Python Mastery: 65 If-Else Technique Examples for Data Manipulation in Python

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The “if-else” technique involves using conditional statements to manipulate or filter data in a DataFrame based on specified conditions. In pandas, these conditions are often applied using functions like np.where(), df.loc[], df.apply(), np.select(), or other methods that allow for conditional operations.

This technique allows for the implementation of logic similar to traditional if-else statements found in programming languages, but it’s adapted to the structure of pandas DataFrames. By leveraging these functions, users can easily update existing columns, create new columns, filter rows, and perform various other conditional operations on their data. It provides a flexible and powerful way to handle data based on different conditions, making it a key aspect of data manipulation and analysis in pandas.

While the syntax and methods may vary, the underlying concept aligns with the if-else technique in the context of pandas data manipulation.

1. df.loc[] for If-Else Conditions:

• df.loc[] can be used for updating values based on "if-else" conditions.

df.loc[df['Score'] >= 70, 'Result'] = 'Pass'

2. np.where() for If-Else Conditions: