More Data Cleaning with Custom Functions (Data Cleaning Part 2) - Pandas | Python ~ Episode 7

Hey Coders! Badly formatted data can be as much of a problem as missing data. Thankfully, pandas provides a suite of tools to format your data in the exact way you want. With methods such as apply() and applymap(), you can quite literally define your own functions to pass the data through. For instance, you can remove special characters from a column of all strings, or calculate the residual of floats to the line of best fit. These functions plus replace() and drop() are what we'll be learning today!

Check out the source code on GitHub:

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🕒 Timestamps
0:00 Intro
0:55 Importing pandas and displaying our data
1:12 Replace() matched strings with another value
4:31 Apply() a custom function on your data
9:25 Applymap() across each element in your DataFrame
11:39 Drop() rows and columns from your DataFrame
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