myeda 0.1.9

One-line Exploratory Data Analysis (EDA) library

myeda

A lightweight Exploratory Data Analysis (EDA) library that provides one-line statistical summaries and optional visualizations for faster data understanding.

Features

One-line EDA summaries

Missing value analysis

Descriptive statistics

Optional visualizations (explicit, not automatic)

Clean, modular API

Beginner-friendly and extensible

Installation

pip install myeda

Basic Usage

import pandas as pd from myeda import overview, report

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

overview(df) report(df)

Visualizations (Explicit & Optional)

from myeda.viz import ( plot_numeric_distribution, plot_boxplot, plot_categorical_counts, plot_correlation_heatmap )

plot_numeric_distribution(df, "Age") plot_boxplot(df, "Fare") plot_categorical_counts(df, "Sex") plot_correlation_heatmap(df)

Visualizations are never automatic — you control when to plot.

Project Structure

EDA/
|-- examples/
|   |-- titanic_dataset.csv
|   `-- titanic_demo.ipynb
|
|-- myeda/
|   |-- __init__.py
|   |-- report.py
|   |
|   |-- core/
|   |   |-- overview.py
|   |   |-- missing.py
|   |   `-- statistics.py
|   |
|   `-- viz/
|       `-- visualization.py
|
|-- tests/
|   `-- test_statistics.py
|
|-- setup.py
|-- pyproject.toml
|-- requirements.txt
|-- README.md
|-- LICENSE
`-- .gitignore

Module Responsibilities

core/overview.py

Dataset shape

Column types

Basic dataset information

core/missing.py

Missing value counts

Missing percentage per column

core/statistics.py

Mean, median, mode

Variance, standard deviation

Numerical summaries

viz/visualization.py

Numeric distributions

Boxplots

Categorical counts

Correlation heatmaps

Examples

Check the examples/ directory for:

Titanic dataset

Jupyter notebook demonstrating full EDA workflow

How users can import EVERYTHING (README-ready)

Dataset overview

from myeda import dataset_overview

dataset_overview(df)

Missing-value analysis

from myeda import missing_overview, missing_summary

missing_overview(df)
missing_summary(df)

Statistical summaries

from myeda import numeric_summary, categorical_summary

numeric_summary(df)
categorical_summary(df)

Visualizations (explicit & optional)

from myeda import (
    plot_numeric_distribution,
    plot_boxplot,
    plot_categorical_counts,
    plot_correlation_heatmap,
)

plot_numeric_distribution(df, "Age")
plot_boxplot(df, "Fare")
plot_categorical_counts(df, "Sex")
plot_correlation_heatmap(df)

Full EDA (recommended)

from myeda import EDAReport

eda = EDAReport(df)
results = eda.run()

Testing

pytest

License

MIT License

Copyright (c) 2026 Khaja Mubashir Arsalan

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Author

Khaja Mubashir Arsalan