jan883-eda 0.2.1

Library that provides helperfunctions for data science preprocessing and exploratory data analysis.

jan883-eda

A collection of utility functions for data analysis, preprocessing, model evaluation, and clustering in Python. Designed to streamline the workflow of data scientists and machine learning practitioners.

Installation

Install the package via pip:

pip install jan883-eda

Usage

Below are examples demonstrating how to use some of the key functions in the package. These examples assume you have a DataFrame (your_dataframe) or feature matrix (X) and target vector (y) ready.

Exploratory Data Analysis (EDA)

• Inspect DataFrame:

from jan883_eda import inspect_df

inspect_df(your_dataframe)

This displays the head, shape, description, NaN values, and duplicates of the DataFrame.

• Column Summary:

from jan883_eda import column_summary

summary = column_summary(your_dataframe)
print(summary)

Data Preprocessing

• Update Column Names:

from jan883_eda import update_column_names

updated_df = update_column_names(your_dataframe)

• Label Encoding:

from jan883_eda import label_encode_column

encoded_df = label_encode_column(your_dataframe, 'column_name')

Model Evaluation

• Evaluate Classification Model:

from jan883_eda import evaluate_classification_model
from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier()
evaluate_classification_model(model, X, y)

• Test Multiple Regression Models:

from jan883_eda import best_regression_models

results = best_regression_models(X, y)
print(results)

Functions Overview

The package provides a variety of functions grouped by their purpose:

• EDA Functions: inspect_df, column_summary, univariate_analysis, and more.
• Data Preprocessing: update_column_names, label_encode_column, one_hot_encode_column, scale_X_train_X_test, and more.
• Model Evaluation: evaluate_classification_model, evaluate_regression_model, best_classification_models, best_regression_models, and more.
• Clustering Analysis: plot_elbow_method, plot_intercluster_distance, plot_silhouette_visualizer, and more.

For a complete list of functions and their detailed documentation, refer to the docstrings within the source code or the official documentation.

Requirements

The following dependencies are required to use the package:

• Python >= 3.6
• pandas >= 1.0.0
• numpy >= 1.18.0
• matplotlib >= 3.0.0
• seaborn >= 0.10.0
• scikit-learn >= 0.22.0
• yellowbrick >= 1.0.0
• imblearn >= 0.7.0

These will be automatically installed when you install the package via pip, assuming the package is properly configured with a setup.py or pyproject.toml file.

License

This package is distributed under the MIT License. See the LICENSE file for more information.

Contact

For questions, bug reports, or contributions, please visit the GitHub repository or contact the author at [e-mail].

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This README.md provides a clear and concise overview of the package, including its purpose, installation instructions, usage examples, function categories, dependencies, licensing, and contact information, making it suitable for PyPI.