Datasis 0.1

A short description of your package

Datasis - A Python Library for Data Analysis

Introduction

Datasis is a data analysis library built using pandas, numpy, matplotlib, and seaborn. It provides tools for loading, cleaning, analyzing data, handling outliers and duplicates, as well as filtering, grouping, and sorting data.

Requirements

Make sure you have the following libraries installed before using Datasis:

pip install Datasis

IF you want a new version :

pip install update Datasis

How to Use

1. Import the Class and Create an Object

from Datasis import Datasis as da

2. Load Data

da.load_data("data.csv")  # Or any Excel or JSON file

3. Display Data

print(da.show_data())  # Display the first 5 rows
print(da.tail())       # Display the last 5 rows

4. Get Data Info and Statistics

print(da.get_info())
print(da.get_statistics())

5. Handle Missing Values

print(da.missing_values())  # Show count of missing values per column
print(da.fill_missing(strategy='mean'))  # Replace missing values with the mean

6. Detect and Handle Outliers

print(da.outlier_detection("column_name"))  # Detect outliers
print(da.drop_outliers("column_name"))  # Remove outliers

7. Remove Duplicates

print(da.drop_duplicates())

8. Filter, Group, and Sort Data

print(da.filter_data("column_name > 50"))  # Filter data based on a condition
print(da.group_by("category"))  # Group data by a specific column
print(da.sort_data("column_name", ascending=False))  # Sort data in descending order

9. Save Data After Modifications

da.save_data("cleaned_data.csv")

Contribution

If you'd like to improve the code or add new features, feel free to submit a pull request on GitHub.

License

This library is licensed under the MIT License. See the LICENSE file for more information.

Contributing

Contributions are welcome! Feel free to submit issues or pull requests to improve the library.

Author

Created by Ibrahem abo kila. For any inquiries, please contact me at [e-mail].