How to Structure a Data Analysis Project in Python: A Complete Guide

How to Structure a Data Analysis Project in Python: A Complete Guide

Here’s your updated data analysis project structure, incorporating tests and reports with detailed suggestions.

📂 Project Structure

my_data_analysis_project/
│── data/  
│   ├── raw/            # Unprocessed source data  
│   ├── interim/        # Temporary files during processing  
│   ├── processed/      # Cleaned and transformed data  
│   ├── final/          # Final, analysis-ready datasets  
│  
│── notebooks/          # Jupyter notebooks for exploration  
│  
│── src/               # Python scripts for data processing  
│   ├── data_preprocessing.py    # Data cleaning functions  
│   ├── eda.py                  # Exploratory Data Analysis  
│   ├── feature_engineering.py   # Create new features  
│   ├── model_training.py        # Train models  
│   ├── visualization.py         # Generate plots  
│  
│── reports/            # Reports and visualizations  
│   ├── eda_report.html         # Auto-generated EDA report  
│   ├── model_evaluation.pdf    # Model performance summary  
│   ├── summary_findings.pdf    # Final analysis summary  
│   ├── plots/                  # Folder for generated plots  
│  
│── tests/              # Unit tests for data and models  
│   ├── test_data_processing.py  # Test data cleaning  
│   ├── test_feature_engineering.py # Test feature creation  
│   ├── test_model_training.py   # Validate model performance  
│  
│── docs/               # Project documentation  
│── .gitignore          # Ignore large files and virtual environments  
│── requirements.txt    # Python dependencies  
│── README.md           # Project overview and setup instructions  
│── config.yaml         # Configuration file for paths and parameters  

📁 tests/ – Unit Tests for Reliability

Tests ensure the data processing, feature engineering, and models work correctly.

Suggested Tests

1. Data Integrity Checks:
   • Ensure expected columns exist
   • Check for missing values and duplicates
   • Validate correct data types
2. Data Processing Tests:
   • Ensure missing values are handled
   • Validate data transformations
3. Feature Engineering Tests:
   • Confirm new feature calculations are correct
4. Model Performance Checks:
   • Verify model accuracy is within expected range
   • Ensure predictions are valid (no NaNs)

Example Test File: tests/test_data_processing.py

import pandas as pd
import pytest
from src.data_preprocessing import clean_data  

def test_no_missing_values():
    df = pd.DataFrame({"A": [1, 2, None], "B": [4, 5, 6]})
    cleaned_df = clean_data(df)
    assert cleaned_df.isnull().sum().sum() == 0

Run tests with:

pytest tests/

📁 reports/ – Final Reports and Insights

This folder stores EDA reports, model evaluations, and visualizations.

Suggested Reports

1. Exploratory Data Analysis (EDA) Report
   • Summary of dataset, missing values, distributions
   • Auto-generated using pandas-profiling

     from pandas_profiling import ProfileReport
     report = ProfileReport(df)
     report.to_file("reports/eda_report.html")
     

2. Model Performance Report
   • Accuracy, precision-recall, confusion matrix
   • Save as model_evaluation.pdf
3. Final Summary Report
   • Key insights, trends, and business recommendations
   • Save as summary_findings.pdf
4. Visualization Reports
   • Store graphs and charts as PNG/JPG

     import matplotlib.pyplot as plt  
     plt.savefig("reports/plots/histogram.png")
     

✅ Best Practices

✔ Modular Code: Keep reusable functions in src/
✔ Version Control: Use Git for tracking changes
✔ Automated Testing: Run tests with pytest
✔ Clear Documentation: Write README and docstrings
✔ Automate Reports: Use Python scripts for report generation

This structure makes your project organized, scalable, and production-ready. Let me know if you need help setting up a template!