How can I replace values in a specific Pandas DataFrame column based on a condition?

Pandas DataFrame: Replace Specific Column Values Based on Condition

In a DataFrame, it's often necessary to replace specific values within a column based on a predefined condition. Consider the following DataFrame:

                 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

Suppose we need to replace all values greater than 1990 in the 'First Season' column with 1. To achieve this, the following command can be utilized:

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

This line selectively targets the 'First Season' column based on the condition specified in the square brackets (df['First Season'] > 1990). The = sign assigns the value 1 to the selected elements, ensuring that only the 'First Season' column is affected.

The resulting DataFrame will appear as follows:

                 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

It's important to note that the syntax for this operation involves two key components:

• df.loc[, ]: This selects the rows and columns specified by the boolean mask.
• = : This assigns the specified value to the selected column(s).

Furthermore, if the goal is to create a boolean indicator instead of replacing values, the condition can be used to generate a boolean Series, which can then be converted to integers by casting its dtype to int. This will transform True and False values into 1 and 0, respectively.

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