How to Selectively Replace Column Values in a Pandas DataFrame Based on Conditions?

Pandas DataFrame: Replacing Column Values Based on Conditions

In this question, the goal is to selectively replace values in a DataFrame's column based on a condition. Given a DataFrame containing football teams and their first season, we want to replace all values in the 'First Season' column that exceed 1990 with 1.

The provided solution, df.loc[(df['First Season'] > 1990)] = 1, incorrectly replaces all values in the selected rows, not just the target column. To address this, we need to specify the column to be modified.

The correct syntax for this modification is:

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

Here's how it works:

• df.loc[(df['First Season'] > 1990)]: This selects the rows where the 'First Season' value is greater than 1990, effectively generating the labels to index into the DataFrame.
• 'First Season': This optionally specifies the column in which the replacement should occur.

After executing this line of code, only the 'First Season' values that meet the specified condition will be replaced with 1, preserving the rest of the DataFrame.

Additional Considerations:

If the desired outcome is a boolean indicator, instead of replacing values with 1, we can use the boolean condition to generate a boolean Series and cast it to integer data type:

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

This will convert True values to 1 and False values to 0, creating a boolean indicator in the 'First Season' column.

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