datamop is a data cleaning and wrangling package designed to streamline the preprocessing of datasets. Whether you meet missing values, inconsistent categorical columns or need scaling for numeric columns when dealing with data. datamop provides a simple and consistent solution to automate and simplify these repetitive tasks.
The following are core functions of this package:
-
sweep_nulls(): Handle missing values such as imputation or removal, based on user preference. -
column_encoder(): Encodes categorical columns using either one-hot encoding or ordinal encoding, based on user preference. -
column_scaler(): Scales numerical columns, including Min-Max scaling and Z-score standardization, based on user preference.
datamop fits into Python data preprocessing ecosystem by offering a more lightweight and user-friendly alternative to complex libraries like pandas, scikit-learn. datamop focuses specifically on handling missing values, encoding categorical columns and normalizing numerical columns. datamop changes scikit-learn tasks performed by modules like SimpleImputer, OneHotEncoder, OrdinalEncoder and StandardScaler with fewer steps and easier customization.
Similar functionality can be found in:
-
pandas (
fillna(), etc.): pandas documentation -
scikit-learn (
SimpleImputer,OneHotEncoder,LabelEncoder,MinMaxScaler, etc.): scikit-learn preprocessing
The authors of this project are Sepehr Heydarian, Ximin Xu, and Essie Zhang.
$ pip install datamop- TODO
Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.
datamop was created by Sepehr Heydarian, Ximin Xu, Essie Zhang. It is licensed under the terms of the MIT license.
datamop was created with cookiecutter and the py-pkgs-cookiecutter template.