Package: prepup-linux
💻 PrepupBuilt with ♥️ bySudhanshu MukherjeePyPI version(https://pypi.org/project/prepup-linux/)LinuxmacOSLicensePrepup is a free open-source package that lets you inspect, explore, visualize, and perform pre-processing tasks on datasets in your windows/macOS terminal.InstallationPrepup can be installed using the Pip package manager.!pip install PrepupMotivationDeveloping an efficient and user-friendly command line tool for data pre-processing to handle various tasks such as missing data, data formatting, and cleaning, with a simple interface and scalability for large datasets.File Format SupportedCSVEXCELPARQUETWhy you should use Prepup?It's SuperfastPrepup is built on Polars which is an alternative to pandas and helps you load and manipulate the DataFrames faster.AnalyticalPrepup handles tasks ranging from the shape of data to the Standardizing of the feature before training the model. It does it right on the terminal.CompatiblePrepup supports CSV, EXCEL, and PARQUET formats making it compatible to go with different file formats.Non-DestructivePrepup doesn't alter your raw data, It saves pre-processed data only when the user specifies the path.Lives in your TerminalPrepup is terminal-based and has specific entry points designed for using it instantly.Command Line Arguments available in PREPUP🕵️ Prepup "File name or File path" -inspectinspect flag takes the dataframe and returns the Features available, Features datatype, and missing values present in the Dataset.File Name: If the current working directory is same as the file location or FILE PATH🧭 Prepup "File name or File path" -exploreexplore flag takes the dataframe and returns the Features available, Features datatype, Correlation between features, Detects Outliers, Checks Normal Distribution, Checks Skewness, Checks Kurtosis, and also allows the option to check if the dataset is Imbalanced.File Name: If the current working directory is same as the file location or FILE PATH📊 Prepup "File name or File path" -visualizevisualize flag plots of the feature distribution directly on the terminal.File Name: If the current working directory is same as the file location or FILE PATH🔥 Prepup "File name or File path" -imputeThere are 8 different strategies available to impute missing data using PrepupFile Name: If the current working directory is same as the file location or FILE PATHOption 1 - Drops the Missing DataOption 2 - Impute Missing values with a Specific valueOption 3 - Impute Missing values with Mean.Option 4 - Impute Missing values with Median.Option 5 - Impute Missing value based on the distribution of existing columns.Option 6 - Impute Missing values based on Forward Fill Strategy where missing values are imputed based on the previous data points.Option 7 - Impute Missing values based on Backward Strategy where missing values are imputed based on the next data points.Option 8 - Impute missing values based on K-Nearest Neighbors.🌐 Prepup "File name or File path" -standardizeStandardize allows you to standardize the dataset using two different methods:Robust ScalerStandard ScalerRobust Scaler is recommended if there are outliers present and you feel they can have an influence on the Machine Learning model.Standard Scaler is the go-to function if you want to standardize the dataset before training the model on it.File Name: If the current working directory is same as the file location or FILE PATHLicenseFree software: MIT licensePackage LinkGithub:https://github.com/sudhanshumukherjeexx/prepup-linuxDocumentation:https://sudhanshumukherjeexx.github.io/prepup-linux