"ok","timestamp":1737062552620,"user_tz":-60,"elapsed":5,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"c19a56cf-9388-4090-be52-b8e06ead8f84"},"id":"la1Q1bZ0-B-L","execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{},"execution_count":9}]},{"cell_type":"code","execution_count":10,"id":"f5aade17-79f8-4dfa-bd8f-547727c207cb","metadata":{"id":"f5aade17-79f8-4dfa-bd8f-547727c207cb","executionInfo":{"status":"ok","timestamp":1737062552620,"user_tz":-60,"elapsed":3,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["quantitative_variables = ['circonference_cm','hauteur_m']"]},{"cell_type":"markdown","id":"aec48b3b-3131-48b0-96a6-9d3a962162f1","metadata":{"id":"aec48b3b-3131-48b0-96a6-9d3a962162f1"},"source":["### Looking for data anomalies"]},{"cell_type":"markdown","id":"bd284b56-394b-4677-b271-91f374e04b01","metadata":{"id":"bd284b56-394b-4677-b271-91f374e04b01"},"source":["#### Outliers"]},{"cell_type":"code","execution_count":11,"id":"c0b79f60-4033-43ad-a8e2-7ba6022ea6f4","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":849},"id":"c0b79f60-4033-43ad-a8e2-7ba6022ea6f4","executionInfo":{"status":"ok","timestamp":1737062555058,"user_tz":-60,"elapsed":2441,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"be8a95c4-c24c-4ae5-f2c2-50b608f8eab7"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["
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Boxplots in order to briefly find outliers\n","for variable in quantitative_variables:\n"," plot_univariate_variable_analysis(df, variable, 'boxplot')"]},{"cell_type":"markdown","id":"96db7b30-d700-47f0-950c-012817006a2b","metadata":{"id":"96db7b30-d700-47f0-950c-012817006a2b"},"source":["It seems that there are many outliers on data"]},{"cell_type":"markdown","id":"580de3b3-db0e-44cd-b271-2c619dc8dee4","metadata":{"id":"580de3b3-db0e-44cd-b271-2c619dc8dee4"},"source":["#### NaN"]},{"cell_type":"code","execution_count":12,"id":"cf0c8910-8bf2-4e28-a919-efacd17a0a2a","metadata":{"id":"cf0c8910-8bf2-4e28-a919-efacd17a0a2a","executionInfo":{"status":"ok","timestamp":1737062555059,"user_tz":-60,"elapsed":3,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["import missingno as msno\n","import numpy as np"]},{"cell_type":"code","execution_count":13,"id":"f9ba7bc9-8b7b-4203-8e59-6fbd7d0bc5cd","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"f9ba7bc9-8b7b-4203-8e59-6fbd7d0bc5cd","executionInfo":{"status":"ok","timestamp":1737062556368,"user_tz":-60,"elapsed":1313,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"9a36f968-1cae-48fa-e6e4-288cf963cecf"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 0, 0, 1, 0, 169235, 200137, 0, 0,\n"," 1497, 16, 1752, 163360, 0, 0, 67205, 63098,\n"," 0, 0])"]},"metadata":{},"execution_count":13}],"source":["# number of 'nan' for each column\n","column_nb_of_nans = np.array([], dtype='int')\n","for column in df.columns:\n"," where_nan = df[df[column].isna()]\n"," column_nb_of_nans = np.concatenate((column_nb_of_nans,[len(where_nan)]))\n","column_nb_of_nans"]},{"cell_type":"code","execution_count":14,"id":"4c0424ef-ae93-4b94-847c-4c8341051aa6","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":107},"id":"4c0424ef-ae93-4b94-847c-4c8341051aa6","scrolled":true,"executionInfo":{"status":"ok","timestamp":1737062556798,"user_tz":-60,"elapsed":434,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"36154d15-5ffd-48e2-f046-632428de3cef"},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of nan for each variable that contains minimum 1 occurrence:\n"]},{"output_type":"execute_result","data":{"text/plain":[" domanialite complement_addresse numero libelle_francais genre espece \\\n","0 1 169235 200137 1497 16 1752 \n","\n"," variete stade_developpement remarquable \n","0 163360 67205 63098 "],"text/html":["\n","
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"columns_with_nans_df","summary":"{\n \"name\": \"columns_with_nans_df\",\n \"rows\": 1,\n \"fields\": [\n {\n \"column\": \"domanialite\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 1,\n \"max\": 1,\n \"num_unique_values\": 1,\n \"samples\": [\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"complement_addresse\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 169235,\n \"max\": 169235,\n \"num_unique_values\": 1,\n \"samples\": [\n 169235\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"numero\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 200137,\n \"max\": 200137,\n \"num_unique_values\": 1,\n \"samples\": [\n 200137\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"libelle_francais\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 1497,\n \"max\": 1497,\n \"num_unique_values\": 1,\n \"samples\": [\n 1497\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"genre\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 16,\n \"max\": 16,\n \"num_unique_values\": 1,\n \"samples\": [\n 16\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"espece\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 1752,\n \"max\": 1752,\n \"num_unique_values\": 1,\n \"samples\": [\n 1752\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"variete\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 163360,\n \"max\": 163360,\n \"num_unique_values\": 1,\n \"samples\": [\n 163360\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"stade_developpement\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 67205,\n \"max\": 67205,\n \"num_unique_values\": 1,\n \"samples\": [\n 67205\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"remarquable\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 63098,\n \"max\": 63098,\n \"num_unique_values\": 1,\n \"samples\": [\n 63098\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":14}],"source":["# dataframe with number of nans per column only when one nan or more\n","where_nans = np.where(column_nb_of_nans>0)\n","columns_with_nans_df = pd.DataFrame([column_nb_of_nans[where_nans]], columns = df.columns[where_nans])\n","print('Number of nan for each variable that contains minimum 1 occurrence:')\n","columns_with_nans_df"]},{"cell_type":"code","execution_count":15,"id":"6d1dbd66-cc76-4724-9a23-eeefb13bf725","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":610},"id":"6d1dbd66-cc76-4724-9a23-eeefb13bf725","executionInfo":{"status":"ok","timestamp":1737062561949,"user_tz":-60,"elapsed":5152,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"e3b9d24a-db1c-48b4-a9a3-ae1b62fbba54"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":15},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# stade_developpement and remarquable are missing around 30% values. Variete is missing 80 percent values\n","msno.bar(df)"]},{"cell_type":"code","execution_count":16,"id":"ef1eb4db-5bfb-49a2-a4f9-cdc5249ad271","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":796},"id":"ef1eb4db-5bfb-49a2-a4f9-cdc5249ad271","executionInfo":{"status":"ok","timestamp":1737062564994,"user_tz":-60,"elapsed":3047,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"3ac44711-3e87-4027-e8c1-f08dc7bfbaa2"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":16},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# missing values correlation between stade_developpement and remarquable. Look at which trees they are\n","msno.heatmap(df)"]},{"cell_type":"markdown","id":"e173bf2d-060c-4610-bbf1-ef35f4a20552","metadata":{"id":"e173bf2d-060c-4610-bbf1-ef35f4a20552"},"source":["## 3) Cleaning anomalies"]},{"cell_type":"code","execution_count":17,"id":"57fe3e6a-3828-4f45-bede-a3422cff30c3","metadata":{"id":"57fe3e6a-3828-4f45-bede-a3422cff30c3","executionInfo":{"status":"ok","timestamp":1737062564994,"user_tz":-60,"elapsed":15,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["df_refined = df.copy()"]},{"cell_type":"markdown","id":"c5535b2a-fd5e-4b16-a1d3-bdc974e55dd0","metadata":{"id":"c5535b2a-fd5e-4b16-a1d3-bdc974e55dd0"},"source":["### Outliers (min and max)"]},{"cell_type":"markdown","id":"76dfdc46-4b19-4f63-a854-82f07a168921","metadata":{"id":"76dfdc46-4b19-4f63-a854-82f07a168921"},"source":["##### Max"]},{"cell_type":"markdown","id":"eeee8c4a-af66-4be0-9637-2054e67e7af5","metadata":{"id":"eeee8c4a-af66-4be0-9637-2054e67e7af5"},"source":["Height :"]},{"cell_type":"code","execution_count":18,"id":"ac8fcbb6-dafa-4f63-9caa-36f99a3a9297","metadata":{"id":"ac8fcbb6-dafa-4f63-9caa-36f99a3a9297","executionInfo":{"status":"ok","timestamp":1737062564994,"user_tz":-60,"elapsed":15,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# based on the tallest tree height : https://en.wikipedia.org/wiki/Hyperion_(tree)\n","# max_height = 116 # in meters\n","# tallest tree in Paris is supposed to be this one (40 m), so the others heights are wrong : https://france3-regions.francetvinfo.fr/paris-ile-de-france/paris/mais-quel-est-le-plus-grand-arbre-de-paris-2982575.html\n","max_height = 45 # in meters"]},{"cell_type":"code","execution_count":19,"id":"c3115746-c299-495f-8a13-b1e3e5ebc6cd","metadata":{"id":"c3115746-c299-495f-8a13-b1e3e5ebc6cd","executionInfo":{"status":"ok","timestamp":1737062564994,"user_tz":-60,"elapsed":14,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["max_height_outlier_df = df_refined[(df_refined['hauteur_m'] >= max_height)]"]},{"cell_type":"code","execution_count":20,"id":"ba6ca618-9335-4f54-8215-1545e821e3da","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":895},"id":"ba6ca618-9335-4f54-8215-1545e821e3da","scrolled":true,"executionInfo":{"status":"ok","timestamp":1737062564994,"user_tz":-60,"elapsed":14,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"c3b39980-eb60-4b6a-c95d-2856c063c926"},"outputs":[{"output_type":"stream","name":"stdout","text":["height outliers trees (479 occurrencies):\n"]},{"output_type":"execute_result","data":{"text/plain":[" id type_emplacement domanialite arrondissement \\\n","4534 105885 Arbre Jardin PARIS 13E ARRDT \n","4537 105888 Arbre Jardin PARIS 13E ARRDT \n","5392 106800 Arbre Jardin PARIS 7E ARRDT \n","5469 106880 Arbre Jardin PARIS 7E ARRDT \n","5599 107029 Arbre Jardin PARIS 7E ARRDT \n","... ... ... ... ... \n","194812 2018093 Arbre Alignement PARIS 18E ARRDT \n","197026 2020675 Arbre Jardin PARIS 18E ARRDT \n","197060 2020711 Arbre Alignement PARIS 19E ARRDT \n","197136 2020792 Arbre Jardin PARIS 14E ARRDT \n","197146 2020802 Arbre Jardin PARIS 14E ARRDT \n","\n"," complement_addresse numero \\\n","4534 NaN NaN \n","4537 NaN NaN \n","5392 NaN NaN \n","5469 NaN NaN \n","5599 NaN NaN \n","... ... ... \n","194812 95 NaN \n","197026 NaN NaN \n","197060 NaN NaN \n","197136 NaN NaN \n","197146 NaN NaN \n","\n"," lieu id_emplacement \\\n","4534 SQUARE DE CHOISY 277 \n","4537 SQUARE DE CHOISY 280 \n","5392 JARDIN DU CHAMP DE MARS A0120683 \n","5469 JARDIN DU CHAMP DE MARS P0070763 \n","5599 JARDIN DU CHAMP DE MARS A0041225 \n","... ... ... \n","194812 RUE DE LA CHAPELLE 1501013 \n","197026 JARDINIERES T3 BOULEVARD NEY CHAPELLE / 2 AVEN... 101016 \n","197060 QUAI DE LA CHARENTE 201004 \n","197136 SQUARE AUGUSTE RENOIR / 205 RUE RAYMOND LOSSERAND 103005 \n","197146 SQUARE AUGUSTE RENOIR / 205 RUE RAYMOND LOSSERAND 104004 \n","\n"," libelle_francais genre espece variete circonference_cm \\\n","4534 Fevier Gleditsia triacanthos NaN 45 \n","4537 Tulipier Liriodendron tulipifera NaN 45 \n","5392 Tilleul Tilia n. sp. NaN 100 \n","5469 If Taxus baccata NaN 61 \n","5599 Platane Platanus x hispanica NaN 41 \n","... ... ... ... ... ... \n","194812 Platane Platanus x hispanica NaN 35 \n","197026 Pin Pinus nigra NaN 50 \n","197060 Aulne Alnus cordata NaN 30 \n","197136 Erable Acer platanoides NaN 145 \n","197146 Erable Acer platanoides NaN 150 \n","\n"," hauteur_m stade_developpement remarquable geo_point_2d_a \\\n","4534 45 NaN NaN 48.83 \n","4537 45 NaN NaN 48.83 \n","5392 1510 A 0.00 48.86 \n","5469 65 A 0.00 48.85 \n","5599 68 JA 0.00 48.86 \n","... ... ... ... ... \n","194812 65 J 0.00 48.90 \n","197026 65 J 0.00 48.90 \n","197060 55 JA 0.00 48.90 \n","197136 125 A 0.00 48.83 \n","197146 105 A 0.00 48.83 \n","\n"," geo_point_2d_b \n","4534 2.36 \n","4537 2.36 \n","5392 2.30 \n","5469 2.30 \n","5599 2.30 \n","... ... \n","194812 2.36 \n","197026 2.36 \n","197060 2.38 \n","197136 2.31 \n","197146 2.31 \n","\n","[479 rows x 18 columns]"],"text/html":["\n","
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idtype_emplacementdomanialitearrondissementcomplement_addressenumerolieuid_emplacementlibelle_francaisgenreespecevarietecirconference_cmhauteur_mstade_developpementremarquablegeo_point_2d_ageo_point_2d_b
4534105885ArbreJardinPARIS 13E ARRDTNaNNaNSQUARE DE CHOISY277FevierGleditsiatriacanthosNaN4545NaNNaN48.832.36
4537105888ArbreJardinPARIS 13E ARRDTNaNNaNSQUARE DE CHOISY280TulipierLiriodendrontulipiferaNaN4545NaNNaN48.832.36
5392106800ArbreJardinPARIS 7E ARRDTNaNNaNJARDIN DU CHAMP DE MARSA0120683TilleulTilian. sp.NaN1001510A0.0048.862.30
5469106880ArbreJardinPARIS 7E ARRDTNaNNaNJARDIN DU CHAMP DE MARSP0070763IfTaxusbaccataNaN6165A0.0048.852.30
5599107029ArbreJardinPARIS 7E ARRDTNaNNaNJARDIN DU CHAMP DE MARSA0041225PlatanePlatanusx hispanicaNaN4168JA0.0048.862.30
.........................................................
1948122018093ArbreAlignementPARIS 18E ARRDT95NaNRUE DE LA CHAPELLE1501013PlatanePlatanusx hispanicaNaN3565J0.0048.902.36
1970262020675ArbreJardinPARIS 18E ARRDTNaNNaNJARDINIERES T3 BOULEVARD NEY CHAPELLE / 2 AVEN...101016PinPinusnigraNaN5065J0.0048.902.36
1970602020711ArbreAlignementPARIS 19E ARRDTNaNNaNQUAI DE LA CHARENTE201004AulneAlnuscordataNaN3055JA0.0048.902.38
1971362020792ArbreJardinPARIS 14E ARRDTNaNNaNSQUARE AUGUSTE RENOIR / 205 RUE RAYMOND LOSSERAND103005ErableAcerplatanoidesNaN145125A0.0048.832.31
1971462020802ArbreJardinPARIS 14E ARRDTNaNNaNSQUARE AUGUSTE RENOIR / 205 RUE RAYMOND LOSSERAND104004ErableAcerplatanoidesNaN150105A0.0048.832.31
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479 rows × 18 columns

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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"max_height_outlier_df","summary":"{\n \"name\": \"max_height_outlier_df\",\n \"rows\": 479,\n \"fields\": [\n {\n \"column\": \"id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 887737,\n \"min\": 105885,\n \"max\": 2020802,\n \"num_unique_values\": 479,\n \"samples\": [\n 226659,\n 2008767,\n 202895\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"type_emplacement\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Arbre\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"domanialite\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"Jardin\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"arrondissement\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 22,\n \"samples\": [\n \"PARIS 13E ARRDT\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"complement_addresse\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 56,\n \"samples\": [\n \"11 \\u00e0 27\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"numero\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": null,\n \"max\": null,\n \"num_unique_values\": 0,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"lieu\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 177,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"id_emplacement\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 440,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"libelle_francais\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 39,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"genre\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 36,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"espece\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 57,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"variete\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 19,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"circonference_cm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 428,\n \"min\": 0,\n \"max\": 7540,\n \"num_unique_values\": 80,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hauteur_m\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 40285,\n \"min\": 45,\n \"max\": 881818,\n \"num_unique_values\": 102,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"stade_developpement\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"remarquable\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0654416970922002,\n \"min\": 0.0,\n \"max\": 1.0,\n \"num_unique_values\": 2,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"geo_point_2d_a\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.025974849186796975,\n \"min\": 48.810646078,\n \"max\": 48.9089446402,\n \"num_unique_values\": 479,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"geo_point_2d_b\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.035886893415735685,\n \"min\": 2.2528752018,\n \"max\": 2.4613206192,\n \"num_unique_values\": 479,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":20}],"source":["print('height outliers trees ('+ str(len(max_height_outlier_df)) + ' occurrencies):')\n","max_height_outlier_df\n"]},{"cell_type":"code","execution_count":21,"id":"bf489b0f-45f5-4879-8c56-b69f6b79f89e","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":489},"id":"bf489b0f-45f5-4879-8c56-b69f6b79f89e","scrolled":true,"executionInfo":{"status":"ok","timestamp":1737062564994,"user_tz":-60,"elapsed":11,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"88793feb-60c1-4363-f936-cf7a43dae03a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["hauteur_m\n","45 19\n","46 1\n","47 1\n","50 13\n","52 1\n"," ..\n","1815 1\n","2015 1\n","2525 1\n","5155 1\n","881818 1\n","Name: count, Length: 102, dtype: int64"],"text/html":["
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count
hauteur_m
4519
461
471
5013
521
......
18151
20151
25251
51551
8818181
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102 rows × 1 columns

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"]},"metadata":{},"execution_count":21}],"source":["max_height_outlier_df.value_counts('hauteur_m').sort_index(ascending=True)"]},{"cell_type":"code","execution_count":22,"id":"c788b243-2521-4f58-8d1f-3463ec0bf45c","metadata":{"id":"c788b243-2521-4f58-8d1f-3463ec0bf45c","scrolled":true,"executionInfo":{"status":"ok","timestamp":1737062564994,"user_tz":-60,"elapsed":9,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# remove outlier value\n","df_refined.loc[df_refined['hauteur_m'] == 881818, 'hauteur_m'] = np.nan"]},{"cell_type":"code","source":["for variable in quantitative_variables:\n"," plot_univariate_variable_analysis(df_refined, variable, 'boxplot')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":849},"id":"K9ZrNsYvsDxk","executionInfo":{"status":"ok","timestamp":1737062566942,"user_tz":-60,"elapsed":1956,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"408da53e-c277-4dff-f93a-e068b303f6b3"},"id":"K9ZrNsYvsDxk","execution_count":23,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","execution_count":24,"id":"76b470a5-7542-44a0-b262-fd9e325b4a43","metadata":{"id":"76b470a5-7542-44a0-b262-fd9e325b4a43","executionInfo":{"status":"ok","timestamp":1737062566942,"user_tz":-60,"elapsed":67,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# need in order to divide by 100 the too high heights\n","df_refined['hauteur_m'] = df_refined['hauteur_m'].astype(float)"]},{"cell_type":"code","execution_count":25,"id":"e0cffc30-00b3-439c-9501-9e94b1f57e0c","metadata":{"id":"e0cffc30-00b3-439c-9501-9e94b1f57e0c","executionInfo":{"status":"ok","timestamp":1737062566943,"user_tz":-60,"elapsed":66,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# Too high remaining heights may have been measured in cms\n","df_refined.loc[df_refined['hauteur_m'] >= max_height, 'hauteur_m'] = df_refined.loc[df_refined['hauteur_m'] >= max_height, 'hauteur_m']/100"]},{"cell_type":"code","execution_count":26,"id":"TVbNwJ0DF9zQ","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TVbNwJ0DF9zQ","executionInfo":{"status":"ok","timestamp":1737062566943,"user_tz":-60,"elapsed":65,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"af8b9f10-e138-4a17-9d4b-f16e17bcd042"},"outputs":[{"output_type":"stream","name":"stdout","text":["Amount of to high heights : 479.\n","1 value has been removed (set to 'NaN').\n","478 values may have been measured in cms --> converted in meters.\n"]}],"source":["print(f\"Amount of to high heights : {len(max_height_outlier_df)}.\\n1 value has been removed (set to 'NaN').\\n{len(max_height_outlier_df)-1} values may have been measured in cms --> converted in meters.\")"]},{"cell_type":"markdown","id":"1e751c26-fc8f-41a9-bd27-2d1f899f4866","metadata":{"id":"1e751c26-fc8f-41a9-bd27-2d1f899f4866"},"source":["Girth :"]},{"cell_type":"code","execution_count":27,"id":"0b2af1bd-9fc8-405f-b810-433044a0ea7a","metadata":{"id":"0b2af1bd-9fc8-405f-b810-433044a0ea7a","executionInfo":{"status":"ok","timestamp":1737062566943,"user_tz":-60,"elapsed":63,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# based on the biggest tree girth (circumference) : https://www.guinnessworldrecords.com/world-records/living-tree-with-greatest-girth\n","max_girth = 36 # in meters"]},{"cell_type":"code","execution_count":28,"id":"dceb4f45-c91d-4234-a5d1-c87d1fcc6403","metadata":{"id":"dceb4f45-c91d-4234-a5d1-c87d1fcc6403","executionInfo":{"status":"ok","timestamp":1737062566943,"user_tz":-60,"elapsed":63,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["max_girth_outlier_df = df_refined[(df_refined['circonference_cm']/100 > max_girth)]"]},{"cell_type":"code","execution_count":29,"id":"a97d6d26-f82a-463b-ad00-69d4676beb7e","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"collapsed":true,"id":"a97d6d26-f82a-463b-ad00-69d4676beb7e","scrolled":true,"executionInfo":{"status":"ok","timestamp":1737062566943,"user_tz":-60,"elapsed":62,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"8aedd841-8816-45e4-a550-e7130a0e7a67"},"outputs":[{"output_type":"stream","name":"stdout","text":["girth outliers trees (20 occurrencies):\n"]},{"output_type":"execute_result","data":{"text/plain":[" id type_emplacement domanialite arrondissement \\\n","71787 190080 Arbre DASCO PARIS 20E ARRDT \n","82951 203295 Arbre Alignement PARIS 18E ARRDT \n","90605 211362 Arbre Alignement PARIS 5E ARRDT \n","91256 212024 Arbre Alignement PARIS 12E ARRDT \n","97354 218274 Arbre Alignement PARIS 7E ARRDT \n","103437 224609 Arbre Alignement PARIS 18E ARRDT \n","133546 256868 Arbre Alignement PARIS 19E ARRDT \n","147235 270997 Arbre Alignement PARIS 7E ARRDT \n","147634 271402 Arbre Alignement PARIS 7E ARRDT \n","148106 271884 Arbre Alignement PARIS 7E ARRDT \n","148481 272263 Arbre Alignement PARIS 7E ARRDT \n","149056 272885 Arbre Alignement PARIS 18E ARRDT \n","151364 275451 Arbre Alignement PARIS 7E ARRDT \n","152303 276396 Arbre Alignement PARIS 7E ARRDT \n","160432 284823 Arbre Alignement PARIS 18E ARRDT \n","163918 288407 Arbre Alignement PARIS 12E ARRDT \n","164616 289121 Arbre Alignement PARIS 12E ARRDT \n","167606 292180 Arbre Alignement PARIS 18E ARRDT \n","170367 295047 Arbre Alignement PARIS 5E ARRDT \n","173343 298211 Arbre Alignement PARIS 5E ARRDT \n","\n"," complement_addresse numero lieu \\\n","71787 NaN NaN PLAINE (9) ELEM B \n","82951 NaN NaN RUE D AUBERVILLIERS \n","90605 34 NaN BOULEVARD SAINT MARCEL \n","91256 NaN NaN RUE DE CHALON \n","97354 27 NaN AVENUE RAPP \n","103437 NaN NaN BOULEVARD BARBES \n","133546 NaN NaN AVENUE JEAN JAURES \n","147235 NaN NaN QUAI BRANLY \n","147634 NaN NaN AVENUE BOSQUET \n","148106 NaN NaN AVENUE BOSQUET \n","148481 14 NaN BOULEVARD DES INVALIDES \n","149056 NaN NaN PLACE DE LA CHAPELLE \n","151364 NaN NaN ESPLANADE DES INVALIDES \n","152303 NaN NaN ESPLANADE DES INVALIDES \n","160432 NaN NaN AVENUE DE LA PORTE DE CLIGNANCOURT \n","163918 NaN NaN BOULEVARD DE PICPUS \n","164616 NaN NaN RUE FRANCOIS TRUFFAUT \n","167606 16 NaN RUE JEAN COCTEAU \n","170367 16 NaN RUE POLIVEAU \n","173343 NaN NaN BOULEVARD DE PORT ROYAL \n","\n"," id_emplacement libelle_francais genre espece \\\n","71787 11110006 Tilleul Tilia tomentosa \n","82951 301004 Erable Acer monspessulanum \n","90605 204025 Cedrele Toona sinensis \n","91256 202005 Micocoulier Celtis australis \n","97354 501003 Platane Platanus occidentalis \n","103437 1303003 Platane Platanus x hispanica \n","133546 1501006 Platane Platanus x hispanica \n","147235 503005 Paulownia Paulownia tomentosa \n","147634 1202016 Platane Platanus occidentalis \n","148106 501022 Platane Platanus occidentalis \n","148481 402009 Platane Platanus occidentalis \n","149056 101026 Platane Platanus x hispanica \n","151364 410005 Tilleul Tilia tomentosa \n","152303 402011 Tilleul Tilia tomentosa \n","160432 202005 Platane Platanus orientalis \n","163918 1604017 Sophora Sophora japonica \n","164616 301005 Platane Platanus x hispanica \n","167606 202027 Noisetier de Byzance Corylus colurna \n","170367 202015 Platane Platanus x hispanica \n","173343 402001 Marronnier Aesculus x carnea \n","\n"," variete circonference_cm hauteur_m stade_developpement \\\n","71787 NaN 18589 15.00 A \n","82951 NaN 5545 0.56 J \n","90605 NaN 20130 16.00 A \n","91256 NaN 30140 10.00 A \n","97354 NaN 8110 18.00 A \n","103437 NaN 9035 6.00 J \n","133546 NaN 4090 10.00 A \n","147235 NaN 9155 15.00 A \n","147634 NaN 65110 16.00 A \n","148106 NaN 6595 15.00 A \n","148481 NaN 6120 18.00 A \n","149056 NaN 250255 22.00 A \n","151364 NaN 7540 8.15 JA \n","152303 NaN 9085 12.00 A \n","160432 NaN 120170 20.00 A \n","163918 NaN 6085 5.00 JA \n","164616 NaN 9095 10.00 A \n","167606 NaN 3630 5.00 J \n","170367 NaN 6125 20.00 A \n","173343 Briotii' 79120 10.00 A \n","\n"," remarquable geo_point_2d_a geo_point_2d_b \n","71787 0.00 48.85 2.40 \n","82951 0.00 48.89 2.37 \n","90605 0.00 48.84 2.36 \n","91256 0.00 48.85 2.37 \n","97354 0.00 48.86 2.30 \n","103437 0.00 48.89 2.35 \n","133546 0.00 48.89 2.39 \n","147235 0.00 48.86 2.29 \n","147634 0.00 48.86 2.31 \n","148106 0.00 48.86 2.30 \n","148481 0.00 48.85 2.31 \n","149056 0.00 48.88 2.36 \n","151364 0.00 48.86 2.31 \n","152303 0.00 48.86 2.31 \n","160432 0.00 48.90 2.34 \n","163918 0.00 48.85 2.40 \n","164616 0.00 48.83 2.39 \n","167606 0.00 48.90 2.35 \n","170367 0.00 48.84 2.36 \n","173343 0.00 48.84 2.35 "],"text/html":["\n","
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idtype_emplacementdomanialitearrondissementcomplement_addressenumerolieuid_emplacementlibelle_francaisgenreespecevarietecirconference_cmhauteur_mstade_developpementremarquablegeo_point_2d_ageo_point_2d_b
71787190080ArbreDASCOPARIS 20E ARRDTNaNNaNPLAINE (9) ELEM B11110006TilleulTiliatomentosaNaN1858915.00A0.0048.852.40
82951203295ArbreAlignementPARIS 18E ARRDTNaNNaNRUE D AUBERVILLIERS301004ErableAcermonspessulanumNaN55450.56J0.0048.892.37
90605211362ArbreAlignementPARIS 5E ARRDT34NaNBOULEVARD SAINT MARCEL204025CedreleToonasinensisNaN2013016.00A0.0048.842.36
91256212024ArbreAlignementPARIS 12E ARRDTNaNNaNRUE DE CHALON202005MicocoulierCeltisaustralisNaN3014010.00A0.0048.852.37
97354218274ArbreAlignementPARIS 7E ARRDT27NaNAVENUE RAPP501003PlatanePlatanusoccidentalisNaN811018.00A0.0048.862.30
103437224609ArbreAlignementPARIS 18E ARRDTNaNNaNBOULEVARD BARBES1303003PlatanePlatanusx hispanicaNaN90356.00J0.0048.892.35
133546256868ArbreAlignementPARIS 19E ARRDTNaNNaNAVENUE JEAN JAURES1501006PlatanePlatanusx hispanicaNaN409010.00A0.0048.892.39
147235270997ArbreAlignementPARIS 7E ARRDTNaNNaNQUAI BRANLY503005PaulowniaPaulowniatomentosaNaN915515.00A0.0048.862.29
147634271402ArbreAlignementPARIS 7E ARRDTNaNNaNAVENUE BOSQUET1202016PlatanePlatanusoccidentalisNaN6511016.00A0.0048.862.31
148106271884ArbreAlignementPARIS 7E ARRDTNaNNaNAVENUE BOSQUET501022PlatanePlatanusoccidentalisNaN659515.00A0.0048.862.30
148481272263ArbreAlignementPARIS 7E ARRDT14NaNBOULEVARD DES INVALIDES402009PlatanePlatanusoccidentalisNaN612018.00A0.0048.852.31
149056272885ArbreAlignementPARIS 18E ARRDTNaNNaNPLACE DE LA CHAPELLE101026PlatanePlatanusx hispanicaNaN25025522.00A0.0048.882.36
151364275451ArbreAlignementPARIS 7E ARRDTNaNNaNESPLANADE DES INVALIDES410005TilleulTiliatomentosaNaN75408.15JA0.0048.862.31
152303276396ArbreAlignementPARIS 7E ARRDTNaNNaNESPLANADE DES INVALIDES402011TilleulTiliatomentosaNaN908512.00A0.0048.862.31
160432284823ArbreAlignementPARIS 18E ARRDTNaNNaNAVENUE DE LA PORTE DE CLIGNANCOURT202005PlatanePlatanusorientalisNaN12017020.00A0.0048.902.34
163918288407ArbreAlignementPARIS 12E ARRDTNaNNaNBOULEVARD DE PICPUS1604017SophoraSophorajaponicaNaN60855.00JA0.0048.852.40
164616289121ArbreAlignementPARIS 12E ARRDTNaNNaNRUE FRANCOIS TRUFFAUT301005PlatanePlatanusx hispanicaNaN909510.00A0.0048.832.39
167606292180ArbreAlignementPARIS 18E ARRDT16NaNRUE JEAN COCTEAU202027Noisetier de ByzanceCoryluscolurnaNaN36305.00J0.0048.902.35
170367295047ArbreAlignementPARIS 5E ARRDT16NaNRUE POLIVEAU202015PlatanePlatanusx hispanicaNaN612520.00A0.0048.842.36
173343298211ArbreAlignementPARIS 5E ARRDTNaNNaNBOULEVARD DE PORT ROYAL402001MarronnierAesculusx carneaBriotii'7912010.00A0.0048.842.35
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"max_girth_outlier_df","summary":"{\n \"name\": \"max_girth_outlier_df\",\n \"rows\": 20,\n \"fields\": [\n {\n \"column\": \"id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 34759,\n \"min\": 190080,\n \"max\": 298211,\n \"num_unique_values\": 20,\n \"samples\": [\n 190080,\n 292180,\n 288407\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"type_emplacement\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Arbre\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"domanialite\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"Alignement\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"arrondissement\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 6,\n \"samples\": [\n \"PARIS 20E ARRDT\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"complement_addresse\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"27\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"numero\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": null,\n \"max\": null,\n \"num_unique_values\": 0,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"lieu\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 18,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"id_emplacement\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 19,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"libelle_francais\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 9,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"genre\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 9,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"espece\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 10,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"variete\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"circonference_cm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 59471,\n \"min\": 3630,\n \"max\": 250255,\n \"num_unique_values\": 20,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hauteur_m\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5.839249658898595,\n \"min\": 0.56,\n \"max\": 22.0,\n \"num_unique_values\": 11,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"stade_developpement\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"remarquable\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.0,\n \"max\": 0.0,\n \"num_unique_values\": 1,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"geo_point_2d_a\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.020935631218655087,\n \"min\": 48.8345576057,\n \"max\": 48.8996027298,\n \"num_unique_values\": 20,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"geo_point_2d_b\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.03448434237606588,\n \"min\": 2.292727369,\n \"max\": 2.4008649434,\n \"num_unique_values\": 20,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":29}],"source":["print('girth outliers trees ('+ str(len(max_girth_outlier_df)) + ' occurrencies):')\n","max_girth_outlier_df"]},{"cell_type":"code","execution_count":30,"id":"3535207c-ddca-4791-afc8-924bdf424cbb","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":742},"id":"3535207c-ddca-4791-afc8-924bdf424cbb","executionInfo":{"status":"ok","timestamp":1737062566943,"user_tz":-60,"elapsed":56,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"e770ae74-760e-4527-97b9-3c3d361e3182"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["circonference_cm\n","3630 1\n","4090 1\n","120170 1\n","79120 1\n","65110 1\n","30140 1\n","20130 1\n","18589 1\n","9155 1\n","9095 1\n","9085 1\n","9035 1\n","8110 1\n","7540 1\n","6595 1\n","6125 1\n","6120 1\n","6085 1\n","5545 1\n","250255 1\n","Name: count, dtype: int64"],"text/html":["
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count
circonference_cm
36301
40901
1201701
791201
651101
301401
201301
185891
91551
90951
90851
90351
81101
75401
65951
61251
61201
60851
55451
2502551
\n","

"]},"metadata":{},"execution_count":30}],"source":["max_girth_outlier_df.value_counts('circonference_cm')"]},{"cell_type":"code","execution_count":31,"id":"e20288eb-4c78-4b56-b411-5d0c74599659","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":460},"id":"e20288eb-4c78-4b56-b411-5d0c74599659","scrolled":true,"executionInfo":{"status":"ok","timestamp":1737062566944,"user_tz":-60,"elapsed":54,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"4fa4d9d1-1291-45aa-ef2e-747de62a21fc"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["hauteur_m\n","10.00 4\n","15.00 3\n","5.00 2\n","16.00 2\n","18.00 2\n","20.00 2\n","0.56 1\n","6.00 1\n","8.15 1\n","12.00 1\n","22.00 1\n","Name: count, dtype: int64"],"text/html":["
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count
hauteur_m
10.004
15.003
5.002
16.002
18.002
20.002
0.561
6.001
8.151
12.001
22.001
\n","

"]},"metadata":{},"execution_count":31}],"source":["max_girth_outlier_df.value_counts('hauteur_m')"]},{"cell_type":"code","execution_count":32,"id":"Lw7drXq5HvUS","metadata":{"id":"Lw7drXq5HvUS","executionInfo":{"status":"ok","timestamp":1737062566944,"user_tz":-60,"elapsed":52,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# Set circonference_cm values to NaN for these indexes\n","df_refined.loc[max_girth_outlier_df.index, 'circonference_cm'] = np.nan"]},{"cell_type":"code","execution_count":33,"id":"7_jcJ0ufG4Qk","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7_jcJ0ufG4Qk","executionInfo":{"status":"ok","timestamp":1737062566944,"user_tz":-60,"elapsed":52,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"e7a03437-b2af-4015-d591-cd0a7546005e"},"outputs":[{"output_type":"stream","name":"stdout","text":["Amount of to high girths : 20.\n","All values have been set to \"NaN\" and the cases will be reported\n"]}],"source":["print(f'Amount of to high girths : {len(max_girth_outlier_df)}.\\nAll values have been set to \"NaN\" and the cases will be reported')"]},{"cell_type":"markdown","id":"747b4e39-2678-40e9-af04-2e1caa765226","metadata":{"id":"747b4e39-2678-40e9-af04-2e1caa765226"},"source":["##### Min"]},{"cell_type":"markdown","id":"8f65de59-ef68-43b1-be1b-9c3ddada8372","metadata":{"id":"8f65de59-ef68-43b1-be1b-9c3ddada8372"},"source":["Height :"]},{"cell_type":"code","execution_count":34,"id":"6a24f15e-6a9e-46d0-acc1-fb7bf281ec30","metadata":{"id":"6a24f15e-6a9e-46d0-acc1-fb7bf281ec30","executionInfo":{"status":"ok","timestamp":1737062566944,"user_tz":-60,"elapsed":47,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# based on the tallest tree height : https://en.wikipedia.org/wiki/Hyperion_(tree)\n","min_height = 0 # in meters"]},{"cell_type":"code","execution_count":35,"id":"10c2ce83-27b5-43f1-af4f-5d80ac2ba48c","metadata":{"id":"10c2ce83-27b5-43f1-af4f-5d80ac2ba48c","executionInfo":{"status":"ok","timestamp":1737062566944,"user_tz":-60,"elapsed":46,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["min_height_outlier_df = df_refined[(df_refined['hauteur_m'] <= min_height)]"]},{"cell_type":"code","execution_count":36,"id":"4dfbde45-9d17-4a5f-af04-282461462235","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":146},"id":"4dfbde45-9d17-4a5f-af04-282461462235","executionInfo":{"status":"ok","timestamp":1737062566944,"user_tz":-60,"elapsed":46,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"bc42741e-ad98-4124-d280-a926786bdf5a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["hauteur_m\n","0.00 39219\n","Name: count, dtype: int64"],"text/html":["
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count
hauteur_m
0.0039219
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"]},"metadata":{},"execution_count":36}],"source":["# no negative value : ok\n","min_height_outlier_df.value_counts('hauteur_m')"]},{"cell_type":"code","execution_count":37,"id":"fa7fd3b4-7e48-4289-b005-939f565b889e","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":999},"id":"fa7fd3b4-7e48-4289-b005-939f565b889e","scrolled":true,"executionInfo":{"status":"ok","timestamp":1737062566944,"user_tz":-60,"elapsed":44,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"b6e95c76-ebcb-4a76-952f-4cdeb933d2bf"},"outputs":[{"output_type":"stream","name":"stdout","text":["height outliers trees (39219 occurrencies):\n"]},{"output_type":"execute_result","data":{"text/plain":[" id type_emplacement domanialite arrondissement \\\n","4 99878 Arbre Jardin PARIS 17E ARRDT \n","5 99879 Arbre Jardin PARIS 17E ARRDT \n","6 99880 Arbre Jardin PARIS 17E ARRDT \n","11 99885 Arbre Jardin PARIS 16E ARRDT \n","12 99887 Arbre Jardin PARIS 16E ARRDT \n","... ... ... ... ... \n","200132 2024741 Arbre Jardin BOIS DE VINCENNES \n","200133 2024742 Arbre Jardin BOIS DE VINCENNES \n","200134 2024743 Arbre Jardin BOIS DE VINCENNES \n","200135 2024744 Arbre Jardin BOIS DE VINCENNES \n","200136 2024745 Arbre Jardin BOIS DE VINCENNES \n","\n"," complement_addresse numero \\\n","4 NaN NaN \n","5 NaN NaN \n","6 NaN NaN \n","11 NaN NaN \n","12 NaN NaN \n","... ... ... \n","200132 NaN NaN \n","200133 NaN NaN \n","200134 NaN NaN \n","200135 NaN NaN \n","200136 NaN NaN \n","\n"," lieu id_emplacement \\\n","4 PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING 000G0037 \n","5 PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING 000G0036 \n","6 PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING 000G0035 \n","11 JARDIN DU RANELAGH 20001 \n","12 JARDIN DU RANELAGH 20003 \n","... ... ... \n","200132 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170151 \n","200133 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170152 \n","200134 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170153 \n","200135 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170154 \n","200136 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170155 \n","\n"," libelle_francais genre espece variete \\\n","4 Arbre à miel Tetradium daniellii NaN \n","5 Arbre à miel Tetradium daniellii NaN \n","6 Arbre à miel Tetradium daniellii NaN \n","11 Hêtre Fagus sylvatica Atropunicea' \n","12 Micocoulier Celtis occidentalis NaN \n","... ... ... ... ... \n","200132 NaN Castanea fargesii NaN \n","200133 Arbre aux mouchoirs Davidia involucrata NaN \n","200134 Peuplier Populus simonii NaN \n","200135 Chêne Quercus n. sp. NaN \n","200136 Raisinier Hovenia dulcis NaN \n","\n"," circonference_cm hauteur_m stade_developpement remarquable \\\n","4 38.00 0.00 NaN NaN \n","5 38.00 0.00 NaN NaN \n","6 37.00 0.00 NaN NaN \n","11 30.00 0.00 NaN NaN \n","12 205.00 0.00 NaN NaN \n","... ... ... ... ... \n","200132 0.00 0.00 NaN 0.00 \n","200133 0.00 0.00 NaN 0.00 \n","200134 0.00 0.00 NaN 0.00 \n","200135 0.00 0.00 NaN 0.00 \n","200136 0.00 0.00 NaN 0.00 \n","\n"," geo_point_2d_a geo_point_2d_b \n","4 48.89 2.32 \n","5 48.89 2.32 \n","6 48.89 2.32 \n","11 48.86 2.27 \n","12 48.86 2.27 \n","... ... ... \n","200132 48.82 2.46 \n","200133 48.82 2.46 \n","200134 48.82 2.46 \n","200135 48.82 2.46 \n","200136 48.82 2.45 \n","\n","[39219 rows x 18 columns]"],"text/html":["\n","
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idtype_emplacementdomanialitearrondissementcomplement_addressenumerolieuid_emplacementlibelle_francaisgenreespecevarietecirconference_cmhauteur_mstade_developpementremarquablegeo_point_2d_ageo_point_2d_b
499878ArbreJardinPARIS 17E ARRDTNaNNaNPARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING000G0037Arbre à mielTetradiumdanielliiNaN38.000.00NaNNaN48.892.32
599879ArbreJardinPARIS 17E ARRDTNaNNaNPARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING000G0036Arbre à mielTetradiumdanielliiNaN38.000.00NaNNaN48.892.32
699880ArbreJardinPARIS 17E ARRDTNaNNaNPARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING000G0035Arbre à mielTetradiumdanielliiNaN37.000.00NaNNaN48.892.32
1199885ArbreJardinPARIS 16E ARRDTNaNNaNJARDIN DU RANELAGH20001HêtreFagussylvaticaAtropunicea'30.000.00NaNNaN48.862.27
1299887ArbreJardinPARIS 16E ARRDTNaNNaNJARDIN DU RANELAGH20003MicocoulierCeltisoccidentalisNaN205.000.00NaNNaN48.862.27
.........................................................
2001322024741ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170151NaNCastaneafargesiiNaN0.000.00NaN0.0048.822.46
2001332024742ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170152Arbre aux mouchoirsDavidiainvolucrataNaN0.000.00NaN0.0048.822.46
2001342024743ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170153PeuplierPopulussimoniiNaN0.000.00NaN0.0048.822.46
2001352024744ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170154ChêneQuercusn. sp.NaN0.000.00NaN0.0048.822.46
2001362024745ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170155RaisinierHoveniadulcisNaN0.000.00NaN0.0048.822.45
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39219 rows × 18 columns

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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"min_height_outlier_df","summary":"{\n \"name\": \"min_height_outlier_df\",\n \"rows\": 39219,\n \"fields\": [\n {\n \"column\": \"id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 635080,\n \"min\": 99878,\n \"max\": 2024745,\n \"num_unique_values\": 39219,\n \"samples\": [\n 123467,\n 2011241,\n 2020241\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"type_emplacement\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Arbre\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"domanialite\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"Jardin\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"arrondissement\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 25,\n \"samples\": [\n \"PARIS 13E ARRDT\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"complement_addresse\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 120,\n \"samples\": [\n \"15 (XVI-7603)\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"numero\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": null,\n \"max\": null,\n \"num_unique_values\": 0,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"lieu\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2397,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"id_emplacement\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 24787,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"libelle_francais\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 170,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"genre\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 156,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"espece\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 462,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"variete\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 256,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"circonference_cm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 51.57964421106181,\n \"min\": 0.0,\n \"max\": 900.0,\n \"num_unique_values\": 298,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hauteur_m\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.0,\n \"max\": 0.0,\n \"num_unique_values\": 1,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"stade_developpement\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"remarquable\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.0,\n \"max\": 0.0,\n \"num_unique_values\": 1,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"geo_point_2d_a\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.04342193848100632,\n \"min\": 48.754504992,\n \"max\": 48.9106150304,\n \"num_unique_values\": 39213,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"geo_point_2d_b\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0577776801177361,\n \"min\": 2.2102412288,\n \"max\": 2.4621650013,\n \"num_unique_values\": 39214,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":37}],"source":["# Maybe freshly planted trees are 0 meters tall and/or 0 is a default value when not measured\n","print('height outliers trees ('+ str(len(min_height_outlier_df)) + ' occurrencies):')\n","min_height_outlier_df"]},{"cell_type":"code","execution_count":38,"id":"ad996a3c-d4b9-4ff6-8e39-eca8054157ff","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"ad996a3c-d4b9-4ff6-8e39-eca8054157ff","executionInfo":{"status":"ok","timestamp":1737062566945,"user_tz":-60,"elapsed":38,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"68397898-f18b-4d89-c5bc-af2677f5491e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" stade_developpement counts\n","0 NaN 36952\n","1 A 1191\n","2 JA 577\n","3 J 474\n","4 M 25"],"text/html":["\n","
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stade_developpementcounts
0NaN36952
1A1191
2JA577
3J474
4M25
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"min_height_value_counts","summary":"{\n \"name\": \"min_height_value_counts\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"stade_developpement\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"JA\",\n \"M\",\n \"A\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"counts\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 16277,\n \"min\": 25,\n \"max\": 36952,\n \"num_unique_values\": 5,\n \"samples\": [\n 1191,\n 25,\n 577\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":38}],"source":["# A = Adult, JA = Jeune Adulte, J = Jeune, M = Mature\n","# Majority has no stade_developpement value. Probably freshly planted grain. Recommend to not ignore these trees.\n","# For the others, it may probably be a fill value. I recommend setting the height values as NaN\n","min_height_value_counts = min_height_outlier_df.value_counts('stade_developpement', dropna=False).reset_index(name='counts')\n","min_height_value_counts"]},{"cell_type":"code","execution_count":39,"id":"34d32b28-1a5f-4df0-a620-4c418befd07c","metadata":{"id":"34d32b28-1a5f-4df0-a620-4c418befd07c","executionInfo":{"status":"ok","timestamp":1737062566945,"user_tz":-60,"elapsed":36,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# giving NaN value instead of 0 for already existing trees in order to signal missing data\n","zero_height_but_existing_development_state = (df_refined['hauteur_m'] <= min_height) & (df_refined['stade_developpement'].isin(['A', 'JA', 'J', 'M']))\n","# display(df_refined[zero_girth_but_existing_development_state].head(1).transpose())\n","df_refined.loc[zero_height_but_existing_development_state,'hauteur_m'] = np.nan\n","\n","# giving NaN value instead of 0 for already existing trees in order to signal missing data\n","# df_refined.loc[(df_refined['hauteur_m'] <= min_height) & (df_refined['stade_developpement'].isin(['A', 'JA', 'J', 'M'])),\n","# 'hauteur_m'] = np.nan\n","# check 1 or 2 trees tagged as 'A' or 'JA' (or other) to see if these really is a positive height"]},{"cell_type":"code","execution_count":40,"id":"zILrPvh-JjKp","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zILrPvh-JjKp","executionInfo":{"status":"ok","timestamp":1737062566945,"user_tz":-60,"elapsed":35,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"5cba9d21-3806-483e-ae80-f5e1555c5bb4"},"outputs":[{"output_type":"stream","name":"stdout","text":["2267 trees have a zero height value whereas they belong to one of these categories : ['A', 'JA', 'J', 'M'] --> height value set to 'NaN'\n","36952 trees have a zero height with no 'stade_developpement' value. They probably are freshly planted grains : height value is kept zero\n"]}],"source":["print(f\"{min_height_value_counts['counts'].sum()-min_height_value_counts['counts'][0]} trees have a zero height value whereas they belong to one of these categories : ['A', 'JA', 'J', 'M'] --> height value set to 'NaN'\")\n","print(f\"{min_height_value_counts['counts'][0]} trees have a zero height with no 'stade_developpement' value. They probably are freshly planted grains : height value is kept zero\")\n"]},{"cell_type":"markdown","id":"27fbf69c-619d-40ee-8514-2b3c3470007b","metadata":{"id":"27fbf69c-619d-40ee-8514-2b3c3470007b"},"source":["Girth :"]},{"cell_type":"code","execution_count":41,"id":"0d1a2d77-c7d8-4944-9a58-dc045519c731","metadata":{"id":"0d1a2d77-c7d8-4944-9a58-dc045519c731","executionInfo":{"status":"ok","timestamp":1737062566945,"user_tz":-60,"elapsed":31,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# based on the biggest tree girth (circumference) : https://www.guinnessworldrecords.com/world-records/living-tree-with-greatest-girth\n","min_girth = 0 # in meters"]},{"cell_type":"code","execution_count":42,"id":"fe7e6808-fa76-4e9c-bddf-27474aa9f28c","metadata":{"id":"fe7e6808-fa76-4e9c-bddf-27474aa9f28c","executionInfo":{"status":"ok","timestamp":1737062566945,"user_tz":-60,"elapsed":30,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["min_girth_outlier_df = df_refined[df_refined['circonference_cm'] <= min_girth]"]},{"cell_type":"code","execution_count":43,"id":"6baa2bd7-0136-451c-aa81-4f61c30ba125","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":146},"id":"6baa2bd7-0136-451c-aa81-4f61c30ba125","scrolled":true,"executionInfo":{"status":"ok","timestamp":1737062566946,"user_tz":-60,"elapsed":30,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"db6ed9e6-7bb2-41a2-ef32-47adf07fa2d0"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["circonference_cm\n","0.00 25867\n","Name: count, dtype: int64"],"text/html":["
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count
circonference_cm
0.0025867
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"]},"metadata":{},"execution_count":43}],"source":["# no negative value : ok\n","min_girth_outlier_df.value_counts('circonference_cm')"]},{"cell_type":"code","execution_count":44,"id":"9f015caa-3e56-437c-9e6d-e315754d33a7","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"9f015caa-3e56-437c-9e6d-e315754d33a7","scrolled":true,"executionInfo":{"status":"ok","timestamp":1737062567527,"user_tz":-60,"elapsed":610,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"e26ad911-abc4-4fd8-99be-c9db6f8d3617"},"outputs":[{"output_type":"stream","name":"stdout","text":["girth outliers trees (25867 occurrencies):\n"]},{"output_type":"execute_result","data":{"text/plain":[" id type_emplacement domanialite arrondissement \\\n","1778 101922 Arbre Jardin PARIS 19E ARRDT \n","2332 102591 Arbre Jardin PARIS 17E ARRDT \n","2333 102592 Arbre Jardin PARIS 17E ARRDT \n","2405 102744 Arbre Jardin PARIS 20E ARRDT \n","2430 102773 Arbre Jardin PARIS 17E ARRDT \n","... ... ... ... ... \n","200132 2024741 Arbre Jardin BOIS DE VINCENNES \n","200133 2024742 Arbre Jardin BOIS DE VINCENNES \n","200134 2024743 Arbre Jardin BOIS DE VINCENNES \n","200135 2024744 Arbre Jardin BOIS DE VINCENNES \n","200136 2024745 Arbre Jardin BOIS DE VINCENNES \n","\n"," complement_addresse numero \\\n","1778 NaN NaN \n","2332 NaN NaN \n","2333 NaN NaN \n","2405 NaN NaN \n","2430 NaN NaN \n","... ... ... \n","200132 NaN NaN \n","200133 NaN NaN \n","200134 NaN NaN \n","200135 NaN NaN \n","200136 NaN NaN \n","\n"," lieu id_emplacement \\\n","1778 PARC DES BUTTES CHAUMONT C0400032 \n","2332 PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING 000B0052 \n","2333 PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING 000B0053 \n","2405 SQUARE LEON FRAPIE 2 \n","2430 PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING 000B0021 \n","... ... ... \n","200132 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170151 \n","200133 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170152 \n","200134 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170153 \n","200135 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170154 \n","200136 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170155 \n","\n"," libelle_francais genre espece variete \\\n","1778 Erable Acer platanoides NaN \n","2332 Magnolia Magnolia x soulangeana Heaven Scent' \n","2333 Magnolia Magnolia x soulangeana Heaven Scent' \n","2405 Platane Platanus x hispanica NaN \n","2430 Magnolia Magnolia x soulangeana Sundew' \n","... ... ... ... ... \n","200132 NaN Castanea fargesii NaN \n","200133 Arbre aux mouchoirs Davidia involucrata NaN \n","200134 Peuplier Populus simonii NaN \n","200135 Chêne Quercus n. sp. NaN \n","200136 Raisinier Hovenia dulcis NaN \n","\n"," circonference_cm hauteur_m stade_developpement remarquable \\\n","1778 0.00 0.00 NaN NaN \n","2332 0.00 0.00 NaN NaN \n","2333 0.00 0.00 NaN NaN \n","2405 0.00 NaN JA 0.00 \n","2430 0.00 0.00 NaN NaN \n","... ... ... ... ... \n","200132 0.00 0.00 NaN 0.00 \n","200133 0.00 0.00 NaN 0.00 \n","200134 0.00 0.00 NaN 0.00 \n","200135 0.00 0.00 NaN 0.00 \n","200136 0.00 0.00 NaN 0.00 \n","\n"," geo_point_2d_a geo_point_2d_b \n","1778 48.88 2.38 \n","2332 48.89 2.32 \n","2333 48.89 2.32 \n","2405 48.87 2.41 \n","2430 48.89 2.32 \n","... ... ... \n","200132 48.82 2.46 \n","200133 48.82 2.46 \n","200134 48.82 2.46 \n","200135 48.82 2.46 \n","200136 48.82 2.45 \n","\n","[25867 rows x 18 columns]"],"text/html":["\n","
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idtype_emplacementdomanialitearrondissementcomplement_addressenumerolieuid_emplacementlibelle_francaisgenreespecevarietecirconference_cmhauteur_mstade_developpementremarquablegeo_point_2d_ageo_point_2d_b
1778101922ArbreJardinPARIS 19E ARRDTNaNNaNPARC DES BUTTES CHAUMONTC0400032ErableAcerplatanoidesNaN0.000.00NaNNaN48.882.38
2332102591ArbreJardinPARIS 17E ARRDTNaNNaNPARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING000B0052MagnoliaMagnoliax soulangeanaHeaven Scent'0.000.00NaNNaN48.892.32
2333102592ArbreJardinPARIS 17E ARRDTNaNNaNPARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING000B0053MagnoliaMagnoliax soulangeanaHeaven Scent'0.000.00NaNNaN48.892.32
2405102744ArbreJardinPARIS 20E ARRDTNaNNaNSQUARE LEON FRAPIE2PlatanePlatanusx hispanicaNaN0.00NaNJA0.0048.872.41
2430102773ArbreJardinPARIS 17E ARRDTNaNNaNPARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING000B0021MagnoliaMagnoliax soulangeanaSundew'0.000.00NaNNaN48.892.32
.........................................................
2001322024741ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170151NaNCastaneafargesiiNaN0.000.00NaN0.0048.822.46
2001332024742ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170152Arbre aux mouchoirsDavidiainvolucrataNaN0.000.00NaN0.0048.822.46
2001342024743ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170153PeuplierPopulussimoniiNaN0.000.00NaN0.0048.822.46
2001352024744ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170154ChêneQuercusn. sp.NaN0.000.00NaN0.0048.822.46
2001362024745ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170155RaisinierHoveniadulcisNaN0.000.00NaN0.0048.822.45
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25867 rows × 18 columns

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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"min_girth_outlier_df","summary":"{\n \"name\": \"min_girth_outlier_df\",\n \"rows\": 25867,\n \"fields\": [\n {\n \"column\": \"id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 721969,\n \"min\": 101922,\n \"max\": 2024745,\n \"num_unique_values\": 25867,\n \"samples\": [\n 2016263,\n 145520,\n 169124\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"type_emplacement\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Arbre\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"domanialite\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"Jardin\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"arrondissement\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 24,\n \"samples\": [\n \"PARIS 12E ARRDT\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"complement_addresse\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 60,\n \"samples\": [\n \"2 (face)\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"numero\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": null,\n \"max\": null,\n \"num_unique_values\": 0,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"lieu\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2101,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"id_emplacement\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 21389,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"libelle_francais\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 152,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"genre\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 141,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"espece\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 412,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"variete\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 134,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"circonference_cm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.0,\n \"max\": 0.0,\n \"num_unique_values\": 1,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hauteur_m\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.9520728384383992,\n \"min\": 0.0,\n \"max\": 40.0,\n \"num_unique_values\": 20,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"stade_developpement\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"remarquable\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.0,\n \"max\": 0.0,\n \"num_unique_values\": 1,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"geo_point_2d_a\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.04898659494827352,\n \"min\": 48.754504992,\n \"max\": 48.9104979364,\n \"num_unique_values\": 25862,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"geo_point_2d_b\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.058212997253484625,\n \"min\": 2.2260475851,\n \"max\": 2.4621650013,\n \"num_unique_values\": 25863,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":44}],"source":["# Maybe freshly planted trees have 0 cms girth and/or 0 is a default value when not measured\n","print('girth outliers trees ('+ str(len(min_girth_outlier_df)) + ' occurrencies):')\n","min_girth_outlier_df"]},{"cell_type":"code","execution_count":45,"id":"6cb2665b-7f8f-4004-b68a-449a6965b461","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"6cb2665b-7f8f-4004-b68a-449a6965b461","executionInfo":{"status":"ok","timestamp":1737062567527,"user_tz":-60,"elapsed":22,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"1d78791d-098a-46f1-a168-a5accee3e653"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" stade_developpement counts\n","0 NaN 25586\n","1 J 126\n","2 A 81\n","3 JA 51\n","4 M 23"],"text/html":["\n","
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stade_developpementcounts
0NaN25586
1J126
2A81
3JA51
4M23
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"min_girth_value_counts","summary":"{\n \"name\": \"min_girth_value_counts\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"stade_developpement\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"A\",\n \"M\",\n \"J\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"counts\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 11411,\n \"min\": 23,\n \"max\": 25586,\n \"num_unique_values\": 5,\n \"samples\": [\n 126,\n 23,\n 81\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":45}],"source":["# A = Adult, JA = Jeune Adulte, J = Jeune, M = Mature\n","# Majority has no stade_developpement value. Probably freshly planted grain. Recommend to not ignore these trees.\n","# For the others, it may probably be a fill value. I recommend setting the height values as NaN\n","min_girth_value_counts = min_girth_outlier_df.value_counts('stade_developpement', dropna=False).reset_index(name='counts')\n","min_girth_value_counts"]},{"cell_type":"code","execution_count":46,"id":"bf4cf441-c5af-4ebc-9ab7-92dfaeefe10c","metadata":{"id":"bf4cf441-c5af-4ebc-9ab7-92dfaeefe10c","executionInfo":{"status":"ok","timestamp":1737062567528,"user_tz":-60,"elapsed":22,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# giving NaN value instead of 0 for already existing trees in order to signal missing data\n","zero_girth_but_existing_development_state = (df_refined['circonference_cm'] <= min_girth) & (df_refined['stade_developpement'].isin(['A', 'JA', 'J', 'M']))\n","# display(df_refined[zero_girth_but_existing_development_state].head(1).transpose())\n","df_refined.loc[zero_girth_but_existing_development_state,'circonference_cm'] = np.nan\n","# same question as minimum height (see above)"]},{"cell_type":"code","execution_count":47,"id":"BLeJcjd0MJub","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BLeJcjd0MJub","executionInfo":{"status":"ok","timestamp":1737062567528,"user_tz":-60,"elapsed":21,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"35ae275f-671c-4718-d138-954a5212a3d9"},"outputs":[{"output_type":"stream","name":"stdout","text":["281 trees have a zero girth value whereas they belong to one of these categories : ['A', 'JA', 'J', 'M'] --> girth value set to 'NaN'\n","25586 trees have a zero girth with no 'stade_developpement' value. They probably are freshly planted grains : girth value is kept zero\n"]}],"source":["print(f\"{min_girth_value_counts['counts'].sum()-min_girth_value_counts['counts'][0]} trees have a zero girth value whereas they belong to one of these categories : ['A', 'JA', 'J', 'M'] --> girth value set to 'NaN'\")\n","print(f\"{min_girth_value_counts['counts'][0]} trees have a zero girth with no 'stade_developpement' value. They probably are freshly planted grains : girth value is kept zero\")\n"]},{"cell_type":"markdown","id":"1MPpoe9IOLP1","metadata":{"id":"1MPpoe9IOLP1"},"source":["##### Girth and Height coherence"]},{"cell_type":"code","execution_count":48,"id":"e3e28272-33a0-428a-80e8-975140d30330","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e3e28272-33a0-428a-80e8-975140d30330","executionInfo":{"status":"ok","timestamp":1737062567528,"user_tz":-60,"elapsed":18,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"1cb1e637-0963-4a69-dfd2-7b00754616a4"},"outputs":[{"output_type":"stream","name":"stdout","text":["25264 trees are both 0 m tall and 0 cms girth --> they probably are freshly planted grains\n","11688 trees are 0 m tall but have strictly positive and coherent girth --> height values are set to 'NaN' \n","322 trees have strictly positive and coherent height but 0 cm girth --> girth values are set to 'NaN' \n"]}],"source":["from IPython.display import display\n","\n","# search if trees 0 meters height trees and 0 cms girth trees are the same\n","both_zero = ((df_refined['hauteur_m'] <= 0) & (df_refined['circonference_cm'] <= 0)).sum()\n","print(f\"{both_zero} trees are both 0 m tall and 0 cms girth --> they probably are freshly planted grains\")\n","\n","height_zero_girth_non_zero = (df_refined['hauteur_m'] <= 0) & (df_refined['circonference_cm'] > 0)\n","# display(df_refined[height_zero_girth_non_zero].head(1).transpose())\n","df_refined.loc[height_zero_girth_non_zero,'hauteur_m'] = np.nan\n","print(f\"{height_zero_girth_non_zero.sum()} trees are 0 m tall but have strictly positive and coherent girth --> height values are set to 'NaN' \")\n","\n","non_zero_height_zero_girth = (df_refined['hauteur_m'] > 0) & (df_refined['circonference_cm'] <= 0)\n","# display(df_refined[non_zero_height_zero_girth].head(1).transpose())\n","df_refined.loc[non_zero_height_zero_girth,'circonference_cm'] = np.nan\n","print(f\"{non_zero_height_zero_girth.sum()} trees have strictly positive and coherent height but 0 cm girth --> girth values are set to 'NaN' \")\n","\n"]},{"cell_type":"markdown","id":"26f2d2ae-4489-4d6e-b217-16125f372597","metadata":{"id":"26f2d2ae-4489-4d6e-b217-16125f372597"},"source":["### NaN Values"]},{"cell_type":"markdown","id":"bf1ee819-3acf-420e-8d17-3699bbc1b4ac","metadata":{"id":"bf1ee819-3acf-420e-8d17-3699bbc1b4ac"},"source":["```\n","# domanialite can be found when looking for nearest neighbors\n","# complement_adresse and numero often do not exist\n","# Maybe can we do something about the missing values for \"libelle_francais\", \"genre\", \"espece\", \"variete\"\n","# stade_developpement and remarkable missing values presences seem to be correlated (according to previous analysis)\n","```\n","\n","\n"]},{"cell_type":"code","execution_count":49,"id":"01c33327-96f8-4cca-bd48-af40c023344d","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":89},"id":"01c33327-96f8-4cca-bd48-af40c023344d","scrolled":true,"executionInfo":{"status":"ok","timestamp":1737062567528,"user_tz":-60,"elapsed":15,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"b9b94480-90c3-4b3a-c383-ffe4f7c35c1e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" domanialite complement_addresse numero libelle_francais genre espece \\\n","0 1 169235 200137 1497 16 1752 \n","\n"," variete stade_developpement remarquable \n","0 163360 67205 63098 "],"text/html":["\n","
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domanialitecomplement_addressenumerolibelle_francaisgenreespecevarietestade_developpementremarquable
0116923520013714971617521633606720563098
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"columns_with_nans_df","summary":"{\n \"name\": \"columns_with_nans_df\",\n \"rows\": 1,\n \"fields\": [\n {\n \"column\": \"domanialite\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 1,\n \"max\": 1,\n \"num_unique_values\": 1,\n \"samples\": [\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"complement_addresse\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 169235,\n \"max\": 169235,\n \"num_unique_values\": 1,\n \"samples\": [\n 169235\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"numero\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 200137,\n \"max\": 200137,\n \"num_unique_values\": 1,\n \"samples\": [\n 200137\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"libelle_francais\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 1497,\n \"max\": 1497,\n \"num_unique_values\": 1,\n \"samples\": [\n 1497\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"genre\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 16,\n \"max\": 16,\n \"num_unique_values\": 1,\n \"samples\": [\n 16\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"espece\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 1752,\n \"max\": 1752,\n \"num_unique_values\": 1,\n \"samples\": [\n 1752\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"variete\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 163360,\n \"max\": 163360,\n \"num_unique_values\": 1,\n \"samples\": [\n 163360\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"stade_developpement\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 67205,\n \"max\": 67205,\n \"num_unique_values\": 1,\n \"samples\": [\n 67205\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"remarquable\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 63098,\n \"max\": 63098,\n \"num_unique_values\": 1,\n \"samples\": [\n 63098\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":49}],"source":["columns_with_nans_df"]},{"cell_type":"markdown","id":"hBvGSxoNq7H2","metadata":{"id":"hBvGSxoNq7H2"},"source":["#### domanialite"]},{"cell_type":"code","execution_count":50,"id":"z_8W9BlA4wnW","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"z_8W9BlA4wnW","executionInfo":{"status":"ok","timestamp":1737062567528,"user_tz":-60,"elapsed":14,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"a0e0a3ba-f162-4b32-e0e0-2eba5d095e85"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[48.85761995, 2.32096211],\n"," [48.85765607, 2.32103142],\n"," [48.85770501, 2.32106057],\n"," ...,\n"," [48.82290714, 2.45797007],\n"," [48.82252234, 2.45595555],\n"," [48.82044479, 2.45485622]])"]},"metadata":{},"execution_count":50}],"source":["df_refined[['geo_point_2d_a','geo_point_2d_b']].values"]},{"cell_type":"code","execution_count":51,"id":"D27qtLjtqEnA","metadata":{"id":"D27qtLjtqEnA","executionInfo":{"status":"ok","timestamp":1737062567528,"user_tz":-60,"elapsed":12,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["from data_analysis_functions import compute_distances"]},{"cell_type":"code","execution_count":52,"id":"cIuY0Unh2g7r","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cIuY0Unh2g7r","executionInfo":{"status":"ok","timestamp":1737062567528,"user_tz":-60,"elapsed":12,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"70ddfa96-8143-4ab4-cb95-dc8cd3c57f14"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[48.84954716, 2.41418967]])"]},"metadata":{},"execution_count":52}],"source":["nan_indexes = df_refined[df_refined['domanialite'].isna()].index\n","nan_indexes\n","df_refined.loc[nan_indexes, ['geo_point_2d_a','geo_point_2d_b']].values"]},{"cell_type":"code","execution_count":53,"id":"70FaYnAPq1N4","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":71946,"status":"ok","timestamp":1737062639465,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"70FaYnAPq1N4","outputId":"5683878c-4a12-4c1d-9c6b-db1cc21be630"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mean distance between tree 197239 and its 3 nearest neighbours: 6.02 meters\n","Missing values for domanialite: Jardin\n"]}],"source":["for index in nan_indexes:\n"," distances = compute_distances(df_refined, index)\n"," # find three nearest neighbours\n"," three_nearest_neighbours = distances.nsmallest(4)[1:]\n"," print(f'Mean distance between tree {index} and its 3 nearest neighbours: {three_nearest_neighbours.mean().round(2)} meters')\n"," missing_domanialite = df_refined.loc[three_nearest_neighbours.index,'domanialite'].unique()\n"," if len(missing_domanialite) == 1:\n"," print(f'Missing values for domanialite: {missing_domanialite[0]}')\n"," df_refined.loc[index, 'domanialite'] = missing_domanialite[0]\n"," else:\n"," print(f'Too many values in missing_domanialite: {missing_domanialite}')"]},{"cell_type":"markdown","id":"cD1g4VFyJgH6","metadata":{"id":"cD1g4VFyJgH6"},"source":["#### genre"]},{"cell_type":"code","execution_count":54,"id":"balAs4z2Je3F","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":550},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1737062639465,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"balAs4z2Je3F","outputId":"db78d6c9-f750-44fb-869f-447f3880be21"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" genre espece variete libelle_francais\n","195404 NaN NaN NaN NaN\n","195409 NaN NaN NaN NaN\n","195410 NaN NaN NaN NaN\n","195475 NaN NaN NaN NaN\n","195476 NaN NaN NaN NaN\n","195487 NaN NaN NaN NaN\n","195496 NaN NaN NaN NaN\n","195497 NaN NaN NaN NaN\n","195499 NaN NaN NaN NaN\n","195502 NaN NaN NaN NaN\n","195503 NaN NaN NaN NaN\n","195505 NaN NaN NaN NaN\n","195509 NaN NaN NaN NaN\n","195514 NaN NaN NaN NaN\n","195518 NaN NaN NaN NaN\n","195519 NaN NaN NaN NaN"],"text/html":["\n","
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genreespecevarietelibelle_francais
195404NaNNaNNaNNaN
195409NaNNaNNaNNaN
195410NaNNaNNaNNaN
195475NaNNaNNaNNaN
195476NaNNaNNaNNaN
195487NaNNaNNaNNaN
195496NaNNaNNaNNaN
195497NaNNaNNaNNaN
195499NaNNaNNaNNaN
195502NaNNaNNaNNaN
195503NaNNaNNaNNaN
195505NaNNaNNaNNaN
195509NaNNaNNaNNaN
195514NaNNaNNaNNaN
195518NaNNaNNaNNaN
195519NaNNaNNaNNaN
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"genre_nan_rows","repr_error":"0"}},"metadata":{},"execution_count":54}],"source":["# catch each line where \"genre\" value is nan. Catch only 'genre','espece','variete','libelle_francais' columns\n","\n","# Assuming df_refined is your DataFrame as defined in the provided code.\n","\n","genre_nan_rows = df_refined[df_refined['genre'].isna()][['genre', 'espece', 'variete', 'libelle_francais']]\n","\n","genre_nan_rows"]},{"cell_type":"markdown","id":"MgBXUNswKdtn","metadata":{"id":"MgBXUNswKdtn"},"source":["Cannot find \"genre\" value since all the other values for \"espece\", \"variete\", \"libelle_francais\" are \"nan\"."]},{"cell_type":"markdown","id":"jPyVZ2cQLLJ0","metadata":{"id":"jPyVZ2cQLLJ0"},"source":["#### libelle_francais"]},{"cell_type":"code","execution_count":55,"id":"ACltGPqrSWo0","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":457,"status":"ok","timestamp":1737062639914,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"ACltGPqrSWo0","outputId":"a8729550-dbac-4366-cc1b-f46bdbdfa335"},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of different libelle_francais values: 193\n","True\n"]}],"source":["print(f\"Number of different libelle_francais values: {len(np.unique(df_refined['libelle_francais'].astype(str)))}\")\n","# nan is in the list. We will continue ignoring it in order to potentially find a correspondance between libelle_francais and genre variable\n","print('nan' in np.unique(df_refined['libelle_francais'].astype(str)))"]},{"cell_type":"code","execution_count":56,"id":"GbXIdFi6T5rR","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1737062639914,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"GbXIdFi6T5rR","outputId":"ec1153cd-8df5-48ab-c581-b2655cf31652"},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of different libelle_francais values: 192\n","Possible correlation between libelle_francais and genre:\n","Number of 'libelle_francais' with one 'genre': 182\n","Number of 'libelle_francais' with multiple 'genre': 10\n"]}],"source":["# not the most useful\n","# For each value of \"libelle_francais\", find when there is only one genre possible.\n","# Count the cases when there is only one genre related to libelle_francais and the cases where it is not.\n","# Please consider the \"nan\" cases in another counter variable\n","\n","# Count different 'genre' values for each 'libelle_francais'. NaNs are ignored\n","libelle_genre_counts = df_refined.groupby('libelle_francais')['genre'].nunique()\n","\n","one_genre_count = (libelle_genre_counts == 1).sum()\n","multiple_genres_count = (libelle_genre_counts > 1).sum()\n","\n","nan_genre_count = df_refined[(df_refined['libelle_francais'].notna()) & (df_refined['genre'].isna())].shape[0]\n","\n","print(f\"Number of different libelle_francais values: {len(libelle_genre_counts)}\")\n","print('Possible correlation between libelle_francais and genre:')\n","print(f\"Number of 'libelle_francais' with one 'genre': {one_genre_count}\")\n","print(f\"Number of 'libelle_francais' with multiple 'genre': {multiple_genres_count}\")"]},{"cell_type":"code","execution_count":57,"id":"pEffiwi8gfAH","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1737062639914,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"pEffiwi8gfAH","outputId":"15d85929-e170-46eb-c1db-77c934963961"},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of different genre values: 175\n","Possible correlation between genre and libelle_francais:\n","Number of 'genre' with one libelle_francais: 159\n","Number of 'genre' with multiple libelle_francais: 13\n","Number of 'genre' with no libelle_francais: 3\n","\n","Genre that did not match any libelle_francais value: genre\n","Crataegomespilus 0\n","Podocarpus 0\n","x Sorbopyrus 0\n","Name: libelle_francais, dtype: int64\n"]}],"source":["# For each value of \"genre\", find when there is only one libelle_francais possible.\n","# Count the cases when there is only one libelle_francais related to genre and the cases where it is not.\n","# Please consider the \"nan\" cases in another counter variable\n","\n","# Count different 'libelle_francais' values for each 'genre'. NaNs are ignored\n","genre_libelle_counts = df_refined.groupby('genre')['libelle_francais'].nunique()\n","\n","one_libelle_count = (genre_libelle_counts == 1).sum()\n","multiple_libelles_count = (genre_libelle_counts > 1).sum()\n","no_libelles_count = (genre_libelle_counts < 1).sum()\n","\n","print(f\"Number of different genre values: {len(genre_libelle_counts)}\")\n","print('Possible correlation between genre and libelle_francais:')\n","print(f\"Number of 'genre' with one libelle_francais: {one_libelle_count}\")\n","print(f\"Number of 'genre' with multiple libelle_francais: {multiple_libelles_count}\")\n","print(f\"Number of 'genre' with no libelle_francais: {no_libelles_count}\")\n","print(f\"\\nGenre that did not match any libelle_francais value: {genre_libelle_counts[genre_libelle_counts < 1]}\")\n","\n"]},{"cell_type":"code","execution_count":58,"id":"dVhtzF2YeW-_","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3289,"status":"ok","timestamp":1737062643201,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"dVhtzF2YeW-_","outputId":"99292d34-7fd8-41c0-b0dc-878084eedc2a"},"outputs":[{"output_type":"stream","name":"stdout","text":["One libelle mapping dictionary: (159 values)\n","\n","More than one libelle mapping dictionary: (13 values)\n","\n","No libelle mapping dictionary: (3 values)\n","{'Crataegomespilus': None, 'Podocarpus': None, 'x Sorbopyrus': None}\n"]}],"source":["# Generate a dictionary that contains matching libelle_francais for each genre, based on the df_refined dataframe\n","\n","# Create a dictionary that maps each 'genre' to its corresponding 'libelle_francais'\n","# Only include 'genre' that have a unique 'libelle_francais'\n","only_one_libelle_mapping = {}\n","more_than_one_libelle_mapping = {}\n","no_libelle_mapping = {}\n","\n","for genre in df_refined['genre'].dropna().unique(): # Iterating over 'genre'\n"," libelles = df_refined[df_refined['genre'] == genre]['libelle_francais'].dropna().unique() # Getting 'libelle_francais'\n"," if len(libelles) == 1:\n"," only_one_libelle_mapping[genre] = libelles[0] # Mapping genre to single libelle_francais\n"," elif len(libelles) > 1:\n"," more_than_one_libelle_mapping[genre] = libelles # Mapping genre to multiple libelle_francais\n"," else:\n"," no_libelle_mapping[genre] = None # Mapping genre to no libelle_francais\n","\n","\n","from pprint import pprint\n","print(f\"One libelle mapping dictionary: ({len(only_one_libelle_mapping)} values)\")\n","# pprint(only_one_libelle_mapping)\n","print()\n","print(f\"More than one libelle mapping dictionary: ({len(more_than_one_libelle_mapping)} values)\")\n","# pprint(more_than_one_libelle_mapping)\n","print()\n","print(f\"No libelle mapping dictionary: ({len(no_libelle_mapping)} values)\")\n","pprint(no_libelle_mapping)"]},{"cell_type":"code","execution_count":59,"id":"9F7qfwNmjsl-","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":607},"executionInfo":{"elapsed":14,"status":"ok","timestamp":1737062643201,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"9F7qfwNmjsl-","outputId":"7b859b05-f52b-4359-cae8-be9fb41bbfa3"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# prompt: plot bars with len of each dictionnary\n","\n","import matplotlib.pyplot as plt\n","\n","# Assuming you have the dictionaries: only_one_libelle_mapping, more_than_one_libelle_mapping, no_libelle_mapping\n","\n","dictionaries = {\n"," \"= 1\": only_one_libelle_mapping,\n"," \"> 1\": more_than_one_libelle_mapping,\n"," \"= 0\": no_libelle_mapping,\n","}\n","\n","dictionary_names = list(dictionaries.keys())\n","lengths = [len(dictionaries[name]) for name in dictionary_names]\n","\n","plt.figure(figsize=(10, 6))\n","plt.bar(dictionary_names, lengths)\n","plt.xlabel(\"Distinct libelle_francais corresponding values per genre\")\n","plt.ylabel(\"Ocurrences\")\n","plt.tight_layout()\n","plt.show()"]},{"cell_type":"code","execution_count":60,"id":"XFSIHEGQjZfa","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1737062643201,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"XFSIHEGQjZfa","outputId":"500f1c3c-dc45-42ba-bd51-8963a897772d"},"outputs":[{"output_type":"stream","name":"stdout","text":["{'x Sorbopyrus': None, 'Podocarpus': None, 'Crataegomespilus': None}\n"," genre espece variete libelle_francais\n","199193 x Sorbopyrus n. sp. NaN NaN\n","199527 Podocarpus macrophyllus NaN NaN\n","199528 Podocarpus macrophyllus NaN NaN\n","200040 Crataegomespilus grandiflora NaN NaN\n","200049 Crataegomespilus grandiflora NaN NaN\n","\"x Sorbopyrus\":\"Poirier de Bollwiller\" entry correctly added to the dictionary\n","\"Podocarpus\":\"Pin des Bouddhistes\" entry correctly added to the dictionary\n","\"Crataegomespilus\":\"Neflier à grandes feuilles\" entry correctly added to the dictionary\n"]}],"source":["# Case \"=0\" corresponding libelle_francais values per genre : add the information\n","print(no_libelle_mapping)\n","print(df_refined.loc[df_refined['genre'].isin(no_libelle_mapping.keys()),['genre','espece','variete','libelle_francais']])\n","# https://lapepinieredufruitier.fr/categorie-produit/poirier/sorbopyrus/\n","# https://fr.wikipedia.org/wiki/Podocarpus_macrophyllus\n","# https://www.jardin-ecologique.fr/boutique/toutes-nos-plantes/plantes-mediterraneennes/plantes-pour-haies/haie-gourmande/crataegomespilus-grandifolia-neflier-a-grandes-feuilles/#:~:text=Le%20Crataegomespilus%20est%20un%20hybride,ou%20aussi%20x%20Crataegomespilus%20grandiflora\n","no_libelle_mapping['x Sorbopyrus'] = 'Poirier de Bollwiller'\n","no_libelle_mapping['Podocarpus'] = 'Pin des Bouddhistes'\n","no_libelle_mapping['Crataegomespilus'] = 'Neflier à grandes feuilles'\n","\n","for index, row in no_libelle_mapping.items():\n"," if index not in only_one_libelle_mapping.keys():\n"," only_one_libelle_mapping[index] = row\n"," print(f'\"{index}\":\"{row}\" entry correctly added to the dictionary')\n"," else:\n"," print(f'Problem with \"{index}\" : found in both dictionaries')"]},{"cell_type":"code","execution_count":61,"id":"Bm65nzDZnJuv","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1737062643202,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"Bm65nzDZnJuv","outputId":"76b60506-de08-46d4-cc96-8508f938b6e0"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'Poirier de Bollwiller'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":61}],"source":["only_one_libelle_mapping['x Sorbopyrus']"]},{"cell_type":"code","execution_count":62,"id":"Gia4bWbb3vWr","metadata":{"executionInfo":{"elapsed":13638,"status":"ok","timestamp":1737062656831,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"Gia4bWbb3vWr"},"outputs":[],"source":["# for each line in df_refined where libelle_francais is missing (nan value),\n","# if genre value is not nan and genre value is in only_one_libelle_mapping dictionary, use the corresponding value in only_one_libelle_mapping as libelle_francais value\n","\n","for index, row in df_refined.iterrows():\n"," if pd.isna(row['libelle_francais']):\n"," if not pd.isna(row['genre']) and row['genre'] in only_one_libelle_mapping:\n"," df_refined.loc[index, 'libelle_francais'] = only_one_libelle_mapping[row['genre']]"]},{"cell_type":"code","execution_count":63,"id":"UuUT5S3m44Bp","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":15,"status":"ok","timestamp":1737062656831,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"UuUT5S3m44Bp","outputId":"f1e16e49-4eb7-48b2-d3f4-566a1c24dc22"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 0, 0, 0, 0, 169235, 200137, 0, 0,\n"," 304, 16, 1752, 163360, 623, 13956, 67205, 63098,\n"," 0, 0])"]},"metadata":{},"execution_count":63}],"source":["# number of 'nan' for each column\n","column_nb_of_nans = np.array([], dtype='int')\n","for column in df_refined.columns:\n"," where_nan = df_refined[df_refined[column].isna()]\n"," column_nb_of_nans = np.concatenate((column_nb_of_nans,[len(where_nan)]))\n","column_nb_of_nans"]},{"cell_type":"code","execution_count":64,"id":"fSQZsKAo44Q_","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":107},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1737062656831,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"fSQZsKAo44Q_","outputId":"758a959e-3bef-47e6-f7bf-e77391b71371","scrolled":true},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of nan for each variable that is concerned by 1 nan or more:\n"]},{"output_type":"execute_result","data":{"text/plain":[" complement_addresse numero libelle_francais genre espece variete \\\n","0 169235 200137 304 16 1752 163360 \n","\n"," circonference_cm hauteur_m stade_developpement remarquable \n","0 623 13956 67205 63098 "],"text/html":["\n","
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complement_addressenumerolibelle_francaisgenreespecevarietecirconference_cmhauteur_mstade_developpementremarquable
0169235200137304161752163360623139566720563098
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Arbre de fer', 'Parrotie de Perse'],\n"," dtype=object),\n"," 'Gymnocladus': array(['Chicot du Canada', 'Fevier'], dtype=object),\n"," 'Laurus': array(['Laurier sauce', 'Laurier'], dtype=object),\n"," 'Cotinus': array(['Arbre à perruque', \"Fustet d' Amérique\"], dtype=object)}"]},"metadata":{},"execution_count":65}],"source":["# Case \">1 \" corresponding libelle_francais values per genre : check espece and (eventually) variete\n","more_than_one_libelle_mapping"]},{"cell_type":"code","execution_count":66,"id":"I2lF8TSH2TDZ","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"executionInfo":{"elapsed":2582,"status":"ok","timestamp":1737062659407,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"I2lF8TSH2TDZ","outputId":"292720b7-73e5-4045-d955-da482dea3dd7"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" libelle_francais genre espece variete\n","0 Prunus n. sp. Prunus n. sp. NaN\n","1 Cerisier à fleurs Prunus serrulata NaN\n","2 Cerisier à fleurs Prunus serrulata Kanzan'\n","3 Cerisier à fleurs Prunus serrulata Hizakura'\n","4 Cerisier à fleurs Prunus serrulata Fugenzo'\n",".. ... ... ... ...\n","285 Fevier Gymnocladus n. sp. NaN\n","286 Laurier sauce Laurus nobilis NaN\n","287 Laurier Laurus n. sp. NaN\n","288 Arbre à perruque Cotinus coggygria NaN\n","289 Fustet d' Amérique Cotinus obovatus NaN\n","\n","[290 rows x 4 columns]"],"text/html":["\n","
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libelle_francaisgenreespecevariete
0Prunus n. sp.Prunusn. sp.NaN
1Cerisier à fleursPrunusserrulataNaN
2Cerisier à fleursPrunusserrulataKanzan'
3Cerisier à fleursPrunusserrulataHizakura'
4Cerisier à fleursPrunusserrulataFugenzo'
...............
285FevierGymnocladusn. sp.NaN
286Laurier sauceLaurusnobilisNaN
287LaurierLaurusn. sp.NaN
288Arbre à perruqueCotinuscoggygriaNaN
289Fustet d' AmériqueCotinusobovatusNaN
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290 rows × 4 columns

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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"matching_libelle_francais","summary":"{\n \"name\": \"matching_libelle_francais\",\n \"rows\": 290,\n \"fields\": [\n {\n \"column\": \"libelle_francais\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 44,\n \"samples\": [\n \"Parrotie de Perse\",\n \"Poirier\",\n \"Poirier \\u00e0 fleurs\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"genre\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 13,\n \"samples\": [\n \"Laurus\",\n \"Parrotia\",\n \"Prunus\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"espece\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 118,\n \"samples\": [\n \"prunifolia\",\n \"fecunda\",\n \"x subhirtella\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"variete\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 154,\n \"samples\": [\n \"Plena'\",\n \"Reinette Blanche du Canada'\",\n \"Gibsii'\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":66}],"source":["# For each couple (genre, libelle_francais) with genre the key of more_than_one_libelle_mapping and libelle_francais taking\n","# its value in the different corresponding lists, find if there are only one or more corresponding couple \"espece\" value in df_refined that fits the couple.\n","# If there is exactly one possible \"espece\" value, fill a dataframe with libelle_francais, genre and espece values.\n","# A 4th column for \"variete\" contains a NaN value. If there is more than one possible \"espece\" value, then check the different values for \"variete\" and fill the dataframe\n","# with the different cases\n","\n","# Create an empty DataFrame to store the results\n","matching_libelle_francais = pd.DataFrame(columns=['libelle_francais', 'genre', 'espece', 'variete'])\n","\n","# Iterate through the 'more_than_one_libelle_mapping' dictionary\n","for genre, libelles_francais in more_than_one_libelle_mapping.items():\n"," for libelle_francais in libelles_francais:\n"," # Find corresponding rows in df_refined\n"," matching_rows = df_refined[(df_refined['genre'] == genre) & (df_refined['libelle_francais'] == libelle_francais)]\n","\n"," # Check the number of unique 'espece' values\n"," unique_especes = matching_rows['espece'].unique()\n"," num_unique_especes = len(unique_especes)\n","\n"," if num_unique_especes == 1:\n"," # Exactly one 'espece' value\n"," espece = unique_especes[0]\n"," new_row = pd.DataFrame({'libelle_francais': [libelle_francais],\n"," 'genre': [genre],\n"," 'espece': [espece],\n"," 'variete': [np.nan]})\n"," matching_libelle_francais = pd.concat([matching_libelle_francais, new_row], ignore_index=True)\n"," elif num_unique_especes > 1:\n"," # More than one 'espece' value, check 'variete'\n"," for espece in unique_especes:\n"," variete_values = matching_rows[matching_rows['espece'] == espece]['variete'].unique()\n"," for variete in variete_values:\n"," new_row = pd.DataFrame({'libelle_francais': [libelle_francais],\n"," 'genre': [genre],\n"," 'espece': [espece],\n"," 'variete': [variete]})\n"," matching_libelle_francais = pd.concat([matching_libelle_francais, new_row], ignore_index=True)\n","\n","# Display the resulting DataFrame\n","matching_libelle_francais"]},{"cell_type":"code","execution_count":67,"id":"x2cpyOBisR1S","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1737062659407,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"x2cpyOBisR1S","outputId":"b100b9b3-073c-425e-dbd6-6f2a43b89c0c"},"outputs":[{"output_type":"stream","name":"stdout","text":["id 100589\n","type_emplacement Arbre\n","domanialite Jardin\n","arrondissement PARIS 7E ARRDT\n","complement_addresse NaN\n","numero NaN\n","lieu JARDIN DU CHAMP DE MARS\n","id_emplacement P0120631\n","libelle_francais NaN\n","genre Crataegus\n","espece japonica\n","variete NaN\n","circonference_cm 43.00\n","hauteur_m 3.00\n","stade_developpement A\n","remarquable 0.00\n","geo_point_2d_a 48.86\n","geo_point_2d_b 2.30\n","Name: 528, dtype: object\n"]},{"output_type":"execute_result","data":{"text/plain":[" libelle_francais genre espece \\\n","225 Aubépine Crataegus laevigata \n","226 Aubépine Crataegus laevigata \n","227 Aubépine Crataegus laevigata \n","228 Aubépine Crataegus x media \n","229 Aubépine Crataegus x media \n","230 Aubépine Crataegus x media \n","231 Aubépine Crataegus x lavalleei \n","232 Aubépine Crataegus x lavalleei \n","233 Aubépine Crataegus crus-galli \n","234 Aubépine Crataegus crus-galli \n","235 Aubépine Crataegus monogyna var. monogyna \n","236 Aubépine Crataegus monogyna \n","237 Aubépine Crataegus viridis \n","238 Aubépine Crataegus chrysocarpa var. phoenicea \n","239 Aubépine Crataegus x lettermanii \n","240 Aubépine Crataegus x sorbifolia \n","241 Aubépine Crataegus x persimilis \n","242 Aubépine Crataegus fecunda \n","243 Aubépine Crataegus canbyi \n","244 Aubépine Crataegus spathulata \n","245 Aubépine Crataegus aprica \n","246 Aubépine Crataegus ambigua \n","247 Aubépine Crataegus venustula \n","248 Aubepine Crataegus n. sp. \n","249 Aubepine Crataegus monogyna \n","250 Aubepine Crataegus rotundifolia var. aboriginum \n","251 Aubepine Crataegus champlainensis \n","\n"," variete \n","225 NaN \n","226 Paul's Scarlet Hamlet' \n","227 Plena' \n","228 Rubra Plena' \n","229 Punicea' \n","230 Paul's Scarlet' \n","231 Carrierei' \n","232 NaN \n","233 Salicifolia' \n","234 NaN \n","235 NaN \n","236 Stricta' \n","237 NaN \n","238 NaN \n","239 NaN \n","240 NaN \n","241 NaN \n","242 NaN \n","243 NaN \n","244 NaN \n","245 NaN \n","246 NaN \n","247 NaN \n","248 NaN \n","249 NaN \n","250 NaN \n","251 NaN "],"text/html":["\n","
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libelle_francaisgenreespecevariete
225AubépineCrataeguslaevigataNaN
226AubépineCrataeguslaevigataPaul's Scarlet Hamlet'
227AubépineCrataeguslaevigataPlena'
228AubépineCrataegusx mediaRubra Plena'
229AubépineCrataegusx mediaPunicea'
230AubépineCrataegusx mediaPaul's Scarlet'
231AubépineCrataegusx lavalleeiCarrierei'
232AubépineCrataegusx lavalleeiNaN
233AubépineCrataeguscrus-galliSalicifolia'
234AubépineCrataeguscrus-galliNaN
235AubépineCrataegusmonogyna var. monogynaNaN
236AubépineCrataegusmonogynaStricta'
237AubépineCrataegusviridisNaN
238AubépineCrataeguschrysocarpa var. phoeniceaNaN
239AubépineCrataegusx lettermaniiNaN
240AubépineCrataegusx sorbifoliaNaN
241AubépineCrataegusx persimilisNaN
242AubépineCrataegusfecundaNaN
243AubépineCrataeguscanbyiNaN
244AubépineCrataegusspathulataNaN
245AubépineCrataegusapricaNaN
246AubépineCrataegusambiguaNaN
247AubépineCrataegusvenustulaNaN
248AubepineCrataegusn. sp.NaN
249AubepineCrataegusmonogynaNaN
250AubepineCrataegusrotundifolia var. aboriginumNaN
251AubepineCrataeguschamplainensisNaN
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","summary":"{\n \"name\": \" (matching_libelle_francais['genre'] == row['genre'])]\",\n \"rows\": 27,\n \"fields\": [\n {\n \"column\": \"libelle_francais\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"Aubepine\",\n \"Aub\\u00e9pine\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"genre\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Crataegus\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"espece\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 20,\n \"samples\": [\n \"laevigata\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"variete\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 8,\n \"samples\": [\n \"Plena'\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":67}],"source":["row = df_refined[df_refined['libelle_francais'].isna()].iloc[0]\n","print(row)\n","matching_libelle_francais[\n"," (matching_libelle_francais['genre'] == row['genre'])]"]},{"cell_type":"code","execution_count":68,"id":"14KyAGnNqLaD","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"elapsed":669,"status":"ok","timestamp":1737062660069,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"14KyAGnNqLaD","outputId":"9618ce17-a00c-4f52-84f1-159c0ab6afcd"},"outputs":[{"output_type":"stream","name":"stdout","text":["Amount of 'libelle_francais' found: 142\n","Amount of 'libelle_francais' not found: 162\n"]}],"source":["# in df_refined, for each row where a nan value is found at 'libelle_francais' column, check the different \"libelle_francais\" corresponding values in matching_libelle_francais\n","# that fit the couple (\"genre\",\"espece\"). If there is only one possible \"libelle_francais\" value, then attribute it to the considered row.\n","# If not, also check the \"variete\" value and find out if there only is one \"libelle_francais\" possible matching value. If still not, note this case as unsolvable.\n","\n","index_found = []\n","index_not_found = []\n","\n","# Iterate through rows with NaN 'libelle_francais' in df_refined\n","for index, row in df_refined[df_refined['libelle_francais'].isna()].iterrows():\n"," # Find matching rows in matching_libelle_francais based on 'genre' and 'espece'\n","\n"," matching_rows = matching_libelle_francais[\n"," (matching_libelle_francais['genre'].astype(str) == str(row['genre'])) &\n"," (matching_libelle_francais['espece'].astype(str) == str(row['espece']))\n"," ]\n","\n"," # Check the number of unique 'libelle_francais' values\n"," num_unique_libelles = len(matching_rows['libelle_francais'].unique())\n","\n"," if num_unique_libelles == 1:\n"," # Only one possible 'libelle_francais'\n"," df_refined.loc[index, 'libelle_francais'] = matching_rows['libelle_francais'].iloc[0]\n"," # print(f\"'libelle_francais' value found : for index {index}\")\n"," index_found.append(index)\n","\n"," elif num_unique_libelles > 1:\n"," # Multiple possible 'libelle_francais', check 'variete'\n"," matching_rows_variete = matching_rows[\n"," matching_rows['variete'].astype(str) == str(row['variete'])\n"," ]\n"," num_unique_libelles_variete = len(matching_rows_variete['libelle_francais'].unique())\n","\n"," if num_unique_libelles_variete == 1:\n"," # Only one possible 'libelle_francais' after considering 'variete'\n"," df_refined.loc[index, 'libelle_francais'] = matching_rows_variete['libelle_francais'].iloc[0]\n"," # print(f\"'libelle_francais' value found : for index {index}\")\n"," index_found.append(index)\n"," else: # > 1\n"," # Still multiple possible 'libelle_francais', mark as unsolvable\n"," # print(f\"'libelle_francais' value not found : for index {index} : too many possible values\")\n"," index_not_found.append(index)\n","\n"," else: # num_unique_libelles == 0\n"," # No possible 'libelle_francais', mark as unsolvable\n"," # print(f\"'libelle_francais' value not found : for index {index} : no matching 'libelle_francais' value\")\n"," index_not_found.append(index)\n","\n","print(f\"Amount of 'libelle_francais' found: {len(index_found)}\")\n","print(f\"Amount of 'libelle_francais' not found: {len(index_not_found)}\")"]},{"cell_type":"code","execution_count":69,"id":"m3S60h5Z_Dip","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1737062660069,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"m3S60h5Z_Dip","outputId":"01ddfa92-6482-4051-97ed-a30f427c7ef2"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" genre espece variete libelle_francais\n","528 Crataegus japonica NaN NaN\n","5282 Sorbus aria Pissardii' NaN\n","8900 Pyrus Fruit n. sp. Chanticleer' NaN\n","12375 Corylus cerasifera NaN NaN\n","12959 Parrotia persica Chanticleer' NaN\n","... ... ... ... ...\n","199405 Crataegus speciosa NaN NaN\n","199984 Malus purpurea NaN NaN\n","199993 Malus sargentii NaN NaN\n","199996 Malus sargentii NaN NaN\n","200128 Parrotia n. sp. NaN NaN\n","\n","[162 rows x 4 columns]"],"text/html":["\n","
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genreespecevarietelibelle_francais
528CrataegusjaponicaNaNNaN
5282SorbusariaPissardii'NaN
8900PyrusFruit n. sp.Chanticleer'NaN
12375CoryluscerasiferaNaNNaN
12959ParrotiapersicaChanticleer'NaN
...............
199405CrataegusspeciosaNaNNaN
199984MaluspurpureaNaNNaN
199993MalussargentiiNaNNaN
199996MalussargentiiNaNNaN
200128Parrotian. sp.NaNNaN
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162 rows × 4 columns

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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"remaining_cases","repr_error":"0"}},"metadata":{},"execution_count":69}],"source":["remaining_cases = df_refined.loc[index_not_found][['genre','espece','variete','libelle_francais']].copy()\n","remaining_cases"]},{"cell_type":"code","execution_count":70,"id":"En8bvFMcBWt3","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1737062660069,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"En8bvFMcBWt3","outputId":"a4ca5c6e-d700-41cf-8139-3f3550ad1c0c"},"outputs":[{"output_type":"stream","name":"stdout","text":["Different remaining cases : 51\n"," genre espece variete libelle_francais\n","12375 Corylus cerasifera NaN NaN\n","123822 Corylus pseudoacacia Unifolia' NaN\n","528 Crataegus japonica NaN NaN\n","199405 Crataegus speciosa NaN NaN\n","194059 Crataegus NaN NaN NaN\n","160955 Fraxinus betulus Fastigiata' NaN\n","161675 Fraxinus betulus NaN NaN\n","107494 Fraxinus calleryana Chanticleer' NaN\n","199101 Fraxinus chinensis NaN NaN\n","176284 Fraxinus minor NaN NaN\n","199154 Fraxinus pensylvanicum NaN NaN\n","198339 Fraxinus platyphyllos NaN NaN\n","108830 Fraxinus pseudoacacia Unifolia' NaN\n","94873 Fraxinus tulipifera NaN NaN\n","191688 Fraxinus NaN NaN NaN\n","141292 Gymnocladus NaN NaN NaN\n","170317 Ligustrum laevigata NaN NaN\n","194061 Ligustrum NaN NaN NaN\n","176508 Malus minor NaN NaN\n","80830 Malus nigra NaN NaN\n","197049 Malus padus NaN NaN\n","194678 Malus pumila Shinseiki' NaN\n","199984 Malus purpurea NaN NaN\n","199993 Malus sargentii NaN NaN\n","15330 Malus siliquastrum NaN NaN\n","19383 Malus NaN NaN NaN\n","15882 Ostrya avium NaN NaN\n","114634 Parrotia n. sp. NaN NaN\n","129676 Parrotia persica Baumannii' NaN\n","12959 Parrotia persica Chanticleer' NaN\n","80093 Parrotia sylvatica NaN NaN\n","73591 Prunus bignonioides NaN NaN\n","107662 Prunus calleryana Chanticleer' NaN\n","192112 Prunus cordata NaN NaN\n","191849 Prunus negundo NaN NaN\n","156348 Prunus pseudoacacia Unifolia' NaN\n","187246 Prunus pseudoplatanus NaN NaN\n","69885 Prunus NaN NaN NaN\n","8900 Pyrus Fruit n. sp. Chanticleer' NaN\n","146565 Pyrus alba Raket' NaN\n","166560 Pyrus australis NaN NaN\n","95678 Pyrus carpinifolia NaN NaN\n","107892 Pyrus controversa NaN NaN\n","187928 Pyrus domestica Bon chrétien Williams' NaN\n","193962 Pyrus domestica Drap d'Or' NaN\n","82969 Pyrus negundo NaN NaN\n","108675 Pyrus NaN NaN NaN\n","5282 Sorbus aria Pissardii' NaN\n","197045 Sorbus monspessulanum NaN NaN\n","15366 Sorbus tomentosa NaN NaN\n","195404 NaN NaN NaN NaN\n"]}],"source":["distinct_remaining_cases = remaining_cases.drop_duplicates().sort_values(by=['genre','espece','variete'])\n","print(f'Different remaining cases : {len(distinct_remaining_cases)}')\n","print(distinct_remaining_cases)\n","\n","# could go further but we will stop here"]},{"cell_type":"code","execution_count":71,"id":"OCR53Q0D1SaT","metadata":{"executionInfo":{"elapsed":7,"status":"ok","timestamp":1737062660069,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"OCR53Q0D1SaT"},"outputs":[],"source":["# some analysis and nan replacement need to be done on espece and variete\n","# impossible to have variete if only have genre and espece... Can take very long time"]},{"cell_type":"markdown","source":["#### espece"],"metadata":{"id":"jFJCBiY03t72"},"id":"jFJCBiY03t72"},{"cell_type":"code","source":["# prompt: take all the rows where \"espece\" is equal to nan. Then select the cases when variete is not nan\n","\n","# Assuming df_refined is your DataFrame\n","espece_nan_rows = df_refined[df_refined['espece'].isna()]\n","variete_not_nan_rows = espece_nan_rows[espece_nan_rows['variete'].notna()]\n","variete_not_nan_rows\n","\n","# same approach, based on finding the value based on the other fields. If possible, find cases where other both other fields and value are given"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":755},"id":"hB-pqw4S3epD","executionInfo":{"status":"ok","timestamp":1737062660069,"user_tz":-60,"elapsed":7,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"7c1b74b6-f5ca-41dd-c098-803579e43829"},"id":"hB-pqw4S3epD","execution_count":72,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" id type_emplacement domanialite arrondissement \\\n","1725 101858 Arbre Jardin PARIS 19E ARRDT \n","2323 102582 Arbre Jardin PARIS 16E ARRDT \n","2733 103215 Arbre Jardin PARIS 17E ARRDT \n","2742 103224 Arbre Jardin PARIS 17E ARRDT \n","3944 104827 Arbre Jardin PARIS 13E ARRDT \n","... ... ... ... ... \n","198666 2022738 Arbre Alignement PARIS 11E ARRDT \n","198667 2022739 Arbre Alignement PARIS 11E ARRDT \n","198668 2022740 Arbre Alignement PARIS 11E ARRDT \n","198680 2022752 Arbre Alignement PARIS 11E ARRDT \n","198681 2022754 Arbre Alignement PARIS 11E ARRDT \n","\n"," complement_addresse numero \\\n","1725 NaN NaN \n","2323 82 à 90 NaN \n","2733 NaN NaN \n","2742 NaN NaN \n","3944 NaN NaN \n","... ... ... \n","198666 NaN NaN \n","198667 NaN NaN \n","198668 NaN NaN \n","198680 NaN NaN \n","198681 NaN NaN \n","\n"," lieu id_emplacement \\\n","1725 PARC DES BUTTES CHAUMONT D0030002 \n","2323 JARDIN DE L AVENUE FOCH / 10 AVENUE FOCH 408032 \n","2733 PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING 000B0167 \n","2742 PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING 000B0188 \n","3944 SQUARE RENE LE GALL 10255 \n","... ... ... \n","198666 PLACE JEAN FERRAT 301001 \n","198667 PLACE JEAN FERRAT 301002 \n","198668 PLACE JEAN FERRAT 301003 \n","198680 PLACE JEAN FERRAT 105001 \n","198681 BOULEVARD DE BELLEVILLE 101001 \n","\n"," libelle_francais genre espece variete \\\n","1725 Pommier à fleurs Malus NaN Aldenhamensis' \n","2323 Cornouiller Cornus NaN Eddie's White Wonder' \n","2733 Cerisier à fleurs Prunus NaN Accolade' \n","2742 Cerisier à fleurs Prunus NaN Accolade' \n","3944 Pommier à fleurs Malus NaN Golden Hornet' \n","... ... ... ... ... \n","198666 Orme Ulmus NaN Nanguen' LUTECE \n","198667 Orme Ulmus NaN Nanguen' LUTECE \n","198668 Orme Ulmus NaN Nanguen' LUTECE \n","198680 Orme Ulmus NaN Nanguen' LUTECE \n","198681 Orme Ulmus NaN Nanguen' LUTECE \n","\n"," circonference_cm hauteur_m stade_developpement remarquable \\\n","1725 40.00 4.00 NaN NaN \n","2323 25.00 3.00 JA 0.00 \n","2733 32.00 NaN NaN NaN \n","2742 60.00 NaN NaN NaN \n","3944 40.00 4.00 NaN NaN \n","... ... ... ... ... \n","198666 30.00 5.00 NaN 0.00 \n","198667 20.00 5.00 NaN 0.00 \n","198668 20.00 5.00 NaN 0.00 \n","198680 20.00 5.00 NaN 0.00 \n","198681 35.00 7.00 NaN 0.00 \n","\n"," geo_point_2d_a geo_point_2d_b \n","1725 48.88 2.39 \n","2323 48.87 2.28 \n","2733 48.89 2.32 \n","2742 48.89 2.32 \n","3944 48.83 2.35 \n","... ... ... \n","198666 48.87 2.38 \n","198667 48.87 2.38 \n","198668 48.87 2.38 \n","198680 48.87 2.38 \n","198681 48.87 2.38 \n","\n","[1389 rows x 18 columns]"],"text/html":["\n","
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idtype_emplacementdomanialitearrondissementcomplement_addressenumerolieuid_emplacementlibelle_francaisgenreespecevarietecirconference_cmhauteur_mstade_developpementremarquablegeo_point_2d_ageo_point_2d_b
1725101858ArbreJardinPARIS 19E ARRDTNaNNaNPARC DES BUTTES CHAUMONTD0030002Pommier à fleursMalusNaNAldenhamensis'40.004.00NaNNaN48.882.39
2323102582ArbreJardinPARIS 16E ARRDT82 à 90NaNJARDIN DE L AVENUE FOCH / 10 AVENUE FOCH408032CornouillerCornusNaNEddie's White Wonder'25.003.00JA0.0048.872.28
2733103215ArbreJardinPARIS 17E ARRDTNaNNaNPARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING000B0167Cerisier à fleursPrunusNaNAccolade'32.00NaNNaNNaN48.892.32
2742103224ArbreJardinPARIS 17E ARRDTNaNNaNPARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING000B0188Cerisier à fleursPrunusNaNAccolade'60.00NaNNaNNaN48.892.32
3944104827ArbreJardinPARIS 13E ARRDTNaNNaNSQUARE RENE LE GALL10255Pommier à fleursMalusNaNGolden Hornet'40.004.00NaNNaN48.832.35
.........................................................
1986662022738ArbreAlignementPARIS 11E ARRDTNaNNaNPLACE JEAN FERRAT301001OrmeUlmusNaNNanguen' LUTECE30.005.00NaN0.0048.872.38
1986672022739ArbreAlignementPARIS 11E ARRDTNaNNaNPLACE JEAN FERRAT301002OrmeUlmusNaNNanguen' LUTECE20.005.00NaN0.0048.872.38
1986682022740ArbreAlignementPARIS 11E ARRDTNaNNaNPLACE JEAN FERRAT301003OrmeUlmusNaNNanguen' LUTECE20.005.00NaN0.0048.872.38
1986802022752ArbreAlignementPARIS 11E ARRDTNaNNaNPLACE JEAN FERRAT105001OrmeUlmusNaNNanguen' LUTECE20.005.00NaN0.0048.872.38
1986812022754ArbreAlignementPARIS 11E ARRDTNaNNaNBOULEVARD DE BELLEVILLE101001OrmeUlmusNaNNanguen' LUTECE35.007.00NaN0.0048.872.38
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1389 rows × 18 columns

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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"variete_not_nan_rows","repr_error":"0"}},"metadata":{},"execution_count":72}]},{"cell_type":"markdown","metadata":{"id":"32tkfElqDL3G"},"source":["### Before and After cleaning anomalies"],"id":"32tkfElqDL3G"},{"cell_type":"code","source":["import seaborn as sns\n","\n","\n","# Calculate the number of non-missing values for each column\n","non_missing_original = df.notnull().sum()\n","non_missing_refined = df_refined.notnull().sum()\n","\n","# Prepare data for the bar plot\n","comparison_data = pd.DataFrame({\n"," 'Columns': non_missing_original.index,\n"," 'Original Dataset': non_missing_original.values,\n"," 'Corrected Dataset': non_missing_refined.values\n","}).melt(id_vars='Columns', var_name='Dataset', value_name='Non-Missing Count')\n","\n","\n","# Create the bar plot\n","plt.figure(figsize=(12, 6))\n","ax = sns.barplot(\n"," data=comparison_data,\n"," x='Columns',\n"," y='Non-Missing Count',\n"," hue='Dataset',\n"," palette='pastel'\n",")\n","# Rotate the x-axis labels\n","plt.xticks(rotation=45, ha='right')\n","\n","# Add count annotations to each bar (inside the bars)\n","for p in ax.patches:\n"," height = p.get_height() # Bar height (value of the bar)\n"," if height > 0: # Only annotate bars with positive height\n"," ax.annotate(\n"," f'{int(height)}', # Display the count as an integer\n"," (p.get_x() + p.get_width() / 2., height / 2), # Position inside the bar, vertically centered\n"," ha='center', va='center', fontsize=10, color='black', rotation=90\n"," )\n","\n","# Add plot titles and labels\n","plt.title('Comparison of Non-Missing Values by Column', fontsize=14)\n","plt.ylabel('Number of Non-Missing Values', fontsize=12)\n","plt.xlabel('Columns', fontsize=12)\n","plt.legend(title='Dataset', loc='upper right')\n","plt.grid(axis='y', linestyle='--', alpha=0.7)\n","plt.tight_layout() # Adjust layout to prevent overlap\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":573},"id":"1PMO745VZod2","executionInfo":{"status":"ok","timestamp":1737062662168,"user_tz":-60,"elapsed":1591,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"1b7e704a-78d0-43e6-9d0c-b47209c7d69e"},"execution_count":73,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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Create a DataFrame for plotting\n","diff_df = data_diff = (non_missing_refined - non_missing_original).reset_index()\n","diff_df.columns = ['Columns', 'Difference']\n","\n","# Plot the data using Seaborn\n","plt.figure(figsize=(10, 6))\n","ax = sns.barplot(\n"," data=diff_df,\n"," x=diff_df.columns[0],\n"," y=diff_df.columns[1],\n"," palette=[\"blue\" if x > 0 else \"red\" for x in diff_df['Difference']] # Blue for positive, Red for negative\n",")\n","\n","# Set the y-axis to be centered at 0\n","ax.axhline(0, color='black',linewidth=1)\n","\n","# Add plot titles and labels\n","plt.title('Differences Between Original and Corrected Datasets counts', fontsize=14)\n","plt.ylabel('Difference (Original - Corrected)', fontsize=12)\n","plt.xlabel('Columns', fontsize=12)\n","\n","# Rotate the x-axis labels\n","plt.xticks(rotation=45, ha='right')\n","\n","# Display the values inside the bars\n","for p in ax.patches:\n"," height = p.get_height()\n"," ax.text(\n"," p.get_x() + p.get_width() / 2, # Position at the center of the bar\n"," height/2, # Position at the top of the bar\n"," f'{int(height)}', # Annotate with the number\n"," ha='center', va='center', fontsize=10, color='k', rotation=0\n"," )\n","\n","# Show the plot\n","plt.tight_layout() # Adjust the layout to avoid overlap\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":717},"id":"5D75sJRqs4vW","executionInfo":{"status":"ok","timestamp":1737062662968,"user_tz":-60,"elapsed":815,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"8573649c-9423-4cd1-ea41-888d00186431"},"execution_count":74,"outputs":[{"output_type":"stream","name":"stderr","text":[":7: FutureWarning: \n","\n","Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n","\n"," ax = sns.barplot(\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"id":"5D75sJRqs4vW"},{"cell_type":"code","execution_count":75,"metadata":{"executionInfo":{"elapsed":910,"status":"ok","timestamp":1737062663874,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"8Tx4l94HDGb2","colab":{"base_uri":"https://localhost:8080/","height":849},"outputId":"508580f5-c834-4ec2-9e37-ebc54d7e8d0d"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# boxplots\n","for variable in quantitative_variables:\n"," plot_univariate_variable_analysis(df_refined, variable, 'boxplot')"],"id":"8Tx4l94HDGb2"},{"cell_type":"markdown","id":"V7hc3wnUW7gq","metadata":{"id":"V7hc3wnUW7gq"},"source":["## 4) Analysis"]},{"cell_type":"markdown","id":"uZfu0OOUYRI3","metadata":{"id":"uZfu0OOUYRI3"},"source":["### Naive analysis (after data clean)"]},{"cell_type":"code","execution_count":76,"id":"2SUutH-hYK1E","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1737062663874,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"2SUutH-hYK1E","outputId":"6c8e2042-bb7c-4da3-e17f-049f01c6632b","scrolled":true},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","RangeIndex: 200137 entries, 0 to 200136\n","Data columns (total 18 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 id 200137 non-null int64 \n"," 1 type_emplacement 200137 non-null object \n"," 2 domanialite 200137 non-null object \n"," 3 arrondissement 200137 non-null object \n"," 4 complement_addresse 30902 non-null object \n"," 5 numero 0 non-null float64\n"," 6 lieu 200137 non-null object \n"," 7 id_emplacement 200137 non-null object \n"," 8 libelle_francais 199975 non-null object \n"," 9 genre 200121 non-null object \n"," 10 espece 198385 non-null object \n"," 11 variete 36777 non-null object \n"," 12 circonference_cm 199514 non-null float64\n"," 13 hauteur_m 186181 non-null float64\n"," 14 stade_developpement 132932 non-null object \n"," 15 remarquable 137039 non-null float64\n"," 16 geo_point_2d_a 200137 non-null float64\n"," 17 geo_point_2d_b 200137 non-null float64\n","dtypes: float64(6), int64(1), object(11)\n","memory usage: 27.5+ MB\n"]}],"source":["df_refined.info() # display count of columns, lines and columns names"]},{"cell_type":"code","execution_count":77,"id":"X0QJb_HCYK1E","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1737062663874,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"X0QJb_HCYK1E","outputId":"a67442cd-a0c3-4598-d61f-53d7baae71c9","scrolled":true},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" id numero circonference_cm hauteur_m remarquable \\\n","count 200137.00 0.00 199514.00 186181.00 137039.00 \n","mean 387202.68 NaN 80.26 8.93 0.00 \n","std 545603.24 NaN 67.48 5.97 0.04 \n","min 99874.00 NaN 0.00 0.00 0.00 \n","25% 155927.00 NaN 30.00 5.00 0.00 \n","50% 221078.00 NaN 70.00 9.00 0.00 \n","75% 274102.00 NaN 115.00 12.00 0.00 \n","max 2024745.00 NaN 3120.00 51.55 1.00 \n","\n"," geo_point_2d_a geo_point_2d_b \n","count 200137.00 200137.00 \n","mean 48.85 2.35 \n","std 0.03 0.05 \n","min 48.74 2.21 \n","25% 48.84 2.31 \n","50% 48.85 2.35 \n","75% 48.88 2.39 \n","max 48.91 2.47 "],"text/html":["\n","
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idnumerocirconference_cmhauteur_mremarquablegeo_point_2d_ageo_point_2d_b
count200137.000.00199514.00186181.00137039.00200137.00200137.00
mean387202.68NaN80.268.930.0048.852.35
std545603.24NaN67.485.970.040.030.05
min99874.00NaN0.000.000.0048.742.21
25%155927.00NaN30.005.000.0048.842.31
50%221078.00NaN70.009.000.0048.852.35
75%274102.00NaN115.0012.000.0048.882.39
max2024745.00NaN3120.0051.551.0048.912.47
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","summary":"{\n \"name\": \"## > 250000 cm as circumference, > 800000 m as height\",\n \"rows\": 8,\n \"fields\": [\n {\n \"column\": \"id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 636490.3531323484,\n \"min\": 99874.0,\n \"max\": 2024745.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 387202.68230761925,\n 221078.0,\n 200137.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"numero\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 0.0,\n \"max\": 0.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"circonference_cm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 70371.0990261856,\n \"min\": 0.0,\n \"max\": 199514.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 80.26401656024139\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hauteur_m\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 65820.25619681984,\n \"min\": 0.0,\n \"max\": 186181.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 8.933927092453045\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"remarquable\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 48450.55066982106,\n \"min\": 0.0,\n \"max\": 137039.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 0.0013426834696692183\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"geo_point_2d_a\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 70744.31300132743,\n \"min\": 0.0302341454886933,\n \"max\": 200137.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 48.85449078901268\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"geo_point_2d_b\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 70758.40152298885,\n \"min\": 0.05121963963079915,\n \"max\": 200137.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 2.3482078955462966\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":77}],"source":["df_refined.describe()# general statistics only on number types variables (int and floats)\n","\n","# problems:\n","## 0 as min value for circumference, height\n","## > 250000 cm as circumference, > 800000 m as height"]},{"cell_type":"code","execution_count":78,"id":"02rOaFuYYrxA","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":418,"status":"ok","timestamp":1737062664287,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"02rOaFuYYrxA","outputId":"b3c3f06e-6f36-4a63-b85e-de27555009b0"},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of different species: 539\n"]}],"source":["print(f\"Number of different species: {df_refined['espece'].nunique()}\")"]},{"cell_type":"code","execution_count":79,"id":"5vyyd7-lZ_tT","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1737062664287,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"5vyyd7-lZ_tT","outputId":"56e93bbe-fc4d-4de0-ea00-8081b3dde942"},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of remarquable different species: 66\n"]}],"source":["print(f\"Number of remarquable different species: {df_refined[df_refined['remarquable']==1]['espece'].nunique()}\")"]},{"cell_type":"markdown","id":"-859g19jW-0l","metadata":{"id":"-859g19jW-0l"},"source":["### Univariate Analysis"]},{"cell_type":"code","execution_count":80,"id":"_we2vsHTWuTn","metadata":{"executionInfo":{"elapsed":9,"status":"ok","timestamp":1737062664287,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"_we2vsHTWuTn"},"outputs":[],"source":["interesting_variables = {'domanialite':'pie','arrondissement':'heat_map',\n"," 'lieu':'heat_map', 'libelle_francais':'bars',\n"," 'genre':'bars', 'espece':'bars',\n"," 'variete':'bars','circonference_cm':'hist',\n"," 'hauteur_m':'hist','stade_developpement':'pie',\n"," 'remarquable':'bars_log'}"]},{"cell_type":"code","execution_count":81,"id":"tn6p_aQcXmLM","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"output_embedded_package_id":"13LADlWprO2_fGI3yOouYyyu2hHs99ijc"},"id":"tn6p_aQcXmLM","outputId":"6152b149-0cdb-4d21-8390-8fb3fbaba709","executionInfo":{"status":"ok","timestamp":1737062696721,"user_tz":-60,"elapsed":32443,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}],"source":["for variable in interesting_variables:\n"," plot_univariate_variable_analysis(df_refined, variable, interesting_variables[variable])\n"," print()"]},{"cell_type":"code","execution_count":82,"id":"58c0f807-803f-4cef-9ff6-a827020cb19f","metadata":{"id":"58c0f807-803f-4cef-9ff6-a827020cb19f","executionInfo":{"status":"ok","timestamp":1737062696721,"user_tz":-60,"elapsed":7,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# remains to do\n","## multivariate analysis (not yet)"]},{"cell_type":"code","source":["plot_bivariate_variable_analysis(df_refined, 'remarquable', 'hauteur_m', 'boxplot')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":564},"id":"OQrQVkhob6oR","executionInfo":{"status":"ok","timestamp":1737062696721,"user_tz":-60,"elapsed":7,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"fe96a611-1254-4c7d-fdc5-1ca04d6709e4"},"id":"OQrQVkhob6oR","execution_count":83,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["plot_bivariate_variable_analysis(df_refined, 'remarquable', 'circonference_cm', 'boxplot')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":564},"id":"olxCasOYdXxO","executionInfo":{"status":"ok","timestamp":1737062697779,"user_tz":-60,"elapsed":1063,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"af497138-e681-46bb-aaff-b5065ffd4b76"},"id":"olxCasOYdXxO","execution_count":84,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["variable1_name = 'remarquable'\n","variable2_name = 'hauteur_m'\n","pearsonr, p_value = compute_bivariate_stats_indicators(df_refined, variable1_name, variable2_name)\n","print(f\"Pearson correlation coefficient between {variable1_name} and {variable2_name} : {pearsonr}\") # existing correlation but very weak\n","\n","print()\n","\n","variable1_name = 'remarquable'\n","variable2_name = 'circonference_cm'\n","pearsonr, p_value = compute_bivariate_stats_indicators(df_refined, variable1_name, variable2_name)\n","print(f\"Pearson correlation coefficient between {variable1_name} and {variable2_name} : {pearsonr}\") # existing correlation but very weak"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GtSIhb2Hdcsm","executionInfo":{"status":"ok","timestamp":1737062782320,"user_tz":-60,"elapsed":1522,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"8d1a664b-6b5f-4175-e597-70f236770a10"},"id":"GtSIhb2Hdcsm","execution_count":87,"outputs":[{"output_type":"stream","name":"stdout","text":["Both quantitative variables : Pearson correlation analysis\n","Pearson correlation coefficient between remarquable and hauteur_m : 0.06269852992286187\n","\n","Both quantitative variables : Pearson correlation analysis\n","Pearson correlation coefficient between remarquable and circonference_cm : 0.11668322959396166\n"]}]},{"cell_type":"code","source":["eta_squared, anova_table = compute_bivariate_stats_indicators(df_refined, 'remarquable','espece')\n","print(f\"Eta-squared: {eta_squared}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Rwt35nIpBJa2","executionInfo":{"status":"ok","timestamp":1737062823550,"user_tz":-60,"elapsed":37593,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"0aaeabc1-1de3-4c14-e7f9-a7ebb154cd81"},"id":"Rwt35nIpBJa2","execution_count":88,"outputs":[{"output_type":"stream","name":"stdout","text":["Both quantitative and categorical variables : ANOVA analysis\n","Eta-squared: 0.05512247910689265\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/statsmodels/base/model.py:1894: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 538, but rank is 481\n"," warnings.warn('covariance of constraints does not have full '\n","/content/drive/MyDrive/Colab_Notebooks/Project_2/data_analysis_functions.py:295: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n"," eta_squared = anova_table['sum_sq'][0] / sum(anova_table['sum_sq'])\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"oGVLCs_pAqFi"},"id":"oGVLCs_pAqFi","execution_count":null,"outputs":[]}],"metadata":{"colab":{"provenance":[],"toc_visible":true},"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.12.7"}},"nbformat":4,"nbformat_minor":5} \ No newline at end of file diff --git a/Paris_tree_analysis_clean.ipynb b/Paris_tree_analysis_clean.ipynb deleted file mode 100644 index d6f1026..0000000 --- a/Paris_tree_analysis_clean.ipynb +++ /dev/null @@ -1,3117 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "307bea88-e08c-4787-9d3a-047272601ef1", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Exploring and Analyzing Paris trees data" - ] - }, - { - "cell_type": "markdown", - "id": "39449094-4efa-4627-b19c-6aa521c7a7e8", - "metadata": {}, - "source": [ - "Taken from https://s3-eu-west-1.amazonaws.com/static.oc-static.com/prod/courses/files/AI+Engineer/Project+2+Participez+%C3%A0+un+concours+sur+la+Smart+City/p2-arbres-fr.csv, coming from https://opendata.paris.fr/explore/dataset/les-arbres/map/?dataChart=eyJxdWVyaWVzIjpbeyJjb25maWciOnsiZGF0YXNldCI6Imxlcy1hcmJyZXMiLCJvcHRpb25zIjp7fX0sImNoYXJ0cyI6W3siYWxpZ25Nb250aCI6dHJ1ZSwidHlwZSI6ImNvbHVtbiIsImZ1bmMiOiJBVkciLCJ5QXhpcyI6ImlkYmFzZSIsInNjaWVudGlmaWNEaXNwbGF5Ijp0cnVlLCJjb2xvciI6IiMwMDMzNjYifV0sInhBeGlzIjoidHlwZWVtcGxhY2VtZW50IiwibWF4cG9pbnRzIjo1MCwic29ydCI6IiJ9XSwidGltZXNjYWxlIjoiIiwiZGlzcGxheUxlZ2VuZCI6dHJ1ZSwiYWxpZ25Nb250aCI6dHJ1ZX0%3D&disjunctive.typeemplacement&disjunctive.arrondissement&disjunctive.libellefrancais&disjunctive.genre&disjunctive.espece&disjunctive.varieteoucultivar&disjunctive.stadedeveloppement&disjunctive.remarquable&location=12,48.85984,2.31341&basemap=jawg.streets" - ] - }, - { - "cell_type": "markdown", - "id": "ceeb296c-0ca5-43d6-b5fb-a4620de1fc75", - "metadata": {}, - "source": [ - "## 1) Opening csv file" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "b4458a0a-853f-4a87-bb46-93ac6a9ba08b", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "pd.set_option('display.float_format', '{:.2f}'.format) # don't want scientific notation. Floats are enough\n", - "\n", - "csv_path = \"./p2-trees-fr.csv\"\n", - "df = pd.read_csv(csv_path, sep=';')" - ] - }, - { - "cell_type": "markdown", - "id": "9de7fc9e-a309-46ac-a4f8-74effb1ca249", - "metadata": {}, - "source": [ - "## 2) Naive Analysis" - ] - }, - { - "cell_type": "markdown", - "id": "d555a028-62b6-47ae-841b-13f45533ad3c", - "metadata": {}, - "source": [ - "### Use pandas basic methods" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "5f0dc948-1c0f-4273-b8d5-7b9d0fc80f23", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Size in MB:\n" - ] - }, - { - "data": { - "text/plain": [ - "131.216112" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import sys\n", - "print('Size in MB:')\n", - "sys.getsizeof(df)/(10**6)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "a302ef86-7553-4b38-8734-b0ae5896c584", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 200137 entries, 0 to 200136\n", - "Data columns (total 18 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 id 200137 non-null int64 \n", - " 1 type_emplacement 200137 non-null object \n", - " 2 domanialite 200136 non-null object \n", - " 3 arrondissement 200137 non-null object \n", - " 4 complement_addresse 30902 non-null object \n", - " 5 numero 0 non-null float64\n", - " 6 lieu 200137 non-null object \n", - " 7 id_emplacement 200137 non-null object \n", - " 8 libelle_francais 198640 non-null object \n", - " 9 genre 200121 non-null object \n", - " 10 espece 198385 non-null object \n", - " 11 variete 36777 non-null object \n", - " 12 circonference_cm 200137 non-null int64 \n", - " 13 hauteur_m 200137 non-null int64 \n", - " 14 stade_developpement 132932 non-null object \n", - " 15 remarquable 137039 non-null float64\n", - " 16 geo_point_2d_a 200137 non-null float64\n", - " 17 geo_point_2d_b 200137 non-null float64\n", - "dtypes: float64(4), int64(3), object(11)\n", - "memory usage: 27.5+ MB\n" - ] - } - ], - "source": [ - "df.info() # display count of columns, lines and columns names" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f24becfa-7e0f-45a0-82b9-40e211922f5c", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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idtype_emplacementdomanialitearrondissementcomplement_addressenumerolieuid_emplacementlibelle_francaisgenreespecevarietecirconference_cmhauteur_mstade_developpementremarquablegeo_point_2d_ageo_point_2d_b
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" - ], - "text/plain": [ - " id type_emplacement domanialite arrondissement complement_addresse \\\n", - "0 99874 Arbre Jardin PARIS 7E ARRDT NaN \n", - "1 99875 Arbre Jardin PARIS 7E ARRDT NaN \n", - "\n", - " numero lieu id_emplacement \\\n", - "0 NaN MAIRIE DU 7E 116 RUE DE GRENELLE PARIS 7E 19 \n", - "1 NaN MAIRIE DU 7E 116 RUE DE GRENELLE PARIS 7E 20 \n", - "\n", - " libelle_francais genre espece variete circonference_cm \\\n", - "0 Marronnier Aesculus hippocastanum NaN 20 \n", - "1 If Taxus baccata NaN 65 \n", - "\n", - " hauteur_m stade_developpement remarquable geo_point_2d_a geo_point_2d_b \n", - "0 5 NaN 0.00 48.86 2.32 \n", - "1 8 A NaN 48.86 2.32 " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.head(2) # display 2 first lines" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "5543e915-5701-43ed-81dc-c1dd54715e95", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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idnumerocirconference_cmhauteur_mremarquablegeo_point_2d_ageo_point_2d_b
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" - ], - "text/plain": [ - " id numero circonference_cm hauteur_m remarquable \\\n", - "count 200137.00 0.00 200137.00 200137.00 137039.00 \n", - "mean 387202.68 NaN 83.38 13.11 0.00 \n", - "std 545603.24 NaN 673.19 1971.22 0.04 \n", - "min 99874.00 NaN 0.00 0.00 0.00 \n", - "25% 155927.00 NaN 30.00 5.00 0.00 \n", - "50% 221078.00 NaN 70.00 8.00 0.00 \n", - "75% 274102.00 NaN 115.00 12.00 0.00 \n", - "max 2024745.00 NaN 250255.00 881818.00 1.00 \n", - "\n", - " geo_point_2d_a geo_point_2d_b \n", - "count 200137.00 200137.00 \n", - "mean 48.85 2.35 \n", - "std 0.03 0.05 \n", - "min 48.74 2.21 \n", - "25% 48.84 2.31 \n", - "50% 48.85 2.35 \n", - "75% 48.88 2.39 \n", - "max 48.91 2.47 " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.describe()# general statistics only on number types variables (int and floats)\n", - "\n", - "# problems:\n", - "## 0 as min value for circumference, height\n", - "## > 250000 cm as circumference, > 800000 m as height" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "5c2225ff-7a7c-421c-9bde-1dc02e2e3742", - "metadata": {}, - "outputs": [], - "source": [ - "from data_analysis_functions import plot_univariate_variable_analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "f5aade17-79f8-4dfa-bd8f-547727c207cb", - "metadata": {}, - "outputs": [], - "source": [ - "quantitative_variables = ['circonference_cm','hauteur_m']" - ] - }, - { - "cell_type": "markdown", - "id": "aec48b3b-3131-48b0-96a6-9d3a962162f1", - "metadata": {}, - "source": [ - "### Looking for data anomalies" - ] - }, - { - "cell_type": "markdown", - "id": "bd284b56-394b-4677-b271-91f374e04b01", - "metadata": {}, - "source": [ - "#### Outliers" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "c0b79f60-4033-43ad-a8e2-7ba6022ea6f4", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Boxplots in order to briefly find outliers\n", - "for variable in quantitative_variables:\n", - " plot_univariate_variable_analysis(df, variable, 'boxplot')" - ] - }, - { - "cell_type": "markdown", - "id": "96db7b30-d700-47f0-950c-012817006a2b", - "metadata": {}, - "source": [ - "It seems that there are many outliers on data" - ] - }, - { - "cell_type": "markdown", - "id": "580de3b3-db0e-44cd-b271-2c619dc8dee4", - "metadata": {}, - "source": [ - "#### NaN" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "cf0c8910-8bf2-4e28-a919-efacd17a0a2a", - "metadata": {}, - "outputs": [], - "source": [ - "import missingno as msno\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "f9ba7bc9-8b7b-4203-8e59-6fbd7d0bc5cd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0, 0, 1, 0, 169235, 200137, 0, 0,\n", - " 1497, 16, 1752, 163360, 0, 0, 67205, 63098,\n", - " 0, 0])" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# number of 'nan' for each column\n", - "column_nb_of_nans = np.array([], dtype='int')\n", - "for column in df.columns:\n", - " where_nan = df[df[column].isna()]\n", - " column_nb_of_nans = np.concatenate((column_nb_of_nans,[len(where_nan)]))\n", - "column_nb_of_nans" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "4c0424ef-ae93-4b94-847c-4c8341051aa6", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of nan for each variable that contains minimum 1 occurrence:\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " domanialite complement_addresse numero libelle_francais genre espece \\\n", - "0 1 169235 200137 1497 16 1752 \n", - "\n", - " variete stade_developpement remarquable \n", - "0 163360 67205 63098 " - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# dataframe with number of nans per column only when one nan or more\n", - "where_nans = np.where(column_nb_of_nans>0)\n", - "columns_with_nans_df = pd.DataFrame([column_nb_of_nans[where_nans]], columns = df.columns[where_nans])\n", - "print('Number of nan for each variable that contains minimum 1 occurrence:')\n", - "columns_with_nans_df" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "6d1dbd66-cc76-4724-9a23-eeefb13bf725", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# stade_developpement and remarquable are missing around 30% values. Variete is missing 80 percent values\n", - "msno.bar(df)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "ef1eb4db-5bfb-49a2-a4f9-cdc5249ad271", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# missing values correlation between stade_developpement and remarquable. Look at which trees they are\n", - "msno.heatmap(df)" - ] - }, - { - "cell_type": "markdown", - "id": "e173bf2d-060c-4610-bbf1-ef35f4a20552", - "metadata": {}, - "source": [ - "## 3) Cleaning anomalies" - ] - }, - { - "cell_type": "code", - "execution_count": 143, - "id": "57fe3e6a-3828-4f45-bede-a3422cff30c3", - "metadata": {}, - "outputs": [], - "source": [ - "df_refined = df.copy()" - ] - }, - { - "cell_type": "markdown", - "id": "c5535b2a-fd5e-4b16-a1d3-bdc974e55dd0", - "metadata": {}, - "source": [ - "### Outliers (min and max)" - ] - }, - { - "cell_type": "markdown", - "id": "76dfdc46-4b19-4f63-a854-82f07a168921", - "metadata": {}, - "source": [ - "##### Max" - ] - }, - { - "cell_type": "markdown", - "id": "eeee8c4a-af66-4be0-9637-2054e67e7af5", - "metadata": {}, - "source": [ - "Height :" - ] - }, - { - "cell_type": "code", - "execution_count": 144, - "id": "ac8fcbb6-dafa-4f63-9caa-36f99a3a9297", - "metadata": {}, - "outputs": [], - "source": [ - "# based on the tallest tree height : https://en.wikipedia.org/wiki/Hyperion_(tree)\n", - "max_height = 116 # in meters" - ] - }, - { - "cell_type": "code", - "execution_count": 145, - "id": "c3115746-c299-495f-8a13-b1e3e5ebc6cd", - "metadata": {}, - "outputs": [], - "source": [ - "max_height_outlier_df = df_refined[(df_refined['hauteur_m'] > max_height)]" - ] - }, - { - "cell_type": "code", - "execution_count": 146, - "id": "ba6ca618-9335-4f54-8215-1545e821e3da", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "height outliers trees (87 occurrencies):\n" - ] - }, - { - "data": { - "text/html": [ - "
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idtype_emplacementdomanialitearrondissementcomplement_addressenumerolieuid_emplacementlibelle_francaisgenreespecevarietecirconference_cmhauteur_mstade_developpementremarquablegeo_point_2d_ageo_point_2d_b
5392106800ArbreJardinPARIS 7E ARRDTNaNNaNJARDIN DU CHAMP DE MARSA0120683TilleulTilian. sp.NaN1001510A0.0048.862.30
6473107951ArbreJardinPARIS 7E ARRDTNaNNaNJARDIN DU CHAMP DE MARS / C01 / ALLEE LEON BOU...A00202003PlatanePlatanusx hispanicaNaN112117A0.0048.862.29
12536115062ArbreJardinPARIS 16E ARRDT11 à 27NaNJARDIN DE L AVENUE FOCH / 10 AVENUE FOCH10303ErableAcern. sp.NaN47255J0.0048.872.29
15373118412ArbreJardinPARIS 7E ARRDTNaNNaNSQUARE DE L'ABBE ESQUERRE17PaulowniaPaulowniatomentosaNaN145810A0.0048.852.31
24099128185ArbreJardinPARIS 17E ARRDTNaNNaNSQUARE ANDRE ULMANN27PlatanePlatanusx hispanicaNaN130125A0.0048.892.30
.........................................................
1878732008880ArbreDJSPARIS 13E ARRDTNaNNaNSTADE CHARLETTY / 99 BOULEVARD KELLERMANN91MicocoulierCeltisaustralisNaN10120JA0.0048.822.35
1878742008881ArbreDJSPARIS 13E ARRDTNaNNaNSTADE CHARLETTY / 99 BOULEVARD KELLERMANN92MicocoulierCeltisaustralisNaN10120JA0.0048.822.35
1890532011417ArbreJardinBOIS DE VINCENNESNaNNaNPARC FLORAL DE PARIS / ROUTE DE LA PYRAMIDE180088PinPinusmugoNaN60150JA0.0048.832.44
1894412011863ArbreDFPEPARIS 16E ARRDTNaNNaNERLANGER (9-11) CRECHE48970005MarronnierAesculushippocastanumBaumannii'8157NaN0.0048.852.26
1971362020792ArbreJardinPARIS 14E ARRDTNaNNaNSQUARE AUGUSTE RENOIR / 205 RUE RAYMOND LOSSERAND103005ErableAcerplatanoidesNaN145125A0.0048.832.31
\n", - "

87 rows × 18 columns

\n", - "
" - ], - "text/plain": [ - " id type_emplacement domanialite arrondissement \\\n", - "5392 106800 Arbre Jardin PARIS 7E ARRDT \n", - "6473 107951 Arbre Jardin PARIS 7E ARRDT \n", - "12536 115062 Arbre Jardin PARIS 16E ARRDT \n", - "15373 118412 Arbre Jardin PARIS 7E ARRDT \n", - "24099 128185 Arbre Jardin PARIS 17E ARRDT \n", - "... ... ... ... ... \n", - "187873 2008880 Arbre DJS PARIS 13E ARRDT \n", - "187874 2008881 Arbre DJS PARIS 13E ARRDT \n", - "189053 2011417 Arbre Jardin BOIS DE VINCENNES \n", - "189441 2011863 Arbre DFPE PARIS 16E ARRDT \n", - "197136 2020792 Arbre Jardin PARIS 14E ARRDT \n", - "\n", - " complement_addresse numero \\\n", - "5392 NaN NaN \n", - "6473 NaN NaN \n", - "12536 11 à 27 NaN \n", - "15373 NaN NaN \n", - "24099 NaN NaN \n", - "... ... ... \n", - "187873 NaN NaN \n", - "187874 NaN NaN \n", - "189053 NaN NaN \n", - "189441 NaN NaN \n", - "197136 NaN NaN \n", - "\n", - " lieu id_emplacement \\\n", - "5392 JARDIN DU CHAMP DE MARS A0120683 \n", - "6473 JARDIN DU CHAMP DE MARS / C01 / ALLEE LEON BOU... A00202003 \n", - "12536 JARDIN DE L AVENUE FOCH / 10 AVENUE FOCH 10303 \n", - "15373 SQUARE DE L'ABBE ESQUERRE 17 \n", - "24099 SQUARE ANDRE ULMANN 27 \n", - "... ... ... \n", - "187873 STADE CHARLETTY / 99 BOULEVARD KELLERMANN 91 \n", - "187874 STADE CHARLETTY / 99 BOULEVARD KELLERMANN 92 \n", - "189053 PARC FLORAL DE PARIS / ROUTE DE LA PYRAMIDE 180088 \n", - "189441 ERLANGER (9-11) CRECHE 48970005 \n", - "197136 SQUARE AUGUSTE RENOIR / 205 RUE RAYMOND LOSSERAND 103005 \n", - "\n", - " libelle_francais genre espece variete \\\n", - "5392 Tilleul Tilia n. sp. NaN \n", - "6473 Platane Platanus x hispanica NaN \n", - "12536 Erable Acer n. sp. NaN \n", - "15373 Paulownia Paulownia tomentosa NaN \n", - "24099 Platane Platanus x hispanica NaN \n", - "... ... ... ... ... \n", - "187873 Micocoulier Celtis australis NaN \n", - "187874 Micocoulier Celtis australis NaN \n", - "189053 Pin Pinus mugo NaN \n", - "189441 Marronnier Aesculus hippocastanum Baumannii' \n", - "197136 Erable Acer platanoides NaN \n", - "\n", - " circonference_cm hauteur_m stade_developpement remarquable \\\n", - "5392 100 1510 A 0.00 \n", - "6473 112 117 A 0.00 \n", - "12536 47 255 J 0.00 \n", - "15373 145 810 A 0.00 \n", - "24099 130 125 A 0.00 \n", - "... ... ... ... ... \n", - "187873 10 120 JA 0.00 \n", - "187874 10 120 JA 0.00 \n", - "189053 60 150 JA 0.00 \n", - "189441 8 157 NaN 0.00 \n", - "197136 145 125 A 0.00 \n", - "\n", - " geo_point_2d_a geo_point_2d_b \n", - "5392 48.86 2.30 \n", - "6473 48.86 2.29 \n", - "12536 48.87 2.29 \n", - "15373 48.85 2.31 \n", - "24099 48.89 2.30 \n", - "... ... ... \n", - "187873 48.82 2.35 \n", - "187874 48.82 2.35 \n", - "189053 48.83 2.44 \n", - "189441 48.85 2.26 \n", - "197136 48.83 2.31 \n", - "\n", - "[87 rows x 18 columns]" - ] - }, - "execution_count": 146, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print('height outliers trees ('+ str(len(max_height_outlier_df)) + ' occurrencies):')\n", - "max_height_outlier_df\n" - ] - }, - { - "cell_type": "code", - "execution_count": 147, - "id": "bf489b0f-45f5-4879-8c56-b69f6b79f89e", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "hauteur_m\n", - "120 7\n", - "155 5\n", - "118 5\n", - "125 4\n", - "810 4\n", - "117 3\n", - "205 3\n", - "1515 3\n", - "150 3\n", - "510 2\n", - "160 2\n", - "610 2\n", - "1510 2\n", - "1512 1\n", - "1511 1\n", - "5155 1\n", - "618 1\n", - "2525 1\n", - "714 1\n", - "720 1\n", - "2015 1\n", - "815 1\n", - "818 1\n", - "1815 1\n", - "1010 1\n", - "1113 1\n", - "1211 1\n", - "1216 1\n", - "1610 1\n", - "1514 1\n", - "615 1\n", - "260 1\n", - "608 1\n", - "159 1\n", - "122 1\n", - "128 1\n", - "129 1\n", - "130 1\n", - "138 1\n", - "148 1\n", - "154 1\n", - "156 1\n", - "157 1\n", - "170 1\n", - "512 1\n", - "180 1\n", - "190 1\n", - "204 1\n", - "216 1\n", - "218 1\n", - "219 1\n", - "220 1\n", - "255 1\n", - "327 1\n", - "881818 1\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 147, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "max_height_outlier_df.value_counts('hauteur_m')" - ] - }, - { - "cell_type": "code", - "execution_count": 148, - "id": "c788b243-2521-4f58-8d1f-3463ec0bf45c", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# remove outlier\n", - "df_refined = df_refined[df_refined['hauteur_m']!=881818]" - ] - }, - { - "cell_type": "code", - "execution_count": 149, - "id": "76b470a5-7542-44a0-b262-fd9e325b4a43", - "metadata": {}, - "outputs": [], - "source": [ - "# need in order to divide by 100 the too high heights\n", - "df_refined['hauteur_m'] = df_refined['hauteur_m'].astype(float)" - ] - }, - { - "cell_type": "code", - "execution_count": 150, - "id": "e0cffc30-00b3-439c-9501-9e94b1f57e0c", - "metadata": {}, - "outputs": [], - "source": [ - "# Too high remaining heights may have been measured in cms\n", - "df_refined.loc[df_refined['hauteur_m'] > max_height, 'hauteur_m'] = df_refined.loc[df_refined['hauteur_m'] > max_height, 'hauteur_m']/100" - ] - }, - { - "cell_type": "markdown", - "id": "1e751c26-fc8f-41a9-bd27-2d1f899f4866", - "metadata": {}, - "source": [ - "Girth :" - ] - }, - { - "cell_type": "code", - "execution_count": 151, - "id": "0b2af1bd-9fc8-405f-b810-433044a0ea7a", - "metadata": {}, - "outputs": [], - "source": [ - "# based on the biggest tree girth (circumference) : https://www.guinnessworldrecords.com/world-records/living-tree-with-greatest-girth\n", - "max_girth = 36 # in meters" - ] - }, - { - "cell_type": "code", - "execution_count": 152, - "id": "dceb4f45-c91d-4234-a5d1-c87d1fcc6403", - "metadata": {}, - "outputs": [], - "source": [ - "max_girth_outlier_df = df_refined[(df_refined['circonference_cm']/100 > max_girth)]" - ] - }, - { - "cell_type": "code", - "execution_count": 153, - "id": "a97d6d26-f82a-463b-ad00-69d4676beb7e", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "girth outliers trees (20 occurrencies):\n" - ] - }, - { - "data": { - "text/html": [ - "
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idtype_emplacementdomanialitearrondissementcomplement_addressenumerolieuid_emplacementlibelle_francaisgenreespecevarietecirconference_cmhauteur_mstade_developpementremarquablegeo_point_2d_ageo_point_2d_b
71787190080ArbreDASCOPARIS 20E ARRDTNaNNaNPLAINE (9) ELEM B11110006TilleulTiliatomentosaNaN1858915.00A0.0048.852.40
82951203295ArbreAlignementPARIS 18E ARRDTNaNNaNRUE D AUBERVILLIERS301004ErableAcermonspessulanumNaN554556.00J0.0048.892.37
90605211362ArbreAlignementPARIS 5E ARRDT34NaNBOULEVARD SAINT MARCEL204025CedreleToonasinensisNaN2013016.00A0.0048.842.36
91256212024ArbreAlignementPARIS 12E ARRDTNaNNaNRUE DE CHALON202005MicocoulierCeltisaustralisNaN3014010.00A0.0048.852.37
97354218274ArbreAlignementPARIS 7E ARRDT27NaNAVENUE RAPP501003PlatanePlatanusoccidentalisNaN811018.00A0.0048.862.30
103437224609ArbreAlignementPARIS 18E ARRDTNaNNaNBOULEVARD BARBES1303003PlatanePlatanusx hispanicaNaN90356.00J0.0048.892.35
133546256868ArbreAlignementPARIS 19E ARRDTNaNNaNAVENUE JEAN JAURES1501006PlatanePlatanusx hispanicaNaN409010.00A0.0048.892.39
147235270997ArbreAlignementPARIS 7E ARRDTNaNNaNQUAI BRANLY503005PaulowniaPaulowniatomentosaNaN915515.00A0.0048.862.29
147634271402ArbreAlignementPARIS 7E ARRDTNaNNaNAVENUE BOSQUET1202016PlatanePlatanusoccidentalisNaN6511016.00A0.0048.862.31
148106271884ArbreAlignementPARIS 7E ARRDTNaNNaNAVENUE BOSQUET501022PlatanePlatanusoccidentalisNaN659515.00A0.0048.862.30
148481272263ArbreAlignementPARIS 7E ARRDT14NaNBOULEVARD DES INVALIDES402009PlatanePlatanusoccidentalisNaN612018.00A0.0048.852.31
149056272885ArbreAlignementPARIS 18E ARRDTNaNNaNPLACE DE LA CHAPELLE101026PlatanePlatanusx hispanicaNaN25025522.00A0.0048.882.36
151364275451ArbreAlignementPARIS 7E ARRDTNaNNaNESPLANADE DES INVALIDES410005TilleulTiliatomentosaNaN75408.15JA0.0048.862.31
152303276396ArbreAlignementPARIS 7E ARRDTNaNNaNESPLANADE DES INVALIDES402011TilleulTiliatomentosaNaN908512.00A0.0048.862.31
160432284823ArbreAlignementPARIS 18E ARRDTNaNNaNAVENUE DE LA PORTE DE CLIGNANCOURT202005PlatanePlatanusorientalisNaN12017020.00A0.0048.902.34
163918288407ArbreAlignementPARIS 12E ARRDTNaNNaNBOULEVARD DE PICPUS1604017SophoraSophorajaponicaNaN60855.00JA0.0048.852.40
164616289121ArbreAlignementPARIS 12E ARRDTNaNNaNRUE FRANCOIS TRUFFAUT301005PlatanePlatanusx hispanicaNaN909510.00A0.0048.832.39
167606292180ArbreAlignementPARIS 18E ARRDT16NaNRUE JEAN COCTEAU202027Noisetier de ByzanceCoryluscolurnaNaN36305.00J0.0048.902.35
170367295047ArbreAlignementPARIS 5E ARRDT16NaNRUE POLIVEAU202015PlatanePlatanusx hispanicaNaN612520.00A0.0048.842.36
173343298211ArbreAlignementPARIS 5E ARRDTNaNNaNBOULEVARD DE PORT ROYAL402001MarronnierAesculusx carneaBriotii'7912010.00A0.0048.842.35
\n", - "
" - ], - "text/plain": [ - " id type_emplacement domanialite arrondissement \\\n", - "71787 190080 Arbre DASCO PARIS 20E ARRDT \n", - "82951 203295 Arbre Alignement PARIS 18E ARRDT \n", - "90605 211362 Arbre Alignement PARIS 5E ARRDT \n", - "91256 212024 Arbre Alignement PARIS 12E ARRDT \n", - "97354 218274 Arbre Alignement PARIS 7E ARRDT \n", - "103437 224609 Arbre Alignement PARIS 18E ARRDT \n", - "133546 256868 Arbre Alignement PARIS 19E ARRDT \n", - "147235 270997 Arbre Alignement PARIS 7E ARRDT \n", - "147634 271402 Arbre Alignement PARIS 7E ARRDT \n", - "148106 271884 Arbre Alignement PARIS 7E ARRDT \n", - "148481 272263 Arbre Alignement PARIS 7E ARRDT \n", - "149056 272885 Arbre Alignement PARIS 18E ARRDT \n", - "151364 275451 Arbre Alignement PARIS 7E ARRDT \n", - "152303 276396 Arbre Alignement PARIS 7E ARRDT \n", - "160432 284823 Arbre Alignement PARIS 18E ARRDT \n", - "163918 288407 Arbre Alignement PARIS 12E ARRDT \n", - "164616 289121 Arbre Alignement PARIS 12E ARRDT \n", - "167606 292180 Arbre Alignement PARIS 18E ARRDT \n", - "170367 295047 Arbre Alignement PARIS 5E ARRDT \n", - "173343 298211 Arbre Alignement PARIS 5E ARRDT \n", - "\n", - " complement_addresse numero lieu \\\n", - "71787 NaN NaN PLAINE (9) ELEM B \n", - "82951 NaN NaN RUE D AUBERVILLIERS \n", - "90605 34 NaN BOULEVARD SAINT MARCEL \n", - "91256 NaN NaN RUE DE CHALON \n", - "97354 27 NaN AVENUE RAPP \n", - "103437 NaN NaN BOULEVARD BARBES \n", - "133546 NaN NaN AVENUE JEAN JAURES \n", - "147235 NaN NaN QUAI BRANLY \n", - "147634 NaN NaN AVENUE BOSQUET \n", - "148106 NaN NaN AVENUE BOSQUET \n", - "148481 14 NaN BOULEVARD DES INVALIDES \n", - "149056 NaN NaN PLACE DE LA CHAPELLE \n", - "151364 NaN NaN ESPLANADE DES INVALIDES \n", - "152303 NaN NaN ESPLANADE DES INVALIDES \n", - "160432 NaN NaN AVENUE DE LA PORTE DE CLIGNANCOURT \n", - "163918 NaN NaN BOULEVARD DE PICPUS \n", - "164616 NaN NaN RUE FRANCOIS TRUFFAUT \n", - "167606 16 NaN RUE JEAN COCTEAU \n", - "170367 16 NaN RUE POLIVEAU \n", - "173343 NaN NaN BOULEVARD DE PORT ROYAL \n", - "\n", - " id_emplacement libelle_francais genre espece \\\n", - "71787 11110006 Tilleul Tilia tomentosa \n", - "82951 301004 Erable Acer monspessulanum \n", - "90605 204025 Cedrele Toona sinensis \n", - "91256 202005 Micocoulier Celtis australis \n", - "97354 501003 Platane Platanus occidentalis \n", - "103437 1303003 Platane Platanus x hispanica \n", - "133546 1501006 Platane Platanus x hispanica \n", - "147235 503005 Paulownia Paulownia tomentosa \n", - "147634 1202016 Platane Platanus occidentalis \n", - "148106 501022 Platane Platanus occidentalis \n", - "148481 402009 Platane Platanus occidentalis \n", - "149056 101026 Platane Platanus x hispanica \n", - "151364 410005 Tilleul Tilia tomentosa \n", - "152303 402011 Tilleul Tilia tomentosa \n", - "160432 202005 Platane Platanus orientalis \n", - "163918 1604017 Sophora Sophora japonica \n", - "164616 301005 Platane Platanus x hispanica \n", - "167606 202027 Noisetier de Byzance Corylus colurna \n", - "170367 202015 Platane Platanus x hispanica \n", - "173343 402001 Marronnier Aesculus x carnea \n", - "\n", - " variete circonference_cm hauteur_m stade_developpement \\\n", - "71787 NaN 18589 15.00 A \n", - "82951 NaN 5545 56.00 J \n", - "90605 NaN 20130 16.00 A \n", - "91256 NaN 30140 10.00 A \n", - "97354 NaN 8110 18.00 A \n", - "103437 NaN 9035 6.00 J \n", - "133546 NaN 4090 10.00 A \n", - "147235 NaN 9155 15.00 A \n", - "147634 NaN 65110 16.00 A \n", - "148106 NaN 6595 15.00 A \n", - "148481 NaN 6120 18.00 A \n", - "149056 NaN 250255 22.00 A \n", - "151364 NaN 7540 8.15 JA \n", - "152303 NaN 9085 12.00 A \n", - "160432 NaN 120170 20.00 A \n", - "163918 NaN 6085 5.00 JA \n", - "164616 NaN 9095 10.00 A \n", - "167606 NaN 3630 5.00 J \n", - "170367 NaN 6125 20.00 A \n", - "173343 Briotii' 79120 10.00 A \n", - "\n", - " remarquable geo_point_2d_a geo_point_2d_b \n", - "71787 0.00 48.85 2.40 \n", - "82951 0.00 48.89 2.37 \n", - "90605 0.00 48.84 2.36 \n", - "91256 0.00 48.85 2.37 \n", - "97354 0.00 48.86 2.30 \n", - "103437 0.00 48.89 2.35 \n", - "133546 0.00 48.89 2.39 \n", - "147235 0.00 48.86 2.29 \n", - "147634 0.00 48.86 2.31 \n", - "148106 0.00 48.86 2.30 \n", - "148481 0.00 48.85 2.31 \n", - "149056 0.00 48.88 2.36 \n", - "151364 0.00 48.86 2.31 \n", - "152303 0.00 48.86 2.31 \n", - "160432 0.00 48.90 2.34 \n", - "163918 0.00 48.85 2.40 \n", - "164616 0.00 48.83 2.39 \n", - "167606 0.00 48.90 2.35 \n", - "170367 0.00 48.84 2.36 \n", - "173343 0.00 48.84 2.35 " - ] - }, - "execution_count": 153, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print('girth outliers trees ('+ str(len(max_girth_outlier_df)) + ' occurrencies):')\n", - "max_girth_outlier_df" - ] - }, - { - "cell_type": "code", - "execution_count": 154, - "id": "3535207c-ddca-4791-afc8-924bdf424cbb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "circonference_cm\n", - "3630 1\n", - "4090 1\n", - "120170 1\n", - "79120 1\n", - "65110 1\n", - "30140 1\n", - "20130 1\n", - "18589 1\n", - "9155 1\n", - "9095 1\n", - "9085 1\n", - "9035 1\n", - "8110 1\n", - "7540 1\n", - "6595 1\n", - "6125 1\n", - "6120 1\n", - "6085 1\n", - "5545 1\n", - "250255 1\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 154, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "max_girth_outlier_df.value_counts('circonference_cm')" - ] - }, - { - "cell_type": "code", - "execution_count": 155, - "id": "e20288eb-4c78-4b56-b411-5d0c74599659", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "hauteur_m\n", - "10.00 4\n", - "15.00 3\n", - "5.00 2\n", - "16.00 2\n", - "18.00 2\n", - "20.00 2\n", - "6.00 1\n", - "8.15 1\n", - "12.00 1\n", - "22.00 1\n", - "56.00 1\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 155, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "max_girth_outlier_df.value_counts('hauteur_m')" - ] - }, - { - "cell_type": "code", - "execution_count": 156, - "id": "e2331f68-1cad-4e2a-a8d1-2d7ef066fa8b", - "metadata": {}, - "outputs": [], - "source": [ - "# Report these problematic girths. Remove these outliers\n", - "df_refined = df_refined[df_refined['circonference_cm']/100 < max_girth]\n", - "## maybe change values to nans" - ] - }, - { - "cell_type": "markdown", - "id": "747b4e39-2678-40e9-af04-2e1caa765226", - "metadata": {}, - "source": [ - "##### Min" - ] - }, - { - "cell_type": "markdown", - "id": "8f65de59-ef68-43b1-be1b-9c3ddada8372", - "metadata": {}, - "source": [ - "Height :" - ] - }, - { - "cell_type": "code", - "execution_count": 158, - "id": "6a24f15e-6a9e-46d0-acc1-fb7bf281ec30", - "metadata": {}, - "outputs": [], - "source": [ - "# based on the tallest tree height : https://en.wikipedia.org/wiki/Hyperion_(tree)\n", - "min_height = 0 # in meters" - ] - }, - { - "cell_type": "code", - "execution_count": 159, - "id": "10c2ce83-27b5-43f1-af4f-5d80ac2ba48c", - "metadata": {}, - "outputs": [], - "source": [ - "min_height_outlier_df = df_refined[(df_refined['hauteur_m'] <= min_height)]" - ] - }, - { - "cell_type": "code", - "execution_count": 164, - "id": "4dfbde45-9d17-4a5f-af04-282461462235", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "hauteur_m\n", - "0.00 39219\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 164, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# no negative value : ok\n", - "min_height_outlier_df.value_counts('hauteur_m')" - ] - }, - { - "cell_type": "code", - "execution_count": 160, - "id": "fa7fd3b4-7e48-4289-b005-939f565b889e", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "height outliers trees (39219 occurrencies):\n" - ] - }, - { - "data": { - "text/html": [ - "
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idtype_emplacementdomanialitearrondissementcomplement_addressenumerolieuid_emplacementlibelle_francaisgenreespecevarietecirconference_cmhauteur_mstade_developpementremarquablegeo_point_2d_ageo_point_2d_b
499878ArbreJardinPARIS 17E ARRDTNaNNaNPARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING000G0037Arbre à mielTetradiumdanielliiNaN380.00NaNNaN48.892.32
599879ArbreJardinPARIS 17E ARRDTNaNNaNPARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING000G0036Arbre à mielTetradiumdanielliiNaN380.00NaNNaN48.892.32
699880ArbreJardinPARIS 17E ARRDTNaNNaNPARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING000G0035Arbre à mielTetradiumdanielliiNaN370.00NaNNaN48.892.32
1199885ArbreJardinPARIS 16E ARRDTNaNNaNJARDIN DU RANELAGH20001HêtreFagussylvaticaAtropunicea'300.00NaNNaN48.862.27
1299887ArbreJardinPARIS 16E ARRDTNaNNaNJARDIN DU RANELAGH20003MicocoulierCeltisoccidentalisNaN2050.00NaNNaN48.862.27
.........................................................
2001322024741ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170151NaNCastaneafargesiiNaN00.00NaN0.0048.822.46
2001332024742ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170152Arbre aux mouchoirsDavidiainvolucrataNaN00.00NaN0.0048.822.46
2001342024743ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170153PeuplierPopulussimoniiNaN00.00NaN0.0048.822.46
2001352024744ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170154ChêneQuercusn. sp.NaN00.00NaN0.0048.822.46
2001362024745ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170155RaisinierHoveniadulcisNaN00.00NaN0.0048.822.45
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39219 rows × 18 columns

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" - ], - "text/plain": [ - " id type_emplacement domanialite arrondissement \\\n", - "4 99878 Arbre Jardin PARIS 17E ARRDT \n", - "5 99879 Arbre Jardin PARIS 17E ARRDT \n", - "6 99880 Arbre Jardin PARIS 17E ARRDT \n", - "11 99885 Arbre Jardin PARIS 16E ARRDT \n", - "12 99887 Arbre Jardin PARIS 16E ARRDT \n", - "... ... ... ... ... \n", - "200132 2024741 Arbre Jardin BOIS DE VINCENNES \n", - "200133 2024742 Arbre Jardin BOIS DE VINCENNES \n", - "200134 2024743 Arbre Jardin BOIS DE VINCENNES \n", - "200135 2024744 Arbre Jardin BOIS DE VINCENNES \n", - "200136 2024745 Arbre Jardin BOIS DE VINCENNES \n", - "\n", - " complement_addresse numero \\\n", - "4 NaN NaN \n", - "5 NaN NaN \n", - "6 NaN NaN \n", - "11 NaN NaN \n", - "12 NaN NaN \n", - "... ... ... \n", - "200132 NaN NaN \n", - "200133 NaN NaN \n", - "200134 NaN NaN \n", - "200135 NaN NaN \n", - "200136 NaN NaN \n", - "\n", - " lieu id_emplacement \\\n", - "4 PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING 000G0037 \n", - "5 PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING 000G0036 \n", - "6 PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING 000G0035 \n", - "11 JARDIN DU RANELAGH 20001 \n", - "12 JARDIN DU RANELAGH 20003 \n", - "... ... ... \n", - "200132 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170151 \n", - "200133 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170152 \n", - "200134 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170153 \n", - "200135 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170154 \n", - "200136 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170155 \n", - "\n", - " libelle_francais genre espece variete \\\n", - "4 Arbre à miel Tetradium daniellii NaN \n", - "5 Arbre à miel Tetradium daniellii NaN \n", - "6 Arbre à miel Tetradium daniellii NaN \n", - "11 Hêtre Fagus sylvatica Atropunicea' \n", - "12 Micocoulier Celtis occidentalis NaN \n", - "... ... ... ... ... \n", - "200132 NaN Castanea fargesii NaN \n", - "200133 Arbre aux mouchoirs Davidia involucrata NaN \n", - "200134 Peuplier Populus simonii NaN \n", - "200135 Chêne Quercus n. sp. NaN \n", - "200136 Raisinier Hovenia dulcis NaN \n", - "\n", - " circonference_cm hauteur_m stade_developpement remarquable \\\n", - "4 38 0.00 NaN NaN \n", - "5 38 0.00 NaN NaN \n", - "6 37 0.00 NaN NaN \n", - "11 30 0.00 NaN NaN \n", - "12 205 0.00 NaN NaN \n", - "... ... ... ... ... \n", - "200132 0 0.00 NaN 0.00 \n", - "200133 0 0.00 NaN 0.00 \n", - "200134 0 0.00 NaN 0.00 \n", - "200135 0 0.00 NaN 0.00 \n", - "200136 0 0.00 NaN 0.00 \n", - "\n", - " geo_point_2d_a geo_point_2d_b \n", - "4 48.89 2.32 \n", - "5 48.89 2.32 \n", - "6 48.89 2.32 \n", - "11 48.86 2.27 \n", - "12 48.86 2.27 \n", - "... ... ... \n", - "200132 48.82 2.46 \n", - "200133 48.82 2.46 \n", - "200134 48.82 2.46 \n", - "200135 48.82 2.46 \n", - "200136 48.82 2.45 \n", - "\n", - "[39219 rows x 18 columns]" - ] - }, - "execution_count": 160, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Maybe freshly planted trees are 0 meters tall and/or 0 is a default value when not measured\n", - "print('height outliers trees ('+ str(len(min_height_outlier_df)) + ' occurrencies):')\n", - "min_height_outlier_df" - ] - }, - { - "cell_type": "code", - "execution_count": 163, - "id": "ad996a3c-d4b9-4ff6-8e39-eca8054157ff", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "stade_developpement\n", - "NaN 36952\n", - "A 1191\n", - "JA 577\n", - "J 474\n", - "M 25\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 163, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# A = Adult, JA = Jeune Adulte, J = Jeune, M = Mature\n", - "# Majority has no stade_developpement value. Probably freshly planted grain. Recommend to not ignore these trees. \n", - "# For the others, it may probably be a fill value. I recommend setting the height values as NaN\n", - "min_height_outlier_df.value_counts('stade_developpement', dropna=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 170, - "id": "34d32b28-1a5f-4df0-a620-4c418befd07c", - "metadata": {}, - "outputs": [], - "source": [ - "# giving NaN value instead of 0 for already existing trees in order to signal missing data\n", - "df_refined.loc[(df_refined['hauteur_m'] <= min_height) & (df_refined['stade_developpement'].isin(['A', 'JA', 'J', 'M'])),\n", - " 'hauteur_m'] = np.nan\n", - "# check 1 or 2 trees tagged as 'A' or 'JA' (or other) to see if these really is a positive height" - ] - }, - { - "cell_type": "markdown", - "id": "27fbf69c-619d-40ee-8514-2b3c3470007b", - "metadata": {}, - "source": [ - "Girth :" - ] - }, - { - "cell_type": "code", - "execution_count": 176, - "id": "0d1a2d77-c7d8-4944-9a58-dc045519c731", - "metadata": {}, - "outputs": [], - "source": [ - "# based on the biggest tree girth (circumference) : https://www.guinnessworldrecords.com/world-records/living-tree-with-greatest-girth\n", - "min_girth = 0 # in meters" - ] - }, - { - "cell_type": "code", - "execution_count": 177, - "id": "fe7e6808-fa76-4e9c-bddf-27474aa9f28c", - "metadata": {}, - "outputs": [], - "source": [ - "min_girth_outlier_df = df_refined[df_refined['circonference_cm'] <= min_girth]" - ] - }, - { - "cell_type": "code", - "execution_count": 178, - "id": "6baa2bd7-0136-451c-aa81-4f61c30ba125", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "circonference_cm\n", - "0 25867\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 178, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# no negative value : ok\n", - "min_girth_outlier_df.value_counts('circonference_cm')" - ] - }, - { - "cell_type": "code", - "execution_count": 179, - "id": "9f015caa-3e56-437c-9e6d-e315754d33a7", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "girth outliers trees (25867 occurrencies):\n" - ] - }, - { - "data": { - "text/html": [ - "
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1778101922ArbreJardinPARIS 19E ARRDTNaNNaNPARC DES BUTTES CHAUMONTC0400032ErableAcerplatanoidesNaN00.00NaNNaN48.882.38
2332102591ArbreJardinPARIS 17E ARRDTNaNNaNPARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING000B0052MagnoliaMagnoliax soulangeanaHeaven Scent'00.00NaNNaN48.892.32
2333102592ArbreJardinPARIS 17E ARRDTNaNNaNPARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING000B0053MagnoliaMagnoliax soulangeanaHeaven Scent'00.00NaNNaN48.892.32
2405102744ArbreJardinPARIS 20E ARRDTNaNNaNSQUARE LEON FRAPIE2PlatanePlatanusx hispanicaNaN0NaNJA0.0048.872.41
2430102773ArbreJardinPARIS 17E ARRDTNaNNaNPARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING000B0021MagnoliaMagnoliax soulangeanaSundew'00.00NaNNaN48.892.32
.........................................................
2001322024741ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170151NaNCastaneafargesiiNaN00.00NaN0.0048.822.46
2001332024742ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170152Arbre aux mouchoirsDavidiainvolucrataNaN00.00NaN0.0048.822.46
2001342024743ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170153PeuplierPopulussimoniiNaN00.00NaN0.0048.822.46
2001352024744ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170154ChêneQuercusn. sp.NaN00.00NaN0.0048.822.46
2001362024745ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170155RaisinierHoveniadulcisNaN00.00NaN0.0048.822.45
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25867 rows × 18 columns

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" - ], - "text/plain": [ - " id type_emplacement domanialite arrondissement \\\n", - "1778 101922 Arbre Jardin PARIS 19E ARRDT \n", - "2332 102591 Arbre Jardin PARIS 17E ARRDT \n", - "2333 102592 Arbre Jardin PARIS 17E ARRDT \n", - "2405 102744 Arbre Jardin PARIS 20E ARRDT \n", - "2430 102773 Arbre Jardin PARIS 17E ARRDT \n", - "... ... ... ... ... \n", - "200132 2024741 Arbre Jardin BOIS DE VINCENNES \n", - "200133 2024742 Arbre Jardin BOIS DE VINCENNES \n", - "200134 2024743 Arbre Jardin BOIS DE VINCENNES \n", - "200135 2024744 Arbre Jardin BOIS DE VINCENNES \n", - "200136 2024745 Arbre Jardin BOIS DE VINCENNES \n", - "\n", - " complement_addresse numero \\\n", - "1778 NaN NaN \n", - "2332 NaN NaN \n", - "2333 NaN NaN \n", - "2405 NaN NaN \n", - "2430 NaN NaN \n", - "... ... ... \n", - "200132 NaN NaN \n", - "200133 NaN NaN \n", - "200134 NaN NaN \n", - "200135 NaN NaN \n", - "200136 NaN NaN \n", - "\n", - " lieu id_emplacement \\\n", - "1778 PARC DES BUTTES CHAUMONT C0400032 \n", - "2332 PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING 000B0052 \n", - "2333 PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING 000B0053 \n", - "2405 SQUARE LEON FRAPIE 2 \n", - "2430 PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING 000B0021 \n", - "... ... ... \n", - "200132 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170151 \n", - "200133 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170152 \n", - "200134 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170153 \n", - "200135 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170154 \n", - "200136 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170155 \n", - "\n", - " libelle_francais genre espece variete \\\n", - "1778 Erable Acer platanoides NaN \n", - "2332 Magnolia Magnolia x soulangeana Heaven Scent' \n", - "2333 Magnolia Magnolia x soulangeana Heaven Scent' \n", - "2405 Platane Platanus x hispanica NaN \n", - "2430 Magnolia Magnolia x soulangeana Sundew' \n", - "... ... ... ... ... \n", - "200132 NaN Castanea fargesii NaN \n", - "200133 Arbre aux mouchoirs Davidia involucrata NaN \n", - "200134 Peuplier Populus simonii NaN \n", - "200135 Chêne Quercus n. sp. NaN \n", - "200136 Raisinier Hovenia dulcis NaN \n", - "\n", - " circonference_cm hauteur_m stade_developpement remarquable \\\n", - "1778 0 0.00 NaN NaN \n", - "2332 0 0.00 NaN NaN \n", - "2333 0 0.00 NaN NaN \n", - "2405 0 NaN JA 0.00 \n", - "2430 0 0.00 NaN NaN \n", - "... ... ... ... ... \n", - "200132 0 0.00 NaN 0.00 \n", - "200133 0 0.00 NaN 0.00 \n", - "200134 0 0.00 NaN 0.00 \n", - "200135 0 0.00 NaN 0.00 \n", - "200136 0 0.00 NaN 0.00 \n", - "\n", - " geo_point_2d_a geo_point_2d_b \n", - "1778 48.88 2.38 \n", - "2332 48.89 2.32 \n", - "2333 48.89 2.32 \n", - "2405 48.87 2.41 \n", - "2430 48.89 2.32 \n", - "... ... ... \n", - "200132 48.82 2.46 \n", - "200133 48.82 2.46 \n", - "200134 48.82 2.46 \n", - "200135 48.82 2.46 \n", - "200136 48.82 2.45 \n", - "\n", - "[25867 rows x 18 columns]" - ] - }, - "execution_count": 179, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Maybe freshly planted trees have 0 cms girth and/or 0 is a default value when not measured\n", - "print('girth outliers trees ('+ str(len(min_girth_outlier_df)) + ' occurrencies):')\n", - "min_girth_outlier_df" - ] - }, - { - "cell_type": "code", - "execution_count": 185, - "id": "6cb2665b-7f8f-4004-b68a-449a6965b461", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "stade_developpement\n", - "NaN 25586\n", - "J 126\n", - "A 81\n", - "JA 51\n", - "M 23\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 185, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# A = Adult, JA = Jeune Adulte, J = Jeune, M = Mature\n", - "# Majority has no stade_developpement value. Probably freshly planted grain. Recommend to not ignore these trees. \n", - "# For the others, it may probably be a fill value. I recommend setting the height values as NaN\n", - "min_girth_outlier_df.value_counts('stade_developpement', dropna=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 186, - "id": "bf4cf441-c5af-4ebc-9ab7-92dfaeefe10c", - "metadata": {}, - "outputs": [], - "source": [ - "# giving NaN value instead of 0 for already existing trees in order to signal missing data\n", - "df_refined.loc[(df_refined['circonference_cm'] <= min_girth) & (df_refined['stade_developpement'].isin(['A', 'JA', 'J', 'M'])),\n", - " 'circonference_cm'] = np.nan\n", - "# same question as minimum height (see above)" - ] - }, - { - "cell_type": "code", - "execution_count": 196, - "id": "e3e28272-33a0-428a-80e8-975140d30330", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "25264 trees are both 0 m tall and 0 cms girth\n", - "11688 trees are 0 m tall and have strictly positive girth\n", - "322 trees have strictly positive height and 0 cms girth\n" - ] - } - ], - "source": [ - "# search if trees 0 meters height trees and 0 cms girth trees are the same\n", - "both_zero = ((df_refined['hauteur_m'] <= 0) & (df_refined['circonference_cm'] <= 0)).sum()\n", - "print(f'{both_zero} trees are both 0 m tall and 0 cms girth')\n", - "\n", - "height_zero_girth_non_zero = ((df_refined['hauteur_m'] <= 0) & (df_refined['circonference_cm'] > 0)).sum()\n", - "print(f'{height_zero_girth_non_zero} trees are 0 m tall and have strictly positive girth')\n", - "\n", - "non_zero_height_zero_girth = ((df_refined['hauteur_m'] > 0) & (df_refined['circonference_cm'] <= 0)).sum()\n", - "print(f'{non_zero_height_zero_girth} trees have strictly positive height and 0 cms girth')\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "26f2d2ae-4489-4d6e-b217-16125f372597", - "metadata": {}, - "source": [ - "### NaN Values" - ] - }, - { - "cell_type": "markdown", - "id": "bf1ee819-3acf-420e-8d17-3699bbc1b4ac", - "metadata": {}, - "source": [ - "# Maybe can we do something about the missing values for \"libelle_francais\", \"genre\", \"espece\", \"variete\"\n", - "# stade_developpement and remarkable missing values presences seem to be correlated (according to previous analysis)" - ] - }, - { - "cell_type": "code", - "execution_count": 197, - "id": "01c33327-96f8-4cca-bd48-af40c023344d", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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domanialitecomplement_addressenumerolibelle_francaisgenreespecevarietestade_developpementremarquable
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" - ], - "text/plain": [ - " domanialite complement_addresse numero libelle_francais genre espece \\\n", - "0 1 169235 200137 1497 16 1752 \n", - "\n", - " variete stade_developpement remarquable \n", - "0 163360 67205 63098 " - ] - }, - "execution_count": 197, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "columns_with_nans_df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b05576d8-1a5f-4140-9409-710c340a4203", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "58c0f807-803f-4cef-9ff6-a827020cb19f", - "metadata": {}, - "outputs": [], - "source": [ - "# remains to do \n", - "## achieve managing nans\n", - "## univariate analysis\n", - "## multivariate analysis" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Paris_trees_analysis.ipynb b/Paris_trees_analysis.ipynb index 07d020f..173c88b 100644 --- a/Paris_trees_analysis.ipynb +++ b/Paris_trees_analysis.ipynb @@ -1,149807 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "2fc37333-2cee-49f4-a498-aa857728960d", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Exploring and Analyzing Paris trees data" - ] - }, - { - "cell_type": "markdown", - "id": "00f55996-8368-403f-9681-ebf93cc9b675", - "metadata": {}, - "source": [ - "Taken from https://s3-eu-west-1.amazonaws.com/static.oc-static.com/prod/courses/files/AI+Engineer/Project+2+Participez+%C3%A0+un+concours+sur+la+Smart+City/p2-arbres-fr.csv, coming from https://opendata.paris.fr/explore/dataset/les-arbres/map/?dataChart=eyJxdWVyaWVzIjpbeyJjb25maWciOnsiZGF0YXNldCI6Imxlcy1hcmJyZXMiLCJvcHRpb25zIjp7fX0sImNoYXJ0cyI6W3siYWxpZ25Nb250aCI6dHJ1ZSwidHlwZSI6ImNvbHVtbiIsImZ1bmMiOiJBVkciLCJ5QXhpcyI6ImlkYmFzZSIsInNjaWVudGlmaWNEaXNwbGF5Ijp0cnVlLCJjb2xvciI6IiMwMDMzNjYifV0sInhBeGlzIjoidHlwZWVtcGxhY2VtZW50IiwibWF4cG9pbnRzIjo1MCwic29ydCI6IiJ9XSwidGltZXNjYWxlIjoiIiwiZGlzcGxheUxlZ2VuZCI6dHJ1ZSwiYWxpZ25Nb250aCI6dHJ1ZX0%3D&disjunctive.typeemplacement&disjunctive.arrondissement&disjunctive.libellefrancais&disjunctive.genre&disjunctive.espece&disjunctive.varieteoucultivar&disjunctive.stadedeveloppement&disjunctive.remarquable&location=12,48.85984,2.31341&basemap=jawg.streets" - ] - }, - { - "cell_type": "markdown", - "id": "fbde0d3c-52bf-47db-a9d9-97cffa4b75a2", - "metadata": {}, - "source": [ - "## 1) Opening csv file" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "47f591da-19b2-4972-9fa8-f84bb909bec3", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "pd.set_option('display.float_format', '{:.2f}'.format) # don't want scientific notation. Floats are enough\n", - "\n", - "csv_path = \"./p2-trees-fr.csv\"\n", - "df = pd.read_csv(csv_path, sep=';')" - ] - }, - { - "cell_type": "markdown", - "id": "c1f5c665-5623-4a26-9dd4-a3a1471938cc", - "metadata": {}, - "source": [ - "## 2) Naive Analysis" - ] - }, - { - "cell_type": "markdown", - "id": "8484f605-2a28-46c8-99ed-e9594fdf6fb8", - "metadata": {}, - "source": [ - "### Use pandas basic methods" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "0a6d1dc0-c9c0-4868-95fe-fb076c3272f5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Size in MB:\n" - ] - }, - { - "data": { - "text/plain": [ - "131.216112" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import sys\n", - "print('Size in MB:')\n", - "sys.getsizeof(df)/(10**6)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "de7d0337-c19a-403c-b11b-483c3b36396d", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 200137 entries, 0 to 200136\n", - "Data columns (total 18 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 id 200137 non-null int64 \n", - " 1 type_emplacement 200137 non-null object \n", - " 2 domanialite 200136 non-null object \n", - " 3 arrondissement 200137 non-null object \n", - " 4 complement_addresse 30902 non-null object \n", - " 5 numero 0 non-null float64\n", - " 6 lieu 200137 non-null object \n", - " 7 id_emplacement 200137 non-null object \n", - " 8 libelle_francais 198640 non-null object \n", - " 9 genre 200121 non-null object \n", - " 10 espece 198385 non-null object \n", - " 11 variete 36777 non-null object \n", - " 12 circonference_cm 200137 non-null int64 \n", - " 13 hauteur_m 200137 non-null int64 \n", - " 14 stade_developpement 132932 non-null object \n", - " 15 remarquable 137039 non-null float64\n", - " 16 geo_point_2d_a 200137 non-null float64\n", - " 17 geo_point_2d_b 200137 non-null float64\n", - "dtypes: float64(4), int64(3), object(11)\n", - "memory usage: 27.5+ MB\n" - ] - } - ], - "source": [ - "df.info() # display count of columns, lines and columns names" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "db73c32b-9264-4629-9a3f-f355dd60585c", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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idtype_emplacementdomanialitearrondissementcomplement_addressenumerolieuid_emplacementlibelle_francaisgenreespecevarietecirconference_cmhauteur_mstade_developpementremarquablegeo_point_2d_ageo_point_2d_b
099874ArbreJardinPARIS 7E ARRDTNaNNaNMAIRIE DU 7E 116 RUE DE GRENELLE PARIS 7E19MarronnierAesculushippocastanumNaN205NaN0.0048.862.32
199875ArbreJardinPARIS 7E ARRDTNaNNaNMAIRIE DU 7E 116 RUE DE GRENELLE PARIS 7E20IfTaxusbaccataNaN658ANaN48.862.32
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" - ], - "text/plain": [ - " id type_emplacement domanialite arrondissement complement_addresse \\\n", - "0 99874 Arbre Jardin PARIS 7E ARRDT NaN \n", - "1 99875 Arbre Jardin PARIS 7E ARRDT NaN \n", - "\n", - " numero lieu id_emplacement \\\n", - "0 NaN MAIRIE DU 7E 116 RUE DE GRENELLE PARIS 7E 19 \n", - "1 NaN MAIRIE DU 7E 116 RUE DE GRENELLE PARIS 7E 20 \n", - "\n", - " libelle_francais genre espece variete circonference_cm \\\n", - "0 Marronnier Aesculus hippocastanum NaN 20 \n", - "1 If Taxus baccata NaN 65 \n", - "\n", - " hauteur_m stade_developpement remarquable geo_point_2d_a geo_point_2d_b \n", - "0 5 NaN 0.00 48.86 2.32 \n", - "1 8 A NaN 48.86 2.32 " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.head(2) # display 2 first lines" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "19a84af9-8b18-4c29-9ede-37a3de29f542", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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idnumerocirconference_cmhauteur_mremarquablegeo_point_2d_ageo_point_2d_b
count200137.000.00200137.00200137.00137039.00200137.00200137.00
mean387202.68NaN83.3813.110.0048.852.35
std545603.24NaN673.191971.220.040.030.05
min99874.00NaN0.000.000.0048.742.21
25%155927.00NaN30.005.000.0048.842.31
50%221078.00NaN70.008.000.0048.852.35
75%274102.00NaN115.0012.000.0048.882.39
max2024745.00NaN250255.00881818.001.0048.912.47
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" - ], - "text/plain": [ - " id numero circonference_cm hauteur_m remarquable \\\n", - "count 200137.00 0.00 200137.00 200137.00 137039.00 \n", - "mean 387202.68 NaN 83.38 13.11 0.00 \n", - "std 545603.24 NaN 673.19 1971.22 0.04 \n", - "min 99874.00 NaN 0.00 0.00 0.00 \n", - "25% 155927.00 NaN 30.00 5.00 0.00 \n", - "50% 221078.00 NaN 70.00 8.00 0.00 \n", - "75% 274102.00 NaN 115.00 12.00 0.00 \n", - "max 2024745.00 NaN 250255.00 881818.00 1.00 \n", - "\n", - " geo_point_2d_a geo_point_2d_b \n", - "count 200137.00 200137.00 \n", - "mean 48.85 2.35 \n", - "std 0.03 0.05 \n", - "min 48.74 2.21 \n", - "25% 48.84 2.31 \n", - "50% 48.85 2.35 \n", - "75% 48.88 2.39 \n", - "max 48.91 2.47 " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.describe()# general statistics only on number types variables (int and floats)\n", - "\n", - "# problems:\n", - "## 0 as min value for circumference, height\n", - "## > 250000 cm as circumference, > 800000 m as height" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "39814cb4-bb3c-4448-a5af-a95d26923b67", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "type_emplacement\n", - "Arbre 200137\n", - "Name: count, dtype: int64\n", - "\n", - "domanialite\n", - "Alignement 104949\n", - "Jardin 46262\n", - "CIMETIERE 31926\n", - "DASCO 6422\n", - "PERIPHERIQUE 5327\n", - "DJS 3900\n", - "DFPE 1325\n", - "DAC 21\n", - "DASES 4\n", - "Name: count, dtype: int64\n", - "\n", - "arrondissement\n", - "PARIS 15E ARRDT 17151\n", - "PARIS 13E ARRDT 16712\n", - "PARIS 16E ARRDT 16403\n", - "PARIS 20E ARRDT 15340\n", - "PARIS 19E ARRDT 13709\n", - "PARIS 12E ARRDT 12600\n", - "SEINE-SAINT-DENIS 11570\n", - "BOIS DE VINCENNES 11510\n", - "PARIS 14E ARRDT 11399\n", - "PARIS 17E ARRDT 10762\n", - "PARIS 18E ARRDT 10011\n", - "PARIS 7E ARRDT 8617\n", - "VAL-DE-MARNE 7580\n", - "PARIS 8E ARRDT 7245\n", - "PARIS 11E ARRDT 5658\n", - "HAUTS-DE-SEINE 5298\n", - "BOIS DE BOULOGNE 3978\n", - "PARIS 10E ARRDT 3385\n", - "PARIS 4E ARRDT 2740\n", - "PARIS 5E ARRDT 2368\n", - "PARIS 6E ARRDT 1764\n", - "PARIS 1ER ARRDT 1413\n", - "PARIS 3E ARRDT 1209\n", - "PARIS 9E ARRDT 1167\n", - "PARIS 2E ARRDT 548\n", - "Name: count, dtype: int64\n", - "\n", - "lieu\n", - "PARC FLORAL DE PARIS / ROUTE DE LA PYRAMIDE 2995\n", - "PARC DES BUTTES CHAUMONT 2331\n", - "PARC ANDRE CITROEN 2095\n", - "PARC OMNISPORT SUZANNE LENGLEN / 7 BOULEVARD DES FRERES VOISIN 1478\n", - "INSEP / AVENUE DU TREMBLAY 1293\n", - " ... \n", - "CIMETIERE ST VINCENT / DIV 5 1\n", - "ROUTE DU LAC A BAGATELLE 1\n", - "ROUTE DE LA PORTE SAINT-JAMES 1\n", - "PENTE DE GRAVELLE - AVENUE DE GRAVELLE / ROUTE NOUVELLE 1\n", - "CIMETIERE DE PANTIN / DIV 66 1\n", - "Name: count, Length: 6921, dtype: int64\n", - "\n", - "id_emplacement\n", - "101001 1324\n", - "101002 1241\n", - "101003 1128\n", - "202001 1032\n", - "101004 1020\n", - " ... \n", - "A15100096004 1\n", - "A15100096003 1\n", - "A15100096002 1\n", - "A15100096001 1\n", - "720170155 1\n", - "Name: count, Length: 69040, dtype: int64\n", - "\n", - "libelle_francais\n", - "Platane 42508\n", - "Marronnier 25207\n", - "Tilleul 21305\n", - "Erable 18389\n", - "Sophora 11797\n", - " ... \n", - "Cordyline 1\n", - "Camphrier 1\n", - "Jujubier 1\n", - "Maackie 1\n", - "Garrya 1\n", - "Name: count, Length: 192, dtype: int64\n", - "\n", - "genre\n", - "Platanus 42591\n", - "Aesculus 25341\n", - "Tilia 21550\n", - "Acer 18471\n", - "Sophora 11830\n", - " ... \n", - "Sycopsis 1\n", - "Enkianthus 1\n", - "Euscaphis 1\n", - "Phyllanthus 1\n", - "Washingtonia 1\n", - "Name: count, Length: 175, dtype: int64\n", - "\n", - "espece\n", - "x hispanica 36409\n", - "hippocastanum 20039\n", - "japonica 11822\n", - "n. sp. 9063\n", - "tomentosa 8962\n", - " ... \n", - "polycarpa 1\n", - "x gondouinii 1\n", - "lusitanica subsp.azorica 1\n", - "oliveri 1\n", - "delavayi subsp. potaninii 1\n", - "Name: count, Length: 539, dtype: int64\n", - "\n", - "variete\n", - "Baumannii' 4538\n", - "Briotii' 2827\n", - "Euchlora' 2756\n", - "Chanticleer' 2595\n", - "Fastigiata' 2483\n", - " ... \n", - "Conspicua' 1\n", - "Concordia' 1\n", - "Kojou-no-mai' 1\n", - "Nizetii' 1\n", - "Grosse Pomme Noire' 1\n", - "Name: count, Length: 436, dtype: int64\n", - "\n", - "hauteur_m\n", - "0 39219\n", - "10 28632\n", - "5 26345\n", - "15 17228\n", - "8 13628\n", - " ... \n", - "97 1\n", - "5155 1\n", - "815 1\n", - "218 1\n", - "157 1\n", - "Name: count, Length: 143, dtype: int64\n", - "\n", - "stade_developpement\n", - "A 64438\n", - "JA 35444\n", - "J 26937\n", - "M 6113\n", - "Name: count, dtype: int64\n", - "\n", - "remarquable\n", - "0.00 136855\n", - "1.00 184\n", - "Name: count, dtype: int64\n", - "\n", - "geo_point_2d_a\n", - "48.83 2\n", - "48.84 2\n", - "48.84 2\n", - "48.76 2\n", - "48.86 2\n", - " ..\n", - "48.84 1\n", - "48.84 1\n", - "48.84 1\n", - "48.84 1\n", - "48.82 1\n", - "Name: count, Length: 200107, dtype: int64\n", - "\n", - "geo_point_2d_b\n", - "2.30 2\n", - "2.37 2\n", - "2.39 2\n", - "2.31 2\n", - "2.34 2\n", - " ..\n", - "2.28 1\n", - "2.28 1\n", - "2.28 1\n", - "2.28 1\n", - "2.45 1\n", - "Name: count, Length: 200114, dtype: int64\n", - "\n" - ] - } - ], - "source": [ - "# for column in df.columns[1:]:\n", - " # df[column].value_counts()\n", - "# df.columns\n", - "# distribution discrète\n", - "for column in ['type_emplacement', 'domanialite', 'arrondissement',\n", - " 'lieu', 'id_emplacement', 'libelle_francais', 'genre', 'espece', 'variete',\n", - " 'hauteur_m', 'stade_developpement', 'remarquable', 'geo_point_2d_a',\n", - " 'geo_point_2d_b']:\n", - " print(df[column].value_counts())\n", - " print()\n", - "# print(column + ':')\n", - "# print(df[column].value_counts())\n", - "# print()" - ] - }, - { - "cell_type": "markdown", - "id": "c3ad81a0-ba4b-4737-9fe7-0434a05df987", - "metadata": {}, - "source": [ - "--> Statistic indicators seem to be coherent except max and min. When min == 0, is there 0 for other variables" - ] - }, - { - "cell_type": "markdown", - "id": "3c27d18f-28e5-4ab1-9841-e8c052f7d8ba", - "metadata": {}, - "source": [ - "### Looking for data anomalies" - ] - }, - { - "cell_type": "markdown", - "id": "2b021e60-5ec2-4fc8-adc3-70a6895de590", - "metadata": {}, - "source": [ - "#### Nan values" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "8dc879a1-e6a7-49d9-bdce-3cde4f9180e5", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['id', 'type_emplacement', 'domanialite', 'arrondissement',\n", - " 'complement_addresse', 'numero', 'lieu', 'id_emplacement',\n", - " 'libelle_francais', 'genre', 'espece', 'variete', 'circonference_cm',\n", - " 'hauteur_m', 'stade_developpement', 'remarquable', 'geo_point_2d_a',\n", - " 'geo_point_2d_b'],\n", - " dtype='object')" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.columns # columns (type of data)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "dc1a2e62-599e-482a-bbd2-74d3bf5ad727", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "30c92999-69cd-4158-b257-fc1557095c3e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0, 0, 1, 0, 169235, 200137, 0, 0,\n", - " 1497, 16, 1752, 163360, 0, 0, 67205, 63098,\n", - " 0, 0])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# number of 'nan' for each column\n", - "column_nb_of_nans = np.array([], dtype='int')\n", - "for column in df.columns:\n", - " where_nan = df[df[column].isna()]\n", - " column_nb_of_nans = np.concatenate((column_nb_of_nans,[len(where_nan)]))\n", - "column_nb_of_nans" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "1a78605d-63af-4c9b-accc-3f9f7da5197f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of nan for each variable that contains minimum 1 occurrence:\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " domanialite complement_addresse numero libelle_francais genre espece \\\n", - "0 1 169235 200137 1497 16 1752 \n", - "\n", - " variete stade_developpement remarquable \n", - "0 163360 67205 63098 " - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# dataframe with number of nans per column only when one nan or more\n", - "where_nans = np.where(column_nb_of_nans>0)\n", - "columns_with_nans_df = pd.DataFrame([column_nb_of_nans[where_nans]], columns = df.columns[where_nans])\n", - "print('Number of nan for each variable that contains minimum 1 occurrence:')\n", - "columns_with_nans_df" - ] - }, - { - "cell_type": "markdown", - "id": "36270ac3-4085-433c-ae0b-86a95b66debe", - "metadata": {}, - "source": [ - "#### Other outliers (min and max)" - ] - }, - { - "cell_type": "markdown", - "id": "7c1b1f8d-94fe-4401-8e8d-65c26212e18c", - "metadata": {}, - "source": [ - "##### Max" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "18ed125d-c9c1-4da5-a7e9-96c5dfe02064", - "metadata": {}, - "outputs": [], - "source": [ - "# based on the tallest tree height : https://en.wikipedia.org/wiki/Hyperion_(tree)\n", - "max_height = 116 # in meters\n", - "# based on the biggest tree girth (circumference) : https://www.guinnessworldrecords.com/world-records/living-tree-with-greatest-girth\n", - "max_girth = 36 # in meters" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "efd5e73a-1135-4866-9839-6eebb220c94a", - "metadata": {}, - "outputs": [], - "source": [ - "max_height_outlier_df = df[(df['hauteur_m'] > max_height)]\n", - "max_girth_outlier_df = df[(df['circonference_cm']/100 > max_girth)]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "b74111f5-80d8-44de-94fa-6501835f7db6", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "height outliers trees (87 occurrencies):\n" - ] - }, - { - "data": { - "text/html": [ - "
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12536115062ArbreJardinPARIS 16E ARRDT11 à 27NaNJARDIN DE L AVENUE FOCH / 10 AVENUE FOCH10303ErableAcern. sp.NaN47255J0.0048.872.29
15373118412ArbreJardinPARIS 7E ARRDTNaNNaNSQUARE DE L'ABBE ESQUERRE17PaulowniaPaulowniatomentosaNaN145810A0.0048.852.31
24099128185ArbreJardinPARIS 17E ARRDTNaNNaNSQUARE ANDRE ULMANN27PlatanePlatanusx hispanicaNaN130125A0.0048.892.30
.........................................................
1878732008880ArbreDJSPARIS 13E ARRDTNaNNaNSTADE CHARLETTY / 99 BOULEVARD KELLERMANN91MicocoulierCeltisaustralisNaN10120JA0.0048.822.35
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87 rows × 18 columns

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" - ], - "text/plain": [ - " id type_emplacement domanialite arrondissement \\\n", - "5392 106800 Arbre Jardin PARIS 7E ARRDT \n", - "6473 107951 Arbre Jardin PARIS 7E ARRDT \n", - "12536 115062 Arbre Jardin PARIS 16E ARRDT \n", - "15373 118412 Arbre Jardin PARIS 7E ARRDT \n", - "24099 128185 Arbre Jardin PARIS 17E ARRDT \n", - "... ... ... ... ... \n", - "187873 2008880 Arbre DJS PARIS 13E ARRDT \n", - "187874 2008881 Arbre DJS PARIS 13E ARRDT \n", - "189053 2011417 Arbre Jardin BOIS DE VINCENNES \n", - "189441 2011863 Arbre DFPE PARIS 16E ARRDT \n", - "197136 2020792 Arbre Jardin PARIS 14E ARRDT \n", - "\n", - " complement_addresse numero \\\n", - "5392 NaN NaN \n", - "6473 NaN NaN \n", - "12536 11 à 27 NaN \n", - "15373 NaN NaN \n", - "24099 NaN NaN \n", - "... ... ... \n", - "187873 NaN NaN \n", - "187874 NaN NaN \n", - "189053 NaN NaN \n", - "189441 NaN NaN \n", - "197136 NaN NaN \n", - "\n", - " lieu id_emplacement \\\n", - "5392 JARDIN DU CHAMP DE MARS A0120683 \n", - "6473 JARDIN DU CHAMP DE MARS / C01 / ALLEE LEON BOU... A00202003 \n", - "12536 JARDIN DE L AVENUE FOCH / 10 AVENUE FOCH 10303 \n", - "15373 SQUARE DE L'ABBE ESQUERRE 17 \n", - "24099 SQUARE ANDRE ULMANN 27 \n", - "... ... ... \n", - "187873 STADE CHARLETTY / 99 BOULEVARD KELLERMANN 91 \n", - "187874 STADE CHARLETTY / 99 BOULEVARD KELLERMANN 92 \n", - "189053 PARC FLORAL DE PARIS / ROUTE DE LA PYRAMIDE 180088 \n", - "189441 ERLANGER (9-11) CRECHE 48970005 \n", - "197136 SQUARE AUGUSTE RENOIR / 205 RUE RAYMOND LOSSERAND 103005 \n", - "\n", - " libelle_francais genre espece variete \\\n", - "5392 Tilleul Tilia n. sp. NaN \n", - "6473 Platane Platanus x hispanica NaN \n", - "12536 Erable Acer n. sp. NaN \n", - "15373 Paulownia Paulownia tomentosa NaN \n", - "24099 Platane Platanus x hispanica NaN \n", - "... ... ... ... ... \n", - "187873 Micocoulier Celtis australis NaN \n", - "187874 Micocoulier Celtis australis NaN \n", - "189053 Pin Pinus mugo NaN \n", - "189441 Marronnier Aesculus hippocastanum Baumannii' \n", - "197136 Erable Acer platanoides NaN \n", - "\n", - " circonference_cm hauteur_m stade_developpement remarquable \\\n", - "5392 100 1510 A 0.00 \n", - "6473 112 117 A 0.00 \n", - "12536 47 255 J 0.00 \n", - "15373 145 810 A 0.00 \n", - "24099 130 125 A 0.00 \n", - "... ... ... ... ... \n", - "187873 10 120 JA 0.00 \n", - "187874 10 120 JA 0.00 \n", - "189053 60 150 JA 0.00 \n", - "189441 8 157 NaN 0.00 \n", - "197136 145 125 A 0.00 \n", - "\n", - " geo_point_2d_a geo_point_2d_b \n", - "5392 48.86 2.30 \n", - "6473 48.86 2.29 \n", - "12536 48.87 2.29 \n", - "15373 48.85 2.31 \n", - "24099 48.89 2.30 \n", - "... ... ... \n", - "187873 48.82 2.35 \n", - "187874 48.82 2.35 \n", - "189053 48.83 2.44 \n", - "189441 48.85 2.26 \n", - "197136 48.83 2.31 \n", - "\n", - "[87 rows x 18 columns]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print('height outliers trees ('+ str(len(max_height_outlier_df)) + ' occurrencies):')\n", - "max_height_outlier_df\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "fe9ec6bc-7472-4417-8057-c17eff3bed71", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "hauteur_m\n", - "120 7\n", - "155 5\n", - "118 5\n", - "125 4\n", - "810 4\n", - "117 3\n", - "205 3\n", - "1515 3\n", - "150 3\n", - "510 2\n", - "160 2\n", - "610 2\n", - "1510 2\n", - "1512 1\n", - "1511 1\n", - "5155 1\n", - "618 1\n", - "2525 1\n", - "714 1\n", - "720 1\n", - "2015 1\n", - "815 1\n", - "818 1\n", - "1815 1\n", - "1010 1\n", - "1113 1\n", - "1211 1\n", - "1216 1\n", - "1610 1\n", - "1514 1\n", - "615 1\n", - "260 1\n", - "608 1\n", - "159 1\n", - "122 1\n", - "128 1\n", - "129 1\n", - "130 1\n", - "138 1\n", - "148 1\n", - "154 1\n", - "156 1\n", - "157 1\n", - "170 1\n", - "512 1\n", - "180 1\n", - "190 1\n", - "204 1\n", - "216 1\n", - "218 1\n", - "219 1\n", - "220 1\n", - "255 1\n", - "327 1\n", - "881818 1\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "max_height_outlier_df.value_counts('hauteur_m')" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "6df7ba62-4999-4c99-9865-238ca327392d", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "girth outliers trees (20 occurrencies):\n" - ] - }, - { - "data": { - "text/html": [ - "
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idtype_emplacementdomanialitearrondissementcomplement_addressenumerolieuid_emplacementlibelle_francaisgenreespecevarietecirconference_cmhauteur_mstade_developpementremarquablegeo_point_2d_ageo_point_2d_b
71787190080ArbreDASCOPARIS 20E ARRDTNaNNaNPLAINE (9) ELEM B11110006TilleulTiliatomentosaNaN1858915A0.0048.852.40
82951203295ArbreAlignementPARIS 18E ARRDTNaNNaNRUE D AUBERVILLIERS301004ErableAcermonspessulanumNaN554556J0.0048.892.37
90605211362ArbreAlignementPARIS 5E ARRDT34NaNBOULEVARD SAINT MARCEL204025CedreleToonasinensisNaN2013016A0.0048.842.36
91256212024ArbreAlignementPARIS 12E ARRDTNaNNaNRUE DE CHALON202005MicocoulierCeltisaustralisNaN3014010A0.0048.852.37
97354218274ArbreAlignementPARIS 7E ARRDT27NaNAVENUE RAPP501003PlatanePlatanusoccidentalisNaN811018A0.0048.862.30
103437224609ArbreAlignementPARIS 18E ARRDTNaNNaNBOULEVARD BARBES1303003PlatanePlatanusx hispanicaNaN90356J0.0048.892.35
133546256868ArbreAlignementPARIS 19E ARRDTNaNNaNAVENUE JEAN JAURES1501006PlatanePlatanusx hispanicaNaN409010A0.0048.892.39
147235270997ArbreAlignementPARIS 7E ARRDTNaNNaNQUAI BRANLY503005PaulowniaPaulowniatomentosaNaN915515A0.0048.862.29
147634271402ArbreAlignementPARIS 7E ARRDTNaNNaNAVENUE BOSQUET1202016PlatanePlatanusoccidentalisNaN6511016A0.0048.862.31
148106271884ArbreAlignementPARIS 7E ARRDTNaNNaNAVENUE BOSQUET501022PlatanePlatanusoccidentalisNaN659515A0.0048.862.30
148481272263ArbreAlignementPARIS 7E ARRDT14NaNBOULEVARD DES INVALIDES402009PlatanePlatanusoccidentalisNaN612018A0.0048.852.31
149056272885ArbreAlignementPARIS 18E ARRDTNaNNaNPLACE DE LA CHAPELLE101026PlatanePlatanusx hispanicaNaN25025522A0.0048.882.36
151364275451ArbreAlignementPARIS 7E ARRDTNaNNaNESPLANADE DES INVALIDES410005TilleulTiliatomentosaNaN7540815JA0.0048.862.31
152303276396ArbreAlignementPARIS 7E ARRDTNaNNaNESPLANADE DES INVALIDES402011TilleulTiliatomentosaNaN908512A0.0048.862.31
160432284823ArbreAlignementPARIS 18E ARRDTNaNNaNAVENUE DE LA PORTE DE CLIGNANCOURT202005PlatanePlatanusorientalisNaN12017020A0.0048.902.34
163918288407ArbreAlignementPARIS 12E ARRDTNaNNaNBOULEVARD DE PICPUS1604017SophoraSophorajaponicaNaN60855JA0.0048.852.40
164616289121ArbreAlignementPARIS 12E ARRDTNaNNaNRUE FRANCOIS TRUFFAUT301005PlatanePlatanusx hispanicaNaN909510A0.0048.832.39
167606292180ArbreAlignementPARIS 18E ARRDT16NaNRUE JEAN COCTEAU202027Noisetier de ByzanceCoryluscolurnaNaN36305J0.0048.902.35
170367295047ArbreAlignementPARIS 5E ARRDT16NaNRUE POLIVEAU202015PlatanePlatanusx hispanicaNaN612520A0.0048.842.36
173343298211ArbreAlignementPARIS 5E ARRDTNaNNaNBOULEVARD DE PORT ROYAL402001MarronnierAesculusx carneaBriotii'7912010A0.0048.842.35
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" - ], - "text/plain": [ - " id type_emplacement domanialite arrondissement \\\n", - "71787 190080 Arbre DASCO PARIS 20E ARRDT \n", - "82951 203295 Arbre Alignement PARIS 18E ARRDT \n", - "90605 211362 Arbre Alignement PARIS 5E ARRDT \n", - "91256 212024 Arbre Alignement PARIS 12E ARRDT \n", - "97354 218274 Arbre Alignement PARIS 7E ARRDT \n", - "103437 224609 Arbre Alignement PARIS 18E ARRDT \n", - "133546 256868 Arbre Alignement PARIS 19E ARRDT \n", - "147235 270997 Arbre Alignement PARIS 7E ARRDT \n", - "147634 271402 Arbre Alignement PARIS 7E ARRDT \n", - "148106 271884 Arbre Alignement PARIS 7E ARRDT \n", - "148481 272263 Arbre Alignement PARIS 7E ARRDT \n", - "149056 272885 Arbre Alignement PARIS 18E ARRDT \n", - "151364 275451 Arbre Alignement PARIS 7E ARRDT \n", - "152303 276396 Arbre Alignement PARIS 7E ARRDT \n", - "160432 284823 Arbre Alignement PARIS 18E ARRDT \n", - "163918 288407 Arbre Alignement PARIS 12E ARRDT \n", - "164616 289121 Arbre Alignement PARIS 12E ARRDT \n", - "167606 292180 Arbre Alignement PARIS 18E ARRDT \n", - "170367 295047 Arbre Alignement PARIS 5E ARRDT \n", - "173343 298211 Arbre Alignement PARIS 5E ARRDT \n", - "\n", - " complement_addresse numero lieu \\\n", - "71787 NaN NaN PLAINE (9) ELEM B \n", - "82951 NaN NaN RUE D AUBERVILLIERS \n", - "90605 34 NaN BOULEVARD SAINT MARCEL \n", - "91256 NaN NaN RUE DE CHALON \n", - "97354 27 NaN AVENUE RAPP \n", - "103437 NaN NaN BOULEVARD BARBES \n", - "133546 NaN NaN AVENUE JEAN JAURES \n", - "147235 NaN NaN QUAI BRANLY \n", - "147634 NaN NaN AVENUE BOSQUET \n", - "148106 NaN NaN AVENUE BOSQUET \n", - "148481 14 NaN BOULEVARD DES INVALIDES \n", - "149056 NaN NaN PLACE DE LA CHAPELLE \n", - "151364 NaN NaN ESPLANADE DES INVALIDES \n", - "152303 NaN NaN ESPLANADE DES INVALIDES \n", - "160432 NaN NaN AVENUE DE LA PORTE DE CLIGNANCOURT \n", - "163918 NaN NaN BOULEVARD DE PICPUS \n", - "164616 NaN NaN RUE FRANCOIS TRUFFAUT \n", - "167606 16 NaN RUE JEAN COCTEAU \n", - "170367 16 NaN RUE POLIVEAU \n", - "173343 NaN NaN BOULEVARD DE PORT ROYAL \n", - "\n", - " id_emplacement libelle_francais genre espece \\\n", - "71787 11110006 Tilleul Tilia tomentosa \n", - "82951 301004 Erable Acer monspessulanum \n", - "90605 204025 Cedrele Toona sinensis \n", - "91256 202005 Micocoulier Celtis australis \n", - "97354 501003 Platane Platanus occidentalis \n", - "103437 1303003 Platane Platanus x hispanica \n", - "133546 1501006 Platane Platanus x hispanica \n", - "147235 503005 Paulownia Paulownia tomentosa \n", - "147634 1202016 Platane Platanus occidentalis \n", - "148106 501022 Platane Platanus occidentalis \n", - "148481 402009 Platane Platanus occidentalis \n", - "149056 101026 Platane Platanus x hispanica \n", - "151364 410005 Tilleul Tilia tomentosa \n", - "152303 402011 Tilleul Tilia tomentosa \n", - "160432 202005 Platane Platanus orientalis \n", - "163918 1604017 Sophora Sophora japonica \n", - "164616 301005 Platane Platanus x hispanica \n", - "167606 202027 Noisetier de Byzance Corylus colurna \n", - "170367 202015 Platane Platanus x hispanica \n", - "173343 402001 Marronnier Aesculus x carnea \n", - "\n", - " variete circonference_cm hauteur_m stade_developpement \\\n", - "71787 NaN 18589 15 A \n", - "82951 NaN 5545 56 J \n", - "90605 NaN 20130 16 A \n", - "91256 NaN 30140 10 A \n", - "97354 NaN 8110 18 A \n", - "103437 NaN 9035 6 J \n", - "133546 NaN 4090 10 A \n", - "147235 NaN 9155 15 A \n", - "147634 NaN 65110 16 A \n", - "148106 NaN 6595 15 A \n", - "148481 NaN 6120 18 A \n", - "149056 NaN 250255 22 A \n", - "151364 NaN 7540 815 JA \n", - "152303 NaN 9085 12 A \n", - "160432 NaN 120170 20 A \n", - "163918 NaN 6085 5 JA \n", - "164616 NaN 9095 10 A \n", - "167606 NaN 3630 5 J \n", - "170367 NaN 6125 20 A \n", - "173343 Briotii' 79120 10 A \n", - "\n", - " remarquable geo_point_2d_a geo_point_2d_b \n", - "71787 0.00 48.85 2.40 \n", - "82951 0.00 48.89 2.37 \n", - "90605 0.00 48.84 2.36 \n", - "91256 0.00 48.85 2.37 \n", - "97354 0.00 48.86 2.30 \n", - "103437 0.00 48.89 2.35 \n", - "133546 0.00 48.89 2.39 \n", - "147235 0.00 48.86 2.29 \n", - "147634 0.00 48.86 2.31 \n", - "148106 0.00 48.86 2.30 \n", - "148481 0.00 48.85 2.31 \n", - "149056 0.00 48.88 2.36 \n", - "151364 0.00 48.86 2.31 \n", - "152303 0.00 48.86 2.31 \n", - "160432 0.00 48.90 2.34 \n", - "163918 0.00 48.85 2.40 \n", - "164616 0.00 48.83 2.39 \n", - "167606 0.00 48.90 2.35 \n", - "170367 0.00 48.84 2.36 \n", - "173343 0.00 48.84 2.35 " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print('girth outliers trees ('+ str(len(max_girth_outlier_df)) + ' occurrencies):')\n", - "max_girth_outlier_df" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "a7a5453d-787f-4c73-a3c2-e3f68b7c2b88", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "circonference_cm\n", - "3630 1\n", - "4090 1\n", - "120170 1\n", - "79120 1\n", - "65110 1\n", - "30140 1\n", - "20130 1\n", - "18589 1\n", - "9155 1\n", - "9095 1\n", - "9085 1\n", - "9035 1\n", - "8110 1\n", - "7540 1\n", - "6595 1\n", - "6125 1\n", - "6120 1\n", - "6085 1\n", - "5545 1\n", - "250255 1\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "max_girth_outlier_df.value_counts('circonference_cm')" - ] - }, - { - "cell_type": "markdown", - "id": "13039a0e-5f50-4290-9d97-03cab5a6f6b7", - "metadata": {}, - "source": [ - "##### Min" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "2311d22a-a4fe-4491-9f34-adee3bcbb5a7", - "metadata": {}, - "outputs": [], - "source": [ - "# based on the tallest tree height : https://en.wikipedia.org/wiki/Hyperion_(tree)\n", - "min_height = 0 # in meters\n", - "# based on the biggest tree girth (circumference) : https://www.guinnessworldrecords.com/world-records/living-tree-with-greatest-girth\n", - "min_girth = 0 # in meters" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "4fb1b6d3-73b7-4eba-aa10-8387bd595787", - "metadata": {}, - "outputs": [], - "source": [ - "min_height_outlier_df = df[(df['hauteur_m'] <= min_height)]\n", - "min_girth_outlier_df = df[(df['circonference_cm']/100 <= min_girth)]" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "49a05f31-e8a2-4a0a-bcca-5c7b9b73b9f8", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "height outliers trees (39219 occurrencies):\n" - ] - }, - { - "data": { - "text/html": [ - "
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699880ArbreJardinPARIS 17E ARRDTNaNNaNPARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING000G0035Arbre à mielTetradiumdanielliiNaN370NaNNaN48.892.32
1199885ArbreJardinPARIS 16E ARRDTNaNNaNJARDIN DU RANELAGH20001HêtreFagussylvaticaAtropunicea'300NaNNaN48.862.27
1299887ArbreJardinPARIS 16E ARRDTNaNNaNJARDIN DU RANELAGH20003MicocoulierCeltisoccidentalisNaN2050NaNNaN48.862.27
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2430102773ArbreJardinPARIS 17E ARRDTNaNNaNPARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING000B0021MagnoliaMagnoliax soulangeanaSundew'00NaNNaN48.892.32
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25867 rows × 18 columns

\n", - "
" - ], - "text/plain": [ - " id type_emplacement domanialite arrondissement \\\n", - "1778 101922 Arbre Jardin PARIS 19E ARRDT \n", - "2332 102591 Arbre Jardin PARIS 17E ARRDT \n", - "2333 102592 Arbre Jardin PARIS 17E ARRDT \n", - "2405 102744 Arbre Jardin PARIS 20E ARRDT \n", - "2430 102773 Arbre Jardin PARIS 17E ARRDT \n", - "... ... ... ... ... \n", - "200132 2024741 Arbre Jardin BOIS DE VINCENNES \n", - "200133 2024742 Arbre Jardin BOIS DE VINCENNES \n", - "200134 2024743 Arbre Jardin BOIS DE VINCENNES \n", - "200135 2024744 Arbre Jardin BOIS DE VINCENNES \n", - "200136 2024745 Arbre Jardin BOIS DE VINCENNES \n", - "\n", - " complement_addresse numero \\\n", - "1778 NaN NaN \n", - "2332 NaN NaN \n", - "2333 NaN NaN \n", - "2405 NaN NaN \n", - "2430 NaN NaN \n", - "... ... ... \n", - "200132 NaN NaN \n", - "200133 NaN NaN \n", - "200134 NaN NaN \n", - "200135 NaN NaN \n", - "200136 NaN NaN \n", - "\n", - " lieu id_emplacement \\\n", - "1778 PARC DES BUTTES CHAUMONT C0400032 \n", - "2332 PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING 000B0052 \n", - "2333 PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING 000B0053 \n", - "2405 SQUARE LEON FRAPIE 2 \n", - "2430 PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING 000B0021 \n", - "... ... ... \n", - "200132 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170151 \n", - "200133 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170152 \n", - "200134 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170153 \n", - "200135 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170154 \n", - "200136 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170155 \n", - "\n", - " libelle_francais genre espece variete \\\n", - "1778 Erable Acer platanoides NaN \n", - "2332 Magnolia Magnolia x soulangeana Heaven Scent' \n", - "2333 Magnolia Magnolia x soulangeana Heaven Scent' \n", - "2405 Platane Platanus x hispanica NaN \n", - "2430 Magnolia Magnolia x soulangeana Sundew' \n", - "... ... ... ... ... \n", - "200132 NaN Castanea fargesii NaN \n", - "200133 Arbre aux mouchoirs Davidia involucrata NaN \n", - "200134 Peuplier Populus simonii NaN \n", - "200135 Chêne Quercus n. sp. NaN \n", - "200136 Raisinier Hovenia dulcis NaN \n", - "\n", - " circonference_cm hauteur_m stade_developpement remarquable \\\n", - "1778 0 0 NaN NaN \n", - "2332 0 0 NaN NaN \n", - "2333 0 0 NaN NaN \n", - "2405 0 0 JA 0.00 \n", - "2430 0 0 NaN NaN \n", - "... ... ... ... ... \n", - "200132 0 0 NaN 0.00 \n", - "200133 0 0 NaN 0.00 \n", - "200134 0 0 NaN 0.00 \n", - "200135 0 0 NaN 0.00 \n", - "200136 0 0 NaN 0.00 \n", - "\n", - " geo_point_2d_a geo_point_2d_b \n", - "1778 48.88 2.38 \n", - "2332 48.89 2.32 \n", - "2333 48.89 2.32 \n", - "2405 48.87 2.41 \n", - "2430 48.89 2.32 \n", - "... ... ... \n", - "200132 48.82 2.46 \n", - "200133 48.82 2.46 \n", - "200134 48.82 2.46 \n", - "200135 48.82 2.46 \n", - "200136 48.82 2.45 \n", - "\n", - "[25867 rows x 18 columns]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print('girth outliers trees ('+ str(len(max_girth_outlier_df)) + ' occurrencies):')\n", - "min_girth_outlier_df" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "3423a9fd-c866-43d1-beb3-e8fa267fa0ba", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "circonference_cm\n", - "0 25867\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "min_girth_outlier_df.value_counts('circonference_cm')" - ] - }, - { - "cell_type": "markdown", - "id": "5eda7ac1-2c90-4079-95e7-c5477eb15fdd", - "metadata": {}, - "source": [ - "# 3) Detailed univariate analysis of each variable" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "2f484a14-cff5-4a3e-8efd-82b29fbdbb5a", - "metadata": {}, - "outputs": [], - "source": [ - "# def variable_univariate_analysis(df, variable_name, variable_type, variable_title_type=None):\n", - "# import numpy as np\n", - "# import seaborn as sns\n", - "# import matplotlib.pyplot as plt\n", - "\n", - "# if variable_title_type == None:\n", - "# variable_title_type = variable_type\n", - " \n", - "# # variable_type can four types\n", - "# variable_types = ['qualitative_nominal','qualitative_ordinal','quantitative_discrete','quantitative_continuous']\n", - "# variable_type = str.lower(variable_type)\n", - "# if variable_type not in variable_types:\n", - "# print('Error : wrong variable_type')\n", - "# else:\n", - "# # bars but can also be pie\n", - "# if variable_type=='qualitative_nominal':\n", - "# value_counts = df[variable_name].value_counts(normalize=True)\n", - "# max_values = 26\n", - "# if len(value_counts) >= 26:\n", - "# top_values = value_counts[0:max_values]\n", - "# others = value_counts[max_values:].sum()\n", - "# if variable_title_type == variable_type:\n", - "# others_label = 'others'\n", - "# else:\n", - "# others_label = '> ' + str(max_values)\n", - "# top_values[others_label] = others\n", - "# value_counts = top_values\n", - "# value_counts.plot(kind='bar')\n", - "# elif variable_type == 'qualitative_ordinal': \n", - "# df.sort_values(by=variable_name, ascending=True, inplace=True)\n", - "# variable_univariate_analysis(df, variable_name, 'qualitative_nominal', variable_type)\n", - "# pass\n", - "# elif variable_type == 'quantitative_discrete':\n", - "# df.sort_values(by=variable_name, ascending=True, inplace=True)\n", - "# value_counts = df[variable_name].value_counts(normalize=True)\n", - "# value_counts.plot(kind='bar')\n", - "# pass\n", - "# elif variable_type == 'quantitative_continuous':\n", - "# sns.histplot(df[variable_name][df[variable_name] <= df[variable].quantile(0.99)], stat='frequency', kde=True)\n", - "# pass\n", - "\n", - "# if variable_type != 'qualitative_ordinal':\n", - "# plt.title(variable_name + ' ( ' + variable_title_type + ' )')\n", - "# plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "5ece6f3a-a152-4a3e-8df5-b6fa0e6c6330", - "metadata": {}, - "outputs": [], - "source": [ - "# import numpy as np\n", - "# import seaborn as sns\n", - "# import matplotlib.pyplot as plt\n", - "# import pandas as pd\n", - "\n", - "# def plot_variable_distribution(df: pd.DataFrame, variable_name: str, variable_type: str, variable_title_type: str = None) -> None:\n", - "# \"\"\"\n", - "# Plots the distribution of a variable based on its type.\n", - "\n", - "# Args:\n", - "# df (pd.DataFrame): The DataFrame containing the data.\n", - "# variable_name (str): The name of the variable to plot.\n", - "# variable_type (str): The type of the variable (e.g., 'qualitative_nominal', 'qualitative_ordinal', \n", - "# 'quantitative_discrete', 'quantitative_continuous').\n", - "# variable_title_type (str, optional): An optional title type for the variable. Defaults to None.\n", - "\n", - "# Raises:\n", - "# ValueError: If the variable_type is not recognized.\n", - "# \"\"\"\n", - "# if variable_title_type is None:\n", - "# variable_title_type = variable_type\n", - "\n", - "# variable_types = ['qualitative_nominal', 'qualitative_ordinal', 'quantitative_discrete', 'quantitative_continuous']\n", - "# variable_type = variable_type.lower()\n", - "\n", - "# if variable_type not in variable_types:\n", - "# raise ValueError('Error: wrong variable_type')\n", - "\n", - "# if variable_type == 'qualitative_nominal':\n", - "# value_counts = df[variable_name].value_counts(normalize=True)\n", - "# max_values = 26\n", - "# if len(value_counts) >= max_values:\n", - "# top_values = value_counts.head(max_values)\n", - "# others = value_counts.iloc[max_values:].sum()\n", - "# others_label = 'others' if variable_title_type == variable_type else f'> {max_values}'\n", - "# top_values[others_label] = others\n", - "# value_counts = top_values\n", - "# value_counts.plot(kind='bar')\n", - "\n", - "# elif variable_type == 'qualitative_ordinal':\n", - "# df.sort_values(by=variable_name, ascending=True, inplace=True)\n", - "# plot_variable_distribution(df, variable_name, 'qualitative_nominal', variable_type)\n", - "\n", - "# elif variable_type == 'quantitative_discrete':\n", - "# df.sort_values(by=variable_name, ascending=True, inplace=True)\n", - "# value_counts = df[variable_name].value_counts(normalize=True)\n", - "# value_counts.plot(kind='bar')\n", - "\n", - "# elif variable_type == 'quantitative_continuous':\n", - "# sns.histplot(df[variable_name][df[variable_name] <= df[variable_name].quantile(0.99)], stat='frequency', kde=True)\n", - "\n", - "# if variable_type != 'qualitative_ordinal':\n", - "# plt.title(f\"{variable_name} ({variable_title_type})\")\n", - "# plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "id": "a2a44234-f541-403e-9ccf-efd7c4e98601", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import folium\n", - "from folium.plugins import MarkerCluster\n", - "import branca\n", - "from IPython.display import display\n", - "from scipy.spatial import cKDTree\n", - "from geopy.distance import geodesic\n", - "\n", - "\n", - "def plot_variable_distribution(\n", - " df: pd.DataFrame, \n", - " variable_name: str, \n", - " graph_type: str, \n", - " variable_title_type: str = None,\n", - ") -> None:\n", - " \"\"\"\n", - " Plots the distribution of a specified variable in a DataFrame based on the graph type.\n", - "\n", - " Args:\n", - " df (pd.DataFrame): The DataFrame containing the data.\n", - " variable_name (str): The name of the variable to plot.\n", - " graph_type (str): The type of graph to plot (e.g., 'pie', 'heat_map', 'bars', 'hist', 'bars_log').\n", - " variable_title_type (str, optional): An optional title type for the variable. Defaults to None.\n", - "\n", - " Raises:\n", - " ValueError: If the graph_type is not recognized or if latitude/longitude columns are missing.\n", - " \"\"\"\n", - " if variable_title_type is None:\n", - " variable_title_type = graph_type\n", - "\n", - " graph_types = ['pie', 'heat_map', 'bars', 'hist', 'bars_log', 'boxplot']\n", - " graph_type = graph_type.lower()\n", - "\n", - " if graph_type not in graph_types:\n", - " raise ValueError('Error: wrong graph_type')\n", - "\n", - " if graph_type == 'pie':\n", - " value_counts = df[variable_name].value_counts(normalize=True)\n", - " value_counts_percent = value_counts * 100 # Convert to percentage\n", - "\n", - " # Group classes with less than 2% into \"Others\"\n", - " others_count = value_counts_percent[value_counts_percent < 2]\n", - " if not others_count.empty:\n", - " others_label = f\"Others ({', '.join(others_count.index)})\"\n", - " value_counts = value_counts_percent[value_counts_percent >= 2]\n", - " value_counts[others_label] = others_count.sum() # Add the \"Others\" category\n", - "\n", - " plt.pie(\n", - " value_counts, \n", - " labels=[f\"{label} ({count:.1f}%)\" for label, count in zip(value_counts.index, value_counts.values)],\n", - " autopct='%1.1f%%'\n", - " )\n", - " plt.title(f\"{variable_name} Distribution\")\n", - "\n", - " elif graph_type == 'heat_map':\n", - " # Check if the DataFrame contains the necessary geographic columns\n", - " if 'geo_point_2d_a' in df.columns and 'geo_point_2d_b' in df.columns and variable_name in df.columns:\n", - " # Group by the variable of interest (e.g., 'city', 'region') and count the occurrences in each sector\n", - " sector_data = df.groupby([variable_name]).agg({\n", - " 'geo_point_2d_a': 'mean',\n", - " 'geo_point_2d_b': 'mean',\n", - " }).reset_index()\n", - " \n", - " # Add a \"population density\" column based on the number of occurrences of the variable in each sector\n", - " sector_data['counts'] = df.groupby(variable_name).size().values\n", - "\n", - " # Calculate the occurrence percentages\n", - " total_counts = sector_data['counts'].sum()\n", - " sector_data['percentage'] = (sector_data['counts'] / total_counts) * 100\n", - "\n", - " # Common zoom level used for city-level maps in Folium (and Leaflet)\n", - " zoom_level = 12\n", - " # Create a map centered around the average geographic points\n", - " m = folium.Map(location=[sector_data['geo_point_2d_a'].mean(), sector_data['geo_point_2d_b'].mean()], zoom_start=zoom_level)\n", - " \n", - " # Normalize population densities for the heat map\n", - " min_density = sector_data['counts'].min()\n", - " max_density = sector_data['counts'].max()\n", - " \n", - " # Create the colormap with branca\n", - " colormap = branca.colormap.LinearColormap(\n", - " colors=['yellow', 'orange', 'red'], # Choose a classic heatmap palette (yellow-orange-red)\n", - " vmin=min_density, vmax=max_density\n", - " ).to_step(10) # Divide the colormap into 10 steps for the legend\n", - "\n", - " # Extract coordinates of the sectors\n", - " coords = sector_data[['geo_point_2d_a', 'geo_point_2d_b']].values\n", - " tree = cKDTree(coords) # Create the k-d tree with the coordinates\n", - "\n", - " # Initialize the minimum distance to a very large number\n", - " min_distance = float('inf')\n", - " closest_pair = None\n", - "\n", - " # Find the closest pairs\n", - " for i, point in enumerate(coords):\n", - " # Find the closest neighbor from point i (excluding the point itself)\n", - " distances, indices = tree.query(point, k=2) # k=2 to get the closest neighbor and itself\n", - " \n", - " # The second closest will be the nearest (the first is the point itself)\n", - " closest_distance = distances[1]\n", - " closest_index = indices[1]\n", - " \n", - " # If the found distance is smaller, update the minimum distance and associated points\n", - " if closest_distance < min_distance:\n", - " min_distance = closest_distance\n", - " closest_pair = (point, coords[closest_index])\n", - "\n", - " # Convert the minimum Euclidean distance to geodesic distance for more accuracy\n", - " min_distance = geodesic(closest_pair[0], closest_pair[1]).meters\n", - "\n", - " # Limit the circle size to half the smallest distance between two points\n", - " max_circle_radius = min_distance / 2\n", - " \n", - " # Average Earth radius in meters\n", - " EARTH_RADIUS = 6371000\n", - " \n", - " # Calculate the size of a pixel in meters (at the given average latitude)\n", - " lat_rad = np.radians(np.mean(sector_data['geo_point_2d_b']))\n", - " meters_per_pixel = (2 * np.pi * EARTH_RADIUS) / (256 * (2 ** zoom_level)) * np.cos(lat_rad)\n", - " \n", - " max_circle_radius_in_pixels = max_circle_radius / meters_per_pixel\n", - " \n", - " # Add each sector as a colored circle based on population density\n", - " for _, row in sector_data.iterrows():\n", - " color = colormap(row['counts']) # Use the colormap to get the color based on density\n", - " \n", - " # Calculate the size of the marker based on density\n", - " # size = np.log(row['population_density'] + 1) * 5 # Logarithmic for a reasonable scale\n", - " # size = max(10, min(size, max_circle_radius)) # Limit the size to max_circle_radius\n", - " size = max_circle_radius_in_pixels\n", - " \n", - " # Add a circle to the map\n", - " folium.CircleMarker(\n", - " location=[row['geo_point_2d_a'], row['geo_point_2d_b']],\n", - " radius=size, # Circle size based on density and minimum distance\n", - " color=color,\n", - " fill=True,\n", - " fill_color=color, # Fill with the same color\n", - " fill_opacity=0.7,\n", - " popup=f\"{variable_name}: {row[variable_name]}
Trees: {row['counts']}
Percentage: {row['percentage']:.3}%\",\n", - " # Allow the size to adjust based on zoom level\n", - " zoom_on_click=True\n", - " ).add_to(m)\n", - "\n", - " # Add the colormap legend to the map\n", - " colormap.caption = f\"Trees per {variable_name}\"\n", - " colormap.add_to(m)\n", - " \n", - " # Force the legend to the bottom left corner using CSS style\n", - " m.get_root().html.add_child(folium.Element(\"\"\"\n", - " \n", - " \"\"\"))\n", - " \n", - " # Display the map\n", - " display(m)\n", - " m.save(f'./map_{variable_name}.html')\n", - " return # End the function\n", - " \n", - " else:\n", - " raise ValueError(\"The DataFrame must contain 'geo_point_2d_a', 'geo_point_2d_b', and the sector variable to display a map.\")\n", - "\n", - " elif graph_type in ['bars', 'bars_log']:\n", - " value_counts = df[variable_name].value_counts()\n", - "\n", - " # Group smaller classes into \"others\" if there are more than 26\n", - " if len(value_counts) > 26:\n", - " top_categories = value_counts[:25]\n", - " others_count = value_counts[25:].sum()\n", - " value_counts = pd.concat([top_categories, pd.Series({'others': others_count})])\n", - "\n", - " total = value_counts.sum()\n", - " plt.figure(figsize=(10, 6))\n", - " ax = sns.barplot(x=value_counts.index, y=value_counts.values)\n", - " plt.title(f\"{variable_name} Distribution\")\n", - " \n", - " # Add percentages inside the bars\n", - " for p, percentage in zip(ax.patches, value_counts / total * 100):\n", - " rotation = 90 if len(value_counts) > 10 else 0\n", - " ax.annotate(\n", - " f\"{percentage:.1f}%\",\n", - " (p.get_x() + p.get_width() * 0.50, p.get_height() * 0.90),\n", - " ha='center', va='center', fontsize=10, rotation=rotation, color='black'\n", - " )\n", - " \n", - " plt.xlabel(variable_name)\n", - " plt.ylabel('Count')\n", - " if graph_type == 'bars_log':\n", - " plt.yscale('log')\n", - " plt.xticks(rotation=90 if len(value_counts) > 10 else 0) # Rotate labels if too many\n", - " plt.show()\n", - "\n", - " elif graph_type == 'hist':\n", - " plt.figure(figsize=(10, 6))\n", - " sns.histplot(df[variable_name][df[variable_name] <= df[variable].quantile(0.99)], kde=True)\n", - " # sns.histplot(df[variable_name], kde=True, color='blue', bins=15)\n", - " plt.title(f\"{variable_name} Distribution\")\n", - " plt.xlabel(variable_name)\n", - " plt.ylabel('Count')\n", - " plt.show()\n", - "\n", - " elif graph_type == \"boxplot\":\n", - " sns.boxplot(y=df[variable_name][df[variable_name] <= df[variable].quantile(0.99)])\n", - " plt.title(f\"{variable_name} Distribution (Boxplot)\")\n", - " plt.ylabel(variable_name)\n", - " plt.show()\n", - " \n" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "0c375743-614a-4e49-a070-b717bfb64816", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['id', 'type_emplacement', 'domanialite', 'arrondissement',\n", - " 'complement_addresse', 'numero', 'lieu', 'id_emplacement',\n", - " 'libelle_francais', 'genre', 'espece', 'variete', 'circonference_cm',\n", - " 'hauteur_m', 'stade_developpement', 'remarquable', 'geo_point_2d_a',\n", - " 'geo_point_2d_b'],\n", - " dtype='object')" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "d3c85f01-5b87-4d42-b5e2-279001e61636", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "id_emplacement\n", - "101001 1324\n", - "101002 1241\n", - "101003 1128\n", - "202001 1032\n", - "101004 1020\n", - " ... \n", - "80269 1\n", - "80270 1\n", - "80271 1\n", - "80272 1\n", - "Q9630006 1\n", - "Name: count, Length: 69040, dtype: int64" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['id_emplacement'].unique()\n", - "df.value_counts('id_emplacement')" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "03af6a47-2f57-4a6f-9fd2-86bcee87382d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "lieu\n", - "PARC FLORAL DE PARIS / ROUTE DE LA PYRAMIDE 2995\n", - "PARC DES BUTTES CHAUMONT 2331\n", - "PARC ANDRE CITROEN 2095\n", - "PARC OMNISPORT SUZANNE LENGLEN / 7 BOULEVARD DES FRERES VOISIN 1478\n", - "INSEP / AVENUE DU TREMBLAY 1293\n", - " ... \n", - "CIMETIERE DU PERE LACHAISE / AVENUE DES THUYAS / DIV 80 1\n", - "CIMETIERE DE SAINT-OUEN ANCIEN / DIV 10 1\n", - "CIMETIERE DE SAINT-OUEN ANCIEN / DIV 4 1\n", - "CARREFOUR ODEON 1\n", - "POMMARD (33) MAT 1\n", - "Name: count, Length: 6921, dtype: int64" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.value_counts('lieu')" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "6175c0da-56c6-493d-b449-3ea13768dadb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['id', 'type_emplacement', 'domanialite', 'arrondissement',\n", - " 'complement_addresse', 'numero', 'lieu', 'id_emplacement',\n", - " 'libelle_francais', 'genre', 'espece', 'variete', 'circonference_cm',\n", - " 'hauteur_m', 'stade_developpement', 'remarquable', 'geo_point_2d_a',\n", - " 'geo_point_2d_b'],\n", - " dtype='object')" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "e9dc120e-cae3-4fb6-adff-9b172c920c40", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "variete\n", - "Baumannii' 4538\n", - "Briotii' 2827\n", - "Euchlora' 2756\n", - "Chanticleer' 2595\n", - "Fastigiata' 2483\n", - " ... \n", - "Précoce de Saumur' 1\n", - "Profusion' 1\n", - "Porlock' 1\n", - "Hilsop' 1\n", - "Neapolitanum' 1\n", - "Name: count, Length: 436, dtype: int64" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.value_counts('variete')" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "bdd9f57e-6010-4351-91f9-d81813b09fe9", - "metadata": {}, - "outputs": [], - "source": [ - "# interesting_variables = {'domanialite':'qualitative_nominal','arrondissement':'qualitative_nominal',\n", - "# 'lieu':'qualitative_nominal', 'libelle_francais':'qualitative_nominal',\n", - "# 'genre':'qualitative_nominal', 'espece':'qualitative_nominal',\n", - "# 'variete':'qualitative_nominal','circonference_cm':'quantitative_continuous',\n", - "# 'hauteur_m':'quantitative_continuous','stade_developpement':'qualitative_ordinal',\n", - "# 'remarquable':'qualitative_ordinal'}" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "219c74f9-ffb8-468f-a694-4b78b752bbf2", - "metadata": {}, - "outputs": [], - "source": [ - "interesting_variables = {'domanialite':'pie','arrondissement':'heat_map',\n", - " 'lieu':'heat_map', 'libelle_francais':'bars',\n", - " 'genre':'bars', 'espece':'bars',\n", - " 'variete':'bars','circonference_cm':'hist',\n", - " 'hauteur_m':'hist','stade_developpement':'pie',\n", - " 'remarquable':'bars_log'}" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "id": "c1d63cdf-8e99-4f05-87cf-f3a06b0af3dc", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
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", 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", 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", 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", 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", 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for variable in interesting_variables:\n", - " plot_variable_distribution(df, variable, interesting_variables[variable])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "376202c7-3a23-4d16-bf28-c032e0389c03", - "metadata": {}, - "outputs": [], - "source": [ - "interesting_variables" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0a553cb5-6153-4bdf-9a4a-b355f5f7651e", - "metadata": {}, - "outputs": [], - "source": [ - "variable = 'hauteur_m'\n", - "# variable_univariate_analysis(df, variable, interesting_variables[variable])\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "# %matplotlib widget\n", - "# ax = sns.histplot(df[variable], bins=20, kde=True)\n", - "filtered_data = df[variable][df[variable]<=40]\n", - "# sns.histplot(df[variable][df[variable] <= df[variable].quantile(0.99)], stat='frequency', binwidth = 1, binrange=(0,20), kde=True)\n", - "sns.histplot(df[variable][df[variable] <= df[variable].quantile(0.99)], stat='frequency', kde=True)\n", - "# plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "48d702a5-7520-48bc-813f-21d73f8de2a9", - "metadata": {}, - "outputs": [], - "source": [ - "variable = 'domanialite'\n", - "variable_univariate_analysis(df, variable, interesting_variables[variable])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0dccc78f-2ecf-4116-a7b0-d91a6a22f74a", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 112, - "id": "b0faf570-91a6-4d6a-bba3-a15c31cea9a7", - "metadata": {}, - "outputs": [], - "source": [ - "interesting_variables = {'circonference_cm':'boxplot',\n", - " 'hauteur_m':'boxplot'}" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "id": "5611cb99-278b-4f8f-aa91-0cd0cfa72337", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for variable in interesting_variables:\n", - " plot_variable_distribution(df, variable, interesting_variables[variable])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8a1791c2-b9af-4c0f-b939-522e29ed0e7e", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "5a119501-0644-446d-b900-c34e96b4ea8b", - "metadata": {}, - "source": [ - "# 3" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f8f5e415-2581-4f78-8894-376701aa4915", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ffbe2364-8e66-45cd-8c1b-33aed692947e", - "metadata": {}, - "outputs": [], - "source": [ - "# genre and libelle_francais, espece etc. : search for same trees with more metadata : step 4" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "03a745b2-df95-4c31-8211-f03c28b84e70", - "metadata": {}, - "outputs": [], - "source": [ - "# regarder si les arbres avec hauteur et circonférence nulle se rencontrent ou pas. Graines plantées ? \n", - "# Relevés non faits (pourquoi on donnerait une position, alors?)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "19009fc0-3cfa-4f11-8d2e-f5e6a21434b5", - "metadata": {}, - "outputs": [], - "source": [ - "## 4 : traitement valeurs aberrantes\n", - "# report this tree to Paris City : the owner is unknown --> look for similar trees in order to find the right owner : step 4\n", - "df[df['domanialite'].isna()]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "27d917ad-125b-4ab4-8fd9-d156edf1c0b5", - "metadata": {}, - "outputs": [], - "source": [ - "import folium\n", - "import missingno" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2490416f-ae59-40cc-934c-dd834c7a9938", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "399d41e7-d9b3-4704-80bf-4d1f099fd86f", - "metadata": {}, - "outputs": [], - "source": [ - "# pistes amélioration : proposer trajet pour arbres de plus de x mètres (nacelle)\n", - "# regarder sur carte à quoi correspondent les id d'emplacements, par rapport aux lieux" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "66c446a2-fbd2-49a2-8eef-ad39128a7ddd", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +{"cells":[{"cell_type":"markdown","id":"307bea88-e08c-4787-9d3a-047272601ef1","metadata":{"id":"307bea88-e08c-4787-9d3a-047272601ef1"},"source":["# Exploring and Analyzing Paris trees data"]},{"cell_type":"markdown","id":"39449094-4efa-4627-b19c-6aa521c7a7e8","metadata":{"id":"39449094-4efa-4627-b19c-6aa521c7a7e8"},"source":["Taken from https://s3-eu-west-1.amazonaws.com/static.oc-static.com/prod/courses/files/AI+Engineer/Project+2+Participez+%C3%A0+un+concours+sur+la+Smart+City/p2-arbres-fr.csv, coming from https://opendata.paris.fr/explore/dataset/les-arbres/map/?dataChart=eyJxdWVyaWVzIjpbeyJjb25maWciOnsiZGF0YXNldCI6Imxlcy1hcmJyZXMiLCJvcHRpb25zIjp7fX0sImNoYXJ0cyI6W3siYWxpZ25Nb250aCI6dHJ1ZSwidHlwZSI6ImNvbHVtbiIsImZ1bmMiOiJBVkciLCJ5QXhpcyI6ImlkYmFzZSIsInNjaWVudGlmaWNEaXNwbGF5Ijp0cnVlLCJjb2xvciI6IiMwMDMzNjYifV0sInhBeGlzIjoidHlwZWVtcGxhY2VtZW50IiwibWF4cG9pbnRzIjo1MCwic29ydCI6IiJ9XSwidGltZXNjYWxlIjoiIiwiZGlzcGxheUxlZ2VuZCI6dHJ1ZSwiYWxpZ25Nb250aCI6dHJ1ZX0%3D&disjunctive.typeemplacement&disjunctive.arrondissement&disjunctive.libellefrancais&disjunctive.genre&disjunctive.espece&disjunctive.varieteoucultivar&disjunctive.stadedeveloppement&disjunctive.remarquable&location=12,48.85984,2.31341&basemap=jawg.streets"]},{"cell_type":"markdown","id":"ceeb296c-0ca5-43d6-b5fb-a4620de1fc75","metadata":{"id":"ceeb296c-0ca5-43d6-b5fb-a4620de1fc75"},"source":["## 1) Opening csv file"]},{"cell_type":"code","execution_count":1,"id":"rA8K5-0Cmf33","metadata":{"id":"rA8K5-0Cmf33","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1737062546472,"user_tz":-60,"elapsed":2641,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"b849ecc7-fe2f-4c96-cd73-fcd37b003220"},"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n","Working directory: /content/drive/MyDrive/Colab_Notebooks/Project_2\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')\n","import os\n","os.listdir()\n","os.chdir(\"/content/drive/MyDrive/Colab_Notebooks/Project_2\")\n","print(f'Working directory: {os.getcwd()}')"]},{"cell_type":"code","execution_count":2,"id":"b4458a0a-853f-4a87-bb46-93ac6a9ba08b","metadata":{"id":"b4458a0a-853f-4a87-bb46-93ac6a9ba08b","executionInfo":{"status":"ok","timestamp":1737062547662,"user_tz":-60,"elapsed":1192,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["import pandas as pd\n","pd.set_option('display.float_format', '{:.2f}'.format) # don't want scientific notation. Floats are enough\n","\n","csv_path = \"./p2-trees-fr.csv\"\n","df = pd.read_csv(csv_path, sep=';')"]},{"cell_type":"markdown","id":"9de7fc9e-a309-46ac-a4f8-74effb1ca249","metadata":{"id":"9de7fc9e-a309-46ac-a4f8-74effb1ca249"},"source":["## 2) Naive Analysis"]},{"cell_type":"markdown","id":"d555a028-62b6-47ae-841b-13f45533ad3c","metadata":{"id":"d555a028-62b6-47ae-841b-13f45533ad3c"},"source":["### Use pandas basic methods"]},{"cell_type":"code","execution_count":3,"id":"5f0dc948-1c0f-4273-b8d5-7b9d0fc80f23","metadata":{"id":"5f0dc948-1c0f-4273-b8d5-7b9d0fc80f23","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1737062548670,"user_tz":-60,"elapsed":1013,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"49cc84d0-f342-4904-b7fd-6fd0ed44f038"},"outputs":[{"output_type":"stream","name":"stdout","text":["Size in MB:\n"]},{"output_type":"execute_result","data":{"text/plain":["145.955268"]},"metadata":{},"execution_count":3}],"source":["import sys\n","print('Size in MB:')\n","sys.getsizeof(df)/(10**6)"]},{"cell_type":"code","execution_count":4,"id":"a302ef86-7553-4b38-8734-b0ae5896c584","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a302ef86-7553-4b38-8734-b0ae5896c584","scrolled":true,"executionInfo":{"status":"ok","timestamp":1737062548671,"user_tz":-60,"elapsed":8,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"c072acc6-50e3-42ac-d905-a1369a0a6be6"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","RangeIndex: 200137 entries, 0 to 200136\n","Data columns (total 18 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 id 200137 non-null int64 \n"," 1 type_emplacement 200137 non-null object \n"," 2 domanialite 200136 non-null object \n"," 3 arrondissement 200137 non-null object \n"," 4 complement_addresse 30902 non-null object \n"," 5 numero 0 non-null float64\n"," 6 lieu 200137 non-null object \n"," 7 id_emplacement 200137 non-null object \n"," 8 libelle_francais 198640 non-null object \n"," 9 genre 200121 non-null object \n"," 10 espece 198385 non-null object \n"," 11 variete 36777 non-null object \n"," 12 circonference_cm 200137 non-null int64 \n"," 13 hauteur_m 200137 non-null int64 \n"," 14 stade_developpement 132932 non-null object \n"," 15 remarquable 137039 non-null float64\n"," 16 geo_point_2d_a 200137 non-null float64\n"," 17 geo_point_2d_b 200137 non-null float64\n","dtypes: float64(4), int64(3), object(11)\n","memory usage: 27.5+ MB\n"]}],"source":["df.info() # display count of columns, lines and columns names"]},{"cell_type":"code","execution_count":5,"id":"f24becfa-7e0f-45a0-82b9-40e211922f5c","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":270},"id":"f24becfa-7e0f-45a0-82b9-40e211922f5c","scrolled":true,"executionInfo":{"status":"ok","timestamp":1737062548671,"user_tz":-60,"elapsed":6,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"ab0be1d5-2314-4f87-d4bf-a44da82aab5d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" id type_emplacement domanialite arrondissement complement_addresse \\\n","0 99874 Arbre Jardin PARIS 7E ARRDT NaN \n","1 99875 Arbre Jardin PARIS 7E ARRDT NaN \n","\n"," numero lieu id_emplacement \\\n","0 NaN MAIRIE DU 7E 116 RUE DE GRENELLE PARIS 7E 19 \n","1 NaN MAIRIE DU 7E 116 RUE DE GRENELLE PARIS 7E 20 \n","\n"," libelle_francais genre espece variete circonference_cm \\\n","0 Marronnier Aesculus hippocastanum NaN 20 \n","1 If Taxus baccata NaN 65 \n","\n"," hauteur_m stade_developpement remarquable geo_point_2d_a geo_point_2d_b \n","0 5 NaN 0.00 48.86 2.32 \n","1 8 A NaN 48.86 2.32 "],"text/html":["\n","
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199875ArbreJardinPARIS 7E ARRDTNaNNaNMAIRIE DU 7E 116 RUE DE GRENELLE PARIS 7E20IfTaxusbaccataNaN658ANaN48.862.32
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idnumerocirconference_cmhauteur_mremarquablegeo_point_2d_ageo_point_2d_b
count200137.000.00200137.00200137.00137039.00200137.00200137.00
mean387202.68NaN83.3813.110.0048.852.35
std545603.24NaN673.191971.220.040.030.05
min99874.00NaN0.000.000.0048.742.21
25%155927.00NaN30.005.000.0048.842.31
50%221078.00NaN70.008.000.0048.852.35
75%274102.00NaN115.0012.000.0048.882.39
max2024745.00NaN250255.00881818.001.0048.912.47
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","summary":"{\n \"name\": \"## > 250000 cm as circumference, > 800000 m as height\",\n \"rows\": 8,\n \"fields\": [\n {\n \"column\": \"id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 636490.3531323484,\n \"min\": 99874.0,\n \"max\": 2024745.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 387202.68230761925,\n 221078.0,\n 200137.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"numero\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 0.0,\n \"max\": 0.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"circonference_cm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 105028.38312832116,\n \"min\": 0.0,\n \"max\": 250255.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 83.38047937163043\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hauteur_m\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 309559.4466281352,\n \"min\": 0.0,\n \"max\": 881818.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 13.110509301128728\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"remarquable\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 48450.55066982106,\n \"min\": 0.0,\n \"max\": 137039.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 0.0013426834696692183\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"geo_point_2d_a\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 70744.31300132743,\n \"min\": 0.0302341454886933,\n \"max\": 200137.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 48.85449078901268\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"geo_point_2d_b\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 70758.40152298885,\n \"min\": 0.05121963963079915,\n \"max\": 200137.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 2.3482078955462966\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":6}],"source":["df.describe()# general statistics only on number types variables (int and floats)\n","\n","# problems:\n","## 0 as min value for circumference, height\n","## > 250000 cm as circumference, > 800000 m as height"]},{"cell_type":"code","execution_count":86,"id":"5c2225ff-7a7c-421c-9bde-1dc02e2e3742","metadata":{"id":"5c2225ff-7a7c-421c-9bde-1dc02e2e3742","executionInfo":{"status":"ok","timestamp":1737062771291,"user_tz":-60,"elapsed":423,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["from data_analysis_functions import plot_univariate_variable_analysis, plot_bivariate_variable_analysis, compute_bivariate_stats_indicators"]},{"cell_type":"code","source":["type(df[df['espece'].notna()]['espece'].values[0]) == str"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"la1Q1bZ0-B-L","executionInfo":{"status":"ok","timestamp":1737062552620,"user_tz":-60,"elapsed":5,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"c19a56cf-9388-4090-be52-b8e06ead8f84"},"id":"la1Q1bZ0-B-L","execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{},"execution_count":9}]},{"cell_type":"code","execution_count":10,"id":"f5aade17-79f8-4dfa-bd8f-547727c207cb","metadata":{"id":"f5aade17-79f8-4dfa-bd8f-547727c207cb","executionInfo":{"status":"ok","timestamp":1737062552620,"user_tz":-60,"elapsed":3,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["quantitative_variables = ['circonference_cm','hauteur_m']"]},{"cell_type":"markdown","id":"aec48b3b-3131-48b0-96a6-9d3a962162f1","metadata":{"id":"aec48b3b-3131-48b0-96a6-9d3a962162f1"},"source":["### Looking for data anomalies"]},{"cell_type":"markdown","id":"bd284b56-394b-4677-b271-91f374e04b01","metadata":{"id":"bd284b56-394b-4677-b271-91f374e04b01"},"source":["#### Outliers"]},{"cell_type":"code","execution_count":11,"id":"c0b79f60-4033-43ad-a8e2-7ba6022ea6f4","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":849},"id":"c0b79f60-4033-43ad-a8e2-7ba6022ea6f4","executionInfo":{"status":"ok","timestamp":1737062555058,"user_tz":-60,"elapsed":2441,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"be8a95c4-c24c-4ae5-f2c2-50b608f8eab7"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["
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Boxplots in order to briefly find outliers\n","for variable in quantitative_variables:\n"," plot_univariate_variable_analysis(df, variable, 'boxplot')"]},{"cell_type":"markdown","id":"96db7b30-d700-47f0-950c-012817006a2b","metadata":{"id":"96db7b30-d700-47f0-950c-012817006a2b"},"source":["It seems that there are many outliers on data"]},{"cell_type":"markdown","id":"580de3b3-db0e-44cd-b271-2c619dc8dee4","metadata":{"id":"580de3b3-db0e-44cd-b271-2c619dc8dee4"},"source":["#### NaN"]},{"cell_type":"code","execution_count":12,"id":"cf0c8910-8bf2-4e28-a919-efacd17a0a2a","metadata":{"id":"cf0c8910-8bf2-4e28-a919-efacd17a0a2a","executionInfo":{"status":"ok","timestamp":1737062555059,"user_tz":-60,"elapsed":3,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["import missingno as msno\n","import numpy as np"]},{"cell_type":"code","execution_count":13,"id":"f9ba7bc9-8b7b-4203-8e59-6fbd7d0bc5cd","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"f9ba7bc9-8b7b-4203-8e59-6fbd7d0bc5cd","executionInfo":{"status":"ok","timestamp":1737062556368,"user_tz":-60,"elapsed":1313,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"9a36f968-1cae-48fa-e6e4-288cf963cecf"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 0, 0, 1, 0, 169235, 200137, 0, 0,\n"," 1497, 16, 1752, 163360, 0, 0, 67205, 63098,\n"," 0, 0])"]},"metadata":{},"execution_count":13}],"source":["# number of 'nan' for each column\n","column_nb_of_nans = np.array([], dtype='int')\n","for column in df.columns:\n"," where_nan = df[df[column].isna()]\n"," column_nb_of_nans = np.concatenate((column_nb_of_nans,[len(where_nan)]))\n","column_nb_of_nans"]},{"cell_type":"code","execution_count":14,"id":"4c0424ef-ae93-4b94-847c-4c8341051aa6","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":107},"id":"4c0424ef-ae93-4b94-847c-4c8341051aa6","scrolled":true,"executionInfo":{"status":"ok","timestamp":1737062556798,"user_tz":-60,"elapsed":434,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"36154d15-5ffd-48e2-f046-632428de3cef"},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of nan for each variable that contains minimum 1 occurrence:\n"]},{"output_type":"execute_result","data":{"text/plain":[" domanialite complement_addresse numero libelle_francais genre espece \\\n","0 1 169235 200137 1497 16 1752 \n","\n"," variete stade_developpement remarquable \n","0 163360 67205 63098 "],"text/html":["\n","
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"columns_with_nans_df","summary":"{\n \"name\": \"columns_with_nans_df\",\n \"rows\": 1,\n \"fields\": [\n {\n \"column\": \"domanialite\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 1,\n \"max\": 1,\n \"num_unique_values\": 1,\n \"samples\": [\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"complement_addresse\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 169235,\n \"max\": 169235,\n \"num_unique_values\": 1,\n \"samples\": [\n 169235\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"numero\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 200137,\n \"max\": 200137,\n \"num_unique_values\": 1,\n \"samples\": [\n 200137\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"libelle_francais\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 1497,\n \"max\": 1497,\n \"num_unique_values\": 1,\n \"samples\": [\n 1497\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"genre\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 16,\n \"max\": 16,\n \"num_unique_values\": 1,\n \"samples\": [\n 16\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"espece\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 1752,\n \"max\": 1752,\n \"num_unique_values\": 1,\n \"samples\": [\n 1752\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"variete\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 163360,\n \"max\": 163360,\n \"num_unique_values\": 1,\n \"samples\": [\n 163360\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"stade_developpement\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 67205,\n \"max\": 67205,\n \"num_unique_values\": 1,\n \"samples\": [\n 67205\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"remarquable\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 63098,\n \"max\": 63098,\n \"num_unique_values\": 1,\n \"samples\": [\n 63098\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":14}],"source":["# dataframe with number of nans per column only when one nan or more\n","where_nans = np.where(column_nb_of_nans>0)\n","columns_with_nans_df = pd.DataFrame([column_nb_of_nans[where_nans]], columns = df.columns[where_nans])\n","print('Number of nan for each variable that contains minimum 1 occurrence:')\n","columns_with_nans_df"]},{"cell_type":"code","execution_count":15,"id":"6d1dbd66-cc76-4724-9a23-eeefb13bf725","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":610},"id":"6d1dbd66-cc76-4724-9a23-eeefb13bf725","executionInfo":{"status":"ok","timestamp":1737062561949,"user_tz":-60,"elapsed":5152,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"e3b9d24a-db1c-48b4-a9a3-ae1b62fbba54"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":15},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# stade_developpement and remarquable are missing around 30% values. Variete is missing 80 percent values\n","msno.bar(df)"]},{"cell_type":"code","execution_count":16,"id":"ef1eb4db-5bfb-49a2-a4f9-cdc5249ad271","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":796},"id":"ef1eb4db-5bfb-49a2-a4f9-cdc5249ad271","executionInfo":{"status":"ok","timestamp":1737062564994,"user_tz":-60,"elapsed":3047,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"3ac44711-3e87-4027-e8c1-f08dc7bfbaa2"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":16},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# missing values correlation between stade_developpement and remarquable. Look at which trees they are\n","msno.heatmap(df)"]},{"cell_type":"markdown","id":"e173bf2d-060c-4610-bbf1-ef35f4a20552","metadata":{"id":"e173bf2d-060c-4610-bbf1-ef35f4a20552"},"source":["## 3) Cleaning anomalies"]},{"cell_type":"code","execution_count":17,"id":"57fe3e6a-3828-4f45-bede-a3422cff30c3","metadata":{"id":"57fe3e6a-3828-4f45-bede-a3422cff30c3","executionInfo":{"status":"ok","timestamp":1737062564994,"user_tz":-60,"elapsed":15,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["df_refined = df.copy()"]},{"cell_type":"markdown","id":"c5535b2a-fd5e-4b16-a1d3-bdc974e55dd0","metadata":{"id":"c5535b2a-fd5e-4b16-a1d3-bdc974e55dd0"},"source":["### Outliers (min and max)"]},{"cell_type":"markdown","id":"76dfdc46-4b19-4f63-a854-82f07a168921","metadata":{"id":"76dfdc46-4b19-4f63-a854-82f07a168921"},"source":["##### Max"]},{"cell_type":"markdown","id":"eeee8c4a-af66-4be0-9637-2054e67e7af5","metadata":{"id":"eeee8c4a-af66-4be0-9637-2054e67e7af5"},"source":["Height :"]},{"cell_type":"code","execution_count":18,"id":"ac8fcbb6-dafa-4f63-9caa-36f99a3a9297","metadata":{"id":"ac8fcbb6-dafa-4f63-9caa-36f99a3a9297","executionInfo":{"status":"ok","timestamp":1737062564994,"user_tz":-60,"elapsed":15,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# based on the tallest tree height : https://en.wikipedia.org/wiki/Hyperion_(tree)\n","# max_height = 116 # in meters\n","# tallest tree in Paris is supposed to be this one (40 m), so the others heights are wrong : https://france3-regions.francetvinfo.fr/paris-ile-de-france/paris/mais-quel-est-le-plus-grand-arbre-de-paris-2982575.html\n","max_height = 45 # in meters"]},{"cell_type":"code","execution_count":19,"id":"c3115746-c299-495f-8a13-b1e3e5ebc6cd","metadata":{"id":"c3115746-c299-495f-8a13-b1e3e5ebc6cd","executionInfo":{"status":"ok","timestamp":1737062564994,"user_tz":-60,"elapsed":14,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["max_height_outlier_df = df_refined[(df_refined['hauteur_m'] >= max_height)]"]},{"cell_type":"code","execution_count":20,"id":"ba6ca618-9335-4f54-8215-1545e821e3da","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":895},"id":"ba6ca618-9335-4f54-8215-1545e821e3da","scrolled":true,"executionInfo":{"status":"ok","timestamp":1737062564994,"user_tz":-60,"elapsed":14,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"c3b39980-eb60-4b6a-c95d-2856c063c926"},"outputs":[{"output_type":"stream","name":"stdout","text":["height outliers trees (479 occurrencies):\n"]},{"output_type":"execute_result","data":{"text/plain":[" id type_emplacement domanialite arrondissement \\\n","4534 105885 Arbre Jardin PARIS 13E ARRDT \n","4537 105888 Arbre Jardin PARIS 13E ARRDT \n","5392 106800 Arbre Jardin PARIS 7E ARRDT \n","5469 106880 Arbre Jardin PARIS 7E ARRDT \n","5599 107029 Arbre Jardin PARIS 7E ARRDT \n","... ... ... ... ... \n","194812 2018093 Arbre Alignement PARIS 18E ARRDT \n","197026 2020675 Arbre Jardin PARIS 18E ARRDT \n","197060 2020711 Arbre Alignement PARIS 19E ARRDT \n","197136 2020792 Arbre Jardin PARIS 14E ARRDT \n","197146 2020802 Arbre Jardin PARIS 14E ARRDT \n","\n"," complement_addresse numero \\\n","4534 NaN NaN \n","4537 NaN NaN \n","5392 NaN NaN \n","5469 NaN NaN \n","5599 NaN NaN \n","... ... ... \n","194812 95 NaN \n","197026 NaN NaN \n","197060 NaN NaN \n","197136 NaN NaN \n","197146 NaN NaN \n","\n"," lieu id_emplacement \\\n","4534 SQUARE DE CHOISY 277 \n","4537 SQUARE DE CHOISY 280 \n","5392 JARDIN DU CHAMP DE MARS A0120683 \n","5469 JARDIN DU CHAMP DE MARS P0070763 \n","5599 JARDIN DU CHAMP DE MARS A0041225 \n","... ... ... \n","194812 RUE DE LA CHAPELLE 1501013 \n","197026 JARDINIERES T3 BOULEVARD NEY CHAPELLE / 2 AVEN... 101016 \n","197060 QUAI DE LA CHARENTE 201004 \n","197136 SQUARE AUGUSTE RENOIR / 205 RUE RAYMOND LOSSERAND 103005 \n","197146 SQUARE AUGUSTE RENOIR / 205 RUE RAYMOND LOSSERAND 104004 \n","\n"," libelle_francais genre espece variete circonference_cm \\\n","4534 Fevier Gleditsia triacanthos NaN 45 \n","4537 Tulipier Liriodendron tulipifera NaN 45 \n","5392 Tilleul Tilia n. sp. NaN 100 \n","5469 If Taxus baccata NaN 61 \n","5599 Platane Platanus x hispanica NaN 41 \n","... ... ... ... ... ... \n","194812 Platane Platanus x hispanica NaN 35 \n","197026 Pin Pinus nigra NaN 50 \n","197060 Aulne Alnus cordata NaN 30 \n","197136 Erable Acer platanoides NaN 145 \n","197146 Erable Acer platanoides NaN 150 \n","\n"," hauteur_m stade_developpement remarquable geo_point_2d_a \\\n","4534 45 NaN NaN 48.83 \n","4537 45 NaN NaN 48.83 \n","5392 1510 A 0.00 48.86 \n","5469 65 A 0.00 48.85 \n","5599 68 JA 0.00 48.86 \n","... ... ... ... ... \n","194812 65 J 0.00 48.90 \n","197026 65 J 0.00 48.90 \n","197060 55 JA 0.00 48.90 \n","197136 125 A 0.00 48.83 \n","197146 105 A 0.00 48.83 \n","\n"," geo_point_2d_b \n","4534 2.36 \n","4537 2.36 \n","5392 2.30 \n","5469 2.30 \n","5599 2.30 \n","... ... \n","194812 2.36 \n","197026 2.36 \n","197060 2.38 \n","197136 2.31 \n","197146 2.31 \n","\n","[479 rows x 18 columns]"],"text/html":["\n","
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idtype_emplacementdomanialitearrondissementcomplement_addressenumerolieuid_emplacementlibelle_francaisgenreespecevarietecirconference_cmhauteur_mstade_developpementremarquablegeo_point_2d_ageo_point_2d_b
4534105885ArbreJardinPARIS 13E ARRDTNaNNaNSQUARE DE CHOISY277FevierGleditsiatriacanthosNaN4545NaNNaN48.832.36
4537105888ArbreJardinPARIS 13E ARRDTNaNNaNSQUARE DE CHOISY280TulipierLiriodendrontulipiferaNaN4545NaNNaN48.832.36
5392106800ArbreJardinPARIS 7E ARRDTNaNNaNJARDIN DU CHAMP DE MARSA0120683TilleulTilian. sp.NaN1001510A0.0048.862.30
5469106880ArbreJardinPARIS 7E ARRDTNaNNaNJARDIN DU CHAMP DE MARSP0070763IfTaxusbaccataNaN6165A0.0048.852.30
5599107029ArbreJardinPARIS 7E ARRDTNaNNaNJARDIN DU CHAMP DE MARSA0041225PlatanePlatanusx hispanicaNaN4168JA0.0048.862.30
.........................................................
1948122018093ArbreAlignementPARIS 18E ARRDT95NaNRUE DE LA CHAPELLE1501013PlatanePlatanusx hispanicaNaN3565J0.0048.902.36
1970262020675ArbreJardinPARIS 18E ARRDTNaNNaNJARDINIERES T3 BOULEVARD NEY CHAPELLE / 2 AVEN...101016PinPinusnigraNaN5065J0.0048.902.36
1970602020711ArbreAlignementPARIS 19E ARRDTNaNNaNQUAI DE LA CHARENTE201004AulneAlnuscordataNaN3055JA0.0048.902.38
1971362020792ArbreJardinPARIS 14E ARRDTNaNNaNSQUARE AUGUSTE RENOIR / 205 RUE RAYMOND LOSSERAND103005ErableAcerplatanoidesNaN145125A0.0048.832.31
1971462020802ArbreJardinPARIS 14E ARRDTNaNNaNSQUARE AUGUSTE RENOIR / 205 RUE RAYMOND LOSSERAND104004ErableAcerplatanoidesNaN150105A0.0048.832.31
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479 rows × 18 columns

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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"max_height_outlier_df","summary":"{\n \"name\": \"max_height_outlier_df\",\n \"rows\": 479,\n \"fields\": [\n {\n \"column\": \"id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 887737,\n \"min\": 105885,\n \"max\": 2020802,\n \"num_unique_values\": 479,\n \"samples\": [\n 226659,\n 2008767,\n 202895\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"type_emplacement\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Arbre\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"domanialite\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"Jardin\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"arrondissement\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 22,\n \"samples\": [\n \"PARIS 13E ARRDT\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"complement_addresse\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 56,\n \"samples\": [\n \"11 \\u00e0 27\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"numero\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": null,\n \"max\": null,\n \"num_unique_values\": 0,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"lieu\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 177,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"id_emplacement\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 440,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"libelle_francais\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 39,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"genre\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 36,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"espece\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 57,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"variete\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 19,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"circonference_cm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 428,\n \"min\": 0,\n \"max\": 7540,\n \"num_unique_values\": 80,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hauteur_m\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 40285,\n \"min\": 45,\n \"max\": 881818,\n \"num_unique_values\": 102,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"stade_developpement\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"remarquable\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0654416970922002,\n \"min\": 0.0,\n \"max\": 1.0,\n \"num_unique_values\": 2,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"geo_point_2d_a\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.025974849186796975,\n \"min\": 48.810646078,\n \"max\": 48.9089446402,\n \"num_unique_values\": 479,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"geo_point_2d_b\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.035886893415735685,\n \"min\": 2.2528752018,\n \"max\": 2.4613206192,\n \"num_unique_values\": 479,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":20}],"source":["print('height outliers trees ('+ str(len(max_height_outlier_df)) + ' occurrencies):')\n","max_height_outlier_df\n"]},{"cell_type":"code","execution_count":21,"id":"bf489b0f-45f5-4879-8c56-b69f6b79f89e","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":489},"id":"bf489b0f-45f5-4879-8c56-b69f6b79f89e","scrolled":true,"executionInfo":{"status":"ok","timestamp":1737062564994,"user_tz":-60,"elapsed":11,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"88793feb-60c1-4363-f936-cf7a43dae03a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["hauteur_m\n","45 19\n","46 1\n","47 1\n","50 13\n","52 1\n"," ..\n","1815 1\n","2015 1\n","2525 1\n","5155 1\n","881818 1\n","Name: count, Length: 102, dtype: int64"],"text/html":["
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count
hauteur_m
4519
461
471
5013
521
......
18151
20151
25251
51551
8818181
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102 rows × 1 columns

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"]},"metadata":{},"execution_count":21}],"source":["max_height_outlier_df.value_counts('hauteur_m').sort_index(ascending=True)"]},{"cell_type":"code","execution_count":22,"id":"c788b243-2521-4f58-8d1f-3463ec0bf45c","metadata":{"id":"c788b243-2521-4f58-8d1f-3463ec0bf45c","scrolled":true,"executionInfo":{"status":"ok","timestamp":1737062564994,"user_tz":-60,"elapsed":9,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# remove outlier value\n","df_refined.loc[df_refined['hauteur_m'] == 881818, 'hauteur_m'] = np.nan"]},{"cell_type":"code","source":["for variable in quantitative_variables:\n"," plot_univariate_variable_analysis(df_refined, variable, 'boxplot')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":849},"id":"K9ZrNsYvsDxk","executionInfo":{"status":"ok","timestamp":1737062566942,"user_tz":-60,"elapsed":1956,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"408da53e-c277-4dff-f93a-e068b303f6b3"},"id":"K9ZrNsYvsDxk","execution_count":23,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","execution_count":24,"id":"76b470a5-7542-44a0-b262-fd9e325b4a43","metadata":{"id":"76b470a5-7542-44a0-b262-fd9e325b4a43","executionInfo":{"status":"ok","timestamp":1737062566942,"user_tz":-60,"elapsed":67,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# need in order to divide by 100 the too high heights\n","df_refined['hauteur_m'] = df_refined['hauteur_m'].astype(float)"]},{"cell_type":"code","execution_count":25,"id":"e0cffc30-00b3-439c-9501-9e94b1f57e0c","metadata":{"id":"e0cffc30-00b3-439c-9501-9e94b1f57e0c","executionInfo":{"status":"ok","timestamp":1737062566943,"user_tz":-60,"elapsed":66,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# Too high remaining heights may have been measured in cms\n","df_refined.loc[df_refined['hauteur_m'] >= max_height, 'hauteur_m'] = df_refined.loc[df_refined['hauteur_m'] >= max_height, 'hauteur_m']/100"]},{"cell_type":"code","execution_count":26,"id":"TVbNwJ0DF9zQ","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TVbNwJ0DF9zQ","executionInfo":{"status":"ok","timestamp":1737062566943,"user_tz":-60,"elapsed":65,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"af8b9f10-e138-4a17-9d4b-f16e17bcd042"},"outputs":[{"output_type":"stream","name":"stdout","text":["Amount of to high heights : 479.\n","1 value has been removed (set to 'NaN').\n","478 values may have been measured in cms --> converted in meters.\n"]}],"source":["print(f\"Amount of to high heights : {len(max_height_outlier_df)}.\\n1 value has been removed (set to 'NaN').\\n{len(max_height_outlier_df)-1} values may have been measured in cms --> converted in meters.\")"]},{"cell_type":"markdown","id":"1e751c26-fc8f-41a9-bd27-2d1f899f4866","metadata":{"id":"1e751c26-fc8f-41a9-bd27-2d1f899f4866"},"source":["Girth :"]},{"cell_type":"code","execution_count":27,"id":"0b2af1bd-9fc8-405f-b810-433044a0ea7a","metadata":{"id":"0b2af1bd-9fc8-405f-b810-433044a0ea7a","executionInfo":{"status":"ok","timestamp":1737062566943,"user_tz":-60,"elapsed":63,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# based on the biggest tree girth (circumference) : https://www.guinnessworldrecords.com/world-records/living-tree-with-greatest-girth\n","max_girth = 36 # in meters"]},{"cell_type":"code","execution_count":28,"id":"dceb4f45-c91d-4234-a5d1-c87d1fcc6403","metadata":{"id":"dceb4f45-c91d-4234-a5d1-c87d1fcc6403","executionInfo":{"status":"ok","timestamp":1737062566943,"user_tz":-60,"elapsed":63,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["max_girth_outlier_df = df_refined[(df_refined['circonference_cm']/100 > max_girth)]"]},{"cell_type":"code","execution_count":29,"id":"a97d6d26-f82a-463b-ad00-69d4676beb7e","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"collapsed":true,"id":"a97d6d26-f82a-463b-ad00-69d4676beb7e","scrolled":true,"executionInfo":{"status":"ok","timestamp":1737062566943,"user_tz":-60,"elapsed":62,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"8aedd841-8816-45e4-a550-e7130a0e7a67"},"outputs":[{"output_type":"stream","name":"stdout","text":["girth outliers trees (20 occurrencies):\n"]},{"output_type":"execute_result","data":{"text/plain":[" id type_emplacement domanialite arrondissement \\\n","71787 190080 Arbre DASCO PARIS 20E ARRDT \n","82951 203295 Arbre Alignement PARIS 18E ARRDT \n","90605 211362 Arbre Alignement PARIS 5E ARRDT \n","91256 212024 Arbre Alignement PARIS 12E ARRDT \n","97354 218274 Arbre Alignement PARIS 7E ARRDT \n","103437 224609 Arbre Alignement PARIS 18E ARRDT \n","133546 256868 Arbre Alignement PARIS 19E ARRDT \n","147235 270997 Arbre Alignement PARIS 7E ARRDT \n","147634 271402 Arbre Alignement PARIS 7E ARRDT \n","148106 271884 Arbre Alignement PARIS 7E ARRDT \n","148481 272263 Arbre Alignement PARIS 7E ARRDT \n","149056 272885 Arbre Alignement PARIS 18E ARRDT \n","151364 275451 Arbre Alignement PARIS 7E ARRDT \n","152303 276396 Arbre Alignement PARIS 7E ARRDT \n","160432 284823 Arbre Alignement PARIS 18E ARRDT \n","163918 288407 Arbre Alignement PARIS 12E ARRDT \n","164616 289121 Arbre Alignement PARIS 12E ARRDT \n","167606 292180 Arbre Alignement PARIS 18E ARRDT \n","170367 295047 Arbre Alignement PARIS 5E ARRDT \n","173343 298211 Arbre Alignement PARIS 5E ARRDT \n","\n"," complement_addresse numero lieu \\\n","71787 NaN NaN PLAINE (9) ELEM B \n","82951 NaN NaN RUE D AUBERVILLIERS \n","90605 34 NaN BOULEVARD SAINT MARCEL \n","91256 NaN NaN RUE DE CHALON \n","97354 27 NaN AVENUE RAPP \n","103437 NaN NaN BOULEVARD BARBES \n","133546 NaN NaN AVENUE JEAN JAURES \n","147235 NaN NaN QUAI BRANLY \n","147634 NaN NaN AVENUE BOSQUET \n","148106 NaN NaN AVENUE BOSQUET \n","148481 14 NaN BOULEVARD DES INVALIDES \n","149056 NaN NaN PLACE DE LA CHAPELLE \n","151364 NaN NaN ESPLANADE DES INVALIDES \n","152303 NaN NaN ESPLANADE DES INVALIDES \n","160432 NaN NaN AVENUE DE LA PORTE DE CLIGNANCOURT \n","163918 NaN NaN BOULEVARD DE PICPUS \n","164616 NaN NaN RUE FRANCOIS TRUFFAUT \n","167606 16 NaN RUE JEAN COCTEAU \n","170367 16 NaN RUE POLIVEAU \n","173343 NaN NaN BOULEVARD DE PORT ROYAL \n","\n"," id_emplacement libelle_francais genre espece \\\n","71787 11110006 Tilleul Tilia tomentosa \n","82951 301004 Erable Acer monspessulanum \n","90605 204025 Cedrele Toona sinensis \n","91256 202005 Micocoulier Celtis australis \n","97354 501003 Platane Platanus occidentalis \n","103437 1303003 Platane Platanus x hispanica \n","133546 1501006 Platane Platanus x hispanica \n","147235 503005 Paulownia Paulownia tomentosa \n","147634 1202016 Platane Platanus occidentalis \n","148106 501022 Platane Platanus occidentalis \n","148481 402009 Platane Platanus occidentalis \n","149056 101026 Platane Platanus x hispanica \n","151364 410005 Tilleul Tilia tomentosa \n","152303 402011 Tilleul Tilia tomentosa \n","160432 202005 Platane Platanus orientalis \n","163918 1604017 Sophora Sophora japonica \n","164616 301005 Platane Platanus x hispanica \n","167606 202027 Noisetier de Byzance Corylus colurna \n","170367 202015 Platane Platanus x hispanica \n","173343 402001 Marronnier Aesculus x carnea \n","\n"," variete circonference_cm hauteur_m stade_developpement \\\n","71787 NaN 18589 15.00 A \n","82951 NaN 5545 0.56 J \n","90605 NaN 20130 16.00 A \n","91256 NaN 30140 10.00 A \n","97354 NaN 8110 18.00 A \n","103437 NaN 9035 6.00 J \n","133546 NaN 4090 10.00 A \n","147235 NaN 9155 15.00 A \n","147634 NaN 65110 16.00 A \n","148106 NaN 6595 15.00 A \n","148481 NaN 6120 18.00 A \n","149056 NaN 250255 22.00 A \n","151364 NaN 7540 8.15 JA \n","152303 NaN 9085 12.00 A \n","160432 NaN 120170 20.00 A \n","163918 NaN 6085 5.00 JA \n","164616 NaN 9095 10.00 A \n","167606 NaN 3630 5.00 J \n","170367 NaN 6125 20.00 A \n","173343 Briotii' 79120 10.00 A \n","\n"," remarquable geo_point_2d_a geo_point_2d_b \n","71787 0.00 48.85 2.40 \n","82951 0.00 48.89 2.37 \n","90605 0.00 48.84 2.36 \n","91256 0.00 48.85 2.37 \n","97354 0.00 48.86 2.30 \n","103437 0.00 48.89 2.35 \n","133546 0.00 48.89 2.39 \n","147235 0.00 48.86 2.29 \n","147634 0.00 48.86 2.31 \n","148106 0.00 48.86 2.30 \n","148481 0.00 48.85 2.31 \n","149056 0.00 48.88 2.36 \n","151364 0.00 48.86 2.31 \n","152303 0.00 48.86 2.31 \n","160432 0.00 48.90 2.34 \n","163918 0.00 48.85 2.40 \n","164616 0.00 48.83 2.39 \n","167606 0.00 48.90 2.35 \n","170367 0.00 48.84 2.36 \n","173343 0.00 48.84 2.35 "],"text/html":["\n","
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idtype_emplacementdomanialitearrondissementcomplement_addressenumerolieuid_emplacementlibelle_francaisgenreespecevarietecirconference_cmhauteur_mstade_developpementremarquablegeo_point_2d_ageo_point_2d_b
71787190080ArbreDASCOPARIS 20E ARRDTNaNNaNPLAINE (9) ELEM B11110006TilleulTiliatomentosaNaN1858915.00A0.0048.852.40
82951203295ArbreAlignementPARIS 18E ARRDTNaNNaNRUE D AUBERVILLIERS301004ErableAcermonspessulanumNaN55450.56J0.0048.892.37
90605211362ArbreAlignementPARIS 5E ARRDT34NaNBOULEVARD SAINT MARCEL204025CedreleToonasinensisNaN2013016.00A0.0048.842.36
91256212024ArbreAlignementPARIS 12E ARRDTNaNNaNRUE DE CHALON202005MicocoulierCeltisaustralisNaN3014010.00A0.0048.852.37
97354218274ArbreAlignementPARIS 7E ARRDT27NaNAVENUE RAPP501003PlatanePlatanusoccidentalisNaN811018.00A0.0048.862.30
103437224609ArbreAlignementPARIS 18E ARRDTNaNNaNBOULEVARD BARBES1303003PlatanePlatanusx hispanicaNaN90356.00J0.0048.892.35
133546256868ArbreAlignementPARIS 19E ARRDTNaNNaNAVENUE JEAN JAURES1501006PlatanePlatanusx hispanicaNaN409010.00A0.0048.892.39
147235270997ArbreAlignementPARIS 7E ARRDTNaNNaNQUAI BRANLY503005PaulowniaPaulowniatomentosaNaN915515.00A0.0048.862.29
147634271402ArbreAlignementPARIS 7E ARRDTNaNNaNAVENUE BOSQUET1202016PlatanePlatanusoccidentalisNaN6511016.00A0.0048.862.31
148106271884ArbreAlignementPARIS 7E ARRDTNaNNaNAVENUE BOSQUET501022PlatanePlatanusoccidentalisNaN659515.00A0.0048.862.30
148481272263ArbreAlignementPARIS 7E ARRDT14NaNBOULEVARD DES INVALIDES402009PlatanePlatanusoccidentalisNaN612018.00A0.0048.852.31
149056272885ArbreAlignementPARIS 18E ARRDTNaNNaNPLACE DE LA CHAPELLE101026PlatanePlatanusx hispanicaNaN25025522.00A0.0048.882.36
151364275451ArbreAlignementPARIS 7E ARRDTNaNNaNESPLANADE DES INVALIDES410005TilleulTiliatomentosaNaN75408.15JA0.0048.862.31
152303276396ArbreAlignementPARIS 7E ARRDTNaNNaNESPLANADE DES INVALIDES402011TilleulTiliatomentosaNaN908512.00A0.0048.862.31
160432284823ArbreAlignementPARIS 18E ARRDTNaNNaNAVENUE DE LA PORTE DE CLIGNANCOURT202005PlatanePlatanusorientalisNaN12017020.00A0.0048.902.34
163918288407ArbreAlignementPARIS 12E ARRDTNaNNaNBOULEVARD DE PICPUS1604017SophoraSophorajaponicaNaN60855.00JA0.0048.852.40
164616289121ArbreAlignementPARIS 12E ARRDTNaNNaNRUE FRANCOIS TRUFFAUT301005PlatanePlatanusx hispanicaNaN909510.00A0.0048.832.39
167606292180ArbreAlignementPARIS 18E ARRDT16NaNRUE JEAN COCTEAU202027Noisetier de ByzanceCoryluscolurnaNaN36305.00J0.0048.902.35
170367295047ArbreAlignementPARIS 5E ARRDT16NaNRUE POLIVEAU202015PlatanePlatanusx hispanicaNaN612520.00A0.0048.842.36
173343298211ArbreAlignementPARIS 5E ARRDTNaNNaNBOULEVARD DE PORT ROYAL402001MarronnierAesculusx carneaBriotii'7912010.00A0.0048.842.35
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"max_girth_outlier_df","summary":"{\n \"name\": \"max_girth_outlier_df\",\n \"rows\": 20,\n \"fields\": [\n {\n \"column\": \"id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 34759,\n \"min\": 190080,\n \"max\": 298211,\n \"num_unique_values\": 20,\n \"samples\": [\n 190080,\n 292180,\n 288407\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"type_emplacement\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Arbre\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"domanialite\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"Alignement\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"arrondissement\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 6,\n \"samples\": [\n \"PARIS 20E ARRDT\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"complement_addresse\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"27\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"numero\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": null,\n \"max\": null,\n \"num_unique_values\": 0,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"lieu\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 18,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"id_emplacement\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 19,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"libelle_francais\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 9,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"genre\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 9,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"espece\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 10,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"variete\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"circonference_cm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 59471,\n \"min\": 3630,\n \"max\": 250255,\n \"num_unique_values\": 20,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hauteur_m\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5.839249658898595,\n \"min\": 0.56,\n \"max\": 22.0,\n \"num_unique_values\": 11,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"stade_developpement\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"remarquable\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.0,\n \"max\": 0.0,\n \"num_unique_values\": 1,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"geo_point_2d_a\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.020935631218655087,\n \"min\": 48.8345576057,\n \"max\": 48.8996027298,\n \"num_unique_values\": 20,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"geo_point_2d_b\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.03448434237606588,\n \"min\": 2.292727369,\n \"max\": 2.4008649434,\n \"num_unique_values\": 20,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":29}],"source":["print('girth outliers trees ('+ str(len(max_girth_outlier_df)) + ' occurrencies):')\n","max_girth_outlier_df"]},{"cell_type":"code","execution_count":30,"id":"3535207c-ddca-4791-afc8-924bdf424cbb","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":742},"id":"3535207c-ddca-4791-afc8-924bdf424cbb","executionInfo":{"status":"ok","timestamp":1737062566943,"user_tz":-60,"elapsed":56,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"e770ae74-760e-4527-97b9-3c3d361e3182"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["circonference_cm\n","3630 1\n","4090 1\n","120170 1\n","79120 1\n","65110 1\n","30140 1\n","20130 1\n","18589 1\n","9155 1\n","9095 1\n","9085 1\n","9035 1\n","8110 1\n","7540 1\n","6595 1\n","6125 1\n","6120 1\n","6085 1\n","5545 1\n","250255 1\n","Name: count, dtype: int64"],"text/html":["
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count
circonference_cm
36301
40901
1201701
791201
651101
301401
201301
185891
91551
90951
90851
90351
81101
75401
65951
61251
61201
60851
55451
2502551
\n","

"]},"metadata":{},"execution_count":30}],"source":["max_girth_outlier_df.value_counts('circonference_cm')"]},{"cell_type":"code","execution_count":31,"id":"e20288eb-4c78-4b56-b411-5d0c74599659","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":460},"id":"e20288eb-4c78-4b56-b411-5d0c74599659","scrolled":true,"executionInfo":{"status":"ok","timestamp":1737062566944,"user_tz":-60,"elapsed":54,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"4fa4d9d1-1291-45aa-ef2e-747de62a21fc"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["hauteur_m\n","10.00 4\n","15.00 3\n","5.00 2\n","16.00 2\n","18.00 2\n","20.00 2\n","0.56 1\n","6.00 1\n","8.15 1\n","12.00 1\n","22.00 1\n","Name: count, dtype: int64"],"text/html":["
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count
hauteur_m
10.004
15.003
5.002
16.002
18.002
20.002
0.561
6.001
8.151
12.001
22.001
\n","

"]},"metadata":{},"execution_count":31}],"source":["max_girth_outlier_df.value_counts('hauteur_m')"]},{"cell_type":"code","execution_count":32,"id":"Lw7drXq5HvUS","metadata":{"id":"Lw7drXq5HvUS","executionInfo":{"status":"ok","timestamp":1737062566944,"user_tz":-60,"elapsed":52,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# Set circonference_cm values to NaN for these indexes\n","df_refined.loc[max_girth_outlier_df.index, 'circonference_cm'] = np.nan"]},{"cell_type":"code","execution_count":33,"id":"7_jcJ0ufG4Qk","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7_jcJ0ufG4Qk","executionInfo":{"status":"ok","timestamp":1737062566944,"user_tz":-60,"elapsed":52,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"e7a03437-b2af-4015-d591-cd0a7546005e"},"outputs":[{"output_type":"stream","name":"stdout","text":["Amount of to high girths : 20.\n","All values have been set to \"NaN\" and the cases will be reported\n"]}],"source":["print(f'Amount of to high girths : {len(max_girth_outlier_df)}.\\nAll values have been set to \"NaN\" and the cases will be reported')"]},{"cell_type":"markdown","id":"747b4e39-2678-40e9-af04-2e1caa765226","metadata":{"id":"747b4e39-2678-40e9-af04-2e1caa765226"},"source":["##### Min"]},{"cell_type":"markdown","id":"8f65de59-ef68-43b1-be1b-9c3ddada8372","metadata":{"id":"8f65de59-ef68-43b1-be1b-9c3ddada8372"},"source":["Height :"]},{"cell_type":"code","execution_count":34,"id":"6a24f15e-6a9e-46d0-acc1-fb7bf281ec30","metadata":{"id":"6a24f15e-6a9e-46d0-acc1-fb7bf281ec30","executionInfo":{"status":"ok","timestamp":1737062566944,"user_tz":-60,"elapsed":47,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# based on the tallest tree height : https://en.wikipedia.org/wiki/Hyperion_(tree)\n","min_height = 0 # in meters"]},{"cell_type":"code","execution_count":35,"id":"10c2ce83-27b5-43f1-af4f-5d80ac2ba48c","metadata":{"id":"10c2ce83-27b5-43f1-af4f-5d80ac2ba48c","executionInfo":{"status":"ok","timestamp":1737062566944,"user_tz":-60,"elapsed":46,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["min_height_outlier_df = df_refined[(df_refined['hauteur_m'] <= min_height)]"]},{"cell_type":"code","execution_count":36,"id":"4dfbde45-9d17-4a5f-af04-282461462235","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":146},"id":"4dfbde45-9d17-4a5f-af04-282461462235","executionInfo":{"status":"ok","timestamp":1737062566944,"user_tz":-60,"elapsed":46,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"bc42741e-ad98-4124-d280-a926786bdf5a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["hauteur_m\n","0.00 39219\n","Name: count, dtype: int64"],"text/html":["
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count
hauteur_m
0.0039219
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"]},"metadata":{},"execution_count":36}],"source":["# no negative value : ok\n","min_height_outlier_df.value_counts('hauteur_m')"]},{"cell_type":"code","execution_count":37,"id":"fa7fd3b4-7e48-4289-b005-939f565b889e","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":999},"id":"fa7fd3b4-7e48-4289-b005-939f565b889e","scrolled":true,"executionInfo":{"status":"ok","timestamp":1737062566944,"user_tz":-60,"elapsed":44,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"b6e95c76-ebcb-4a76-952f-4cdeb933d2bf"},"outputs":[{"output_type":"stream","name":"stdout","text":["height outliers trees (39219 occurrencies):\n"]},{"output_type":"execute_result","data":{"text/plain":[" id type_emplacement domanialite arrondissement \\\n","4 99878 Arbre Jardin PARIS 17E ARRDT \n","5 99879 Arbre Jardin PARIS 17E ARRDT \n","6 99880 Arbre Jardin PARIS 17E ARRDT \n","11 99885 Arbre Jardin PARIS 16E ARRDT \n","12 99887 Arbre Jardin PARIS 16E ARRDT \n","... ... ... ... ... \n","200132 2024741 Arbre Jardin BOIS DE VINCENNES \n","200133 2024742 Arbre Jardin BOIS DE VINCENNES \n","200134 2024743 Arbre Jardin BOIS DE VINCENNES \n","200135 2024744 Arbre Jardin BOIS DE VINCENNES \n","200136 2024745 Arbre Jardin BOIS DE VINCENNES \n","\n"," complement_addresse numero \\\n","4 NaN NaN \n","5 NaN NaN \n","6 NaN NaN \n","11 NaN NaN \n","12 NaN NaN \n","... ... ... \n","200132 NaN NaN \n","200133 NaN NaN \n","200134 NaN NaN \n","200135 NaN NaN \n","200136 NaN NaN \n","\n"," lieu id_emplacement \\\n","4 PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING 000G0037 \n","5 PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING 000G0036 \n","6 PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING 000G0035 \n","11 JARDIN DU RANELAGH 20001 \n","12 JARDIN DU RANELAGH 20003 \n","... ... ... \n","200132 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170151 \n","200133 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170152 \n","200134 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170153 \n","200135 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170154 \n","200136 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170155 \n","\n"," libelle_francais genre espece variete \\\n","4 Arbre à miel Tetradium daniellii NaN \n","5 Arbre à miel Tetradium daniellii NaN \n","6 Arbre à miel Tetradium daniellii NaN \n","11 Hêtre Fagus sylvatica Atropunicea' \n","12 Micocoulier Celtis occidentalis NaN \n","... ... ... ... ... \n","200132 NaN Castanea fargesii NaN \n","200133 Arbre aux mouchoirs Davidia involucrata NaN \n","200134 Peuplier Populus simonii NaN \n","200135 Chêne Quercus n. sp. NaN \n","200136 Raisinier Hovenia dulcis NaN \n","\n"," circonference_cm hauteur_m stade_developpement remarquable \\\n","4 38.00 0.00 NaN NaN \n","5 38.00 0.00 NaN NaN \n","6 37.00 0.00 NaN NaN \n","11 30.00 0.00 NaN NaN \n","12 205.00 0.00 NaN NaN \n","... ... ... ... ... \n","200132 0.00 0.00 NaN 0.00 \n","200133 0.00 0.00 NaN 0.00 \n","200134 0.00 0.00 NaN 0.00 \n","200135 0.00 0.00 NaN 0.00 \n","200136 0.00 0.00 NaN 0.00 \n","\n"," geo_point_2d_a geo_point_2d_b \n","4 48.89 2.32 \n","5 48.89 2.32 \n","6 48.89 2.32 \n","11 48.86 2.27 \n","12 48.86 2.27 \n","... ... ... \n","200132 48.82 2.46 \n","200133 48.82 2.46 \n","200134 48.82 2.46 \n","200135 48.82 2.46 \n","200136 48.82 2.45 \n","\n","[39219 rows x 18 columns]"],"text/html":["\n","
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idtype_emplacementdomanialitearrondissementcomplement_addressenumerolieuid_emplacementlibelle_francaisgenreespecevarietecirconference_cmhauteur_mstade_developpementremarquablegeo_point_2d_ageo_point_2d_b
499878ArbreJardinPARIS 17E ARRDTNaNNaNPARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING000G0037Arbre à mielTetradiumdanielliiNaN38.000.00NaNNaN48.892.32
599879ArbreJardinPARIS 17E ARRDTNaNNaNPARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING000G0036Arbre à mielTetradiumdanielliiNaN38.000.00NaNNaN48.892.32
699880ArbreJardinPARIS 17E ARRDTNaNNaNPARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING000G0035Arbre à mielTetradiumdanielliiNaN37.000.00NaNNaN48.892.32
1199885ArbreJardinPARIS 16E ARRDTNaNNaNJARDIN DU RANELAGH20001HêtreFagussylvaticaAtropunicea'30.000.00NaNNaN48.862.27
1299887ArbreJardinPARIS 16E ARRDTNaNNaNJARDIN DU RANELAGH20003MicocoulierCeltisoccidentalisNaN205.000.00NaNNaN48.862.27
.........................................................
2001322024741ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170151NaNCastaneafargesiiNaN0.000.00NaN0.0048.822.46
2001332024742ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170152Arbre aux mouchoirsDavidiainvolucrataNaN0.000.00NaN0.0048.822.46
2001342024743ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170153PeuplierPopulussimoniiNaN0.000.00NaN0.0048.822.46
2001352024744ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170154ChêneQuercusn. sp.NaN0.000.00NaN0.0048.822.46
2001362024745ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170155RaisinierHoveniadulcisNaN0.000.00NaN0.0048.822.45
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39219 rows × 18 columns

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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"min_height_outlier_df","summary":"{\n \"name\": \"min_height_outlier_df\",\n \"rows\": 39219,\n \"fields\": [\n {\n \"column\": \"id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 635080,\n \"min\": 99878,\n \"max\": 2024745,\n \"num_unique_values\": 39219,\n \"samples\": [\n 123467,\n 2011241,\n 2020241\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"type_emplacement\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Arbre\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"domanialite\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"Jardin\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"arrondissement\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 25,\n \"samples\": [\n \"PARIS 13E ARRDT\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"complement_addresse\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 120,\n \"samples\": [\n \"15 (XVI-7603)\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"numero\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": null,\n \"max\": null,\n \"num_unique_values\": 0,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"lieu\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2397,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"id_emplacement\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 24787,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"libelle_francais\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 170,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"genre\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 156,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"espece\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 462,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"variete\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 256,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"circonference_cm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 51.57964421106181,\n \"min\": 0.0,\n \"max\": 900.0,\n \"num_unique_values\": 298,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hauteur_m\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.0,\n \"max\": 0.0,\n \"num_unique_values\": 1,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"stade_developpement\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"remarquable\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.0,\n \"max\": 0.0,\n \"num_unique_values\": 1,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"geo_point_2d_a\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.04342193848100632,\n \"min\": 48.754504992,\n \"max\": 48.9106150304,\n \"num_unique_values\": 39213,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"geo_point_2d_b\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0577776801177361,\n \"min\": 2.2102412288,\n \"max\": 2.4621650013,\n \"num_unique_values\": 39214,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":37}],"source":["# Maybe freshly planted trees are 0 meters tall and/or 0 is a default value when not measured\n","print('height outliers trees ('+ str(len(min_height_outlier_df)) + ' occurrencies):')\n","min_height_outlier_df"]},{"cell_type":"code","execution_count":38,"id":"ad996a3c-d4b9-4ff6-8e39-eca8054157ff","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"ad996a3c-d4b9-4ff6-8e39-eca8054157ff","executionInfo":{"status":"ok","timestamp":1737062566945,"user_tz":-60,"elapsed":38,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"68397898-f18b-4d89-c5bc-af2677f5491e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" stade_developpement counts\n","0 NaN 36952\n","1 A 1191\n","2 JA 577\n","3 J 474\n","4 M 25"],"text/html":["\n","
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stade_developpementcounts
0NaN36952
1A1191
2JA577
3J474
4M25
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"min_height_value_counts","summary":"{\n \"name\": \"min_height_value_counts\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"stade_developpement\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"JA\",\n \"M\",\n \"A\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"counts\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 16277,\n \"min\": 25,\n \"max\": 36952,\n \"num_unique_values\": 5,\n \"samples\": [\n 1191,\n 25,\n 577\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":38}],"source":["# A = Adult, JA = Jeune Adulte, J = Jeune, M = Mature\n","# Majority has no stade_developpement value. Probably freshly planted grain. Recommend to not ignore these trees.\n","# For the others, it may probably be a fill value. I recommend setting the height values as NaN\n","min_height_value_counts = min_height_outlier_df.value_counts('stade_developpement', dropna=False).reset_index(name='counts')\n","min_height_value_counts"]},{"cell_type":"code","execution_count":39,"id":"34d32b28-1a5f-4df0-a620-4c418befd07c","metadata":{"id":"34d32b28-1a5f-4df0-a620-4c418befd07c","executionInfo":{"status":"ok","timestamp":1737062566945,"user_tz":-60,"elapsed":36,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# giving NaN value instead of 0 for already existing trees in order to signal missing data\n","zero_height_but_existing_development_state = (df_refined['hauteur_m'] <= min_height) & (df_refined['stade_developpement'].isin(['A', 'JA', 'J', 'M']))\n","# display(df_refined[zero_girth_but_existing_development_state].head(1).transpose())\n","df_refined.loc[zero_height_but_existing_development_state,'hauteur_m'] = np.nan\n","\n","# giving NaN value instead of 0 for already existing trees in order to signal missing data\n","# df_refined.loc[(df_refined['hauteur_m'] <= min_height) & (df_refined['stade_developpement'].isin(['A', 'JA', 'J', 'M'])),\n","# 'hauteur_m'] = np.nan\n","# check 1 or 2 trees tagged as 'A' or 'JA' (or other) to see if these really is a positive height"]},{"cell_type":"code","execution_count":40,"id":"zILrPvh-JjKp","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zILrPvh-JjKp","executionInfo":{"status":"ok","timestamp":1737062566945,"user_tz":-60,"elapsed":35,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"5cba9d21-3806-483e-ae80-f5e1555c5bb4"},"outputs":[{"output_type":"stream","name":"stdout","text":["2267 trees have a zero height value whereas they belong to one of these categories : ['A', 'JA', 'J', 'M'] --> height value set to 'NaN'\n","36952 trees have a zero height with no 'stade_developpement' value. They probably are freshly planted grains : height value is kept zero\n"]}],"source":["print(f\"{min_height_value_counts['counts'].sum()-min_height_value_counts['counts'][0]} trees have a zero height value whereas they belong to one of these categories : ['A', 'JA', 'J', 'M'] --> height value set to 'NaN'\")\n","print(f\"{min_height_value_counts['counts'][0]} trees have a zero height with no 'stade_developpement' value. They probably are freshly planted grains : height value is kept zero\")\n"]},{"cell_type":"markdown","id":"27fbf69c-619d-40ee-8514-2b3c3470007b","metadata":{"id":"27fbf69c-619d-40ee-8514-2b3c3470007b"},"source":["Girth :"]},{"cell_type":"code","execution_count":41,"id":"0d1a2d77-c7d8-4944-9a58-dc045519c731","metadata":{"id":"0d1a2d77-c7d8-4944-9a58-dc045519c731","executionInfo":{"status":"ok","timestamp":1737062566945,"user_tz":-60,"elapsed":31,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# based on the biggest tree girth (circumference) : https://www.guinnessworldrecords.com/world-records/living-tree-with-greatest-girth\n","min_girth = 0 # in meters"]},{"cell_type":"code","execution_count":42,"id":"fe7e6808-fa76-4e9c-bddf-27474aa9f28c","metadata":{"id":"fe7e6808-fa76-4e9c-bddf-27474aa9f28c","executionInfo":{"status":"ok","timestamp":1737062566945,"user_tz":-60,"elapsed":30,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["min_girth_outlier_df = df_refined[df_refined['circonference_cm'] <= min_girth]"]},{"cell_type":"code","execution_count":43,"id":"6baa2bd7-0136-451c-aa81-4f61c30ba125","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":146},"id":"6baa2bd7-0136-451c-aa81-4f61c30ba125","scrolled":true,"executionInfo":{"status":"ok","timestamp":1737062566946,"user_tz":-60,"elapsed":30,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"db6ed9e6-7bb2-41a2-ef32-47adf07fa2d0"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["circonference_cm\n","0.00 25867\n","Name: count, dtype: int64"],"text/html":["
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count
circonference_cm
0.0025867
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"]},"metadata":{},"execution_count":43}],"source":["# no negative value : ok\n","min_girth_outlier_df.value_counts('circonference_cm')"]},{"cell_type":"code","execution_count":44,"id":"9f015caa-3e56-437c-9e6d-e315754d33a7","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"9f015caa-3e56-437c-9e6d-e315754d33a7","scrolled":true,"executionInfo":{"status":"ok","timestamp":1737062567527,"user_tz":-60,"elapsed":610,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"e26ad911-abc4-4fd8-99be-c9db6f8d3617"},"outputs":[{"output_type":"stream","name":"stdout","text":["girth outliers trees (25867 occurrencies):\n"]},{"output_type":"execute_result","data":{"text/plain":[" id type_emplacement domanialite arrondissement \\\n","1778 101922 Arbre Jardin PARIS 19E ARRDT \n","2332 102591 Arbre Jardin PARIS 17E ARRDT \n","2333 102592 Arbre Jardin PARIS 17E ARRDT \n","2405 102744 Arbre Jardin PARIS 20E ARRDT \n","2430 102773 Arbre Jardin PARIS 17E ARRDT \n","... ... ... ... ... \n","200132 2024741 Arbre Jardin BOIS DE VINCENNES \n","200133 2024742 Arbre Jardin BOIS DE VINCENNES \n","200134 2024743 Arbre Jardin BOIS DE VINCENNES \n","200135 2024744 Arbre Jardin BOIS DE VINCENNES \n","200136 2024745 Arbre Jardin BOIS DE VINCENNES \n","\n"," complement_addresse numero \\\n","1778 NaN NaN \n","2332 NaN NaN \n","2333 NaN NaN \n","2405 NaN NaN \n","2430 NaN NaN \n","... ... ... \n","200132 NaN NaN \n","200133 NaN NaN \n","200134 NaN NaN \n","200135 NaN NaN \n","200136 NaN NaN \n","\n"," lieu id_emplacement \\\n","1778 PARC DES BUTTES CHAUMONT C0400032 \n","2332 PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING 000B0052 \n","2333 PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING 000B0053 \n","2405 SQUARE LEON FRAPIE 2 \n","2430 PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING 000B0021 \n","... ... ... \n","200132 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170151 \n","200133 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170152 \n","200134 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170153 \n","200135 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170154 \n","200136 ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F... 720170155 \n","\n"," libelle_francais genre espece variete \\\n","1778 Erable Acer platanoides NaN \n","2332 Magnolia Magnolia x soulangeana Heaven Scent' \n","2333 Magnolia Magnolia x soulangeana Heaven Scent' \n","2405 Platane Platanus x hispanica NaN \n","2430 Magnolia Magnolia x soulangeana Sundew' \n","... ... ... ... ... \n","200132 NaN Castanea fargesii NaN \n","200133 Arbre aux mouchoirs Davidia involucrata NaN \n","200134 Peuplier Populus simonii NaN \n","200135 Chêne Quercus n. sp. NaN \n","200136 Raisinier Hovenia dulcis NaN \n","\n"," circonference_cm hauteur_m stade_developpement remarquable \\\n","1778 0.00 0.00 NaN NaN \n","2332 0.00 0.00 NaN NaN \n","2333 0.00 0.00 NaN NaN \n","2405 0.00 NaN JA 0.00 \n","2430 0.00 0.00 NaN NaN \n","... ... ... ... ... \n","200132 0.00 0.00 NaN 0.00 \n","200133 0.00 0.00 NaN 0.00 \n","200134 0.00 0.00 NaN 0.00 \n","200135 0.00 0.00 NaN 0.00 \n","200136 0.00 0.00 NaN 0.00 \n","\n"," geo_point_2d_a geo_point_2d_b \n","1778 48.88 2.38 \n","2332 48.89 2.32 \n","2333 48.89 2.32 \n","2405 48.87 2.41 \n","2430 48.89 2.32 \n","... ... ... \n","200132 48.82 2.46 \n","200133 48.82 2.46 \n","200134 48.82 2.46 \n","200135 48.82 2.46 \n","200136 48.82 2.45 \n","\n","[25867 rows x 18 columns]"],"text/html":["\n","
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idtype_emplacementdomanialitearrondissementcomplement_addressenumerolieuid_emplacementlibelle_francaisgenreespecevarietecirconference_cmhauteur_mstade_developpementremarquablegeo_point_2d_ageo_point_2d_b
1778101922ArbreJardinPARIS 19E ARRDTNaNNaNPARC DES BUTTES CHAUMONTC0400032ErableAcerplatanoidesNaN0.000.00NaNNaN48.882.38
2332102591ArbreJardinPARIS 17E ARRDTNaNNaNPARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING000B0052MagnoliaMagnoliax soulangeanaHeaven Scent'0.000.00NaNNaN48.892.32
2333102592ArbreJardinPARIS 17E ARRDTNaNNaNPARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING000B0053MagnoliaMagnoliax soulangeanaHeaven Scent'0.000.00NaNNaN48.892.32
2405102744ArbreJardinPARIS 20E ARRDTNaNNaNSQUARE LEON FRAPIE2PlatanePlatanusx hispanicaNaN0.00NaNJA0.0048.872.41
2430102773ArbreJardinPARIS 17E ARRDTNaNNaNPARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING000B0021MagnoliaMagnoliax soulangeanaSundew'0.000.00NaNNaN48.892.32
.........................................................
2001322024741ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170151NaNCastaneafargesiiNaN0.000.00NaN0.0048.822.46
2001332024742ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170152Arbre aux mouchoirsDavidiainvolucrataNaN0.000.00NaN0.0048.822.46
2001342024743ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170153PeuplierPopulussimoniiNaN0.000.00NaN0.0048.822.46
2001352024744ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170154ChêneQuercusn. sp.NaN0.000.00NaN0.0048.822.46
2001362024745ArbreJardinBOIS DE VINCENNESNaNNaNARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...720170155RaisinierHoveniadulcisNaN0.000.00NaN0.0048.822.45
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25867 rows × 18 columns

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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"min_girth_outlier_df","summary":"{\n \"name\": \"min_girth_outlier_df\",\n \"rows\": 25867,\n \"fields\": [\n {\n \"column\": \"id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 721969,\n \"min\": 101922,\n \"max\": 2024745,\n \"num_unique_values\": 25867,\n \"samples\": [\n 2016263,\n 145520,\n 169124\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"type_emplacement\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Arbre\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"domanialite\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"Jardin\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"arrondissement\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 24,\n \"samples\": [\n \"PARIS 12E ARRDT\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"complement_addresse\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 60,\n \"samples\": [\n \"2 (face)\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"numero\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": null,\n \"max\": null,\n \"num_unique_values\": 0,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"lieu\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2101,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"id_emplacement\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 21389,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"libelle_francais\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 152,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"genre\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 141,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"espece\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 412,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"variete\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 134,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"circonference_cm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.0,\n \"max\": 0.0,\n \"num_unique_values\": 1,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hauteur_m\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.9520728384383992,\n \"min\": 0.0,\n \"max\": 40.0,\n \"num_unique_values\": 20,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"stade_developpement\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"remarquable\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.0,\n \"max\": 0.0,\n \"num_unique_values\": 1,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"geo_point_2d_a\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.04898659494827352,\n \"min\": 48.754504992,\n \"max\": 48.9104979364,\n \"num_unique_values\": 25862,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"geo_point_2d_b\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.058212997253484625,\n \"min\": 2.2260475851,\n \"max\": 2.4621650013,\n \"num_unique_values\": 25863,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":44}],"source":["# Maybe freshly planted trees have 0 cms girth and/or 0 is a default value when not measured\n","print('girth outliers trees ('+ str(len(min_girth_outlier_df)) + ' occurrencies):')\n","min_girth_outlier_df"]},{"cell_type":"code","execution_count":45,"id":"6cb2665b-7f8f-4004-b68a-449a6965b461","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"6cb2665b-7f8f-4004-b68a-449a6965b461","executionInfo":{"status":"ok","timestamp":1737062567527,"user_tz":-60,"elapsed":22,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"1d78791d-098a-46f1-a168-a5accee3e653"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" stade_developpement counts\n","0 NaN 25586\n","1 J 126\n","2 A 81\n","3 JA 51\n","4 M 23"],"text/html":["\n","
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stade_developpementcounts
0NaN25586
1J126
2A81
3JA51
4M23
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"min_girth_value_counts","summary":"{\n \"name\": \"min_girth_value_counts\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"stade_developpement\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"A\",\n \"M\",\n \"J\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"counts\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 11411,\n \"min\": 23,\n \"max\": 25586,\n \"num_unique_values\": 5,\n \"samples\": [\n 126,\n 23,\n 81\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":45}],"source":["# A = Adult, JA = Jeune Adulte, J = Jeune, M = Mature\n","# Majority has no stade_developpement value. Probably freshly planted grain. Recommend to not ignore these trees.\n","# For the others, it may probably be a fill value. I recommend setting the height values as NaN\n","min_girth_value_counts = min_girth_outlier_df.value_counts('stade_developpement', dropna=False).reset_index(name='counts')\n","min_girth_value_counts"]},{"cell_type":"code","execution_count":46,"id":"bf4cf441-c5af-4ebc-9ab7-92dfaeefe10c","metadata":{"id":"bf4cf441-c5af-4ebc-9ab7-92dfaeefe10c","executionInfo":{"status":"ok","timestamp":1737062567528,"user_tz":-60,"elapsed":22,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# giving NaN value instead of 0 for already existing trees in order to signal missing data\n","zero_girth_but_existing_development_state = (df_refined['circonference_cm'] <= min_girth) & (df_refined['stade_developpement'].isin(['A', 'JA', 'J', 'M']))\n","# display(df_refined[zero_girth_but_existing_development_state].head(1).transpose())\n","df_refined.loc[zero_girth_but_existing_development_state,'circonference_cm'] = np.nan\n","# same question as minimum height (see above)"]},{"cell_type":"code","execution_count":47,"id":"BLeJcjd0MJub","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BLeJcjd0MJub","executionInfo":{"status":"ok","timestamp":1737062567528,"user_tz":-60,"elapsed":21,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"35ae275f-671c-4718-d138-954a5212a3d9"},"outputs":[{"output_type":"stream","name":"stdout","text":["281 trees have a zero girth value whereas they belong to one of these categories : ['A', 'JA', 'J', 'M'] --> girth value set to 'NaN'\n","25586 trees have a zero girth with no 'stade_developpement' value. They probably are freshly planted grains : girth value is kept zero\n"]}],"source":["print(f\"{min_girth_value_counts['counts'].sum()-min_girth_value_counts['counts'][0]} trees have a zero girth value whereas they belong to one of these categories : ['A', 'JA', 'J', 'M'] --> girth value set to 'NaN'\")\n","print(f\"{min_girth_value_counts['counts'][0]} trees have a zero girth with no 'stade_developpement' value. They probably are freshly planted grains : girth value is kept zero\")\n"]},{"cell_type":"markdown","id":"1MPpoe9IOLP1","metadata":{"id":"1MPpoe9IOLP1"},"source":["##### Girth and Height coherence"]},{"cell_type":"code","execution_count":48,"id":"e3e28272-33a0-428a-80e8-975140d30330","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e3e28272-33a0-428a-80e8-975140d30330","executionInfo":{"status":"ok","timestamp":1737062567528,"user_tz":-60,"elapsed":18,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"1cb1e637-0963-4a69-dfd2-7b00754616a4"},"outputs":[{"output_type":"stream","name":"stdout","text":["25264 trees are both 0 m tall and 0 cms girth --> they probably are freshly planted grains\n","11688 trees are 0 m tall but have strictly positive and coherent girth --> height values are set to 'NaN' \n","322 trees have strictly positive and coherent height but 0 cm girth --> girth values are set to 'NaN' \n"]}],"source":["from IPython.display import display\n","\n","# search if trees 0 meters height trees and 0 cms girth trees are the same\n","both_zero = ((df_refined['hauteur_m'] <= 0) & (df_refined['circonference_cm'] <= 0)).sum()\n","print(f\"{both_zero} trees are both 0 m tall and 0 cms girth --> they probably are freshly planted grains\")\n","\n","height_zero_girth_non_zero = (df_refined['hauteur_m'] <= 0) & (df_refined['circonference_cm'] > 0)\n","# display(df_refined[height_zero_girth_non_zero].head(1).transpose())\n","df_refined.loc[height_zero_girth_non_zero,'hauteur_m'] = np.nan\n","print(f\"{height_zero_girth_non_zero.sum()} trees are 0 m tall but have strictly positive and coherent girth --> height values are set to 'NaN' \")\n","\n","non_zero_height_zero_girth = (df_refined['hauteur_m'] > 0) & (df_refined['circonference_cm'] <= 0)\n","# display(df_refined[non_zero_height_zero_girth].head(1).transpose())\n","df_refined.loc[non_zero_height_zero_girth,'circonference_cm'] = np.nan\n","print(f\"{non_zero_height_zero_girth.sum()} trees have strictly positive and coherent height but 0 cm girth --> girth values are set to 'NaN' \")\n","\n"]},{"cell_type":"markdown","id":"26f2d2ae-4489-4d6e-b217-16125f372597","metadata":{"id":"26f2d2ae-4489-4d6e-b217-16125f372597"},"source":["### NaN Values"]},{"cell_type":"markdown","id":"bf1ee819-3acf-420e-8d17-3699bbc1b4ac","metadata":{"id":"bf1ee819-3acf-420e-8d17-3699bbc1b4ac"},"source":["```\n","# domanialite can be found when looking for nearest neighbors\n","# complement_adresse and numero often do not exist\n","# Maybe can we do something about the missing values for \"libelle_francais\", \"genre\", \"espece\", \"variete\"\n","# stade_developpement and remarkable missing values presences seem to be correlated (according to previous analysis)\n","```\n","\n","\n"]},{"cell_type":"code","execution_count":49,"id":"01c33327-96f8-4cca-bd48-af40c023344d","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":89},"id":"01c33327-96f8-4cca-bd48-af40c023344d","scrolled":true,"executionInfo":{"status":"ok","timestamp":1737062567528,"user_tz":-60,"elapsed":15,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"b9b94480-90c3-4b3a-c383-ffe4f7c35c1e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" domanialite complement_addresse numero libelle_francais genre espece \\\n","0 1 169235 200137 1497 16 1752 \n","\n"," variete stade_developpement remarquable \n","0 163360 67205 63098 "],"text/html":["\n","
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domanialitecomplement_addressenumerolibelle_francaisgenreespecevarietestade_developpementremarquable
0116923520013714971617521633606720563098
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"columns_with_nans_df","summary":"{\n \"name\": \"columns_with_nans_df\",\n \"rows\": 1,\n \"fields\": [\n {\n \"column\": \"domanialite\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 1,\n \"max\": 1,\n \"num_unique_values\": 1,\n \"samples\": [\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"complement_addresse\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 169235,\n \"max\": 169235,\n \"num_unique_values\": 1,\n \"samples\": [\n 169235\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"numero\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 200137,\n \"max\": 200137,\n \"num_unique_values\": 1,\n \"samples\": [\n 200137\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"libelle_francais\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 1497,\n \"max\": 1497,\n \"num_unique_values\": 1,\n \"samples\": [\n 1497\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"genre\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 16,\n \"max\": 16,\n \"num_unique_values\": 1,\n \"samples\": [\n 16\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"espece\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 1752,\n \"max\": 1752,\n \"num_unique_values\": 1,\n \"samples\": [\n 1752\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"variete\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 163360,\n \"max\": 163360,\n \"num_unique_values\": 1,\n \"samples\": [\n 163360\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"stade_developpement\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 67205,\n \"max\": 67205,\n \"num_unique_values\": 1,\n \"samples\": [\n 67205\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"remarquable\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 63098,\n \"max\": 63098,\n \"num_unique_values\": 1,\n \"samples\": [\n 63098\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":49}],"source":["columns_with_nans_df"]},{"cell_type":"markdown","id":"hBvGSxoNq7H2","metadata":{"id":"hBvGSxoNq7H2"},"source":["#### domanialite"]},{"cell_type":"code","execution_count":50,"id":"z_8W9BlA4wnW","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"z_8W9BlA4wnW","executionInfo":{"status":"ok","timestamp":1737062567528,"user_tz":-60,"elapsed":14,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"a0e0a3ba-f162-4b32-e0e0-2eba5d095e85"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[48.85761995, 2.32096211],\n"," [48.85765607, 2.32103142],\n"," [48.85770501, 2.32106057],\n"," ...,\n"," [48.82290714, 2.45797007],\n"," [48.82252234, 2.45595555],\n"," [48.82044479, 2.45485622]])"]},"metadata":{},"execution_count":50}],"source":["df_refined[['geo_point_2d_a','geo_point_2d_b']].values"]},{"cell_type":"code","execution_count":51,"id":"D27qtLjtqEnA","metadata":{"id":"D27qtLjtqEnA","executionInfo":{"status":"ok","timestamp":1737062567528,"user_tz":-60,"elapsed":12,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["from data_analysis_functions import compute_distances"]},{"cell_type":"code","execution_count":52,"id":"cIuY0Unh2g7r","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cIuY0Unh2g7r","executionInfo":{"status":"ok","timestamp":1737062567528,"user_tz":-60,"elapsed":12,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"70ddfa96-8143-4ab4-cb95-dc8cd3c57f14"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[48.84954716, 2.41418967]])"]},"metadata":{},"execution_count":52}],"source":["nan_indexes = df_refined[df_refined['domanialite'].isna()].index\n","nan_indexes\n","df_refined.loc[nan_indexes, ['geo_point_2d_a','geo_point_2d_b']].values"]},{"cell_type":"code","execution_count":53,"id":"70FaYnAPq1N4","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":71946,"status":"ok","timestamp":1737062639465,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"70FaYnAPq1N4","outputId":"5683878c-4a12-4c1d-9c6b-db1cc21be630"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mean distance between tree 197239 and its 3 nearest neighbours: 6.02 meters\n","Missing values for domanialite: Jardin\n"]}],"source":["for index in nan_indexes:\n"," distances = compute_distances(df_refined, index)\n"," # find three nearest neighbours\n"," three_nearest_neighbours = distances.nsmallest(4)[1:]\n"," print(f'Mean distance between tree {index} and its 3 nearest neighbours: {three_nearest_neighbours.mean().round(2)} meters')\n"," missing_domanialite = df_refined.loc[three_nearest_neighbours.index,'domanialite'].unique()\n"," if len(missing_domanialite) == 1:\n"," print(f'Missing values for domanialite: {missing_domanialite[0]}')\n"," df_refined.loc[index, 'domanialite'] = missing_domanialite[0]\n"," else:\n"," print(f'Too many values in missing_domanialite: {missing_domanialite}')"]},{"cell_type":"markdown","id":"cD1g4VFyJgH6","metadata":{"id":"cD1g4VFyJgH6"},"source":["#### genre"]},{"cell_type":"code","execution_count":54,"id":"balAs4z2Je3F","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":550},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1737062639465,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"balAs4z2Je3F","outputId":"db78d6c9-f750-44fb-869f-447f3880be21"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" genre espece variete libelle_francais\n","195404 NaN NaN NaN NaN\n","195409 NaN NaN NaN NaN\n","195410 NaN NaN NaN NaN\n","195475 NaN NaN NaN NaN\n","195476 NaN NaN NaN NaN\n","195487 NaN NaN NaN NaN\n","195496 NaN NaN NaN NaN\n","195497 NaN NaN NaN NaN\n","195499 NaN NaN NaN NaN\n","195502 NaN NaN NaN NaN\n","195503 NaN NaN NaN NaN\n","195505 NaN NaN NaN NaN\n","195509 NaN NaN NaN NaN\n","195514 NaN NaN NaN NaN\n","195518 NaN NaN NaN NaN\n","195519 NaN NaN NaN NaN"],"text/html":["\n","
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genreespecevarietelibelle_francais
195404NaNNaNNaNNaN
195409NaNNaNNaNNaN
195410NaNNaNNaNNaN
195475NaNNaNNaNNaN
195476NaNNaNNaNNaN
195487NaNNaNNaNNaN
195496NaNNaNNaNNaN
195497NaNNaNNaNNaN
195499NaNNaNNaNNaN
195502NaNNaNNaNNaN
195503NaNNaNNaNNaN
195505NaNNaNNaNNaN
195509NaNNaNNaNNaN
195514NaNNaNNaNNaN
195518NaNNaNNaNNaN
195519NaNNaNNaNNaN
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"genre_nan_rows","repr_error":"0"}},"metadata":{},"execution_count":54}],"source":["# catch each line where \"genre\" value is nan. Catch only 'genre','espece','variete','libelle_francais' columns\n","\n","# Assuming df_refined is your DataFrame as defined in the provided code.\n","\n","genre_nan_rows = df_refined[df_refined['genre'].isna()][['genre', 'espece', 'variete', 'libelle_francais']]\n","\n","genre_nan_rows"]},{"cell_type":"markdown","id":"MgBXUNswKdtn","metadata":{"id":"MgBXUNswKdtn"},"source":["Cannot find \"genre\" value since all the other values for \"espece\", \"variete\", \"libelle_francais\" are \"nan\"."]},{"cell_type":"markdown","id":"jPyVZ2cQLLJ0","metadata":{"id":"jPyVZ2cQLLJ0"},"source":["#### libelle_francais"]},{"cell_type":"code","execution_count":55,"id":"ACltGPqrSWo0","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":457,"status":"ok","timestamp":1737062639914,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"ACltGPqrSWo0","outputId":"a8729550-dbac-4366-cc1b-f46bdbdfa335"},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of different libelle_francais values: 193\n","True\n"]}],"source":["print(f\"Number of different libelle_francais values: {len(np.unique(df_refined['libelle_francais'].astype(str)))}\")\n","# nan is in the list. We will continue ignoring it in order to potentially find a correspondance between libelle_francais and genre variable\n","print('nan' in np.unique(df_refined['libelle_francais'].astype(str)))"]},{"cell_type":"code","execution_count":56,"id":"GbXIdFi6T5rR","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1737062639914,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"GbXIdFi6T5rR","outputId":"ec1153cd-8df5-48ab-c581-b2655cf31652"},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of different libelle_francais values: 192\n","Possible correlation between libelle_francais and genre:\n","Number of 'libelle_francais' with one 'genre': 182\n","Number of 'libelle_francais' with multiple 'genre': 10\n"]}],"source":["# not the most useful\n","# For each value of \"libelle_francais\", find when there is only one genre possible.\n","# Count the cases when there is only one genre related to libelle_francais and the cases where it is not.\n","# Please consider the \"nan\" cases in another counter variable\n","\n","# Count different 'genre' values for each 'libelle_francais'. NaNs are ignored\n","libelle_genre_counts = df_refined.groupby('libelle_francais')['genre'].nunique()\n","\n","one_genre_count = (libelle_genre_counts == 1).sum()\n","multiple_genres_count = (libelle_genre_counts > 1).sum()\n","\n","nan_genre_count = df_refined[(df_refined['libelle_francais'].notna()) & (df_refined['genre'].isna())].shape[0]\n","\n","print(f\"Number of different libelle_francais values: {len(libelle_genre_counts)}\")\n","print('Possible correlation between libelle_francais and genre:')\n","print(f\"Number of 'libelle_francais' with one 'genre': {one_genre_count}\")\n","print(f\"Number of 'libelle_francais' with multiple 'genre': {multiple_genres_count}\")"]},{"cell_type":"code","execution_count":57,"id":"pEffiwi8gfAH","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1737062639914,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"pEffiwi8gfAH","outputId":"15d85929-e170-46eb-c1db-77c934963961"},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of different genre values: 175\n","Possible correlation between genre and libelle_francais:\n","Number of 'genre' with one libelle_francais: 159\n","Number of 'genre' with multiple libelle_francais: 13\n","Number of 'genre' with no libelle_francais: 3\n","\n","Genre that did not match any libelle_francais value: genre\n","Crataegomespilus 0\n","Podocarpus 0\n","x Sorbopyrus 0\n","Name: libelle_francais, dtype: int64\n"]}],"source":["# For each value of \"genre\", find when there is only one libelle_francais possible.\n","# Count the cases when there is only one libelle_francais related to genre and the cases where it is not.\n","# Please consider the \"nan\" cases in another counter variable\n","\n","# Count different 'libelle_francais' values for each 'genre'. NaNs are ignored\n","genre_libelle_counts = df_refined.groupby('genre')['libelle_francais'].nunique()\n","\n","one_libelle_count = (genre_libelle_counts == 1).sum()\n","multiple_libelles_count = (genre_libelle_counts > 1).sum()\n","no_libelles_count = (genre_libelle_counts < 1).sum()\n","\n","print(f\"Number of different genre values: {len(genre_libelle_counts)}\")\n","print('Possible correlation between genre and libelle_francais:')\n","print(f\"Number of 'genre' with one libelle_francais: {one_libelle_count}\")\n","print(f\"Number of 'genre' with multiple libelle_francais: {multiple_libelles_count}\")\n","print(f\"Number of 'genre' with no libelle_francais: {no_libelles_count}\")\n","print(f\"\\nGenre that did not match any libelle_francais value: {genre_libelle_counts[genre_libelle_counts < 1]}\")\n","\n"]},{"cell_type":"code","execution_count":58,"id":"dVhtzF2YeW-_","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3289,"status":"ok","timestamp":1737062643201,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"dVhtzF2YeW-_","outputId":"99292d34-7fd8-41c0-b0dc-878084eedc2a"},"outputs":[{"output_type":"stream","name":"stdout","text":["One libelle mapping dictionary: (159 values)\n","\n","More than one libelle mapping dictionary: (13 values)\n","\n","No libelle mapping dictionary: (3 values)\n","{'Crataegomespilus': None, 'Podocarpus': None, 'x Sorbopyrus': None}\n"]}],"source":["# Generate a dictionary that contains matching libelle_francais for each genre, based on the df_refined dataframe\n","\n","# Create a dictionary that maps each 'genre' to its corresponding 'libelle_francais'\n","# Only include 'genre' that have a unique 'libelle_francais'\n","only_one_libelle_mapping = {}\n","more_than_one_libelle_mapping = {}\n","no_libelle_mapping = {}\n","\n","for genre in df_refined['genre'].dropna().unique(): # Iterating over 'genre'\n"," libelles = df_refined[df_refined['genre'] == genre]['libelle_francais'].dropna().unique() # Getting 'libelle_francais'\n"," if len(libelles) == 1:\n"," only_one_libelle_mapping[genre] = libelles[0] # Mapping genre to single libelle_francais\n"," elif len(libelles) > 1:\n"," more_than_one_libelle_mapping[genre] = libelles # Mapping genre to multiple libelle_francais\n"," else:\n"," no_libelle_mapping[genre] = None # Mapping genre to no libelle_francais\n","\n","\n","from pprint import pprint\n","print(f\"One libelle mapping dictionary: ({len(only_one_libelle_mapping)} values)\")\n","# pprint(only_one_libelle_mapping)\n","print()\n","print(f\"More than one libelle mapping dictionary: ({len(more_than_one_libelle_mapping)} values)\")\n","# pprint(more_than_one_libelle_mapping)\n","print()\n","print(f\"No libelle mapping dictionary: ({len(no_libelle_mapping)} values)\")\n","pprint(no_libelle_mapping)"]},{"cell_type":"code","execution_count":59,"id":"9F7qfwNmjsl-","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":607},"executionInfo":{"elapsed":14,"status":"ok","timestamp":1737062643201,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"9F7qfwNmjsl-","outputId":"7b859b05-f52b-4359-cae8-be9fb41bbfa3"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# prompt: plot bars with len of each dictionnary\n","\n","import matplotlib.pyplot as plt\n","\n","# Assuming you have the dictionaries: only_one_libelle_mapping, more_than_one_libelle_mapping, no_libelle_mapping\n","\n","dictionaries = {\n"," \"= 1\": only_one_libelle_mapping,\n"," \"> 1\": more_than_one_libelle_mapping,\n"," \"= 0\": no_libelle_mapping,\n","}\n","\n","dictionary_names = list(dictionaries.keys())\n","lengths = [len(dictionaries[name]) for name in dictionary_names]\n","\n","plt.figure(figsize=(10, 6))\n","plt.bar(dictionary_names, lengths)\n","plt.xlabel(\"Distinct libelle_francais corresponding values per genre\")\n","plt.ylabel(\"Ocurrences\")\n","plt.tight_layout()\n","plt.show()"]},{"cell_type":"code","execution_count":60,"id":"XFSIHEGQjZfa","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1737062643201,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"XFSIHEGQjZfa","outputId":"500f1c3c-dc45-42ba-bd51-8963a897772d"},"outputs":[{"output_type":"stream","name":"stdout","text":["{'x Sorbopyrus': None, 'Podocarpus': None, 'Crataegomespilus': None}\n"," genre espece variete libelle_francais\n","199193 x Sorbopyrus n. sp. NaN NaN\n","199527 Podocarpus macrophyllus NaN NaN\n","199528 Podocarpus macrophyllus NaN NaN\n","200040 Crataegomespilus grandiflora NaN NaN\n","200049 Crataegomespilus grandiflora NaN NaN\n","\"x Sorbopyrus\":\"Poirier de Bollwiller\" entry correctly added to the dictionary\n","\"Podocarpus\":\"Pin des Bouddhistes\" entry correctly added to the dictionary\n","\"Crataegomespilus\":\"Neflier à grandes feuilles\" entry correctly added to the dictionary\n"]}],"source":["# Case \"=0\" corresponding libelle_francais values per genre : add the information\n","print(no_libelle_mapping)\n","print(df_refined.loc[df_refined['genre'].isin(no_libelle_mapping.keys()),['genre','espece','variete','libelle_francais']])\n","# https://lapepinieredufruitier.fr/categorie-produit/poirier/sorbopyrus/\n","# https://fr.wikipedia.org/wiki/Podocarpus_macrophyllus\n","# https://www.jardin-ecologique.fr/boutique/toutes-nos-plantes/plantes-mediterraneennes/plantes-pour-haies/haie-gourmande/crataegomespilus-grandifolia-neflier-a-grandes-feuilles/#:~:text=Le%20Crataegomespilus%20est%20un%20hybride,ou%20aussi%20x%20Crataegomespilus%20grandiflora\n","no_libelle_mapping['x Sorbopyrus'] = 'Poirier de Bollwiller'\n","no_libelle_mapping['Podocarpus'] = 'Pin des Bouddhistes'\n","no_libelle_mapping['Crataegomespilus'] = 'Neflier à grandes feuilles'\n","\n","for index, row in no_libelle_mapping.items():\n"," if index not in only_one_libelle_mapping.keys():\n"," only_one_libelle_mapping[index] = row\n"," print(f'\"{index}\":\"{row}\" entry correctly added to the dictionary')\n"," else:\n"," print(f'Problem with \"{index}\" : found in both dictionaries')"]},{"cell_type":"code","execution_count":61,"id":"Bm65nzDZnJuv","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1737062643202,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"Bm65nzDZnJuv","outputId":"76b60506-de08-46d4-cc96-8508f938b6e0"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'Poirier de Bollwiller'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":61}],"source":["only_one_libelle_mapping['x Sorbopyrus']"]},{"cell_type":"code","execution_count":62,"id":"Gia4bWbb3vWr","metadata":{"executionInfo":{"elapsed":13638,"status":"ok","timestamp":1737062656831,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"Gia4bWbb3vWr"},"outputs":[],"source":["# for each line in df_refined where libelle_francais is missing (nan value),\n","# if genre value is not nan and genre value is in only_one_libelle_mapping dictionary, use the corresponding value in only_one_libelle_mapping as libelle_francais value\n","\n","for index, row in df_refined.iterrows():\n"," if pd.isna(row['libelle_francais']):\n"," if not pd.isna(row['genre']) and row['genre'] in only_one_libelle_mapping:\n"," df_refined.loc[index, 'libelle_francais'] = only_one_libelle_mapping[row['genre']]"]},{"cell_type":"code","execution_count":63,"id":"UuUT5S3m44Bp","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":15,"status":"ok","timestamp":1737062656831,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"UuUT5S3m44Bp","outputId":"f1e16e49-4eb7-48b2-d3f4-566a1c24dc22"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 0, 0, 0, 0, 169235, 200137, 0, 0,\n"," 304, 16, 1752, 163360, 623, 13956, 67205, 63098,\n"," 0, 0])"]},"metadata":{},"execution_count":63}],"source":["# number of 'nan' for each column\n","column_nb_of_nans = np.array([], dtype='int')\n","for column in df_refined.columns:\n"," where_nan = df_refined[df_refined[column].isna()]\n"," column_nb_of_nans = np.concatenate((column_nb_of_nans,[len(where_nan)]))\n","column_nb_of_nans"]},{"cell_type":"code","execution_count":64,"id":"fSQZsKAo44Q_","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":107},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1737062656831,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"fSQZsKAo44Q_","outputId":"758a959e-3bef-47e6-f7bf-e77391b71371","scrolled":true},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of nan for each variable that is concerned by 1 nan or more:\n"]},{"output_type":"execute_result","data":{"text/plain":[" complement_addresse numero libelle_francais genre espece variete \\\n","0 169235 200137 304 16 1752 163360 \n","\n"," circonference_cm hauteur_m stade_developpement remarquable \n","0 623 13956 67205 63098 "],"text/html":["\n","
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complement_addressenumerolibelle_francaisgenreespecevarietecirconference_cmhauteur_mstade_developpementremarquable
0169235200137304161752163360623139566720563098
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Arbre de fer', 'Parrotie de Perse'],\n"," dtype=object),\n"," 'Gymnocladus': array(['Chicot du Canada', 'Fevier'], dtype=object),\n"," 'Laurus': array(['Laurier sauce', 'Laurier'], dtype=object),\n"," 'Cotinus': array(['Arbre à perruque', \"Fustet d' Amérique\"], dtype=object)}"]},"metadata":{},"execution_count":65}],"source":["# Case \">1 \" corresponding libelle_francais values per genre : check espece and (eventually) variete\n","more_than_one_libelle_mapping"]},{"cell_type":"code","execution_count":66,"id":"I2lF8TSH2TDZ","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"executionInfo":{"elapsed":2582,"status":"ok","timestamp":1737062659407,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"I2lF8TSH2TDZ","outputId":"292720b7-73e5-4045-d955-da482dea3dd7"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" libelle_francais genre espece variete\n","0 Prunus n. sp. Prunus n. sp. NaN\n","1 Cerisier à fleurs Prunus serrulata NaN\n","2 Cerisier à fleurs Prunus serrulata Kanzan'\n","3 Cerisier à fleurs Prunus serrulata Hizakura'\n","4 Cerisier à fleurs Prunus serrulata Fugenzo'\n",".. ... ... ... ...\n","285 Fevier Gymnocladus n. sp. NaN\n","286 Laurier sauce Laurus nobilis NaN\n","287 Laurier Laurus n. sp. NaN\n","288 Arbre à perruque Cotinus coggygria NaN\n","289 Fustet d' Amérique Cotinus obovatus NaN\n","\n","[290 rows x 4 columns]"],"text/html":["\n","
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libelle_francaisgenreespecevariete
0Prunus n. sp.Prunusn. sp.NaN
1Cerisier à fleursPrunusserrulataNaN
2Cerisier à fleursPrunusserrulataKanzan'
3Cerisier à fleursPrunusserrulataHizakura'
4Cerisier à fleursPrunusserrulataFugenzo'
...............
285FevierGymnocladusn. sp.NaN
286Laurier sauceLaurusnobilisNaN
287LaurierLaurusn. sp.NaN
288Arbre à perruqueCotinuscoggygriaNaN
289Fustet d' AmériqueCotinusobovatusNaN
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290 rows × 4 columns

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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"matching_libelle_francais","summary":"{\n \"name\": \"matching_libelle_francais\",\n \"rows\": 290,\n \"fields\": [\n {\n \"column\": \"libelle_francais\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 44,\n \"samples\": [\n \"Parrotie de Perse\",\n \"Poirier\",\n \"Poirier \\u00e0 fleurs\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"genre\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 13,\n \"samples\": [\n \"Laurus\",\n \"Parrotia\",\n \"Prunus\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"espece\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 118,\n \"samples\": [\n \"prunifolia\",\n \"fecunda\",\n \"x subhirtella\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"variete\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 154,\n \"samples\": [\n \"Plena'\",\n \"Reinette Blanche du Canada'\",\n \"Gibsii'\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":66}],"source":["# For each couple (genre, libelle_francais) with genre the key of more_than_one_libelle_mapping and libelle_francais taking\n","# its value in the different corresponding lists, find if there are only one or more corresponding couple \"espece\" value in df_refined that fits the couple.\n","# If there is exactly one possible \"espece\" value, fill a dataframe with libelle_francais, genre and espece values.\n","# A 4th column for \"variete\" contains a NaN value. If there is more than one possible \"espece\" value, then check the different values for \"variete\" and fill the dataframe\n","# with the different cases\n","\n","# Create an empty DataFrame to store the results\n","matching_libelle_francais = pd.DataFrame(columns=['libelle_francais', 'genre', 'espece', 'variete'])\n","\n","# Iterate through the 'more_than_one_libelle_mapping' dictionary\n","for genre, libelles_francais in more_than_one_libelle_mapping.items():\n"," for libelle_francais in libelles_francais:\n"," # Find corresponding rows in df_refined\n"," matching_rows = df_refined[(df_refined['genre'] == genre) & (df_refined['libelle_francais'] == libelle_francais)]\n","\n"," # Check the number of unique 'espece' values\n"," unique_especes = matching_rows['espece'].unique()\n"," num_unique_especes = len(unique_especes)\n","\n"," if num_unique_especes == 1:\n"," # Exactly one 'espece' value\n"," espece = unique_especes[0]\n"," new_row = pd.DataFrame({'libelle_francais': [libelle_francais],\n"," 'genre': [genre],\n"," 'espece': [espece],\n"," 'variete': [np.nan]})\n"," matching_libelle_francais = pd.concat([matching_libelle_francais, new_row], ignore_index=True)\n"," elif num_unique_especes > 1:\n"," # More than one 'espece' value, check 'variete'\n"," for espece in unique_especes:\n"," variete_values = matching_rows[matching_rows['espece'] == espece]['variete'].unique()\n"," for variete in variete_values:\n"," new_row = pd.DataFrame({'libelle_francais': [libelle_francais],\n"," 'genre': [genre],\n"," 'espece': [espece],\n"," 'variete': [variete]})\n"," matching_libelle_francais = pd.concat([matching_libelle_francais, new_row], ignore_index=True)\n","\n","# Display the resulting DataFrame\n","matching_libelle_francais"]},{"cell_type":"code","execution_count":67,"id":"x2cpyOBisR1S","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1737062659407,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"x2cpyOBisR1S","outputId":"b100b9b3-073c-425e-dbd6-6f2a43b89c0c"},"outputs":[{"output_type":"stream","name":"stdout","text":["id 100589\n","type_emplacement Arbre\n","domanialite Jardin\n","arrondissement PARIS 7E ARRDT\n","complement_addresse NaN\n","numero NaN\n","lieu JARDIN DU CHAMP DE MARS\n","id_emplacement P0120631\n","libelle_francais NaN\n","genre Crataegus\n","espece japonica\n","variete NaN\n","circonference_cm 43.00\n","hauteur_m 3.00\n","stade_developpement A\n","remarquable 0.00\n","geo_point_2d_a 48.86\n","geo_point_2d_b 2.30\n","Name: 528, dtype: object\n"]},{"output_type":"execute_result","data":{"text/plain":[" libelle_francais genre espece \\\n","225 Aubépine Crataegus laevigata \n","226 Aubépine Crataegus laevigata \n","227 Aubépine Crataegus laevigata \n","228 Aubépine Crataegus x media \n","229 Aubépine Crataegus x media \n","230 Aubépine Crataegus x media \n","231 Aubépine Crataegus x lavalleei \n","232 Aubépine Crataegus x lavalleei \n","233 Aubépine Crataegus crus-galli \n","234 Aubépine Crataegus crus-galli \n","235 Aubépine Crataegus monogyna var. monogyna \n","236 Aubépine Crataegus monogyna \n","237 Aubépine Crataegus viridis \n","238 Aubépine Crataegus chrysocarpa var. phoenicea \n","239 Aubépine Crataegus x lettermanii \n","240 Aubépine Crataegus x sorbifolia \n","241 Aubépine Crataegus x persimilis \n","242 Aubépine Crataegus fecunda \n","243 Aubépine Crataegus canbyi \n","244 Aubépine Crataegus spathulata \n","245 Aubépine Crataegus aprica \n","246 Aubépine Crataegus ambigua \n","247 Aubépine Crataegus venustula \n","248 Aubepine Crataegus n. sp. \n","249 Aubepine Crataegus monogyna \n","250 Aubepine Crataegus rotundifolia var. aboriginum \n","251 Aubepine Crataegus champlainensis \n","\n"," variete \n","225 NaN \n","226 Paul's Scarlet Hamlet' \n","227 Plena' \n","228 Rubra Plena' \n","229 Punicea' \n","230 Paul's Scarlet' \n","231 Carrierei' \n","232 NaN \n","233 Salicifolia' \n","234 NaN \n","235 NaN \n","236 Stricta' \n","237 NaN \n","238 NaN \n","239 NaN \n","240 NaN \n","241 NaN \n","242 NaN \n","243 NaN \n","244 NaN \n","245 NaN \n","246 NaN \n","247 NaN \n","248 NaN \n","249 NaN \n","250 NaN \n","251 NaN "],"text/html":["\n","
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libelle_francaisgenreespecevariete
225AubépineCrataeguslaevigataNaN
226AubépineCrataeguslaevigataPaul's Scarlet Hamlet'
227AubépineCrataeguslaevigataPlena'
228AubépineCrataegusx mediaRubra Plena'
229AubépineCrataegusx mediaPunicea'
230AubépineCrataegusx mediaPaul's Scarlet'
231AubépineCrataegusx lavalleeiCarrierei'
232AubépineCrataegusx lavalleeiNaN
233AubépineCrataeguscrus-galliSalicifolia'
234AubépineCrataeguscrus-galliNaN
235AubépineCrataegusmonogyna var. monogynaNaN
236AubépineCrataegusmonogynaStricta'
237AubépineCrataegusviridisNaN
238AubépineCrataeguschrysocarpa var. phoeniceaNaN
239AubépineCrataegusx lettermaniiNaN
240AubépineCrataegusx sorbifoliaNaN
241AubépineCrataegusx persimilisNaN
242AubépineCrataegusfecundaNaN
243AubépineCrataeguscanbyiNaN
244AubépineCrataegusspathulataNaN
245AubépineCrataegusapricaNaN
246AubépineCrataegusambiguaNaN
247AubépineCrataegusvenustulaNaN
248AubepineCrataegusn. sp.NaN
249AubepineCrataegusmonogynaNaN
250AubepineCrataegusrotundifolia var. aboriginumNaN
251AubepineCrataeguschamplainensisNaN
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","summary":"{\n \"name\": \" (matching_libelle_francais['genre'] == row['genre'])]\",\n \"rows\": 27,\n \"fields\": [\n {\n \"column\": \"libelle_francais\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"Aubepine\",\n \"Aub\\u00e9pine\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"genre\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Crataegus\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"espece\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 20,\n \"samples\": [\n \"laevigata\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"variete\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 8,\n \"samples\": [\n \"Plena'\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":67}],"source":["row = df_refined[df_refined['libelle_francais'].isna()].iloc[0]\n","print(row)\n","matching_libelle_francais[\n"," (matching_libelle_francais['genre'] == row['genre'])]"]},{"cell_type":"code","execution_count":68,"id":"14KyAGnNqLaD","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"elapsed":669,"status":"ok","timestamp":1737062660069,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"14KyAGnNqLaD","outputId":"9618ce17-a00c-4f52-84f1-159c0ab6afcd"},"outputs":[{"output_type":"stream","name":"stdout","text":["Amount of 'libelle_francais' found: 142\n","Amount of 'libelle_francais' not found: 162\n"]}],"source":["# in df_refined, for each row where a nan value is found at 'libelle_francais' column, check the different \"libelle_francais\" corresponding values in matching_libelle_francais\n","# that fit the couple (\"genre\",\"espece\"). If there is only one possible \"libelle_francais\" value, then attribute it to the considered row.\n","# If not, also check the \"variete\" value and find out if there only is one \"libelle_francais\" possible matching value. If still not, note this case as unsolvable.\n","\n","index_found = []\n","index_not_found = []\n","\n","# Iterate through rows with NaN 'libelle_francais' in df_refined\n","for index, row in df_refined[df_refined['libelle_francais'].isna()].iterrows():\n"," # Find matching rows in matching_libelle_francais based on 'genre' and 'espece'\n","\n"," matching_rows = matching_libelle_francais[\n"," (matching_libelle_francais['genre'].astype(str) == str(row['genre'])) &\n"," (matching_libelle_francais['espece'].astype(str) == str(row['espece']))\n"," ]\n","\n"," # Check the number of unique 'libelle_francais' values\n"," num_unique_libelles = len(matching_rows['libelle_francais'].unique())\n","\n"," if num_unique_libelles == 1:\n"," # Only one possible 'libelle_francais'\n"," df_refined.loc[index, 'libelle_francais'] = matching_rows['libelle_francais'].iloc[0]\n"," # print(f\"'libelle_francais' value found : for index {index}\")\n"," index_found.append(index)\n","\n"," elif num_unique_libelles > 1:\n"," # Multiple possible 'libelle_francais', check 'variete'\n"," matching_rows_variete = matching_rows[\n"," matching_rows['variete'].astype(str) == str(row['variete'])\n"," ]\n"," num_unique_libelles_variete = len(matching_rows_variete['libelle_francais'].unique())\n","\n"," if num_unique_libelles_variete == 1:\n"," # Only one possible 'libelle_francais' after considering 'variete'\n"," df_refined.loc[index, 'libelle_francais'] = matching_rows_variete['libelle_francais'].iloc[0]\n"," # print(f\"'libelle_francais' value found : for index {index}\")\n"," index_found.append(index)\n"," else: # > 1\n"," # Still multiple possible 'libelle_francais', mark as unsolvable\n"," # print(f\"'libelle_francais' value not found : for index {index} : too many possible values\")\n"," index_not_found.append(index)\n","\n"," else: # num_unique_libelles == 0\n"," # No possible 'libelle_francais', mark as unsolvable\n"," # print(f\"'libelle_francais' value not found : for index {index} : no matching 'libelle_francais' value\")\n"," index_not_found.append(index)\n","\n","print(f\"Amount of 'libelle_francais' found: {len(index_found)}\")\n","print(f\"Amount of 'libelle_francais' not found: {len(index_not_found)}\")"]},{"cell_type":"code","execution_count":69,"id":"m3S60h5Z_Dip","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1737062660069,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"m3S60h5Z_Dip","outputId":"01ddfa92-6482-4051-97ed-a30f427c7ef2"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" genre espece variete libelle_francais\n","528 Crataegus japonica NaN NaN\n","5282 Sorbus aria Pissardii' NaN\n","8900 Pyrus Fruit n. sp. Chanticleer' NaN\n","12375 Corylus cerasifera NaN NaN\n","12959 Parrotia persica Chanticleer' NaN\n","... ... ... ... ...\n","199405 Crataegus speciosa NaN NaN\n","199984 Malus purpurea NaN NaN\n","199993 Malus sargentii NaN NaN\n","199996 Malus sargentii NaN NaN\n","200128 Parrotia n. sp. NaN NaN\n","\n","[162 rows x 4 columns]"],"text/html":["\n","
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genreespecevarietelibelle_francais
528CrataegusjaponicaNaNNaN
5282SorbusariaPissardii'NaN
8900PyrusFruit n. sp.Chanticleer'NaN
12375CoryluscerasiferaNaNNaN
12959ParrotiapersicaChanticleer'NaN
...............
199405CrataegusspeciosaNaNNaN
199984MaluspurpureaNaNNaN
199993MalussargentiiNaNNaN
199996MalussargentiiNaNNaN
200128Parrotian. sp.NaNNaN
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162 rows × 4 columns

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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"remaining_cases","repr_error":"0"}},"metadata":{},"execution_count":69}],"source":["remaining_cases = df_refined.loc[index_not_found][['genre','espece','variete','libelle_francais']].copy()\n","remaining_cases"]},{"cell_type":"code","execution_count":70,"id":"En8bvFMcBWt3","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1737062660069,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"En8bvFMcBWt3","outputId":"a4ca5c6e-d700-41cf-8139-3f3550ad1c0c"},"outputs":[{"output_type":"stream","name":"stdout","text":["Different remaining cases : 51\n"," genre espece variete libelle_francais\n","12375 Corylus cerasifera NaN NaN\n","123822 Corylus pseudoacacia Unifolia' NaN\n","528 Crataegus japonica NaN NaN\n","199405 Crataegus speciosa NaN NaN\n","194059 Crataegus NaN NaN NaN\n","160955 Fraxinus betulus Fastigiata' NaN\n","161675 Fraxinus betulus NaN NaN\n","107494 Fraxinus calleryana Chanticleer' NaN\n","199101 Fraxinus chinensis NaN NaN\n","176284 Fraxinus minor NaN NaN\n","199154 Fraxinus pensylvanicum NaN NaN\n","198339 Fraxinus platyphyllos NaN NaN\n","108830 Fraxinus pseudoacacia Unifolia' NaN\n","94873 Fraxinus tulipifera NaN NaN\n","191688 Fraxinus NaN NaN NaN\n","141292 Gymnocladus NaN NaN NaN\n","170317 Ligustrum laevigata NaN NaN\n","194061 Ligustrum NaN NaN NaN\n","176508 Malus minor NaN NaN\n","80830 Malus nigra NaN NaN\n","197049 Malus padus NaN NaN\n","194678 Malus pumila Shinseiki' NaN\n","199984 Malus purpurea NaN NaN\n","199993 Malus sargentii NaN NaN\n","15330 Malus siliquastrum NaN NaN\n","19383 Malus NaN NaN NaN\n","15882 Ostrya avium NaN NaN\n","114634 Parrotia n. sp. NaN NaN\n","129676 Parrotia persica Baumannii' NaN\n","12959 Parrotia persica Chanticleer' NaN\n","80093 Parrotia sylvatica NaN NaN\n","73591 Prunus bignonioides NaN NaN\n","107662 Prunus calleryana Chanticleer' NaN\n","192112 Prunus cordata NaN NaN\n","191849 Prunus negundo NaN NaN\n","156348 Prunus pseudoacacia Unifolia' NaN\n","187246 Prunus pseudoplatanus NaN NaN\n","69885 Prunus NaN NaN NaN\n","8900 Pyrus Fruit n. sp. Chanticleer' NaN\n","146565 Pyrus alba Raket' NaN\n","166560 Pyrus australis NaN NaN\n","95678 Pyrus carpinifolia NaN NaN\n","107892 Pyrus controversa NaN NaN\n","187928 Pyrus domestica Bon chrétien Williams' NaN\n","193962 Pyrus domestica Drap d'Or' NaN\n","82969 Pyrus negundo NaN NaN\n","108675 Pyrus NaN NaN NaN\n","5282 Sorbus aria Pissardii' NaN\n","197045 Sorbus monspessulanum NaN NaN\n","15366 Sorbus tomentosa NaN NaN\n","195404 NaN NaN NaN NaN\n"]}],"source":["distinct_remaining_cases = remaining_cases.drop_duplicates().sort_values(by=['genre','espece','variete'])\n","print(f'Different remaining cases : {len(distinct_remaining_cases)}')\n","print(distinct_remaining_cases)\n","\n","# could go further but we will stop here"]},{"cell_type":"code","execution_count":71,"id":"OCR53Q0D1SaT","metadata":{"executionInfo":{"elapsed":7,"status":"ok","timestamp":1737062660069,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"OCR53Q0D1SaT"},"outputs":[],"source":["# some analysis and nan replacement need to be done on espece and variete\n","# impossible to have variete if only have genre and espece... Can take very long time"]},{"cell_type":"markdown","source":["#### espece"],"metadata":{"id":"jFJCBiY03t72"},"id":"jFJCBiY03t72"},{"cell_type":"code","source":["# prompt: take all the rows where \"espece\" is equal to nan. Then select the cases when variete is not nan\n","\n","# Assuming df_refined is your DataFrame\n","espece_nan_rows = df_refined[df_refined['espece'].isna()]\n","variete_not_nan_rows = espece_nan_rows[espece_nan_rows['variete'].notna()]\n","variete_not_nan_rows\n","\n","# same approach, based on finding the value based on the other fields. If possible, find cases where other both other fields and value are given"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":755},"id":"hB-pqw4S3epD","executionInfo":{"status":"ok","timestamp":1737062660069,"user_tz":-60,"elapsed":7,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"7c1b74b6-f5ca-41dd-c098-803579e43829"},"id":"hB-pqw4S3epD","execution_count":72,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" id type_emplacement domanialite arrondissement \\\n","1725 101858 Arbre Jardin PARIS 19E ARRDT \n","2323 102582 Arbre Jardin PARIS 16E ARRDT \n","2733 103215 Arbre Jardin PARIS 17E ARRDT \n","2742 103224 Arbre Jardin PARIS 17E ARRDT \n","3944 104827 Arbre Jardin PARIS 13E ARRDT \n","... ... ... ... ... \n","198666 2022738 Arbre Alignement PARIS 11E ARRDT \n","198667 2022739 Arbre Alignement PARIS 11E ARRDT \n","198668 2022740 Arbre Alignement PARIS 11E ARRDT \n","198680 2022752 Arbre Alignement PARIS 11E ARRDT \n","198681 2022754 Arbre Alignement PARIS 11E ARRDT \n","\n"," complement_addresse numero \\\n","1725 NaN NaN \n","2323 82 à 90 NaN \n","2733 NaN NaN \n","2742 NaN NaN \n","3944 NaN NaN \n","... ... ... \n","198666 NaN NaN \n","198667 NaN NaN \n","198668 NaN NaN \n","198680 NaN NaN \n","198681 NaN NaN \n","\n"," lieu id_emplacement \\\n","1725 PARC DES BUTTES CHAUMONT D0030002 \n","2323 JARDIN DE L AVENUE FOCH / 10 AVENUE FOCH 408032 \n","2733 PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING 000B0167 \n","2742 PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING 000B0188 \n","3944 SQUARE RENE LE GALL 10255 \n","... ... ... \n","198666 PLACE JEAN FERRAT 301001 \n","198667 PLACE JEAN FERRAT 301002 \n","198668 PLACE JEAN FERRAT 301003 \n","198680 PLACE JEAN FERRAT 105001 \n","198681 BOULEVARD DE BELLEVILLE 101001 \n","\n"," libelle_francais genre espece variete \\\n","1725 Pommier à fleurs Malus NaN Aldenhamensis' \n","2323 Cornouiller Cornus NaN Eddie's White Wonder' \n","2733 Cerisier à fleurs Prunus NaN Accolade' \n","2742 Cerisier à fleurs Prunus NaN Accolade' \n","3944 Pommier à fleurs Malus NaN Golden Hornet' \n","... ... ... ... ... \n","198666 Orme Ulmus NaN Nanguen' LUTECE \n","198667 Orme Ulmus NaN Nanguen' LUTECE \n","198668 Orme Ulmus NaN Nanguen' LUTECE \n","198680 Orme Ulmus NaN Nanguen' LUTECE \n","198681 Orme Ulmus NaN Nanguen' LUTECE \n","\n"," circonference_cm hauteur_m stade_developpement remarquable \\\n","1725 40.00 4.00 NaN NaN \n","2323 25.00 3.00 JA 0.00 \n","2733 32.00 NaN NaN NaN \n","2742 60.00 NaN NaN NaN \n","3944 40.00 4.00 NaN NaN \n","... ... ... ... ... \n","198666 30.00 5.00 NaN 0.00 \n","198667 20.00 5.00 NaN 0.00 \n","198668 20.00 5.00 NaN 0.00 \n","198680 20.00 5.00 NaN 0.00 \n","198681 35.00 7.00 NaN 0.00 \n","\n"," geo_point_2d_a geo_point_2d_b \n","1725 48.88 2.39 \n","2323 48.87 2.28 \n","2733 48.89 2.32 \n","2742 48.89 2.32 \n","3944 48.83 2.35 \n","... ... ... \n","198666 48.87 2.38 \n","198667 48.87 2.38 \n","198668 48.87 2.38 \n","198680 48.87 2.38 \n","198681 48.87 2.38 \n","\n","[1389 rows x 18 columns]"],"text/html":["\n","
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idtype_emplacementdomanialitearrondissementcomplement_addressenumerolieuid_emplacementlibelle_francaisgenreespecevarietecirconference_cmhauteur_mstade_developpementremarquablegeo_point_2d_ageo_point_2d_b
1725101858ArbreJardinPARIS 19E ARRDTNaNNaNPARC DES BUTTES CHAUMONTD0030002Pommier à fleursMalusNaNAldenhamensis'40.004.00NaNNaN48.882.39
2323102582ArbreJardinPARIS 16E ARRDT82 à 90NaNJARDIN DE L AVENUE FOCH / 10 AVENUE FOCH408032CornouillerCornusNaNEddie's White Wonder'25.003.00JA0.0048.872.28
2733103215ArbreJardinPARIS 17E ARRDTNaNNaNPARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING000B0167Cerisier à fleursPrunusNaNAccolade'32.00NaNNaNNaN48.892.32
2742103224ArbreJardinPARIS 17E ARRDTNaNNaNPARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING000B0188Cerisier à fleursPrunusNaNAccolade'60.00NaNNaNNaN48.892.32
3944104827ArbreJardinPARIS 13E ARRDTNaNNaNSQUARE RENE LE GALL10255Pommier à fleursMalusNaNGolden Hornet'40.004.00NaNNaN48.832.35
.........................................................
1986662022738ArbreAlignementPARIS 11E ARRDTNaNNaNPLACE JEAN FERRAT301001OrmeUlmusNaNNanguen' LUTECE30.005.00NaN0.0048.872.38
1986672022739ArbreAlignementPARIS 11E ARRDTNaNNaNPLACE JEAN FERRAT301002OrmeUlmusNaNNanguen' LUTECE20.005.00NaN0.0048.872.38
1986682022740ArbreAlignementPARIS 11E ARRDTNaNNaNPLACE JEAN FERRAT301003OrmeUlmusNaNNanguen' LUTECE20.005.00NaN0.0048.872.38
1986802022752ArbreAlignementPARIS 11E ARRDTNaNNaNPLACE JEAN FERRAT105001OrmeUlmusNaNNanguen' LUTECE20.005.00NaN0.0048.872.38
1986812022754ArbreAlignementPARIS 11E ARRDTNaNNaNBOULEVARD DE BELLEVILLE101001OrmeUlmusNaNNanguen' LUTECE35.007.00NaN0.0048.872.38
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1389 rows × 18 columns

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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"variete_not_nan_rows","repr_error":"0"}},"metadata":{},"execution_count":72}]},{"cell_type":"markdown","metadata":{"id":"32tkfElqDL3G"},"source":["### Before and After cleaning anomalies"],"id":"32tkfElqDL3G"},{"cell_type":"code","source":["import seaborn as sns\n","\n","\n","# Calculate the number of non-missing values for each column\n","non_missing_original = df.notnull().sum()\n","non_missing_refined = df_refined.notnull().sum()\n","\n","# Prepare data for the bar plot\n","comparison_data = pd.DataFrame({\n"," 'Columns': non_missing_original.index,\n"," 'Original Dataset': non_missing_original.values,\n"," 'Corrected Dataset': non_missing_refined.values\n","}).melt(id_vars='Columns', var_name='Dataset', value_name='Non-Missing Count')\n","\n","\n","# Create the bar plot\n","plt.figure(figsize=(12, 6))\n","ax = sns.barplot(\n"," data=comparison_data,\n"," x='Columns',\n"," y='Non-Missing Count',\n"," hue='Dataset',\n"," palette='pastel'\n",")\n","# Rotate the x-axis labels\n","plt.xticks(rotation=45, ha='right')\n","\n","# Add count annotations to each bar (inside the bars)\n","for p in ax.patches:\n"," height = p.get_height() # Bar height (value of the bar)\n"," if height > 0: # Only annotate bars with positive height\n"," ax.annotate(\n"," f'{int(height)}', # Display the count as an integer\n"," (p.get_x() + p.get_width() / 2., height / 2), # Position inside the bar, vertically centered\n"," ha='center', va='center', fontsize=10, color='black', rotation=90\n"," )\n","\n","# Add plot titles and labels\n","plt.title('Comparison of Non-Missing Values by Column', fontsize=14)\n","plt.ylabel('Number of Non-Missing Values', fontsize=12)\n","plt.xlabel('Columns', fontsize=12)\n","plt.legend(title='Dataset', loc='upper right')\n","plt.grid(axis='y', linestyle='--', alpha=0.7)\n","plt.tight_layout() # Adjust layout to prevent overlap\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":573},"id":"1PMO745VZod2","executionInfo":{"status":"ok","timestamp":1737062662168,"user_tz":-60,"elapsed":1591,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"1b7e704a-78d0-43e6-9d0c-b47209c7d69e"},"execution_count":73,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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Create a DataFrame for plotting\n","diff_df = data_diff = (non_missing_refined - non_missing_original).reset_index()\n","diff_df.columns = ['Columns', 'Difference']\n","\n","# Plot the data using Seaborn\n","plt.figure(figsize=(10, 6))\n","ax = sns.barplot(\n"," data=diff_df,\n"," x=diff_df.columns[0],\n"," y=diff_df.columns[1],\n"," palette=[\"blue\" if x > 0 else \"red\" for x in diff_df['Difference']] # Blue for positive, Red for negative\n",")\n","\n","# Set the y-axis to be centered at 0\n","ax.axhline(0, color='black',linewidth=1)\n","\n","# Add plot titles and labels\n","plt.title('Differences Between Original and Corrected Datasets counts', fontsize=14)\n","plt.ylabel('Difference (Original - Corrected)', fontsize=12)\n","plt.xlabel('Columns', fontsize=12)\n","\n","# Rotate the x-axis labels\n","plt.xticks(rotation=45, ha='right')\n","\n","# Display the values inside the bars\n","for p in ax.patches:\n"," height = p.get_height()\n"," ax.text(\n"," p.get_x() + p.get_width() / 2, # Position at the center of the bar\n"," height/2, # Position at the top of the bar\n"," f'{int(height)}', # Annotate with the number\n"," ha='center', va='center', fontsize=10, color='k', rotation=0\n"," )\n","\n","# Show the plot\n","plt.tight_layout() # Adjust the layout to avoid overlap\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":717},"id":"5D75sJRqs4vW","executionInfo":{"status":"ok","timestamp":1737062662968,"user_tz":-60,"elapsed":815,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"8573649c-9423-4cd1-ea41-888d00186431"},"execution_count":74,"outputs":[{"output_type":"stream","name":"stderr","text":[":7: FutureWarning: \n","\n","Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n","\n"," ax = sns.barplot(\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"id":"5D75sJRqs4vW"},{"cell_type":"code","execution_count":75,"metadata":{"executionInfo":{"elapsed":910,"status":"ok","timestamp":1737062663874,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"8Tx4l94HDGb2","colab":{"base_uri":"https://localhost:8080/","height":849},"outputId":"508580f5-c834-4ec2-9e37-ebc54d7e8d0d"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["
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s+mWW26pxKuC2oCRGdQq/fv3l6+vrwYPHqxx48bp7NmzevPNN9W0adNSIy6PPvqoHn/8ccXHx6tfv37at2+fNm7cWGpoukOHDvLy8tKrr74qu90uPz8/x3ls5s+fr4ceeki33XabRowYoeDgYGVkZGj9+vXq3r2749wro0eP1muvvaa4uDiNGTNGx44d04IFC3TLLbc4TV52Z59//rny8vJUVFSkkydPauvWrfroo49ks9m0du3aCg83vP3225o3b56GDh2q1q1b68yZM3rzzTcVGBjo+PAPCAjQzTffrHfeeUdt2rRRUFCQ2rVrV+Hhq4rccMMN6tGjh5544gnl5+dr7ty5aty4sZ5//nlHn8q+L1WprX379ho5cqQWLVqknJwc9e7dWzt37tTbb7+te+65R3feeadLv09ZhgwZomnTpik3N1eBgYFO2y5cuKAVK1ZIunhI9KefftKCBQtUXFyspKQkR7/Jkyfr//7v/zRgwAA9/fTTCgoK0ttvv6309HS9//778vT0lGEYGj16tAICAhzL1MeNG6f3339fzzzzjPr27avmzZs79tmmTRuNGTNGu3btUkhIiBYvXqzs7Owyv1SU8PHx0auvvqpHHnlEvXv31gMPPKDs7Gz9+c9/VsuWLTVp0iRH306dOkm6eFbquLg4eXl5OS1737RpkwYPHsycmbrEpFVUQJWULH89fvy4U/vlS4ANwzA++ugjIzo62vD39zdatmxpvPrqq8bixYtL9SsqKjJeeOEFo0mTJsZ1111nxMXFGYcOHSq1NNswDOPNN980IiMjDS8vr1LLmTdv3mzExcUZNpvN8Pf3N1q3bm2MGjXK2L17t9M+VqxYYURGRhq+vr5Ghw4djI0bN5a7rPiPf/yjS69TRESEMWjQoFLtkowJEyY4tVX2uUqW0JbcfHx8jODgYKNXr17GrFmzylxCfvn78tVXXxkPPPCAER4ebvj5+RlNmzY17r777lKv0bZt24xOnToZvr6+TkuhR44cadSrV6/M+ip6DWfPnm2EhYUZfn5+Rs+ePY19+/aVenxl3peKart8abZhGEZhYaExc+ZMo1WrVoaPj48RFhZmTJkyxcjLy3PqV977Vd6S8ctlZ2cb3t7exvLly0u9JrpsWXZgYKBx1113GZ9++mmp/fzwww/GsGHDjIYNGxr+/v5Gly5djI8//tix/c9//nOp5daGYRgZGRlGYGCgMXDgwFK/08aNG43o6GjDz8/PiIqKKrW0//Kl2SXeeecdo2PHjoafn58RFBRkJCQkOC2xNwzDuHDhgvHUU08ZwcHBhoeHh9Pr/+9//9uQVObvidrLwzA4RSIAWNWYMWP0/fff6/PPPze7FEkX58S0a9dOH3/8sSnPP3HiRKWlpWnPnj2MzNQhzJkBAAtLSkrSrl27LD//qjqcPHlSf/nLX/TKK68QZOoY5swAFnD8+PEKlwv7+vpW6ZIHqD3Cw8OVl5dndhluoXHjxk4rtVB3EGYAC7j99tsrXMbdu3dvp4tfAkBdwpwZwAK2bt2q8+fPl7u9UaNGjhUeAFDXEGYAAIClMQEYAABYWq2fM1NcXKzMzEw1aNCA2e0AAFiEYRg6c+aMmjdvfsXrstX6MJOZmVnqasEAAMAajhw5ohYtWlTYp9aHmQYNGki6+GJcfrpvAADgnnJzcxUWFub4HK9IrQ8zJYeWAgMDCTMAAFhMZaaIMAEYAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYWq0/aR6A2qmoqEj79+/XqVOnFBQUpOjoaHl5eZldFgATEGYAWE5aWprmzZunrKwsR1toaKjGjx+vXr16mVgZADNwmAmApaSlpSkpKUmRkZFKSUnRhg0blJKSosjISCUlJSktLc3sEgHUMA/DMAyzi7iWcnNzZbPZZLfbuTYTYHFFRUVKSEhQZGSkXnnlFXl6/vf7WHFxsaZPn6709HStWLGCQ06AxVXl85uRGQCWsX//fmVlZSkhIcEpyEiSp6enEhISdPToUe3fv9+kCgGYgTADwDJOnTolSWrVqlWZ20vaS/oBqBsIMwAsIygoSJKUnp5e5vaS9pJ+AOoGwgwAy4iOjlZoaKhWrlyp4uJip23FxcVauXKlmjVrpujoaJMqBGAGwgwAy/Dy8tL48eO1fft2TZ8+Xf/617/0yy+/6F//+pemT5+u7du364knnmDyL1DHsJoJgOWUdZ6ZZs2a6YknnuA8M0AtUZXPb8IMAEviDMBA7VaVz2/OAAzAkry8vNSxY0ezywDgBkydM/PSSy/Jw8PD6RYVFeXYnpeXpwkTJqhx48aqX7++4uPjlZ2dbWLFAADA3Zg+AfiWW27R0aNHHbcvvvjCsW3SpElat26d1qxZo88++0yZmZm69957TawWAAC4G9MPM3l7eys0NLRUu91u11tvvaVVq1apT58+kqQlS5aobdu22rFjh7p161bm/vLz85Wfn++4n5ube20KBwAAbsH0kZmDBw+qefPmioyMVEJCgjIyMiRJe/bsUWFhofr27evoGxUVpfDwcG3fvr3c/SUnJ8tmszluYWFh1/x3AAAA5jE1zHTt2lVLly7VJ598ovnz5ys9PV09e/bUmTNnlJWVJV9fXzVs2NDpMSEhIU7LMS83ZcoU2e12x+3IkSPX+LcAAABmMvUw04ABAxw/R0dHq2vXroqIiNC7776rgIAAl/bp5+cnPz+/6ioRAAC4OdMPM12qYcOGatOmjQ4dOqTQ0FAVFBQoJyfHqU92dnaZc2wAAEDd5FZh5uzZs/rhhx/UrFkzderUST4+PkpNTXVsP3DggDIyMhQTE2NilQAAwJ2Yepjp2Wef1eDBgxUREaHMzEwlJSXJy8tLDzzwgGw2m8aMGaPExEQFBQUpMDBQTz31lGJiYspdyQQAAOoeU8PMzz//rAceeEAnT55UcHCwevTooR07dig4OFiSNGfOHHl6eio+Pl75+fmKi4vTvHnzzCwZAAC4Ga7NBAAA3E5VPr/das4MAABAVRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApRFmAACApXmbXQAAuKKoqEj79+/XqVOnFBQUpOjoaHl5eZldFgATEGYAWE5aWprmzZunrKwsR1toaKjGjx+vXr16mVgZADNwmAmApaSlpSkpKUmRkZFKSUnRhg0blJKSosjISCUlJSktLc3sEgHUMA/DMAyzi7iWcnNzZbPZZLfbFRgYaHY5AK5CUVGREhISFBkZqVdeeUWenv/9PlZcXKzp06crPT1dK1as4JATYHFV+fxmZAaAZezfv19ZWVlKSEhwCjKS5OnpqYSEBB09elT79+83qUIAZiDMALCMU6dOSZJatWpV5vaS9pJ+AOoGwgwAywgKCpIkpaenl7m9pL2kH4C6gTADwDKio6MVGhqqlStX6ty5c5o7d66effZZzZ07V+fOndPKlSvVrFkzRUdHm10qgBrEBGAAlpKWlqYZM2aUu/3ll19meTZQCzABGECttXHjxqvaDqD24aR5ACzj/Pnz2rp1q3x8fPTRRx/pu+++c5wBOCoqSr/61a+0detWnT9/XgEBAWaXC6CGMDIDwDIWLlwoSRo+fLgCAgLUsWNH3XXXXerYsaMCAgI0bNgwp34A6gbCDADL+PnnnyVJAwcOLHN7SXtJPwB1A2EGgGW0aNFCkrRhw4Yyt5e0l/QDUDcQZgBYxrhx4yRJa9asUUFBgdO2goICvffee079ANQNhBkAlhEQEKDu3bursLBQgwYN0sKFC3XkyBEtXLhQgwYNUmFhobp3787kX6CO4TwzACxn2rRp2rp1a6n27t27a9asWSZUBKC6VeXzmzADwJLOnz+vhQsX6ueff1aLFi00btw4RmSAWoQwcwnCDAAA1sMZgAEAQJ1BmAFgSTt37lRsbKzjtnPnTrNLAmAStwozv//97+Xh4aGJEyc62vLy8jRhwgQ1btxY9evXV3x8vLKzs80rEoDpYmNj9fzzzzu1Pf/884qNjTWnIACmcpsws2vXLi1cuFDR0dFO7ZMmTdK6deu0Zs0affbZZ8rMzNS9995rUpUAzHZ5YImJialwO4Dazy0uNHn27FklJCTozTff1CuvvOJot9vteuutt7Rq1Sr16dNHkrRkyRK1bdtWO3bsULdu3cwqGYAJLj2U9Prrrzt9+dm/f7+efvppR78uXbrUeH0AzOEWIzMTJkzQoEGD1LdvX6f2PXv2qLCw0Kk9KipK4eHh2r59e5n7ys/PV25urtMNQO1w6aGly0dxL71/+SEoALWb6WFm9erV+uqrr5ScnFxqW1ZWlnx9fdWwYUOn9pCQEGVlZZW5v+TkZNlsNsctLCzsWpQNwESXH1oq0blz5xquBIA7MDXMHDlyRM8884xWrlwpf3//atnnlClTZLfbHbcjR45Uy34BuI/yRmZ3795dw5UAcAemhpk9e/bo2LFjuu222+Tt7S1vb2999tlnev311+Xt7a2QkBAVFBQoJyfH6XHZ2dkKDQ0tc59+fn4KDAx0ugGoHf7whz84ft6/f7/TtkvvX9oPQO1n6gTgu+66S//85z+d2h555BFFRUXphRdeUFhYmHx8fJSamqr4+HhJ0oEDB5SRkVHuMDOA2uvSSb0lk307d+5cakSGyb9A3WJqmGnQoIHatWvn1FavXj01btzY0T5mzBglJiYqKChIgYGBeuqppxQTE8NKJqCO2rJli9Py68uDzJYtW2q2IACmc4ul2RWZM2eOPD09FR8fr/z8fMXFxWnevHlmlwXARFu2bNHOnTudVi394Q9/YEQGqKO40CQAAHA7XGgSAADUGW5/mAkAylJUVKT9+/fr1KlTCgoKUnR0tLy8vMwuC4AJCDMALCctLU3z5s1zOnlmaGioxo8fr169eplYGQAzcJgJgKWkpaUpKSlJkZGRSklJ0YYNG5SSkqLIyEglJSUpLS3N7BIB1DAmAAOwjKKiIiUkJCgyMlKvvPKKPD3/+32suLhY06dPV3p6ulasWMEhJ8DimAAMoFbav3+/srKylJCQ4BRkJMnT01MJCQk6evRoqbMDA6jdCDMALOPUqVOSpFatWpW5vaS9pB+AuoEJwAAsIygoSJKUnp6uG2+8UR9++KEyMzPVvHlzDRkyROnp6U79ANQNzJkBYBklc2Y8PT2VnZ2toqIixzYvLy+FhITIMAzmzAC1AHNmANRKXl5eat26tTIzM+Xp6alf//rXWrFihX7961/L09NTmZmZioyMJMgAdQwjMwAso6CgQAMGDJC/v7/q1aunY8eOObaFhITo7NmzysvL09/+9jf5+vqaWCmAq1WVz2/mzACwjA8//FBFRUV64oknNGDAgFJnAN6wYYNmz56tDz/8UMOHDze7XAA1hDADwDIyMzMlSTExMfLy8lLHjh2dtsfExDj1A1A3MGcGgGU0b95ckrR9+/Yyt5e0l/QDUDcQZgBYxpAhQ+Tl5aW33npLFy5ccNp24cIFLV68WF5eXhoyZIhJFQIwA2EGgGX4+vpq+PDhOn36tIYPH65169bpxIkTWrdunVM7k3+BuoU5MwAs5fHHH5ckrVmzRrNnz3a0e3l5acSIEY7tAOoOlmYDsKSCgoJSZwBmRAaoPViaDaDWKznkBADMmQEAAJZGmAFgSdu2bVNsbKzjtm3bNrNLAmAS5swAsJzY2Nhyt23ZsqXG6gBw7XChSQC11uVBplOnThVuB1D7MQEYgGVceijptdde02233ea4/9VXXykxMdHR74477qjx+gCYg5EZAJYxdepUx8+XBpnL71/aD0DtR5gBYDmXH1oqER0dXcOVAHAHhBkAlrNnz54y2/fv31/DlQBwB4QZAJbxu9/9zvFzamqqhg4dqn79+mno0KFKTU0tsx+A2o+l2QAspTKrlVieDVgfS7MB1Fr+/v5XtR1A7UOYAWAZWVlZysvLkyRNnjzZaVvJ/by8PGVlZdV4bQDMw2EmAJYxdOhQnT59WqGhoVq9enWp7SNGjFBWVpYaNWqktWvXmlAhgOrCYSYAtdLZs2clSePGjStz+5gxY5z6AagbCDMALKN+/fqSpIULF8put+vJJ5/U8OHD9eSTT8put+utt95y6gegbuAwEwDLyMrK0ogRI67Yb/Xq1QoNDa2BigBcKxxmAlArXR5QfHx8NHLkSPn4+FTYD0DtRpgBYBl2u93pfmFhod5++20VFhZW2A9A7UaYAWAZ06ZNkyR16dJFq1evVqNGjeTj46NGjRpp9erVuv322536AagbvM0uAAAqKzs7W5L08MMPKzQ0tNTy64ceeki7du1y9ANQNzAyA8AyQkJCJEnLli0rc/vy5cud+gGoGwgzACxj1qxZkqSdO3fqwIED6tevn2JjY9WvXz8dOHBAu3btcuoHoG4gzACwDJvNpuuvv17SxRPnlUz8LSwsdJxI7/rrr5fNZjOtRgA1jzADwFKOHj16VdsB1D6EGQCWkZGRoeLiYknS4sWL1a5dOwUHB6tdu3ZavHixJKm4uFgZGRlmlgmghnEGYACW0a9fPxUWFuq6667Thg0bSm0fOHCgfvnlF/n4+GjTpk0mVAigunAGYAC1UskcmZILSl7u4YcfduoHoG7gPDMALMPHx0eFhYV66623dOutt2rs2LGObYsWLXIs2b788gYAajcOMwGwjIyMDMfoS0WWLVum8PDwGqgIwLXCYSYAtVJZAeXWW2+tVD8AtRdhBoBlfP/996Xa/vnPf1aqH4DaizADwDJK5sh4enpq2bJljrkxPj4+WrZsmTw9PZ36AagbmAAMwHLuu+8+hYeHl1p+PXToUL3//vsmVQXALIzMALCcd999t8z2y6+iDaBucGlk5uTJk5oxY4Y2b96sY8eOOc7IWeLUqVPVUhwAXGrRokUaO3asiouLFRsbW2E/AHWHS2HmoYce0qFDhzRmzBiFhITIw8PDpSefP3++5s+fr59++kmSdMstt2jGjBkaMGCAJCkvL0+/+c1vtHr1auXn5ysuLk7z5s1TSEiIS88HwNratGlTrf0A1A4uhZnPP/9cX3zxhdq3b39VT96iRQv9/ve/14033ijDMPT2229ryJAh2rt3r2655RZNmjRJ69ev15o1a2Sz2fTkk0/q3nvv1datW6/qeQEAQO3h0knzbr/9dr3xxhvq1q1btRcUFBSkP/7xjxo2bJiCg4O1atUqDRs2TJL03XffqW3bttq+fXuln5uT5gG1R0WHli63ZcuWa1YHgGuvKp/fLo3MzJs3T5MnT9aMGTPUrl27UqcOdyU0FBUVac2aNTp37pxiYmK0Z88eFRYWqm/fvo4+UVFRCg8PrzDM5OfnKz8/33E/Nze3yrUAcH9lhZWqhB0AtYdLYaZhw4bKzc1Vnz59nNoNw5CHh4eKiooqva9//vOfiomJUV5enurXr6+1a9fq5ptv1tdffy1fX181bNjQqX9ISIiysrLK3V9ycrJmzpxZpd8HAABYl0thJiEhQT4+Plq1atVVTQCWpJtuuklff/217Ha73nvvPY0cOVKfffaZy/ubMmWKEhMTHfdzc3MVFhbm8v4AuKdp06Y5zZ/r3r27idUAMJNLYeabb77R3r17ddNNN111Ab6+vrrhhhskSZ06ddKuXbv05z//Wffff78KCgqUk5PjNDqTnZ2t0NDQcvfn5+cnPz+/q64LgHu7fCEACwOAusulk+Z17txZR44cqe5aJEnFxcXKz89Xp06d5OPjo9TUVMe2AwcOKCMjQzExMdfkuQG4t8pO6mXyL1C3uDQy89RTT+mZZ57Rc889p1tvvbXUBODo6OhK7WfKlCkaMGCAwsPDdebMGa1atUpbtmzRxo0bZbPZNGbMGCUmJiooKEiBgYF66qmnFBMTc01WUQFwf9OmTat0v1mzZl3jagC4C5fCzP333y9JGj16tKPNw8OjyhOAjx07pocfflhHjx6VzWZTdHS0Nm7cqH79+kmS5syZI09PT8XHxzudNA9A3VTZQ0kccgLqFpfOM3P48OEKt0dERLhcUHXjPDNA7XHp0usrLc3mUBNgbdf8PDOVDSuDBg3SX/7yFzVr1syVpwEAALgil8JMZaWlpen8+fPX8ikA1CHdu3d3HEKq6AR5LNMG6haXVjMBgBkqO6mXyb9A3UKYAQAAlkaYAWAZlb32EtdoAuqWazpnBgCuFS40CaAEIzMAAMDSqjwyU1hYqHHjxunFF19Uq1atKuw7depUBQUFuVwcAJSHURgAJao8MuPj46P333+/Un2nTJnidJFIAACA6ubSYaZ77rlHH3zwQTWXAgAV40KTAMri0gTgG2+8US+//LK2bt2qTp06qV69ek7bn3766WopDgAuVZXVTAQaoO5w6dpMFc2V8fDw0I8//nhVRVUnrs0E1B5VmSdDmAGs7Zpfmyk9Pd2lwgCgurA0G0AJlmYDAABLc2lkZvTo0RVuX7x4sUvFAEBlMQoDoIRLYeb06dNO9wsLC/XNN98oJydHffr0qZbCAOByW7ZsqVSIYb4MULe4FGbWrl1bqq24uFhPPPGEWrdufdVFAQAAVFa1zZnx9PRUYmKi5syZU127BAAnXGgSQFmq9UKTP/zwgy5cuFCduwSAMrGaCUAJl8JMYmKi033DMHT06FGtX79eI0eOrJbCAKAiRUVF2r9/v06dOqWgoCBFR0ebXRIAk7gUZvbu3et039PTU8HBwZo9e/YVVzoBQHW46667zC4BgJtwKcxs3ry5uusAAABwicsTgC9cuKBPP/1UCxcu1JkzZyRJmZmZOnv2bLUVBwCXSk1NrdZ+AGoHl0ZmDh8+rP/5n/9RRkaG8vPz1a9fPzVo0ECvvvqq8vPztWDBguquEwAqfWjprrvu4lwzQB3i0sjMM888o86dO+v06dMKCAhwtA8dOpRvRAAAoEa5NDLz+eefa9u2bfL19XVqb9mypf7zn/9US2EAUBGWZgMo4dLITHFxsYqKikq1//zzz2rQoMFVFwUAV1JcXFzhfQB1h0sjM/3799fcuXO1aNEiSZKHh4fOnj2rpKQkDRw4sFoLBICycB04ACU8DMMwqvqgn3/+WXFxcTIMQwcPHlTnzp118OBBNWnSRGlpaWratOm1qNUlubm5stlsstvtCgwMNLscAFeJC00CdUNVPr9dGplp0aKF9u3bp3feeUf79u3T2bNnNWbMGCUkJDhNCAYAALjWXBqZSUtL0x133CFvb+csdOHCBW3btk29evWqtgKvFiMzQO1RlQm+jM4A1nbNR2buvPNOHT16tNThJLvdrjvvvLPMycEAUJ1YzQSghEurmQzDkIeHR6n2kydPql69elddFAAAQGVVaWTm3nvvlXRx9dKoUaPk5+fn2FZyBds77rijeisEgDIwCgOgRJXCjM1mk3RxZKZBgwZOk319fX3VrVs3PfbYY9VbIQAAQAWqFGaWLFki6eKZfp999lkOKQGoUVu2bGFpNoBSXFrNZCWsZgJqD1YzAXXHNV/N1KpVqzInAJf48ccfXdktAABAlbkUZiZOnOh0v7CwUHv37tUnn3yi5557rjrqAoAKsTQbQAmXwswzzzxTZntKSop27959VQUBAABUhUvnmSnPgAED9P7771fnLgGgTJePwjAqA9RdLo3MlOe9995TUFBQde4SABwuX81UXoBh8i9Qt7gUZjp27Og0AdgwDGVlZen48eOaN29etRUHAJe70vJsggxQ97gUZu655x6n+56engoODlZsbKyioqKqoy4AKFd5gYYgA9RNnGcGqKK8vDxlZGSYXQbglsLDw+Xv7292GagFrvl5Zi6Vl5engoICpzZCA2qzjIwMjR071uwyALe0aNEitWnTxuwyUMe4FGbOnTunF154Qe+++65OnjxZantRUdFVFwa4q/DwcC1atMjsMiDp8OHDmjVrlqZNm6aIiAizy4Eu/v8B1DSXwszzzz+vzZs3a/78+XrooYeUkpKi//znP1q4cKF+//vfV3eNgFvx9/fnm6ebiYiI4D0B6jCXwsy6deu0bNkyxcbG6pFHHlHPnj11ww03KCIiQitXrlRCQkJ11wkAAFAml06ad+rUKUVGRkq6OD/m1KlTkqQePXooLS2t+qoDAAC4ApfCTGRkpNLT0yVJUVFRevfddyVdHLFp2LBhtRUHAABwJS6FmUceeUT79u2TJE2ePFkpKSny9/fXpEmTuNAkAACoUS7NmZk0aZLj5759++q7777Tnj17dMMNNyg6OrraigMAALgSl88zk5qaqtTUVB07dkzFxcVO2xYvXnzVhQEAAFSGS4eZZs6cqf79+ys1NVUnTpzQ6dOnnW6VlZycrNtvv10NGjRQ06ZNdc899+jAgQNOffLy8jRhwgQ1btxY9evXV3x8vLKzs10pGwAA1EIujcwsWLBAS5cu1UMPPXRVT/7ZZ59pwoQJuv3223XhwgVNnTpV/fv317fffqt69epJunhIa/369VqzZo1sNpuefPJJ3Xvvvdq6detVPTcAAKgdXAozBQUFuuOOO676yT/55BOn+0uXLlXTpk21Z88e9erVS3a7XW+99ZZWrVqlPn36SJKWLFmitm3baseOHerWrdtV1wAAAKzNpcNMjz76qFatWlXdtchut0uSgoKCJEl79uxRYWGh+vbt6+gTFRWl8PBwbd++vcx95OfnKzc31+kGAABqr0qPzCQmJjp+Li4u1qJFi/Tpp58qOjpaPj4+Tn1fe+21KhdSXFysiRMnqnv37mrXrp0kKSsrS76+vqXOXRMSEqKsrKwy95OcnKyZM2dW+fkBAIA1VTrM7N271+l+hw4dJEnffPONU7uHh4dLhUyYMEHffPONvvjiC5ceX2LKlClOwSs3N1dhYWFXtU8AAOC+Kh1mNm/efM2KePLJJ/Xxxx8rLS1NLVq0cLSHhoaqoKBAOTk5TqMz2dnZCg0NLXNffn5+8vPzu2a1AgAA9+LSnJnqYhiGnnzySa1du1b/+Mc/1KpVK6ftnTp1ko+Pj1JTUx1tBw4cUEZGhmJiYmq6XAAA4IZcPmledZgwYYJWrVqlDz/8UA0aNHDMg7HZbAoICJDNZtOYMWOUmJiooKAgBQYG6qmnnlJMTAwrmQAAgCSTw8z8+fMlSbGxsU7tS5Ys0ahRoyRJc+bMkaenp+Lj45Wfn6+4uDjNmzevhisFAADuytQwYxjGFfv4+/srJSVFKSkpNVARAACwGlPnzAAAAFwtwgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0wgwAALA0b7MLQOVlZ2fLbrebXQbgNg4fPuz0XwAX2Ww2hYSEmF1GjfEwDMMwu4hrKTc3VzabTXa7XYGBgWaX47Ls7Gw9+NDDKizIN7sUAICb8/H104rlyywdaKry+c3IjEXY7XYVFuTrfGRvFfvbzC4HAOCmPPPs0o+fyW63WzrMVAVhxmKK/W0qrtfE7DIAAHAbTAAGAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWRpgBAACWZmqYSUtL0+DBg9W8eXN5eHjogw8+cNpuGIZmzJihZs2aKSAgQH379tXBgwfNKRYAALglU8PMuXPn1L59e6WkpJS5/Q9/+INef/11LViwQF9++aXq1aunuLg45eXl1XClAADAXZl60rwBAwZowIABZW4zDENz587V9OnTNWTIEEnSsmUXT838wQcfaMSIETVZKgAAcFNuO2cmPT1dWVlZ6tu3r6PNZrOpa9eu2r59e7mPy8/PV25urtMNAADUXm4bZrKysiSp1HUlQkJCHNvKkpycLJvN5riFhYVd0zoBAIC53DbMuGrKlCmy2+2O25EjR8wuCQAAXENuG2ZCQ0MlSdnZ2U7t2dnZjm1l8fPzU2BgoNMNAADUXm4bZlq1aqXQ0FClpqY62nJzc/Xll18qJibGxMoAAIA7MXU109mzZ3Xo0CHH/fT0dH399dcKCgpSeHi4Jk6cqFdeeUU33nijWrVqpRdffFHNmzfXPffcY17RAADArZgaZnbv3q0777zTcT8xMVGSNHLkSC1dulTPP/+8zp07p7FjxyonJ0c9evTQJ598In9/f7NKBgAAbsbUMBMbGyvDMMrd7uHhoZdfflkvv/xyDVbl3jzP55hdAgDAjdXFzwlTwwyqLiA9zewSAABwK4QZiznfqpeKAxqaXQYAwE15ns+pc198CTMWUxzQUMX1mphdBgAAbsNtl2YDAABUBmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYGmEGAABYmrfZBaBqPPPsZpcAAHBjdfFzgjBjETabTT6+ftKPn5ldCgDAzfn4+slms5ldRo0hzFhESEiIVixfJru97iVuoDyHDx/WrFmzNG3aNEVERJhdDuA2bDabQkJCzC6jxhBmLCQkJKRO/XEClRUREaE2bdqYXQYAkzABGAAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWBphBgAAWJolwkxKSopatmwpf39/de3aVTt37jS7JAAA4CbcPsy88847SkxMVFJSkr766iu1b99ecXFxOnbsmNmlAQAAN+BtdgFX8tprr+mxxx7TI488IklasGCB1q9fr8WLF2vy5Mml+ufn5ys/P99xPzc3t8ZqRd2Ql5enjIwMs8uApMOHDzv9F+YLDw+Xv7+/2WWgjnHrMFNQUKA9e/ZoypQpjjZPT0/17dtX27dvL/MxycnJmjlzZk2ViDooIyNDY8eONbsMXGLWrFlml4D/b9GiRWrTpo3ZZaCOceswc+LECRUVFSkkJMSpPSQkRN99912Zj5kyZYoSExMd93NzcxUWFnZN60TdEh4erkWLFpldBuCWwsPDzS4BdZBbhxlX+Pn5yc/Pz+wyUIv5+/vzzRMA3IhbTwBu0qSJvLy8lJ2d7dSenZ2t0NBQk6oCAADuxK3DjK+vrzp16qTU1FRHW3FxsVJTUxUTE2NiZQAAwF24/WGmxMREjRw5Up07d1aXLl00d+5cnTt3zrG6CQAA1G1uH2buv/9+HT9+XDNmzFBWVpY6dOigTz75pNSkYAAAUDd5GIZhmF3EtZSbmyubzSa73a7AwECzywEAAJVQlc9vt54zAwAAcCWEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGmEGQAAYGlufwbgq1VyTsDc3FyTKwEAAJVV8rldmXP71vowc+bMGUlSWFiYyZUAAICqOnPmjGw2W4V9av3lDIqLi5WZmakGDRrIw8PD7HIAVKPc3FyFhYXpyJEjXK4EqGUMw9CZM2fUvHlzeXpWPCum1ocZALUX114DIDEBGAAAWBxhBgAAWBphBoBl+fn5KSkpSX5+fmaXAsBEzJkBAACWxsgMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwNMIMAACwtP8HdsJsliTghhAAAAAASUVORK5CYII=\n"},"metadata":{}}],"source":["# boxplots\n","for variable in quantitative_variables:\n"," plot_univariate_variable_analysis(df_refined, variable, 'boxplot')"],"id":"8Tx4l94HDGb2"},{"cell_type":"markdown","id":"V7hc3wnUW7gq","metadata":{"id":"V7hc3wnUW7gq"},"source":["## 4) Analysis"]},{"cell_type":"markdown","id":"uZfu0OOUYRI3","metadata":{"id":"uZfu0OOUYRI3"},"source":["### Naive analysis (after data clean)"]},{"cell_type":"code","execution_count":76,"id":"2SUutH-hYK1E","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1737062663874,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"2SUutH-hYK1E","outputId":"6c8e2042-bb7c-4da3-e17f-049f01c6632b","scrolled":true},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","RangeIndex: 200137 entries, 0 to 200136\n","Data columns (total 18 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 id 200137 non-null int64 \n"," 1 type_emplacement 200137 non-null object \n"," 2 domanialite 200137 non-null object \n"," 3 arrondissement 200137 non-null object \n"," 4 complement_addresse 30902 non-null object \n"," 5 numero 0 non-null float64\n"," 6 lieu 200137 non-null object \n"," 7 id_emplacement 200137 non-null object \n"," 8 libelle_francais 199975 non-null object \n"," 9 genre 200121 non-null object \n"," 10 espece 198385 non-null object \n"," 11 variete 36777 non-null object \n"," 12 circonference_cm 199514 non-null float64\n"," 13 hauteur_m 186181 non-null float64\n"," 14 stade_developpement 132932 non-null object \n"," 15 remarquable 137039 non-null float64\n"," 16 geo_point_2d_a 200137 non-null float64\n"," 17 geo_point_2d_b 200137 non-null float64\n","dtypes: float64(6), int64(1), object(11)\n","memory usage: 27.5+ MB\n"]}],"source":["df_refined.info() # display count of columns, lines and columns names"]},{"cell_type":"code","execution_count":77,"id":"X0QJb_HCYK1E","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1737062663874,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"X0QJb_HCYK1E","outputId":"a67442cd-a0c3-4598-d61f-53d7baae71c9","scrolled":true},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" id numero circonference_cm hauteur_m remarquable \\\n","count 200137.00 0.00 199514.00 186181.00 137039.00 \n","mean 387202.68 NaN 80.26 8.93 0.00 \n","std 545603.24 NaN 67.48 5.97 0.04 \n","min 99874.00 NaN 0.00 0.00 0.00 \n","25% 155927.00 NaN 30.00 5.00 0.00 \n","50% 221078.00 NaN 70.00 9.00 0.00 \n","75% 274102.00 NaN 115.00 12.00 0.00 \n","max 2024745.00 NaN 3120.00 51.55 1.00 \n","\n"," geo_point_2d_a geo_point_2d_b \n","count 200137.00 200137.00 \n","mean 48.85 2.35 \n","std 0.03 0.05 \n","min 48.74 2.21 \n","25% 48.84 2.31 \n","50% 48.85 2.35 \n","75% 48.88 2.39 \n","max 48.91 2.47 "],"text/html":["\n","
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idnumerocirconference_cmhauteur_mremarquablegeo_point_2d_ageo_point_2d_b
count200137.000.00199514.00186181.00137039.00200137.00200137.00
mean387202.68NaN80.268.930.0048.852.35
std545603.24NaN67.485.970.040.030.05
min99874.00NaN0.000.000.0048.742.21
25%155927.00NaN30.005.000.0048.842.31
50%221078.00NaN70.009.000.0048.852.35
75%274102.00NaN115.0012.000.0048.882.39
max2024745.00NaN3120.0051.551.0048.912.47
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","summary":"{\n \"name\": \"## > 250000 cm as circumference, > 800000 m as height\",\n \"rows\": 8,\n \"fields\": [\n {\n \"column\": \"id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 636490.3531323484,\n \"min\": 99874.0,\n \"max\": 2024745.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 387202.68230761925,\n 221078.0,\n 200137.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"numero\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 0.0,\n \"max\": 0.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"circonference_cm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 70371.0990261856,\n \"min\": 0.0,\n \"max\": 199514.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 80.26401656024139\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"hauteur_m\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 65820.25619681984,\n \"min\": 0.0,\n \"max\": 186181.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 8.933927092453045\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"remarquable\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 48450.55066982106,\n \"min\": 0.0,\n \"max\": 137039.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 0.0013426834696692183\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"geo_point_2d_a\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 70744.31300132743,\n \"min\": 0.0302341454886933,\n \"max\": 200137.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 48.85449078901268\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"geo_point_2d_b\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 70758.40152298885,\n \"min\": 0.05121963963079915,\n \"max\": 200137.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 2.3482078955462966\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":77}],"source":["df_refined.describe()# general statistics only on number types variables (int and floats)\n","\n","# problems:\n","## 0 as min value for circumference, height\n","## > 250000 cm as circumference, > 800000 m as height"]},{"cell_type":"code","execution_count":78,"id":"02rOaFuYYrxA","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":418,"status":"ok","timestamp":1737062664287,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"02rOaFuYYrxA","outputId":"b3c3f06e-6f36-4a63-b85e-de27555009b0"},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of different species: 539\n"]}],"source":["print(f\"Number of different species: {df_refined['espece'].nunique()}\")"]},{"cell_type":"code","execution_count":79,"id":"5vyyd7-lZ_tT","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1737062664287,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"5vyyd7-lZ_tT","outputId":"56e93bbe-fc4d-4de0-ea00-8081b3dde942"},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of remarquable different species: 66\n"]}],"source":["print(f\"Number of remarquable different species: {df_refined[df_refined['remarquable']==1]['espece'].nunique()}\")"]},{"cell_type":"markdown","id":"-859g19jW-0l","metadata":{"id":"-859g19jW-0l"},"source":["### Univariate Analysis"]},{"cell_type":"code","execution_count":80,"id":"_we2vsHTWuTn","metadata":{"executionInfo":{"elapsed":9,"status":"ok","timestamp":1737062664287,"user":{"displayName":"Grégoire","userId":"01249298206117462766"},"user_tz":-60},"id":"_we2vsHTWuTn"},"outputs":[],"source":["interesting_variables = {'domanialite':'pie','arrondissement':'heat_map',\n"," 'lieu':'heat_map', 'libelle_francais':'bars',\n"," 'genre':'bars', 'espece':'bars',\n"," 'variete':'bars','circonference_cm':'hist',\n"," 'hauteur_m':'hist','stade_developpement':'pie',\n"," 'remarquable':'bars_log'}"]},{"cell_type":"code","execution_count":81,"id":"tn6p_aQcXmLM","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"output_embedded_package_id":"13LADlWprO2_fGI3yOouYyyu2hHs99ijc"},"id":"tn6p_aQcXmLM","outputId":"6152b149-0cdb-4d21-8390-8fb3fbaba709","executionInfo":{"status":"ok","timestamp":1737062696721,"user_tz":-60,"elapsed":32443,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}],"source":["for variable in interesting_variables:\n"," plot_univariate_variable_analysis(df_refined, variable, interesting_variables[variable])\n"," print()"]},{"cell_type":"code","execution_count":82,"id":"58c0f807-803f-4cef-9ff6-a827020cb19f","metadata":{"id":"58c0f807-803f-4cef-9ff6-a827020cb19f","executionInfo":{"status":"ok","timestamp":1737062696721,"user_tz":-60,"elapsed":7,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"outputs":[],"source":["# remains to do\n","## multivariate analysis (not yet)"]},{"cell_type":"code","source":["plot_bivariate_variable_analysis(df_refined, 'remarquable', 'hauteur_m', 'boxplot')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":564},"id":"OQrQVkhob6oR","executionInfo":{"status":"ok","timestamp":1737062696721,"user_tz":-60,"elapsed":7,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"fe96a611-1254-4c7d-fdc5-1ca04d6709e4"},"id":"OQrQVkhob6oR","execution_count":83,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["plot_bivariate_variable_analysis(df_refined, 'remarquable', 'circonference_cm', 'boxplot')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":564},"id":"olxCasOYdXxO","executionInfo":{"status":"ok","timestamp":1737062697779,"user_tz":-60,"elapsed":1063,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"af497138-e681-46bb-aaff-b5065ffd4b76"},"id":"olxCasOYdXxO","execution_count":84,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["variable1_name = 'remarquable'\n","variable2_name = 'hauteur_m'\n","pearsonr, p_value = compute_bivariate_stats_indicators(df_refined, variable1_name, variable2_name)\n","print(f\"Pearson correlation coefficient between {variable1_name} and {variable2_name} : {pearsonr}\") # existing correlation but very weak\n","\n","print()\n","\n","variable1_name = 'remarquable'\n","variable2_name = 'circonference_cm'\n","pearsonr, p_value = compute_bivariate_stats_indicators(df_refined, variable1_name, variable2_name)\n","print(f\"Pearson correlation coefficient between {variable1_name} and {variable2_name} : {pearsonr}\") # existing correlation but very weak"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GtSIhb2Hdcsm","executionInfo":{"status":"ok","timestamp":1737062782320,"user_tz":-60,"elapsed":1522,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"8d1a664b-6b5f-4175-e597-70f236770a10"},"id":"GtSIhb2Hdcsm","execution_count":87,"outputs":[{"output_type":"stream","name":"stdout","text":["Both quantitative variables : Pearson correlation analysis\n","Pearson correlation coefficient between remarquable and hauteur_m : 0.06269852992286187\n","\n","Both quantitative variables : Pearson correlation analysis\n","Pearson correlation coefficient between remarquable and circonference_cm : 0.11668322959396166\n"]}]},{"cell_type":"code","source":["eta_squared, anova_table = compute_bivariate_stats_indicators(df_refined, 'remarquable','espece')\n","print(f\"Eta-squared: {eta_squared}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Rwt35nIpBJa2","executionInfo":{"status":"ok","timestamp":1737062823550,"user_tz":-60,"elapsed":37593,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"0aaeabc1-1de3-4c14-e7f9-a7ebb154cd81"},"id":"Rwt35nIpBJa2","execution_count":88,"outputs":[{"output_type":"stream","name":"stdout","text":["Both quantitative and categorical variables : ANOVA analysis\n","Eta-squared: 0.05512247910689265\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/statsmodels/base/model.py:1894: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 538, but rank is 481\n"," warnings.warn('covariance of constraints does not have full '\n","/content/drive/MyDrive/Colab_Notebooks/Project_2/data_analysis_functions.py:295: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n"," eta_squared = anova_table['sum_sq'][0] / sum(anova_table['sum_sq'])\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"oGVLCs_pAqFi"},"id":"oGVLCs_pAqFi","execution_count":null,"outputs":[]}],"metadata":{"colab":{"provenance":[],"toc_visible":true},"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.12.7"}},"nbformat":4,"nbformat_minor":5} \ No newline at end of file