TurboImpute 0.1.0

A library for handling missing values in datasets

TurboImpute

TurboImpute is a Python library for handling missing values in datasets. It provides tools for detecting, analyzing, imputing, and visualizing missing data.

Installation

pip install TurboImpute

or

git clone https://github.com/yourusername/TurboImpute.git
cd TurboImpute
pip install .

Usage

To use TurboImpute, follow these steps:

import pandas as pd
import TurboImpute as tp

# Load your dataset
df = pd.read_csv('your_dataset.csv')

# Detect missing values
missing_summary = tp.missing_summary(df)
print(missing_summary)

# Impute missing values using mean
df = tp.impute_mean(df, columns=['column1', 'column2'])

# Remove rows with missing values
df = tp.drop_missing_rows(df)

# Visualize missing values (boxplot for numerical variables)
tp.visualize_missing(df)

Functions Available

Detection (TurboImpute.detection)

• identify_missing(df): Identifies missing values in the dataframe.
• missing_summary(df): Provides a summary of missing values in the dataframe.

Imputation (TurboImpute.imputation)

• impute_mean(df, columns=None): Imputes missing values using mean.
• impute_median(df, columns=None): Imputes missing values using median.
• impute_mode(df, columns=None): Imputes missing values using mode.
• impute_value(df, value, columns=None): Imputes missing values with a specified value.
• impute_ml(df, method='knn', columns=None, **kwargs): Imputes missing values using machine learning methods such as KNN, Decision Tree, or GBM.
• impute_knn(df, columns=None, n_neighbors=5): Imputes missing values using K-Nearest Neighbors.
• impute_dt(df, columns=None, **kwargs): Imputes missing values using Decision Tree regression.
• impute_gbm(df, columns=None, **kwargs): Imputes missing values using Gradient Boosting regression.

Removal (TurboImpute.removal)

• drop_missing_rows(df): Drops rows with missing values.
• drop_missing_columns(df): Drops columns with missing values.

Visualization (TurboImpute.visualization)

• visualize_missing(df): Visualizes missing values using boxplots for numerical variables.

License

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