Pandas DataFrame: replace all values in a column, based on condition

I have a simple DataFrame like the following:

Pandas DataFrame

I want to select all values from the 'First Season' column and replace those that are over 1990 by 1. In this example, only Baltimore Ravens would have the 1996 replaced by 1 (keeping the rest of the data intact).

I have used the following:

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

But, it replaces all the values in that row by 1, and not just the values in the 'First Season' column.

How can I replace just the values from that column?

You need to select that column:

    In [41]:
    df.loc[df['First Season'] > 1990, 'First Season'] = 1
    df

    Out[41]:
                     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

So the syntax here is:

    df.loc[<mask>(here mask is generating the labels to index) , <optional column(s)> ]

You can check the docs and also the 10 minutes to pandas which shows the semantics

EDIT

If you want to generate a boolean indicator then you can just use the boolean condition to generate a boolean Series and cast the dtype to int this will convert True and False to 1 and 0 respectively:

    In [43]:
    df['First Season'] = (df['First Season'] > 1990).astype(int)
    df

    Out[43]:
                     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

From: stackoverflow.com/q/31511997

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