rdatapp 1.0

A recoded data preprocessing library for handling various data cleaning and transformation tasks. The library includes classes for text cleaning, missing value imputation, one-hot encoding, and more.

RDATAPP: Recoded Data Preprocessing Library

Overview

RDATAPP is a comprehensive data preprocessing library designed to handle various data cleaning and transformation tasks. This library includes classes and methods for text cleaning, missing value imputation, one-hot encoding, outlier detection, feature engineering, and more.

Features

• Text Cleaning: Convert text to lowercase, remove punctuation and stopwords, and lemmatize words.
• Missing Value Handling: Impute missing values using mean, median, or a constant value. Alternatively, delete rows with missing values.
• Encoding: One-hot encode and label encode categorical columns.
• Outlier Detection: Detect and remove outliers using the Interquartile Range (IQR) method.
• Scaling: Apply Min-Max scaling and standard scaling to numerical columns.
• Feature Engineering: Create new features by applying functions to existing columns.
• Date-Time Handling: Convert columns to datetime format and extract date parts like year, month, and day.

Installation

You can install RDATAPP from PyPI using pip:

pip install rdatapp

Usage

Below are examples of how to use the different classes and methods provided by RDATAPP.

Text Cleaning

from rdatapp.text_cleaning import TextCleaner

text_cleaner = TextCleaner()
cleaned_text = text_cleaner.clean_text("This is a Sample TEXT, with Punctuation!")
print(cleaned_text)

Missing Value Handling

import pandas as pd
from rdatapp.missing_value_handler import MissingValueHandler

df = pd.DataFrame({'A': [1, 2, None, 4]})
df = MissingValueHandler.impute_mean(df, 'A')
print(df)

Encoding

import pandas as pd
from rdatapp.categorical_encoder import CategoricalEncoder

df = pd.DataFrame({'Category': ['A', 'B', 'A', 'C']})
df = CategoricalEncoder.one_hot_encode(df, 'Category')
print(df)

Outlier Detection

import pandas as pd
from rdatapp.outlier_handler import OutlierHandler

df = pd.DataFrame({'Values': [1, 2, 3, 4, 100]})
df = OutlierHandler.iqr_outlier_detection(df, 'Values')
print(df)

Scaling

import pandas as pd
from rdatapp.scaler import Scaler

df = pd.DataFrame({'Values': [1, 2, 3, 4, 5]})
df = Scaler.min_max_scale(df, 'Values')
print(df)

Feature Engineering

import pandas as pd
from rdatapp.feature_engineer import FeatureEngineer

df = pd.DataFrame({'Values': [1, 2, 3, 4, 5]})
df = FeatureEngineer.create_new_feature(df, 'Values', lambda x: x**2)
print(df)

Date-Time Handling

import pandas as pd
from rdatapp.date_time_handler import DateTimeHandler

df = pd.DataFrame({'Date': ['2021-01-01', '2021-02-01', '2021-03-01']})
df = DateTimeHandler.to_datetime(df, 'Date')
df = DateTimeHandler.extract_date_parts(df, 'Date')
print(df)

Authors

• Izzettin Furkan Özmen - [e-mail] linkedin
• Ismail Cifci - [e-mail] linkedin

License

This project is not licensed. Feel free to use.

Contributing

We welcome contributions! Please contact us via E-mail addresses.

Acknowledgments

Special thanks to the instructors who provided guidance and support throughout the development of this project.

Project Links

• Source

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For any issues, please contact the authors or open an issue on GitHub.