Data Forge is a Python package designed to provide a comprehensive suite of tools specialized in managing and analyzing data. It offers a user-friendly toolkit suitable for individuals of all levels of expertise. This initial release encompasses the following features:

• Effortless reading of datafiles with support for Pandas-compatible extensions.
• Simplified dataframe creation by specifying column names and corresponding values.
• Automatic generation of hexadecimal and numeric identifiers.
• Provision of common statistical analyses for specified columns.
• Histogram plotting functionality.
• Easy visualization of class distribution within the database.
• Data standardization capabilities.
• Feature discretization support.
• Creation of fold columns for conducting stratified k-fold analyses.

This class enables the creation and manipulation of Pandas DataFrames. It supports the following functionalities:

• Reading data from various file formats: CSV, Excel, JSON, Parquet, Feather, and Pickle.
• Generating unique identifiers for rows.
• Adding new data to the DataFrame.
• Displaying the DataFrame.
• Retrieving column names.
• Printing specific columns.

This class extends the PDBuilder class and provides additional functionalities for numerical analysis and preprocessing. It includes the following features:

• Computing statistics for numerical columns.
• Plotting histograms.
• Analyzing data balance.
• Standardizing numerical data.
• Reconstructing data.
• Converting non-numeric columns to numeric values.
• Creating folds for cross-validation.

This toolkit requires the following dependencies:

• pandas
• numpy
• matplotlib
• scikit-learn

To install the dependencies, run:

pip install dforge

You can use this toolkit in your Python projects by importing the necessary classes and functions. Here's an example of how to use the PDBuilder class:

import dforge as df

# Create a DataFrame from data
data = [[1, 'A', 10], [2, 'B', 20], [3, 'C', 30]]
columns = ['ID', 'Category', 'Value']
data_pd = df.PDBuilder(data=data, columns=columns)

# Display DataFrame
data_pd.show_dataset()

# Add new data
new_data = [[4, 'D', 40], [5, 'E', 50]]
data_pd.add_data(new_data)

# Display DataFrame with added data
data_pd.show_dataset()

Stay tuned for upcoming releases, which will incorporate the following enhancements:

• Missing data completion functionality.
• Conversion of dataframes into datasets suitable for machine learning inputs.
• Introduction of PDTextAnalysis for handling features related to text manipulation.