When it comes to data science or data analysis, Python is pretty much always the language of choice. Its library Pandas is the one you cannot, and more importantly, shouldn’t avoid.

Pandas is a predominantly used python data analysis library. It provides many functions and methods to expedite the data analysis process. What makes pandas so common is its functionality, flexibility, and simple syntax.

While Pandas by itself isn’t that difficult to learn, mainly due to the self-explanatory method names, having a cheat sheet is still worthy, especially if you want to code out something quickly. That’s why today I want to put the focus on how I use Pandas to do Exploratory Data Analysis by providing you with the list of my most used methods and also a detailed explanation of those.

Dataset used

I will do the examples on the california housing dataset.This Dataset and code is available in this link.


Here’s how to import the Pandas library and load in the dataset:

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#exploratory-data-analysis #pandas #data-science #python-pandas #data-folkz

Top 20 Pandas Functions which are commonly used for Exploratory Data Analysis.
2.70 GEEK