Metadata-Version: 2.1
Name: data-profiler-kit
Version: 0.1.1
Summary: A library to provide quick and insightful data profiling for pandas DataFrames
Author-email: Najm Eddine Charaf <oldcharaf@gmail.com>
License: MIT License
        
        Copyright (c) 2024 Najm Eddine Charaf
        
        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.
Project-URL: Homepage, https://github.com/cnajmeddine/data-profiler
Keywords: data,profiling,pandas,analytics
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas>=1.0.0
Requires-Dist: numpy>=1.18.0

# DataProfilerKit

A Python library that provides quick and insightful data profiling for pandas DataFrames. It generates detailed reports including missing values analysis, data type information, correlations, outliers, and column statistics in a clear, organized format.

## Installation

```bash
pip install data-profiler-kit
```

## Usage

```python
from dataprofilerkit import DataProfiler
import pandas as pd

# Create or load your DataFrame
df = pd.read_csv('your_data.csv')

# Create a DataProfiler instance
profiler = DataProfiler(df)

# Generate the profile
profile = profiler.generate_profile()

# Access different aspects of the profile
print("Basic Information:")
print(profile['basic_info'])

print("\nMissing Values Analysis:")
print(profile['missing_values'])

print("\nColumn Statistics:")
print(profile['column_stats'])

print("\nDuplicates Analysis:")
print(profile['duplicates'])

print("\nOutliers Analysis:")
print(profile['outliers'])
```

## Core Functionality

- ### Basic DataFrame Information:
    - Number of rows, columns, and total cells.
    - Memory usage of the DataFrame.
    - Data types and their counts.

- ### Missing Value Analysis:
    - Total missing values across the DataFrame.
    - Missing values by column.
    - Percentage of missing values for each column.

- ### Column-wise Analysis:

    - #### Numeric Columns:
        - Descriptive statistics (mean, median, standard deviation, etc.).
        - Skewness and kurtosis.

    - #### Categorical Columns:
        - Count of unique values.
        - Top 5 most frequent values with their percentages.

    - #### Datetime Columns:
        - Minimum and maximum values.
        - Range in days.

- ### Duplicate Detection:
    - Duplicate rows (count and percentage).
    - Duplicate columns (count and list of column names).

- ### Outlier Detection:
    - For numeric columns, detects outliers using:
        - Z-score method (with indices and percentages).
        - Interquartile Range (IQR) method (with indices and percentages).

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the LICENSE file for details.
