Replace value in column B using dictionary if column A is equal to-CodePudding

This is my dataset:

    pos    result
 0   1       AA
 1   1       AB
 2   1       BB
 3   2       CC
 4   2       CA
 5   2       AC
 6   3       AA
 7   3       DD
 8   3       CC
 9   4       DD
10   4       AB
11   4       BA

At the moment, I replace values in a column with this code:

replaceValues = {
    'AA': 'A',
    'BB': 'B',
    'CC': 'C',
    'DD': 'D'
}

df[ 'result' ].replace( to_replace=replaceValues, inplace=True )

and it works great.

But I would like to do the same, but only where column 'pos' are equal to '2' or '3' to get this desired result:

    pos    result
 0   1       AA
 1   1       AB
 2   1       BB
 3   2       C
 4   2       CA
 5   2       AC
 6   3       A
 7   3       D
 8   3       CC
 9   4       DD
10   4       AB
11   4       BA

CodePudding user response：

You can first create replace values then use pandas.mask and only set values for those rows that have pos==2 or pos==3.

rep = df['result'].replace(replaceValues)
df['result'] = df['result'].mask(df['pos'].isin([2,3]), rep)
print(df)

    pos result
0     1     AA
1     1     AB
2     1     BB
3     2      C
4     2     CA
5     2     AC
6     3      A
7     3      D
8     3      C
9     4     DD
10    4     AB
11    4     BA

CodePudding user response：

You can use the .loc to filter your results like this:

df['result'].loc[(df['pos'] == 2) | (df['pos'] == 3)].replace(replaceValues)