descripstats 0.1.1

Extended descriptive statistics for pandas DataFrames

descripstats

descripstats is a lightweight Python package that extends pandas DataFrame.describe() by adding additional descriptive statistics commonly used in exploratory data analysis (EDA).

It is designed to be simple, minimal, and compatible with modern versions of pandas (2.x+).

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✨ Features

In addition to the default pandas describe(), this package provides:

• mad: mean absolute deviation
• variance: variance
• sem: standard error of the mean
• sum: column sums
• skewness: distribution skew
• kurtosis: distribution kurtosis

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📦 Installation

pip install descripstats

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🚀 Usage

Option 1: Direct import

from descripstats import Describe

import pandas as pd

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

stats = Describe(df)
print(stats)

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Option 2: Module import

import descripstats as ds

stats = ds.Describe(df)
print(stats)

⚠️ Note: The correct function name is Describe (not Discribe).

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📊 Example

import pandas as pd
from descripstats import Describe

df = pd.DataFrame({
    "A": [1, 2, 3, 4, 5],
    "B": [10, 20, 30, 40, 50]
})

result = Describe(df)
print(result)

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⚙️ Compatibility

• pandas ≥ 2.0
• numpy ≥ 1.21
• Python ≥ 3.9

This package has been updated to remove deprecated pandas APIs such as DataFrame.mad().

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📁 Documentation

Example notebook:

https://github.com/shoukewei/descripstats/blob/main/docs/example.ipynb

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👤 Author

Developed by Shouke Wei
Deepsim Intelligence Technology Inc., Canada
© 2022–2026

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🧠 Notes

This package is intended for:

• quick EDA (exploratory data analysis)
• educational use
• lightweight statistical summaries

For advanced statistical workflows, consider using:

• pandas
• scipy
• statsmodels