pandas - How to assign a value to the variable based on the existing variables in python? -


i want assign value variable based on existing variable values. give brief explanation of data.

mydata        category  original_wt  predicted_wt   categorized     1 xxxxx      2.5          3.0            original     2 yyyyy      3.5          4.0            predicted     3 zzzzz      3.0          5.0            predicted     4 aaaaa      4.0          2.5            original     5 bbbbb      3.2          5.5            original     6 ccccc      4.6          3.5            predicted

from above data want assign value based on categorized type.
if original categorized value should original_wt of same row.
if predicted categorized value should predicted_wt of same row.

expected output:

mydata    category  original_wt  predicted_wt   categorized   categorized_value 1 xxxxx      2.5          3.0            original      2.5 2 yyyyy      3.5          4.0            predicted     4.0 3 zzzzz      3.0          5.0            predicted     5.0 4 aaaaa      4.0          2.5            original      4.0 5 bbbbb      3.2          5.5            original      3.2 6 ccccc      4.6          3.5            predicted     3.5

tried:

mydata['categorized_value'] =       if mydata['categorized'] == 'original':         mydata['categorized_value'] = mydata['original_wt']      else:      mydata['categorized_value'] = mydata['predicted_wt']

how expected output in python?

use loc , boolean mask set values desire:

in [221]:  df.loc[df['categorized'] == 'original', 'categorized_value'] = df['original_wt'] df.loc[df['categorized'] == 'predicted', 'categorized_value'] = df['predicted_wt'] df out[221]:   category  original_wt  predicted_wt categorized  categorized_value 1    xxxxx          2.5           3.0    original                2.5 2    yyyyy          3.5           4.0   predicted                4.0 3    zzzzz          3.0           5.0   predicted                5.0 4    aaaaa          4.0           2.5    original                4.0 5    bbbbb          3.2           5.5    original                3.2 6    ccccc          4.6           3.5   predicted                3.5

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