klib 0.0.71

Customized data preprocessing functions for frequent tasks.

klib

klib is a Python library for importing, cleaning, analyzing and preprocessing data. Future versions will include model creation and optimization to provide an end-to-end solution.

Installation

Use the package manager pip to install klib.

pip install klib
pip install --upgrade klib

Alternatively, to install this package with conda run:

conda install -c conda-forge klib

Usage

import klib

klib.describe # functions for visualizing datasets
- klib.cat_plot() # returns a visualization of the number and frequency of categorical features.
- klib.corr_mat() # returns a color-encoded correlation matrix
- klib.corr_plot() # returns a color-encoded heatmap, ideal for correlations
- klib.dist_plot() # returns a distribution plot for every numeric feature
- klib.missingval_plot() # returns a figure containing information about missing values

klib.clean # functions for cleaning datasets
- klib.data_cleaning() # perform datacleaning (drop duplicates & empty rows/columns, adjust dtypes,...) on a dataset
- klib.convert_datatypes() # converts existing to more efficient dtypes, also called inside ".data_cleaning()"
- klib.drop_missing() # drops missing values, also called in ".data_cleaning()"

Examples

klib.corr_plot(df) # providing a pd.DataFrame is sufficient, however, plently of settings and options are available
klib.corr_plot(df, split='pos') # displaying only positive correlations

klib.missingval_plot(df) # default representation of missing values in a DataFrame, plenty of settings are available

Contributing

Pull requests and ideas, especially for further functions are welcome. For major changes or feedback, please open an issue first to discuss what you would like to change.

License

MIT