Python Pandas Library Tutorial - Data Cleaning and Manipulation in Pandas
In this video, we use Pandas library to perform common data cleaning and manipulation tasks such as changing data types, splitting columns, and removing values.
Real-life datasets come with a large number of issues and problems such as missing values, having wrong data types, and bad text formatting. Luckily, Pandas library has a lot of built-in functions that can help us to fix these issues. We will go through some of the most common issues that we need to do for data cleaning,
Timecodes:
0:00 INTRO
03:34 Split Columns
06:16 Replace Strings
09:18 Change Column Data Type
10:40 Drop Rows and Columns
14:30 Rename Columns
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👉 Jupyter notebook used in this video: https://github.com/NabuRika/pandas_cr...
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