forgekit 1.0.2

A collection of handy tools for data handling, visualization, and reporting.

forgekit

forgekit is a python3 package designed to simplify data handling, visualization, and reporting. It provides a wide range of functions for cleaning, transforming, visualizing data, and performing machine learning tasks.

Features

• Display and summarize DataFrames
• Export and load CSV files
• Handle missing data, outliers, and invalid values
• Data scaling and transformation (min-max scaling, standardization, log transformation)
• Generate interactive and static plots (matplotlib and Plotly)
• Machine learning utilities (K-Means clustering, feature importance calculation, train-test splitting)

Installation

You can install forgekit by cloning the repository and installing it locally.

1. Clone the repository:

   git clone https://github.com/yourusername/forgekit.git
   

2. Navigate to the package directory and install it:

   cd forgekit
   pip3 install .
   

3. Alternatively, install dependencies directly from requirements.txt:

   pip3 install -r requirements.txt
   

Usage

Here’s an example of how to use forgekit:

import pandas as pd
from forgekit import ForgeKit

# Sample data
data = {
    'Domain': ['example.com', 'example.net'],
    'Price': [10.0, 12.5]
}
df = pd.DataFrame(data)

# Display the DataFrame
ForgeKit.display_dataframe(df)

# Plot the DataFrame
ForgeKit.plot_dataframe(df, kind='bar', title="Domain Prices")

Available Functions

Data Display and Summarization:

• display_dataframe(): Display a DataFrame with a row limit.
• summary_stats(): Show summary statistics of a DataFrame.
• custom_summary(): Show a custom summary of data types, missing values, and statistics.

Data Cleaning:

• impute_missing_data(): Handle missing values with strategies like mean, median, mode, or a constant value.
• remove_outliers(): Remove outliers using the IQR method.
• remove_duplicates(): Remove duplicate rows in the DataFrame.
• clean_text_columns(): Clean text columns by stripping whitespace and converting to lowercase.

Data Transformation:

• minmax_scale(): Scale numerical data between 0 and 1.
• standard_scale(): Standardize numerical data to have zero mean and unit variance.
• log_transform(): Apply log transformation to reduce skewness.

Data Visualization:

• plot_dataframe(): Generate static plots using matplotlib.
• interactive_plot(): Generate interactive plots using Plotly.

Machine Learning Tools:

• kmeans_clustering(): Perform K-Means clustering on the DataFrame.
• train_test_split_data(): Split the DataFrame into training and test sets.
• feature_importance(): Calculate feature importance using a Random Forest classifier.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Feel free to open issues or pull requests if you would like to contribute!