pristinizer 1.0.0

Automatic data cleaning, EDA, and missing data visualization for pandas DataFrames

Pristinizer

Pristinizer is a lightweight Python package for automatic data cleaning, exploratory data analysis (EDA), and missing data visualization for pandas DataFrames.

It helps data scientists and ML engineers quickly clean and understand datasets with minimal effort.

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Features

• Automatic data cleaning

  • Removes duplicate rows
  • Standardizes column names
  • Handles missing values
  • Removes empty rows and columns

• Dataset summary (EDA)

  • Column data types
  • Missing value counts and percentages
  • Unique value counts

• Missing data visualization

  • Missing value matrix
  • Missing value heatmap
  • Missing value bar chart

• Simple and easy-to-use API

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Installation

From PyPI (recommended)

pip install pristinizer

From Source

git clone https://github.com/harmanbajwa2954/Pristinizer-pyProject.git
cd pristinizer
pip install .

Quick Start

import pandas as pd
import pristinizer as ps

# Load dataset
df = pd.read_csv("data.csv")

# Clean dataset
clean_df = ps.clean(df)

# Generate summary
summary = ps.summarize(df)
print(summary)

# Visualize missing data
ps.missing_matrix(df)
ps.missing_heatmap(df)
ps.missing_bar(df)

Examples

Input DataSet

Name	Age	Salary	City
A	25	50000	Delhi
B	NaN	60000	Mumbai
C	30	NaN	NaN

Output

column	datatype	missing_count	missing_%	unique_count
Age	float64	1	33.33	2
Salary	float64	1	33.33	2
City	object	1	33.33	2
Name	object	0	0.00	3

Available Functions

Data Cleaning

ps.clean(df)

Returns cleaned DataFrame.

Dataset Summary

ps.summarize(df)

Returns summary DataFrame containing:

• column name
• datatype
• missing count
• missing percentage
• unique count

Missing Data Visualization

Matrix view:

ps.missing_matrix(df)

Heatmap view:

ps.missing_heatmap(df)

Bar chart view:

ps.missing_bar(df)

Project Structure

pristinizer/
│
├── pristinizer/
│...... ├── init.py
│ ......├── cleaner.py
│.......├── eda.py
│...... ├── visualizer.py
│
├── README.md
├── pyproject.toml
├── LICENSE

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Requirements

• Python ≥ 3.8
• pandas
• matplotlib
• seaborn

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Use Cases

• Machine learning preprocessing
• Exploratory data analysis
• Data science projects
• Data cleaning automation
• Educational purposes

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Future Features

• Outlier detection
• Automatic datatype conversion
• Feature importance analysis
• Full EDA reports
• Integration with ML pipelines

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Contributing

Contributions are welcome.

Steps:

1. Fork the repository
2. Create a new branch
3. Make changes
4. Submit a pull request

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License

MIT License

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Author

Created as a data science utility package to simplify preprocessing workflows.
By – Harmanpreet Singh

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Support

If you find this useful, consider starring the repository on GitHub.