4 - Filtering, Slicing, Indexing Data using Pandas | Comprehensive Pandas Tutorial for Beginners

In this tutorial, we'll look at one of the most fundamental aspects of Data Science - obtaining a subset of data relevant to our needs, using the slicing [ ], .loc[ ], .iloc[ ], .at[ ], .iat[ ], query( ), mask( ), where( ), etc.

We'll also look at indexing data to make our DataFrame more robust, more informative, more readable and ready to be sliced upon!

You can check out my github page to find all the relevant Jupyter Notebooks, datasets and other materials I'd upload with each video tutorial!

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Thanks!

[jonahjohnbaba]

Wow this is nice and I went through your post analysis on GitHub and Its cool. Can you do a tutorial on how to fill in or replace column or row values based on certain conditions met in other columns and rows? This is to clean a data set by filling values mistakenly not entered by the numerator. A single line command to be used in such situations.

[jitendratrivedi]

We also need playlist on data visualization with Matplotlib/Seaborn/Plotly..