In this tutorial, we'll learn Fast and simple function calls for efficient data preparation.
The klib package provides a number of very easily applicable functions with sensible default values that can be used on virtually any DataFrame to assess data quality, gain insight, perform cleaning operations and visualizations which results in a much lighter and more convenient to work with Pandas DataFrame.
While the previous article mainly focused on visualizations, this piece will demonstrate the data cleaning capabilities the latest release of klib has to offer. Specifically, it comes with a number of improvements targeted at facilitating data cleaning and preparation.
For those of you who want to follow along, let’s make sure you have access to the Kaggle API to download the data. For that you need to create an API token in your Kaggle account settings and save it under ~/.kaggle/kaggle.json
We download three datasets using the Kaggle API, unzip them and read the resulting .csv files into pd.DataFrames. We then hand them to klib.data_cleaning() using the default settings and obtain the cleaned DataFrames.
*Alternatively, I encourage you to follow along using your own data! *In this case, just read the data in and pass it to the data_cleaning() function.
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This tutorial explains how to preprocess data using the pandas library. Preprocessing is the process of doing a pre-analysis of data, in…
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