3117 lines
440 KiB
Text
3117 lines
440 KiB
Text
{
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"cells": [
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"cell_type": "markdown",
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"id": "307bea88-e08c-4787-9d3a-047272601ef1",
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"metadata": {
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"editable": true,
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"slideshow": {
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||
"slide_type": ""
|
||
},
|
||
"tags": []
|
||
},
|
||
"source": [
|
||
"# Exploring and Analyzing Paris trees data"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "39449094-4efa-4627-b19c-6aa521c7a7e8",
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"metadata": {},
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"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",
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||
"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,
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||
"id": "a302ef86-7553-4b38-8734-b0ae5896c584",
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||
"metadata": {
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||
"scrolled": true
|
||
},
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||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
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||
"text": [
|
||
"<class 'pandas.core.frame.DataFrame'>\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
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||
},
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||
"outputs": [
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||
{
|
||
"data": {
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||
"text/html": [
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"<div>\n",
|
||
"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
|
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>id</th>\n",
|
||
" <th>type_emplacement</th>\n",
|
||
" <th>domanialite</th>\n",
|
||
" <th>arrondissement</th>\n",
|
||
" <th>complement_addresse</th>\n",
|
||
" <th>numero</th>\n",
|
||
" <th>lieu</th>\n",
|
||
" <th>id_emplacement</th>\n",
|
||
" <th>libelle_francais</th>\n",
|
||
" <th>genre</th>\n",
|
||
" <th>espece</th>\n",
|
||
" <th>variete</th>\n",
|
||
" <th>circonference_cm</th>\n",
|
||
" <th>hauteur_m</th>\n",
|
||
" <th>stade_developpement</th>\n",
|
||
" <th>remarquable</th>\n",
|
||
" <th>geo_point_2d_a</th>\n",
|
||
" <th>geo_point_2d_b</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>99874</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Jardin</td>\n",
|
||
" <td>PARIS 7E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>MAIRIE DU 7E 116 RUE DE GRENELLE PARIS 7E</td>\n",
|
||
" <td>19</td>\n",
|
||
" <td>Marronnier</td>\n",
|
||
" <td>Aesculus</td>\n",
|
||
" <td>hippocastanum</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>20</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.86</td>\n",
|
||
" <td>2.32</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>99875</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Jardin</td>\n",
|
||
" <td>PARIS 7E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>MAIRIE DU 7E 116 RUE DE GRENELLE PARIS 7E</td>\n",
|
||
" <td>20</td>\n",
|
||
" <td>If</td>\n",
|
||
" <td>Taxus</td>\n",
|
||
" <td>baccata</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>65</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>A</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>48.86</td>\n",
|
||
" <td>2.32</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"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": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
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||
" }\n",
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||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>id</th>\n",
|
||
" <th>numero</th>\n",
|
||
" <th>circonference_cm</th>\n",
|
||
" <th>hauteur_m</th>\n",
|
||
" <th>remarquable</th>\n",
|
||
" <th>geo_point_2d_a</th>\n",
|
||
" <th>geo_point_2d_b</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>count</th>\n",
|
||
" <td>200137.00</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>200137.00</td>\n",
|
||
" <td>200137.00</td>\n",
|
||
" <td>137039.00</td>\n",
|
||
" <td>200137.00</td>\n",
|
||
" <td>200137.00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>mean</th>\n",
|
||
" <td>387202.68</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>83.38</td>\n",
|
||
" <td>13.11</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.85</td>\n",
|
||
" <td>2.35</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>std</th>\n",
|
||
" <td>545603.24</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>673.19</td>\n",
|
||
" <td>1971.22</td>\n",
|
||
" <td>0.04</td>\n",
|
||
" <td>0.03</td>\n",
|
||
" <td>0.05</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>min</th>\n",
|
||
" <td>99874.00</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.74</td>\n",
|
||
" <td>2.21</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25%</th>\n",
|
||
" <td>155927.00</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>30.00</td>\n",
|
||
" <td>5.00</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.84</td>\n",
|
||
" <td>2.31</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>50%</th>\n",
|
||
" <td>221078.00</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>70.00</td>\n",
|
||
" <td>8.00</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.85</td>\n",
|
||
" <td>2.35</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>75%</th>\n",
|
||
" <td>274102.00</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>115.00</td>\n",
|
||
" <td>12.00</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.88</td>\n",
|
||
" <td>2.39</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>max</th>\n",
|
||
" <td>2024745.00</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>250255.00</td>\n",
|
||
" <td>881818.00</td>\n",
|
||
" <td>1.00</td>\n",
|
||
" <td>48.91</td>\n",
|
||
" <td>2.47</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"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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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"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": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>domanialite</th>\n",
|
||
" <th>complement_addresse</th>\n",
|
||
" <th>numero</th>\n",
|
||
" <th>libelle_francais</th>\n",
|
||
" <th>genre</th>\n",
|
||
" <th>espece</th>\n",
|
||
" <th>variete</th>\n",
|
||
" <th>stade_developpement</th>\n",
|
||
" <th>remarquable</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>169235</td>\n",
|
||
" <td>200137</td>\n",
|
||
" <td>1497</td>\n",
|
||
" <td>16</td>\n",
|
||
" <td>1752</td>\n",
|
||
" <td>163360</td>\n",
|
||
" <td>67205</td>\n",
|
||
" <td>63098</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"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": [
|
||
"<Axes: >"
|
||
]
|
||
},
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 2400x1000 with 3 Axes>"
|
||
]
|
||
},
|
||
"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": [
|
||
"<Axes: >"
|
||
]
|
||
},
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 2000x1200 with 2 Axes>"
|
||
]
|
||
},
|
||
"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": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>id</th>\n",
|
||
" <th>type_emplacement</th>\n",
|
||
" <th>domanialite</th>\n",
|
||
" <th>arrondissement</th>\n",
|
||
" <th>complement_addresse</th>\n",
|
||
" <th>numero</th>\n",
|
||
" <th>lieu</th>\n",
|
||
" <th>id_emplacement</th>\n",
|
||
" <th>libelle_francais</th>\n",
|
||
" <th>genre</th>\n",
|
||
" <th>espece</th>\n",
|
||
" <th>variete</th>\n",
|
||
" <th>circonference_cm</th>\n",
|
||
" <th>hauteur_m</th>\n",
|
||
" <th>stade_developpement</th>\n",
|
||
" <th>remarquable</th>\n",
|
||
" <th>geo_point_2d_a</th>\n",
|
||
" <th>geo_point_2d_b</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>5392</th>\n",
|
||
" <td>106800</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Jardin</td>\n",
|
||
" <td>PARIS 7E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>JARDIN DU CHAMP DE MARS</td>\n",
|
||
" <td>A0120683</td>\n",
|
||
" <td>Tilleul</td>\n",
|
||
" <td>Tilia</td>\n",
|
||
" <td>n. sp.</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>100</td>\n",
|
||
" <td>1510</td>\n",
|
||
" <td>A</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.86</td>\n",
|
||
" <td>2.30</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6473</th>\n",
|
||
" <td>107951</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Jardin</td>\n",
|
||
" <td>PARIS 7E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>JARDIN DU CHAMP DE MARS / C01 / ALLEE LEON BOU...</td>\n",
|
||
" <td>A00202003</td>\n",
|
||
" <td>Platane</td>\n",
|
||
" <td>Platanus</td>\n",
|
||
" <td>x hispanica</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>112</td>\n",
|
||
" <td>117</td>\n",
|
||
" <td>A</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.86</td>\n",
|
||
" <td>2.29</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12536</th>\n",
|
||
" <td>115062</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Jardin</td>\n",
|
||
" <td>PARIS 16E ARRDT</td>\n",
|
||
" <td>11 à 27</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>JARDIN DE L AVENUE FOCH / 10 AVENUE FOCH</td>\n",
|
||
" <td>10303</td>\n",
|
||
" <td>Erable</td>\n",
|
||
" <td>Acer</td>\n",
|
||
" <td>n. sp.</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>47</td>\n",
|
||
" <td>255</td>\n",
|
||
" <td>J</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.87</td>\n",
|
||
" <td>2.29</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>15373</th>\n",
|
||
" <td>118412</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Jardin</td>\n",
|
||
" <td>PARIS 7E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>SQUARE DE L'ABBE ESQUERRE</td>\n",
|
||
" <td>17</td>\n",
|
||
" <td>Paulownia</td>\n",
|
||
" <td>Paulownia</td>\n",
|
||
" <td>tomentosa</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>145</td>\n",
|
||
" <td>810</td>\n",
|
||
" <td>A</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.85</td>\n",
|
||
" <td>2.31</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>24099</th>\n",
|
||
" <td>128185</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Jardin</td>\n",
|
||
" <td>PARIS 17E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>SQUARE ANDRE ULMANN</td>\n",
|
||
" <td>27</td>\n",
|
||
" <td>Platane</td>\n",
|
||
" <td>Platanus</td>\n",
|
||
" <td>x hispanica</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>130</td>\n",
|
||
" <td>125</td>\n",
|
||
" <td>A</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.89</td>\n",
|
||
" <td>2.30</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>187873</th>\n",
|
||
" <td>2008880</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>DJS</td>\n",
|
||
" <td>PARIS 13E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>STADE CHARLETTY / 99 BOULEVARD KELLERMANN</td>\n",
|
||
" <td>91</td>\n",
|
||
" <td>Micocoulier</td>\n",
|
||
" <td>Celtis</td>\n",
|
||
" <td>australis</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>10</td>\n",
|
||
" <td>120</td>\n",
|
||
" <td>JA</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.82</td>\n",
|
||
" <td>2.35</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>187874</th>\n",
|
||
" <td>2008881</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>DJS</td>\n",
|
||
" <td>PARIS 13E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>STADE CHARLETTY / 99 BOULEVARD KELLERMANN</td>\n",
|
||
" <td>92</td>\n",
|
||
" <td>Micocoulier</td>\n",
|
||
" <td>Celtis</td>\n",
|
||
" <td>australis</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>10</td>\n",
|
||
" <td>120</td>\n",
|
||
" <td>JA</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.82</td>\n",
|
||
" <td>2.35</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>189053</th>\n",
|
||
" <td>2011417</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Jardin</td>\n",
|
||
" <td>BOIS DE VINCENNES</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>PARC FLORAL DE PARIS / ROUTE DE LA PYRAMIDE</td>\n",
|
||
" <td>180088</td>\n",
|
||
" <td>Pin</td>\n",
|
||
" <td>Pinus</td>\n",
|
||
" <td>mugo</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>60</td>\n",
|
||
" <td>150</td>\n",
|
||
" <td>JA</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.83</td>\n",
|
||
" <td>2.44</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>189441</th>\n",
|
||
" <td>2011863</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>DFPE</td>\n",
|
||
" <td>PARIS 16E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>ERLANGER (9-11) CRECHE</td>\n",
|
||
" <td>48970005</td>\n",
|
||
" <td>Marronnier</td>\n",
|
||
" <td>Aesculus</td>\n",
|
||
" <td>hippocastanum</td>\n",
|
||
" <td>Baumannii'</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>157</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.85</td>\n",
|
||
" <td>2.26</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>197136</th>\n",
|
||
" <td>2020792</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Jardin</td>\n",
|
||
" <td>PARIS 14E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>SQUARE AUGUSTE RENOIR / 205 RUE RAYMOND LOSSERAND</td>\n",
|
||
" <td>103005</td>\n",
|
||
" <td>Erable</td>\n",
|
||
" <td>Acer</td>\n",
|
||
" <td>platanoides</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>145</td>\n",
|
||
" <td>125</td>\n",
|
||
" <td>A</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.83</td>\n",
|
||
" <td>2.31</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>87 rows × 18 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"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": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
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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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|
||
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|
||
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|
||
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|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>id</th>\n",
|
||
" <th>type_emplacement</th>\n",
|
||
" <th>domanialite</th>\n",
|
||
" <th>arrondissement</th>\n",
|
||
" <th>complement_addresse</th>\n",
|
||
" <th>numero</th>\n",
|
||
" <th>lieu</th>\n",
|
||
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|
||
" <th>libelle_francais</th>\n",
|
||
" <th>genre</th>\n",
|
||
" <th>espece</th>\n",
|
||
" <th>variete</th>\n",
|
||
" <th>circonference_cm</th>\n",
|
||
" <th>hauteur_m</th>\n",
|
||
" <th>stade_developpement</th>\n",
|
||
" <th>remarquable</th>\n",
|
||
" <th>geo_point_2d_a</th>\n",
|
||
" <th>geo_point_2d_b</th>\n",
|
||
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|
||
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|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>71787</th>\n",
|
||
" <td>190080</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>DASCO</td>\n",
|
||
" <td>PARIS 20E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>PLAINE (9) ELEM B</td>\n",
|
||
" <td>11110006</td>\n",
|
||
" <td>Tilleul</td>\n",
|
||
" <td>Tilia</td>\n",
|
||
" <td>tomentosa</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>18589</td>\n",
|
||
" <td>15.00</td>\n",
|
||
" <td>A</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.85</td>\n",
|
||
" <td>2.40</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>82951</th>\n",
|
||
" <td>203295</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Alignement</td>\n",
|
||
" <td>PARIS 18E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>RUE D AUBERVILLIERS</td>\n",
|
||
" <td>301004</td>\n",
|
||
" <td>Erable</td>\n",
|
||
" <td>Acer</td>\n",
|
||
" <td>monspessulanum</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>5545</td>\n",
|
||
" <td>56.00</td>\n",
|
||
" <td>J</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.89</td>\n",
|
||
" <td>2.37</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>90605</th>\n",
|
||
" <td>211362</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Alignement</td>\n",
|
||
" <td>PARIS 5E ARRDT</td>\n",
|
||
" <td>34</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>BOULEVARD SAINT MARCEL</td>\n",
|
||
" <td>204025</td>\n",
|
||
" <td>Cedrele</td>\n",
|
||
" <td>Toona</td>\n",
|
||
" <td>sinensis</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>20130</td>\n",
|
||
" <td>16.00</td>\n",
|
||
" <td>A</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.84</td>\n",
|
||
" <td>2.36</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>91256</th>\n",
|
||
" <td>212024</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Alignement</td>\n",
|
||
" <td>PARIS 12E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>RUE DE CHALON</td>\n",
|
||
" <td>202005</td>\n",
|
||
" <td>Micocoulier</td>\n",
|
||
" <td>Celtis</td>\n",
|
||
" <td>australis</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>30140</td>\n",
|
||
" <td>10.00</td>\n",
|
||
" <td>A</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.85</td>\n",
|
||
" <td>2.37</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>97354</th>\n",
|
||
" <td>218274</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Alignement</td>\n",
|
||
" <td>PARIS 7E ARRDT</td>\n",
|
||
" <td>27</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>AVENUE RAPP</td>\n",
|
||
" <td>501003</td>\n",
|
||
" <td>Platane</td>\n",
|
||
" <td>Platanus</td>\n",
|
||
" <td>occidentalis</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>8110</td>\n",
|
||
" <td>18.00</td>\n",
|
||
" <td>A</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.86</td>\n",
|
||
" <td>2.30</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>103437</th>\n",
|
||
" <td>224609</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Alignement</td>\n",
|
||
" <td>PARIS 18E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>BOULEVARD BARBES</td>\n",
|
||
" <td>1303003</td>\n",
|
||
" <td>Platane</td>\n",
|
||
" <td>Platanus</td>\n",
|
||
" <td>x hispanica</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>9035</td>\n",
|
||
" <td>6.00</td>\n",
|
||
" <td>J</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.89</td>\n",
|
||
" <td>2.35</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>133546</th>\n",
|
||
" <td>256868</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Alignement</td>\n",
|
||
" <td>PARIS 19E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>AVENUE JEAN JAURES</td>\n",
|
||
" <td>1501006</td>\n",
|
||
" <td>Platane</td>\n",
|
||
" <td>Platanus</td>\n",
|
||
" <td>x hispanica</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>4090</td>\n",
|
||
" <td>10.00</td>\n",
|
||
" <td>A</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.89</td>\n",
|
||
" <td>2.39</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>147235</th>\n",
|
||
" <td>270997</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Alignement</td>\n",
|
||
" <td>PARIS 7E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>QUAI BRANLY</td>\n",
|
||
" <td>503005</td>\n",
|
||
" <td>Paulownia</td>\n",
|
||
" <td>Paulownia</td>\n",
|
||
" <td>tomentosa</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>9155</td>\n",
|
||
" <td>15.00</td>\n",
|
||
" <td>A</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.86</td>\n",
|
||
" <td>2.29</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>147634</th>\n",
|
||
" <td>271402</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Alignement</td>\n",
|
||
" <td>PARIS 7E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>AVENUE BOSQUET</td>\n",
|
||
" <td>1202016</td>\n",
|
||
" <td>Platane</td>\n",
|
||
" <td>Platanus</td>\n",
|
||
" <td>occidentalis</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>65110</td>\n",
|
||
" <td>16.00</td>\n",
|
||
" <td>A</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.86</td>\n",
|
||
" <td>2.31</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>148106</th>\n",
|
||
" <td>271884</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Alignement</td>\n",
|
||
" <td>PARIS 7E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>AVENUE BOSQUET</td>\n",
|
||
" <td>501022</td>\n",
|
||
" <td>Platane</td>\n",
|
||
" <td>Platanus</td>\n",
|
||
" <td>occidentalis</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>6595</td>\n",
|
||
" <td>15.00</td>\n",
|
||
" <td>A</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.86</td>\n",
|
||
" <td>2.30</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>148481</th>\n",
|
||
" <td>272263</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Alignement</td>\n",
|
||
" <td>PARIS 7E ARRDT</td>\n",
|
||
" <td>14</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>BOULEVARD DES INVALIDES</td>\n",
|
||
" <td>402009</td>\n",
|
||
" <td>Platane</td>\n",
|
||
" <td>Platanus</td>\n",
|
||
" <td>occidentalis</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>6120</td>\n",
|
||
" <td>18.00</td>\n",
|
||
" <td>A</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.85</td>\n",
|
||
" <td>2.31</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>149056</th>\n",
|
||
" <td>272885</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Alignement</td>\n",
|
||
" <td>PARIS 18E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>PLACE DE LA CHAPELLE</td>\n",
|
||
" <td>101026</td>\n",
|
||
" <td>Platane</td>\n",
|
||
" <td>Platanus</td>\n",
|
||
" <td>x hispanica</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>250255</td>\n",
|
||
" <td>22.00</td>\n",
|
||
" <td>A</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.88</td>\n",
|
||
" <td>2.36</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>151364</th>\n",
|
||
" <td>275451</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Alignement</td>\n",
|
||
" <td>PARIS 7E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>ESPLANADE DES INVALIDES</td>\n",
|
||
" <td>410005</td>\n",
|
||
" <td>Tilleul</td>\n",
|
||
" <td>Tilia</td>\n",
|
||
" <td>tomentosa</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>7540</td>\n",
|
||
" <td>8.15</td>\n",
|
||
" <td>JA</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.86</td>\n",
|
||
" <td>2.31</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>152303</th>\n",
|
||
" <td>276396</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Alignement</td>\n",
|
||
" <td>PARIS 7E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>ESPLANADE DES INVALIDES</td>\n",
|
||
" <td>402011</td>\n",
|
||
" <td>Tilleul</td>\n",
|
||
" <td>Tilia</td>\n",
|
||
" <td>tomentosa</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>9085</td>\n",
|
||
" <td>12.00</td>\n",
|
||
" <td>A</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.86</td>\n",
|
||
" <td>2.31</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>160432</th>\n",
|
||
" <td>284823</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Alignement</td>\n",
|
||
" <td>PARIS 18E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>AVENUE DE LA PORTE DE CLIGNANCOURT</td>\n",
|
||
" <td>202005</td>\n",
|
||
" <td>Platane</td>\n",
|
||
" <td>Platanus</td>\n",
|
||
" <td>orientalis</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>120170</td>\n",
|
||
" <td>20.00</td>\n",
|
||
" <td>A</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.90</td>\n",
|
||
" <td>2.34</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>163918</th>\n",
|
||
" <td>288407</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Alignement</td>\n",
|
||
" <td>PARIS 12E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>BOULEVARD DE PICPUS</td>\n",
|
||
" <td>1604017</td>\n",
|
||
" <td>Sophora</td>\n",
|
||
" <td>Sophora</td>\n",
|
||
" <td>japonica</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>6085</td>\n",
|
||
" <td>5.00</td>\n",
|
||
" <td>JA</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.85</td>\n",
|
||
" <td>2.40</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>164616</th>\n",
|
||
" <td>289121</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Alignement</td>\n",
|
||
" <td>PARIS 12E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>RUE FRANCOIS TRUFFAUT</td>\n",
|
||
" <td>301005</td>\n",
|
||
" <td>Platane</td>\n",
|
||
" <td>Platanus</td>\n",
|
||
" <td>x hispanica</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>9095</td>\n",
|
||
" <td>10.00</td>\n",
|
||
" <td>A</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.83</td>\n",
|
||
" <td>2.39</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>167606</th>\n",
|
||
" <td>292180</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Alignement</td>\n",
|
||
" <td>PARIS 18E ARRDT</td>\n",
|
||
" <td>16</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>RUE JEAN COCTEAU</td>\n",
|
||
" <td>202027</td>\n",
|
||
" <td>Noisetier de Byzance</td>\n",
|
||
" <td>Corylus</td>\n",
|
||
" <td>colurna</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>3630</td>\n",
|
||
" <td>5.00</td>\n",
|
||
" <td>J</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.90</td>\n",
|
||
" <td>2.35</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>170367</th>\n",
|
||
" <td>295047</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Alignement</td>\n",
|
||
" <td>PARIS 5E ARRDT</td>\n",
|
||
" <td>16</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>RUE POLIVEAU</td>\n",
|
||
" <td>202015</td>\n",
|
||
" <td>Platane</td>\n",
|
||
" <td>Platanus</td>\n",
|
||
" <td>x hispanica</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>6125</td>\n",
|
||
" <td>20.00</td>\n",
|
||
" <td>A</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.84</td>\n",
|
||
" <td>2.36</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>173343</th>\n",
|
||
" <td>298211</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Alignement</td>\n",
|
||
" <td>PARIS 5E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>BOULEVARD DE PORT ROYAL</td>\n",
|
||
" <td>402001</td>\n",
|
||
" <td>Marronnier</td>\n",
|
||
" <td>Aesculus</td>\n",
|
||
" <td>x carnea</td>\n",
|
||
" <td>Briotii'</td>\n",
|
||
" <td>79120</td>\n",
|
||
" <td>10.00</td>\n",
|
||
" <td>A</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.84</td>\n",
|
||
" <td>2.35</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"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": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
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|
||
"\n",
|
||
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|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>id</th>\n",
|
||
" <th>type_emplacement</th>\n",
|
||
" <th>domanialite</th>\n",
|
||
" <th>arrondissement</th>\n",
|
||
" <th>complement_addresse</th>\n",
|
||
" <th>numero</th>\n",
|
||
" <th>lieu</th>\n",
|
||
" <th>id_emplacement</th>\n",
|
||
" <th>libelle_francais</th>\n",
|
||
" <th>genre</th>\n",
|
||
" <th>espece</th>\n",
|
||
" <th>variete</th>\n",
|
||
" <th>circonference_cm</th>\n",
|
||
" <th>hauteur_m</th>\n",
|
||
" <th>stade_developpement</th>\n",
|
||
" <th>remarquable</th>\n",
|
||
" <th>geo_point_2d_a</th>\n",
|
||
" <th>geo_point_2d_b</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>99878</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Jardin</td>\n",
|
||
" <td>PARIS 17E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING</td>\n",
|
||
" <td>000G0037</td>\n",
|
||
" <td>Arbre à miel</td>\n",
|
||
" <td>Tetradium</td>\n",
|
||
" <td>daniellii</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>38</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>48.89</td>\n",
|
||
" <td>2.32</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>99879</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Jardin</td>\n",
|
||
" <td>PARIS 17E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING</td>\n",
|
||
" <td>000G0036</td>\n",
|
||
" <td>Arbre à miel</td>\n",
|
||
" <td>Tetradium</td>\n",
|
||
" <td>daniellii</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>38</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>48.89</td>\n",
|
||
" <td>2.32</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>99880</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Jardin</td>\n",
|
||
" <td>PARIS 17E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING</td>\n",
|
||
" <td>000G0035</td>\n",
|
||
" <td>Arbre à miel</td>\n",
|
||
" <td>Tetradium</td>\n",
|
||
" <td>daniellii</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>37</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>48.89</td>\n",
|
||
" <td>2.32</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>99885</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Jardin</td>\n",
|
||
" <td>PARIS 16E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>JARDIN DU RANELAGH</td>\n",
|
||
" <td>20001</td>\n",
|
||
" <td>Hêtre</td>\n",
|
||
" <td>Fagus</td>\n",
|
||
" <td>sylvatica</td>\n",
|
||
" <td>Atropunicea'</td>\n",
|
||
" <td>30</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>48.86</td>\n",
|
||
" <td>2.27</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>99887</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Jardin</td>\n",
|
||
" <td>PARIS 16E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>JARDIN DU RANELAGH</td>\n",
|
||
" <td>20003</td>\n",
|
||
" <td>Micocoulier</td>\n",
|
||
" <td>Celtis</td>\n",
|
||
" <td>occidentalis</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>205</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>48.86</td>\n",
|
||
" <td>2.27</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>200132</th>\n",
|
||
" <td>2024741</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Jardin</td>\n",
|
||
" <td>BOIS DE VINCENNES</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...</td>\n",
|
||
" <td>720170151</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>Castanea</td>\n",
|
||
" <td>fargesii</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.82</td>\n",
|
||
" <td>2.46</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>200133</th>\n",
|
||
" <td>2024742</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Jardin</td>\n",
|
||
" <td>BOIS DE VINCENNES</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...</td>\n",
|
||
" <td>720170152</td>\n",
|
||
" <td>Arbre aux mouchoirs</td>\n",
|
||
" <td>Davidia</td>\n",
|
||
" <td>involucrata</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.82</td>\n",
|
||
" <td>2.46</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>200134</th>\n",
|
||
" <td>2024743</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Jardin</td>\n",
|
||
" <td>BOIS DE VINCENNES</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...</td>\n",
|
||
" <td>720170153</td>\n",
|
||
" <td>Peuplier</td>\n",
|
||
" <td>Populus</td>\n",
|
||
" <td>simonii</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.82</td>\n",
|
||
" <td>2.46</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>200135</th>\n",
|
||
" <td>2024744</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Jardin</td>\n",
|
||
" <td>BOIS DE VINCENNES</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...</td>\n",
|
||
" <td>720170154</td>\n",
|
||
" <td>Chêne</td>\n",
|
||
" <td>Quercus</td>\n",
|
||
" <td>n. sp.</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.82</td>\n",
|
||
" <td>2.46</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>200136</th>\n",
|
||
" <td>2024745</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Jardin</td>\n",
|
||
" <td>BOIS DE VINCENNES</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...</td>\n",
|
||
" <td>720170155</td>\n",
|
||
" <td>Raisinier</td>\n",
|
||
" <td>Hovenia</td>\n",
|
||
" <td>dulcis</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.82</td>\n",
|
||
" <td>2.45</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>39219 rows × 18 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"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": {
|
||
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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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|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>id</th>\n",
|
||
" <th>type_emplacement</th>\n",
|
||
" <th>domanialite</th>\n",
|
||
" <th>arrondissement</th>\n",
|
||
" <th>complement_addresse</th>\n",
|
||
" <th>numero</th>\n",
|
||
" <th>lieu</th>\n",
|
||
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|
||
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|
||
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|
||
" <th>espece</th>\n",
|
||
" <th>variete</th>\n",
|
||
" <th>circonference_cm</th>\n",
|
||
" <th>hauteur_m</th>\n",
|
||
" <th>stade_developpement</th>\n",
|
||
" <th>remarquable</th>\n",
|
||
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|
||
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|
||
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|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>1778</th>\n",
|
||
" <td>101922</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Jardin</td>\n",
|
||
" <td>PARIS 19E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>PARC DES BUTTES CHAUMONT</td>\n",
|
||
" <td>C0400032</td>\n",
|
||
" <td>Erable</td>\n",
|
||
" <td>Acer</td>\n",
|
||
" <td>platanoides</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>48.88</td>\n",
|
||
" <td>2.38</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2332</th>\n",
|
||
" <td>102591</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Jardin</td>\n",
|
||
" <td>PARIS 17E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING</td>\n",
|
||
" <td>000B0052</td>\n",
|
||
" <td>Magnolia</td>\n",
|
||
" <td>Magnolia</td>\n",
|
||
" <td>x soulangeana</td>\n",
|
||
" <td>Heaven Scent'</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>48.89</td>\n",
|
||
" <td>2.32</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2333</th>\n",
|
||
" <td>102592</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Jardin</td>\n",
|
||
" <td>PARIS 17E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING</td>\n",
|
||
" <td>000B0053</td>\n",
|
||
" <td>Magnolia</td>\n",
|
||
" <td>Magnolia</td>\n",
|
||
" <td>x soulangeana</td>\n",
|
||
" <td>Heaven Scent'</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>48.89</td>\n",
|
||
" <td>2.32</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2405</th>\n",
|
||
" <td>102744</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Jardin</td>\n",
|
||
" <td>PARIS 20E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>SQUARE LEON FRAPIE</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>Platane</td>\n",
|
||
" <td>Platanus</td>\n",
|
||
" <td>x hispanica</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>JA</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.87</td>\n",
|
||
" <td>2.41</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2430</th>\n",
|
||
" <td>102773</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Jardin</td>\n",
|
||
" <td>PARIS 17E ARRDT</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>PARC CLICHY-BATIGNOLLES-MARTIN LUTHER KING</td>\n",
|
||
" <td>000B0021</td>\n",
|
||
" <td>Magnolia</td>\n",
|
||
" <td>Magnolia</td>\n",
|
||
" <td>x soulangeana</td>\n",
|
||
" <td>Sundew'</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>48.89</td>\n",
|
||
" <td>2.32</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>200132</th>\n",
|
||
" <td>2024741</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Jardin</td>\n",
|
||
" <td>BOIS DE VINCENNES</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...</td>\n",
|
||
" <td>720170151</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>Castanea</td>\n",
|
||
" <td>fargesii</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.82</td>\n",
|
||
" <td>2.46</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>200133</th>\n",
|
||
" <td>2024742</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Jardin</td>\n",
|
||
" <td>BOIS DE VINCENNES</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...</td>\n",
|
||
" <td>720170152</td>\n",
|
||
" <td>Arbre aux mouchoirs</td>\n",
|
||
" <td>Davidia</td>\n",
|
||
" <td>involucrata</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.82</td>\n",
|
||
" <td>2.46</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>200134</th>\n",
|
||
" <td>2024743</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Jardin</td>\n",
|
||
" <td>BOIS DE VINCENNES</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...</td>\n",
|
||
" <td>720170153</td>\n",
|
||
" <td>Peuplier</td>\n",
|
||
" <td>Populus</td>\n",
|
||
" <td>simonii</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.82</td>\n",
|
||
" <td>2.46</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>200135</th>\n",
|
||
" <td>2024744</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Jardin</td>\n",
|
||
" <td>BOIS DE VINCENNES</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...</td>\n",
|
||
" <td>720170154</td>\n",
|
||
" <td>Chêne</td>\n",
|
||
" <td>Quercus</td>\n",
|
||
" <td>n. sp.</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.82</td>\n",
|
||
" <td>2.46</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>200136</th>\n",
|
||
" <td>2024745</td>\n",
|
||
" <td>Arbre</td>\n",
|
||
" <td>Jardin</td>\n",
|
||
" <td>BOIS DE VINCENNES</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>ARBORETUM DE L ECOLE DU BREUIL - ROUTE DE LA F...</td>\n",
|
||
" <td>720170155</td>\n",
|
||
" <td>Raisinier</td>\n",
|
||
" <td>Hovenia</td>\n",
|
||
" <td>dulcis</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>48.82</td>\n",
|
||
" <td>2.45</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>25867 rows × 18 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"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": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>domanialite</th>\n",
|
||
" <th>complement_addresse</th>\n",
|
||
" <th>numero</th>\n",
|
||
" <th>libelle_francais</th>\n",
|
||
" <th>genre</th>\n",
|
||
" <th>espece</th>\n",
|
||
" <th>variete</th>\n",
|
||
" <th>stade_developpement</th>\n",
|
||
" <th>remarquable</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>169235</td>\n",
|
||
" <td>200137</td>\n",
|
||
" <td>1497</td>\n",
|
||
" <td>16</td>\n",
|
||
" <td>1752</td>\n",
|
||
" <td>163360</td>\n",
|
||
" <td>67205</td>\n",
|
||
" <td>63098</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"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
|
||
}
|