🍊 Completeness

Data Completeness Evaluation Description

Overview

This metric provides a set of tools for assessing the completeness of tabular data. It is particularly useful during the data preprocessing and cleaning stages of a data analysis workflow.

Components

metric.py (Module)

Description

This module includes the DataCompleteness class, which contains methods to calculate completeness scores for dataframes and individual columns. These methods are instrumental in identifying columns with missing data and quantifying the extent of missingness.

Authors

Faouzi ADJED
Anani DJATO

Class: DataCompleteness

Methods

completeness_tabular(data: pd.DataFrame) -> float: Calculates the average completeness score for a dataframe.
data_completion(data: pd.Series) -> float: Calculates the completeness score for an individual data column.

Example

from dqm.completeness.metric import DataCompleteness
import pandas as pd

def main():
    completeness_evaluator = DataCompleteness()
    df = pd.read_csv('your_data.csv', sep=";")
    overall_score = completeness_evaluator.completeness_tabular(df)
    column_score = completeness_evaluator.data_completion(df['your_column'])
    print(f'Overall Data Completeness Score: {overall_score}')
    print(f'Completeness Score for Column: {column_score}')

if __name__ == "__main__":
    main()