datasense-eda 0.1.0

Datasense: An Explainable EDA library for automated exploratory data analysis, outlier detection, feature importance, and visualization.

datasense 📊

A Python library for automated exploratory data analysis (EDA), data cleaning, and visualization. Built for beginners and analysts to quickly understand and preprocess datasets.

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✨ Features

• Dataset Summary: Overview of shape, dtypes, missing values, duplicates.
• Missing Value Handling: Detect and impute missing values (mean, median, mode, constant, drop).
• Outlier Detection: Identify outliers using Z-score or IQR methods.
• Feature Importance: Calculate and visualize feature importance for regression/classification.
• Time-Series Analysis: Decomposition, rolling statistics, and trend detection.
• Visualizations: Histograms, boxplots, count plots, correlation matrices, scatter plots, pair plots.
• Automated Recommendations: Get actionable insights for data preprocessing.

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🚀 Why Datasense?

• ⚡ One-line automated EDA for quick dataset understanding.
• 🧹 Built-in cleaning and preprocessing to save time.
• 📊 Visual + tabular insights, ready for analysis or ML pipelines.
• 🔧 Beginner-friendly but powerful enough for production workflows.

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Installation

git clone https://github.com/Akash-Sare03/datasense.git
cd datasense
pip install -r requirements.txt

Or install from PyPI: 
pip install datasense

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Quick Start

import pandas as pd
from datasense import analyze

df = pd.read_csv("your_data.csv")

# Run a full EDA
analyze(df, target_col="price")

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Usage Examples

Basic EDA Report

from datasense import analyze

analyze(df, target_col="price", outlier_method="zscore")

Handle Missing Values

from datasense import handle_missing_values

df_clean, report = handle_missing_values(df, method="mean")

Feature Importance

from datasense import feature_importance_calculate

fi_df, markdown_report, task_type = feature_importance_calculate(df, target_col="target", top_n=10)
print(markdown_report)

Time-Series Analysis

from datasense import analyze_timeseries

analyze_timeseries(df, date_col="date", target_col="sales", freq="D")

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Example Notebooks

See practical examples and full workflows in the included Jupyter notebooks:

• Basic EDA Example
• Time-Series Example

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API Reference

Main Functions

• analyze(): Generate a full EDA report.
• summarize_dataset(): Dataset overview.
• handle_missing_values(): Impute or remove missing values.
• detect_outliers(): Find outliers using Z-score or IQR.
• feature_importance_calculate(): Compute feature importance.
• analyze_timeseries(): Decompose and plot time-series data.
• visualize(): Auto-generate plots for numeric/categorical features.

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Contributing

1. Fork the project.
2. Create a feature branch: git checkout -b feature-name
3. Commit changes: git commit -m 'Add feature'
4. Push to the branch: git push origin feature-name
5. Open a pull request.

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License

This project is licensed under the MIT License. See LICENSE for details.

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Deployment

This package is available on PyPI. Install it via:

pip install datasense

Or deploy locally:

python setup.py sdist bdist_wheel
twine upload dist/*

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Support

If you have any questions or issues, please open an issue here.