eda-datapilot 0.2.2

DataPilot - A powerful, automated Exploratory Data Analysis (EDA) library in Python

DataPilot

DataPilot is a Python library for automated exploratory data analysis (EDA). It analyzes a pandas DataFrame and provides dataset summaries, missing-value analysis, numerical and categorical statistics, duplicate detection, correlations, outlier detection, data-quality problems, recommendations, ML insights, visualizations, and an HTML report.

Features

• Dataset shape, memory usage, and data-type summaries
• Missing values and duplicate-row analysis
• Numerical and categorical statistics
• Pearson, Spearman, and Kendall correlations
• IQR and Z-score outlier detection
• Data-quality alerts and preprocessing recommendations
• ML task detection, feature importance, and PCA insights
• Standalone HTML reports with interactive charts

Requirements

• Python 3.10 or newer
• A CSV file or another dataset that can be loaded into a pandas DataFrame

Installation

From the project directory:

python -m venv .venv
.venv\Scripts\activate
python -m pip install --upgrade pip
pip install -e .

To install the test and development dependencies as well:

pip install -e ".[dev]"

Quick Start

Create a Python file such as analyze_data.py:

from pathlib import Path

import pandas as pd
import datapilot


df = pd.read_csv("data/dataset.csv")

# Set target to None when the dataset has no target column.
report = datapilot.analyze(df, target="target_column")

output_path = Path("reports/datapilot_report.html")
output_path.parent.mkdir(parents=True, exist_ok=True)
report.to_html(str(output_path))

print(f"Report saved to: {output_path.resolve()}")
print(f"Quality score: {report.data.get('quality_score')}")

Run the script from the project directory:

python analyze_data.py

Open the generated reports/datapilot_report.html file in a browser.

To save the report and open it automatically:

report.show("reports/datapilot_report.html", open_browser=True)

Step-by-Step Analysis

Use EDA when you need individual analysis results in Python:

import pandas as pd
from datapilot import EDA


df = pd.read_csv("data/dataset.csv")
eda = EDA(df, target="target_column")

summary = eda.summary()
missing = eda.missing()
numeric = eda.numeric()
categorical = eda.categorical()
duplicates = eda.duplicates()
correlation = eda.correlation()
outliers = eda.outliers()
problems = eda.problems()
recommendations = eda.recommendations()
ml_insights = eda.ml_insights()

print(summary)
print(problems)
print(recommendations)

report = eda.report()
report.to_html("reports/detailed_report.html")

Each analysis method returns a Python dictionary, while report.data contains all results from the complete analysis.

Configuration

Use EDAConfig to change sampling, thresholds, outlier detection, or report settings:

import pandas as pd
from datapilot import EDA, EDAConfig


df = pd.read_csv("data/dataset.csv")

config = EDAConfig(sample_size=10_000)
config.thresholds.high_correlation = 0.9
config.outliers.iqr_multiplier = 1.5
config.outliers.z_score_threshold = 3.0
config.report.title = "Dataset EDA Report"
config.report.theme = "light"

eda = EDA(df, target="target_column", config=config)
eda.report().to_html("reports/configured_report.html")

Run the Included Demo

The included demo creates a synthetic dataset with missing values, outliers, duplicates, categorical columns, and a target column. It writes DataPilot_Demo_Report.html to the project directory.

python examples\demo.py

Run Tests

python -m pytest

Public API

The package exports the following public objects:

from datapilot import EDA, EDAConfig, analyze

• analyze(df, target=None) runs a complete analysis.
• EDA(df, target=None, config=None) provides step-by-step analysis.
• EDAConfig configures thresholds, outliers, reports, and sampling.

Project Layout

datapilot/
├── datapilot/              # Library source code
│   ├── analyzer.py         # EDA and analyze APIs
│   ├── config.py           # EDAConfig and report settings
│   ├── report.py           # HTML report generation
│   └── templates/          # Report template
├── examples/demo.py        # Runnable library example
├── tests/                  # Automated tests and sample data
├── pyproject.toml          # Package metadata and dependencies
└── requirements.txt        # Runtime and test dependencies