The article explains two pandas methods for mapping column values in DataFrames: map() for direct value replacement and factorize() for automatic numeric encoding. Both techniques are demonstrated wit
h practical examples focusing on common data cleaning scenarios like gender encoding and grade categorization.
Reasons to Read -- Learn:
how to efficiently convert categorical text data into numerical values for machine learning preprocessing, with specific examples using pandas map() function for gender encoding
how to handle columns with multiple unique values using pandas factorize() method, saving time compared to manual mapping
practical techniques for binarizing multi-category data, such as converting multiple grade levels into pass/fail categories using pandas operations
publisher: @tubelwj
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