mypreprocessinglib 0.1

A comprehensive data preprocessing library for Python

import pandas as pd import numpy as np from mypreprocessinglib.missing_value_handler import missing_value_handler from mypreprocessinglib.outlier_handler import outlier_handler from mypreprocessinglib.scaler import scaler from mypreprocessinglib.text_cleaner import text_cleaner from mypreprocessinglib.feature_engineer import feature_engineer from mypreprocessinglib.data_type_converter import data_type_converter from mypreprocessinglib.categorical_encoder import categorical_encoder from mypreprocessinglib.date_time_handler import date_time_handler

Example usage

data = pd.DataFrame({'A': [1, 2, np.nan, 4], 'B': [np.nan, 6, 7, 8]}) missing_handler = missing_value_handler() missing_values = missing_handler.detect_missing_values(data) print(missing_values)

Example usage

data = pd.DataFrame({'A': [1, 2, 100, 4], 'B': [5, 6, 7, 800]}) outliers = outlier_handler.detect_outliers(data) print(outliers)

Example usage

data = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [4, 3, 2, 1]}) scaled_data = scaler.standardize_data(data) print(scaled_data)

Example usage

text = "This is a sample text, with punctuation and stopwords!" cleaned_text = text_cleaner.clean_text(text) print(cleaned_text)

Example usage

data = pd.DataFrame({'Feature1': [1, 2, 3], 'Feature2': [4, 5, 6]}) data_with_new_feature = feature_engineer.create_new_features(data) print(data_with_new_feature)

Example usage

data = pd.DataFrame({'A': ['1', '2', '3'], 'B': ['4', '5', '6']}) numeric_data = data_type_converter.convert_to_numeric(data, columns=['A', 'B']) print(numeric_data)

Example usage

data = pd.DataFrame({'A': ['cat', 'dog', 'bird'], 'B': ['red', 'blue', 'green']}) encoded_data = categorical_encoder.one_hot_encode(data, columns=['A', 'B']) print(encoded_data)

Example usage

data = pd.DataFrame({'Data': ['2023-01-01', '2023-02-01', '2023-03-01']}) data_with_features = date_time_handler.extract_data_features(data, column='Data') print(data_with_features)