Universal-EDA is a Python library that provides an easy-to-use interface for Exploratory Data Analysis (EDA) and data loading. It supports various datasets and helps streamline the EDA process.
This project was inspired by the process of updating the Simple Linear Regression Project in scikit-learn and discovering that the Boston Housing dataset had been removed in later versions of scikit-learn. Universal-EDA aims to provide an easy way to work with various datasets, including the Boston Housing dataset, for EDA and other machine learning tasks.
- DataExplorer class: Perform EDA on any dataset, including summary statistics, missing values, and visualization
- DataLoader class: Load common datasets from scikit-learn or convert DataFrames to CSV files
Clone the repository and install the required dependencies:
git clone https://github.com/sminerport/universal-EDA.git
cd universal-EDA
pip install -r requirements.txtfrom src import DataExplorer, DataLoader
# Load a sample dataset
data_loader = DataLoader()
df = data_loader.load_toy_dataset('boston')
# Perform EDA on the dataset
data_explorer = DataExplorer(df)
data_explorer.basic_info()
# Save the DataFrame as a CSV file
data_loader.dataframe_to_csv('boston_housing.csv')Sample Python interpreter output:
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
This project is licensed under the MIT License. See the LICENSE file for more information.