How Can I Use Pandas `apply()` to Modify a Single DataFrame Column?

Using apply() to Modify a Single Column in Pandas DataFrames

Similar to modifying an entire DataFrame, pandas provides the apply() function to manipulate specific columns without affecting others. This technique is particularly useful for transforming a specific column while preserving the integrity of the remaining DataFrame.

Let's consider an example DataFrame, df, containing multiple columns:

   a  b
0  1  2
1  2  3
2  3  4
3  4  5

To modify only the values in the first column, 'a', using apply():

df['a'] = df['a'].apply(lambda x: x + 1)

In this example, the lambda function adds 1 to each element in the 'a' column. The result:

   a  b
0  2  2
1  3  3
2  4  4
3  5  5

Demonstrating its flexibility, apply() enables you to perform various operations on a specific column, such as:

• Changing data types: df['name'] = df['name'].apply(str)
• Renaming the column: df.rename(columns={'old_name': 'new_name'}, inplace=True)
• Combining multiple operations: df['total_score'] = df[['score1', 'score2']].apply(lambda x: x.mean())

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