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What you would learn in Data Cleaning With pandas and NumPy course?
Data scientists invest a significant portion working on cleaning data to make them simpler to work with. The 80/20 rule suggests that the first steps in getting and cleaning data account for the majority of the time that is spent on a given project.
If you're beginning to get into the field or plan to get into it, you need to be able to handle chaotic data, whether it is the absence of values, inconsistent formatting, badly formatted records, or outliers that aren't logical.
In this course, you'll learn how to use Python's pandas and NumPy libraries to cleanse the data.
Content of the Course:
Eliminating unneeded columns within the DataFrame
Modifying an Index of an DataFrame
Utilizing .str() methods to clear columns
Affixing columns to make them more easily recognizable label set
Avoid redundant rows in CSV files.
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