Pandas DataFrame: Targeted Value Replacement Based on Conditions

In Pandas, it's often necessary to modify specific values within a DataFrame based on certain criteria. While a common approach is to use loc to select rows, it's crucial to understand how to precisely target a specific column for value modification.

Consider the following DataFrame, where we wish to replace values in the 'First Season' column that exceed 1990 with the integer 1:

                 Team  First Season  Total Games
0      Dallas Cowboys          1960          894
1       Chicago Bears          1920         1357
2   Green Bay Packers          1921         1339
3      Miami Dolphins          1966          792
4    Baltimore Ravens          1996          326
5  San Franciso 49ers          1950         1003

An initial attempt using only the loc function resulted in replacing all values in the selected rows rather than solely the targeted column. To rectify this, we need to explicitly specify the 'First Season' column as the second argument to loc:

df.loc[df['First Season'] > 1990, 'First Season'] = 1

This targeted approach ensures that only the values in the 'First Season' column satisfy the condition are replaced with 1, leaving the other columns unaffected.

                 Team  First Season  Total Games
0      Dallas Cowboys          1960          894
1       Chicago Bears          1920         1357
2   Green Bay Packers          1921         1339
3      Miami Dolphins          1966          792
4    Baltimore Ravens             1          326
5  San Franciso 49ers          1950         1003

Alternatively, if the desired outcome is a boolean indicator, you can employ the condition to create a boolean Series and convert it to integers, where True and False translate to 1 and 0, respectively:

df['First Season'] = (df['First Season'] > 1990).astype(int)

This approach yields a DataFrame with updated values:

                 Team  First Season  Total Games
0      Dallas Cowboys             0          894
1       Chicago Bears             0         1357
2   Green Bay Packers             0         1339
3      Miami Dolphins             0          792
4    Baltimore Ravens             1          326
5  San Franciso 49ers             0         1003