How Can Pandas\' `apply()` Function Modify a Single Column Without Affecting Others?

Using the apply() Function for Selective Column Alteration

In Pandas, the apply() function is a versatile tool for transforming values within a DataFrame. It can be used to modify specific columns while preserving the others.

Question:

How can you utilize the apply() function to modify the values of a single column in a Pandas DataFrame without affecting the remaining columns?

Answer:

To apply a transformation to a particular column, assign the modified column back to itself as follows:

df['column_name'] = df['column_name'].apply(transform_function)

Example:

Consider the following DataFrame:

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

To increment the values in column 'a' while leaving 'b' untouched, use the following code:

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

This will produce the following transformed DataFrame:

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

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