klib library enables us to quickly visualize missing data, perform data cleaning, visualize data distribution plot, visualize correlation plot and visualize categorical column values. klib is a Python library for importing, cleaning, analyzing and preprocessing data. Explanations on key functionalities can be found on Medium / TowardsDataScience in the examples section or on YouTube (Data Professor).

Original Github repo

Usage

!pip install  klib 

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

Examples

Take a look at this starter notebook.

Further examples, as well as applications of the functions can be found here.

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. Take a look at this Github repo.

License

MIT