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

Replace Column Values Based on Condition in Pandas DataFrame

To replace specific values in a DataFrame column based on a condition, the loc indexing method should be utilized correctly. In the provided example, the task is to replace values in the 'First Season' column that exceed 1990 with the value 1.

The code provided in the question, df.loc[(df['First Season'] > 1990)] = 1, replaces all values in the entire row instead of just the 'First Season' column. To accurately target the desired column, the following syntax is required:

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

Here, the loc method selects rows where the 'First Season' column values exceed 1990. The second argument, 'First Season', specifies that only the values in that particular column should be replaced.

To generate a boolean indicator where True corresponds to values over 1990 and False otherwise, the following is recommended:

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

This converts True and False values to 1 and 0, respectively.

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