In this blog, we'll learn Learn how to create DataFrames, configure & reshape them using some of the most common methods/functions from Pandas
This blog follows a first one that I named “Basic DataFrame Manipulation using Pandas” which I published a few months ago, where I tried to give some tips and show short workflows that I use on a regular basis. Most of it, if not all of the content was for beginners, so I decided to do a second one with slightly more complex functions and methods which I broke down into two parts, not to make a super long blog tedious to follow.
Once again, I will try to do this as an exercise starting from creating my own DataFrame (df) and then playing with it as much as I can trying to keep it to maximum an intermediate level. Later on, and after parts I and II of this blog are published, I will focus on writing an advance one following a project with real data.
The content that I will cover is the following:
If by the time I finish with the second part I find that the level is not high enough, then I will modify this content and edit it in this blog as well so that they are both coherent.
Because I am a big basketball fan, I will use once again NBA data, but this time I will take stats from the 2019–2020 recently finished season and I will use the top 25 scorers. With that said, let’s jump straight into the content.
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