Data Preprocessing with Python Pandas - this tutorial explains how to preprocess data using the Pandas library. Preprocessing is the process of doing a pre-analysis of data, in…
Data Preprocessing with Python Pandas
This tutorial explains how to preprocess data using the Pandas library. Preprocessing is the process of doing a pre-analysis of data, in order to transform them into a standard and normalised format. Preprocessing involves the following aspects:
Data Normalisation involves adjusting values measured on different scales to a common scale. When dealing with dataframes, data normalization permits to adjust values referred to different columns to a common scale. This operation is strongly recommended when the columns of a dataframe are considered as input features of a machine learning algorithm, because it permits to give all the features the same weight.
Normalization applies only to columns containing numeric values. Five methods of normalization exist:
In the remainder of the tutorial, we apply each method to a single column. However, if you wanted to use each column of the dataset as input features of a machine learning algorithm, you should apply the same normalisation method to all the columns.
In this tutorial, we use the
pandas library to perform normalization. As an alternative, you could use the preprocessing methods of the
scikit-learn libray. A little note for readers: if you wanted to learn how to use the preprocessing package of
scikit-learn, please drop me a message or a comment to this post :)
You can download the source code of this tutorial as a Jupyter notebook from my Github Data Science Repository.
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