oc-p02-paris-trees/README.md

Paris Trees Analysis Project

📘 Ce projet est également disponible en français 🇫🇷

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

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

📋 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
├── demo/ (complementary work: interactive map)
└── p2-trees-fr.csv

Complementary work (demo/)

An interactive Leaflet map generated from the same CSV, with filters by district and species, three views (density, heatmap, points) and live KPIs. It does not replace the notebook or the original Folium maps. Live page: https://demo.gregoiremureau.com/arbres-paris-carte-interactive.html. Details: demo/README.md.

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.