DataMason 0.1.0a2

A powerful Python package for data analysis and manipulation.

DataMason

DataMason is a powerful Python package designed to make data analysis and manipulation easier for data professionals of all skill levels. It offers a collection of tools to clean, transform, analyze, and visualize datasets, allowing users to uncover insights and make informed decisions.

Features

• Data Cleaning: Easily identify and address data quality issues.
• Data Transformation: Reshape and transform datasets to fit your analysis needs.
• Data Analysis: Perform insightful data analysis to uncover patterns and trends.
• Data Visualization: Create meaningful visualizations that help communicate findings.
• Data Validation: Validate data based on common rules and constraints.

Installation

Install DataMason using pip:

pip install datamason

Usage

Importing the Package

import datamason as dm

Data Cleaning

# Deduplicate rows
cleaned_data = dm.prepare.deduplicate(data)

# Fill gaps (missing values)
filled_data = dm.prepare.fill_gaps(cleaned_data)

# Trim outliers
final_data = dm.prepare.trim_outliers(filled_data)

Data Transformation

# Reshape data
reshaped_data = dm.transform.reshape_data(final_data)

# Encode categorical variables
encoded_data = dm.transform.encode_cats(reshaped_data)

Data Analysis

# Summarize the data
summary = dm.analyze.summarize(encoded_data)

# Find correlations
correlations = dm.analyze.find_correlations(encoded_data)

Data Visualization

# Plot a line chart
dm.visualize.plot_line(encoded_data, 'time', 'value')

# Plot a scatter chart
dm.visualize.plot_scatter(encoded_data, 'feature1', 'feature2')

Data Validation

# Define validation rules
rules = {
    'age': {'type': int, 'min_value': 0, 'max_value': 100},
    'name': {'type': str},
}

# Validate the data
is_valid, errors = dm.validate(encoded_data, rules)

Contributing

We welcome contributions from the community to help improve DataMason. If you're interested in contributing, please follow these steps:

1. Share Your Ideas: Before making significant changes, send an email with your thoughts and recommendations to the contact below. We'll discuss your ideas and how they fit into the project.

2. Fork the Repository: If your idea is approved, you can fork the repository and work on your changes.

3. Submit a Pull Request: Once you've made your changes, submit a pull request for review. Please ensure that your code adheres to the existing coding standards and includes appropriate tests and documentation.

4. Review and Merge: We'll review your pull request and provide feedback if necessary. Upon approval, your contribution will be merged into the project.

5. Acknowledgment: Contributors are valued members of our community, and we'll acknowledge your hard work in our project documentation.

For any questions or further guidance, please contact us at [e-mail]](mailto:[e-mail]).

Thank you for considering contributing to DataMason!

License

MIT License

Contact

[e-mail]