Source of truth : https://git.gregoiremureau.com/openclassroom-ai/oc-p02-paris-trees

git clone https://git.gregoiremureau.com/openclassroom-ai/oc-p02-paris-trees.git
git clone https://git.gregoiremureau.com/openclassroom-ai/oc-p02-paris-trees.git

Dataset: see fetch-data.sh (out of git if large).

Paris Trees Analysis Project

📘 Ce projet est également disponible en français 🇫🇷

📋 Project Context

This project was carried out as part of a data analysis assignment for the City of Paris. The objective is to explore and analyze Paris' tree heritage from a public dataset listing the trees present across the territory of the capital.

The project focuses on the fundamentals of data science: data cleaning, anomaly handling, exploratory analysis, and statistical visualization to prepare the dataset for further analysis or business applications (urban management, biodiversity, planning).

🎯 Objectives

The main objectives of the project are:

1. Explore the dataset: Understand the structure, variable types, and characteristics of Paris' trees
2. Clean the data: Identify and handle missing, aberrant, and inconsistent values
3. Analyze distributions: Perform univariate and bivariate analyses to understand the variables
4. Visualize the data: Create charts and interactive maps to facilitate understanding
5. Prepare the data: Provide a clean and usable dataset for future analyses

📊 Data

The data comes from the City of Paris Open Data platform and contains detailed information about the trees:

• Geographic location (GPS coordinates, district, address)
• Botanical characteristics (genus, species, variety)
• Dimensions (height, circumference)
• Status (remarkable tree, ownership)
• Planting date

The dataset is included in the project under the name p2-trees-fr.csv.

Source: Open Data City of Paris - Trees

📁 Project Structure

├── Paris_trees_analysis.ipynb
├── soutenance_projet2.pptx
├── data_analysis_functions.py
├── map_arrondissement.html
├── map_lieu.html
└── p2-trees-fr.csv

1. Paris_trees_analysis.ipynb

Main Notebook for Paris Trees Analysis

This notebook is the core of the project and covers the entire exploratory analysis process:

Data loading and overview:

• Import of the CSV file containing the tree data
• Display of the first rows for a quick understanding
• Inspection of the DataFrame structure (types, dimensions)

Initial exploratory analysis:

• Use of pandas methods (info(), head(), describe(), value_counts())
• Identification of the dataset size
• Analysis of column types and missing values
• Descriptive statistics for numerical variables

Identification and cleaning of anomalies:

• Outliers:
  • Detection for tree circumference and height
  • Use of statistical methods (IQR, z-score)
  • Correction or replacement of extreme values with NaN
• Missing values:
  • In-depth analysis of columns with NaN
  • Smart imputation for certain variables (domanialite, libelle_francais)
  • Use of correlated columns (genre, espece, variete) for imputation

Univariate analysis:

• Quantitative variables: distribution of height, circumference, planting year
• Categorical variables: breakdown by genus, species, district, remarkable status
• Visualizations: histograms, boxplots, pie charts, barplots

Bivariate analysis:

• Relationships between the remarquable status and quantitative variables (hauteur_m, circonference_cm)
• Relationships between remarquable and categorical variables (espece, genre)
• Correlations between numerical variables
• Statistical tests to validate observations

Geographic analysis:

• Distribution of trees by district
• Interactive mapping of Paris trees
• Identification of areas with high tree density

Export of cleaned data:

• Saving the prepared DataFrame for further analyses

2. soutenance_projet2.pptx

Final Presentation

This presentation summarizes:

• The context and objectives of the project
• The exploration and data cleaning methodology
• The main findings about Paris' tree heritage
• Key statistical analyses and visualizations
• Business insights (remarkable trees, distribution by district, dominant species)
• Recommendations for managing the tree heritage
• Perspectives for improvement and exploitation of the data

3. data_analysis_functions.py

Analysis Utility Functions Module

This Python module contains reusable functions for:

• Data cleaning (duplicate removal, NaN handling)
• Outlier identification (IQR, z-score, percentile methods)
• Outlier treatment (replacement, removal, capping)
• Variable transformation (normalization, encoding)
• Generation of data quality reports
• Standardized visualization functions

These functions were developed to be reusable on other data analysis projects.

4. map_arrondissement.html

Interactive Map of Trees by District

HTML file containing an interactive map (likely Folium or Plotly) that visualizes:

• The distribution of trees by Paris district
• Aggregated statistics by geographic area
• Interactive visualizations allowing zooming and exploring the data
• Potentially indicators on tree density, dominant species by district

5. map_lieu.html

Interactive Map of Trees by Location

HTML file containing a detailed interactive map that presents:

• The precise location of individual trees across the Paris territory
• Interactive markers with detailed information (species, height, circumference)
• Filters to explore trees by characteristics
• Clusters to improve readability in high-density areas

6. p2-trees-fr.csv

Paris Trees Dataset

CSV file containing raw or lightly preprocessed data on Paris trees, including:

• Identifiers and references
• Location (GPS coordinates, district, address)
• Botanical characteristics (genus, species, variety, French name)
• Dimensions (height in meters, circumference in cm)
• Status (remarkable or not, ownership)
• Planting date

🛠️ Technologies Used

• Python: Main language for data analysis
  • pandas, numpy: data manipulation and analysis
  • matplotlib, seaborn: data visualization
  • folium or plotly: interactive mapping
  • scipy: statistical tests
  • scikit-learn: outlier detection
• Jupyter Notebook: development and documentation
• HTML/JavaScript: interactive maps

📈 Methodology

1. Initial exploration: Understanding the structure and content of the dataset
2. Cleaning: Identification and handling of missing and aberrant values
3. Univariate analyses: Study of the distribution of each variable
4. Bivariate analyses: Exploration of relationships between variables
5. Geographic visualization: Interactive mapping of trees
6. Documentation: Presentation of results and insights

🎯 Expected Results

• ✅ In-depth understanding of Paris' tree heritage
• ✅ Cleaned dataset ready for advanced analyses
• ✅ Identification of remarkable trees and their characteristics
• ✅ Insights into the geographic distribution of trees
• ✅ Interactive maps to visually explore the data
• ✅ Reusable Python module for similar analyses

💡 Key Points of the Project

Data Quality

Like any real-world dataset, this dataset presents challenges:

• Missing values: some incomplete botanical characteristics
• Outliers: data entry errors in measurements (impossible heights and circumferences)
• Inconsistencies: variations in formats and naming

Remarkable Trees

Special attention was given to remarkable trees:

• Characteristics differentiating remarkable trees from others
• Relationships between dimensions (height, circumference) and remarkable status
• Geographic distribution of remarkable trees

Geographic Analysis

The project integrates an important spatial dimension:

• Distribution of trees by district
• Identification of areas with high tree density
• Interactive maps for intuitive exploration
• Possibility of identifying geographic patterns

Urban Biodiversity

The analyses reveal information about biodiversity:

• Most frequent tree species in Paris
• Botanical diversity by district
• Average age of trees (via planting dates)

🎓 Skills Developed

• Loading and exploring tabular data
• Cleaning real-world data with anomalies
• Handling missing values with imputation strategies
• Detection and treatment of outliers
• Univariate and bivariate statistical analyses
• Data visualization (charts and maps)
• Development of reusable Python functions
• Communication of data analysis results

👤 Author

Grégoire Mureau

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This project illustrates the fundamentals of exploratory data analysis: exploration, cleaning, visualization, and communication of insights, applied to a real case of urban open data.