How to Speed Up Data Cleaning and Exploratory Data Analysis in Python with klib
In this video, I will be showing you how you can speed up your data science projects (data cleaning and exploratory data analysis) using the klib library in Python. Particularly, the klib library enables the following task: quickly visualize missing data, perform data cleaning, visualize data distribution plot, visualize correlation plot and visualize categorical column values.
Key features:
- visualize missing data using the missingval_plot function
- perform data cleaning using the data_cleaning function
- visualize the data distribution plot using the dist_plot function
- visualize the correlation plot using the corr_plot function
- visualize categorical column values using the cat_plot function
Links for this video:
- Jupyter notebook: https://github.com/dataprofessor/code/blob/master/python/klib.ipynb
- klib Examples on GitHub https://github.com/akanz1/klib/tree/main/examples
- klib on Towards Data Science https://towardsdatascience.com/speed-up-your-data-cleaning-and-preprocessing-with-klib-97191d320f80
- klib Documentation https://klib.readthedocs.io/en/latest/
Installing klib: (Choose one)
pip install klib
conda install -c conda-forge klib
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