semvec/pypi-packages huggingface dataset:
eda-profiler 0.1.2
A lightweight package for detailed Exploratory Data Analysis on pandas DataFrames.
Requires Python: >=3.8

# EDA Profiler

A lightweight yet comprehensive Python package for performing Exploratory Data Analysis (EDA) on a pandas DataFrame.

`eda_profiler` quickly generates a detailed profile report for each column in your dataset, providing essential statistics for data cleaning, feature engineering, and initial data understanding.

## Features

-   Calculates counts and percentages of missing values.
-   Differentiates between numerical and categorical columns to provide relevant stats.
-   For **numerical** columns, it computes:
    -   Standard descriptive stats (mean, std, min, max).
    -   A full range of percentiles (1%, 5%, 10%, 25%, 50%, 75%, 90%, 95%, 99%).
    -   Distribution shape metrics: Skewness and Kurtosis.
    -   Dispersion metrics: IQR and Coefficient of Variation.
    -   Count of zero values.
-   For **categorical** columns, it computes:
    -   Cardinality (unique value count).
    -   If value count is <=10 then it will show the unique values as well.
    -   Mode (most frequent value), its frequency, and percentage.

## How to Use

The package provides a single, easy-to-use function: `profile_df`.

```python
import pandas as pd
import numpy as np
from eda_profiler import profile_df

# 1. Create a sample DataFrame
data = {
    'numeric_col': np.random.randn(100) * 100,
    'categorical_col': np.random.choice(['A', 'B', 'C'], 100, p=[0.6, 0.3, 0.1]),
    'mixed_col_with_nan': [1, 2, np.nan, 4, 5, 1, 2, np.nan] * 12 + [1,2,np.nan, 4]
}
df = pd.DataFrame(data)

# 2. Generate the EDA profile
eda_summary = profile_df(df)

# 3. Print the summary
# Transposing (.T) is often useful for readability
print(eda_summary.T)

```

## Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue.

## License

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