Cleanlyze 0.1v

Cleanlyze is a Python library designed to streamline data processing tasks. With Cleanlyze, you can efficiently clean, transform, and visualize your data using powerful tools like pandas, numpy, and scikit-learn.

Features

• Data Cleaning: Handle missing values, remove duplicates, and manage outliers with ease.
• Data Transformation: Normalize, scale, encode data, and create polynomial features for better machine learning models.
• Visualization: Create plots for data distribution, histograms, and correlation matrices.
• File I/O: Seamlessly read and write data from CSV, Excel, and other file formats.

Commands

• remove_duplicates: 'Remove duplicate rows from DataFrame.',
• handle_missing_values: 'Handle missing values with specified strategy.',
• normalize_data: 'Normalize the DataFrame using Min-Max scaling.',
• create_polynomial_features: 'Create polynomial features of a specified degree.',
• plot_histogram: 'Plot a histogram for a specific column.',
• plot_correlation_matrix: 'Plot the correlation matrix of the DataFrame.',
• read_csv: 'Read a CSV file into a DataFrame.',
• write_csv: 'Write the DataFrame to a CSV file.'

Installation

To install Cleanlyze, simply run:

pip install cleanlyze

Example

import cleanlyze as cz
import pandas as pd

# Load your data
df = pd.read_csv('data.csv')

# Clean the data by removing duplicates and handling missing values
df_cleaned = cz.cleaning.remove_duplicates(df)
df_cleaned = cz.cleaning.handle_missing_values(df_cleaned)

# Normalize the data
df_normalized = cz.transformation.normalize_data(df_cleaned)

# Plot the distribution of a column
cz.visualization.plot_histogram(df_normalized, 'column_name')

Dependencies

• Cleanlyze requires the following libraries:

• pandas

• numpy

• scikit-learn

• matplotlib

• seaborn

• scipy

These will be installed automatically when you install Cleanlyze.

• Contributing

Contributions are welcome! If you'd like to contribute, please fork the repository and submit a pull request.

• License This project is licensed under the MIT License.