Welcome to PandasParadise, a comprehensive toolkit for data analysis and manipulation using the powerful pandas library. Created by Himel, this project aims to simplify and enhance your data processing workflows.
PandasParadise offers a suite of utilities designed to streamline common data manipulation tasks, making it easier for data scientists and analysts to clean, transform, and visualize data.
- Data Cleaning: Effortlessly handle missing values, duplicates, and outliers.
- Data Transformation: Advanced functions for merging, grouping, and reshaping datasets.
- Visualization Tools: Seamless integration with
matplotlibandseabornfor data visualization. - Performance Enhancements: Techniques and utilities to optimize data processing tasks.
- User-Friendly Documentation: Detailed documentation with examples to help you get started quickly.
To install PandasParadise, make sure you have Python 3.6 or higher. You can install the package via pip:
pip install pandasparadiseHere’s a quick example to demonstrate how to use PandasParadise:
import pandas as pd
from pandasparadise import cleaner, transformer, visualizer
# Load a sample dataset
df = pd.read_csv('data/sample_data.csv')
# Clean the dataset by filling missing values
clean_df = cleaner.fill_missing_values(df, method='median')
# Transform the dataset by grouping
grouped_df = transformer.group_data(clean_df, by='category')
# Visualize the transformed data
visualizer.plot_boxplot(grouped_df, column='value')For more detailed usage and examples, please refer to the documentation.
Contains scripts and notebooks related to handling and processing categorical data.
Contains scripts for customizing pandas settings and options to enhance usability.
Provides tools and functions for cleaning datasets, including handling missing values and removing duplicates.
Includes methods for generating descriptive statistics and summaries of datasets.
Contains examples and utilities for applying functions across pandas DataFrame and Series objects.
Demonstrates how to use the groupby function in pandas to group and aggregate data.
Provides examples and tools for reading from and writing to various data formats (CSV, Excel, SQL, etc.).
Shows different ways to iterate over pandas DataFrame and Series objects efficiently.
Contains examples of using nlargest() and nsmallest() methods to find the largest and smallest values in a dataset.
A comprehensive guide to working with pandas DataFrame objects, covering various methods and operations.
A comprehensive guide to working with pandas Series objects, covering various methods and operations.
Includes scripts for sorting DataFrame and Series objects based on various criteria.
Contains examples of using string methods to manipulate text data in pandas.
Provides examples of creating visualizations using matplotlib, seaborn, and pandas built-in plotting.
Demonstrates the use of window functions in pandas for performing rolling and expanding operations.
We welcome contributions from the community! If you would like to contribute, please follow these steps:
- Fork the repository on GitHub.
- Create a new branch (
git checkout -b feature/new-feature). - Make your changes and commit them (
git commit -m 'Add new feature'). - Push your branch to GitHub (
git push origin feature/new-feature). - Open a pull request and describe your changes.
Please ensure your code follows our coding standards and includes appropriate tests.
This project is licensed under the MIT License. See the LICENSE file for more details.
For questions, feedback, or suggestions, feel free to open an issue on GitHub or contact the maintainer directly:
- Himel Sarder
- Dept. of CSE, BSFMSTU
- Bangladesh
- Email: Click here..
Thank you for using PandasParadise! We hope it makes your data analysis tasks easier and more enjoyable.