DataFrame Manipulation using Pandas — Part I

DataFrame Manipulation using Pandas — Part I

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:

  1. Creating a DataFrame from a dictionary
  2. Configurations and Settings
  3. Combining DataFrames
  4. Reshaping your dataFrame
  5. Using the Datetime module
  6. Using the groupby() method/function
  7. Slicing using .loc and .iloc

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.

pandas towards-data-science data-science python dataframes

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