Replacing The Value In A Column Based On A Single Vectorized Condition
I tried the following code with multiple conditions and worked perfectly, somehow whatever I am trying with a single condition it won't work. It will simply return the values "1" & "SD" for every row no matter if the value of Col_A is True. Please let me know any suggestions, the weird part is that I am not getting any error whatsoever.
condition = [ df1['Col_A'] == 'TRUE' ] result1 = ['1'] result2 = ['SD'] df1['Col_B'] = np.select(condition, result1, 'NA') df1['Col_C'] = np.select(condition, result2, 'NA') df1``` Input Col_A Col_B Col_C 0 TRUE NA NA 1 FALSE 1001 Valid 2 TRUE NA NA 3 TRUE NA NA Output Col_A Col_B Col_C 0 TRUE NA NA 1 FALSE NA NA 2 TRUE NA NA 3 TRUE NA NA Desired Output Col_A Col_B Col_C 0 TRUE 1 SD 1 FALSE 1001 Valid 2 TRUE 1 SD 3 TRUE 1 SD
You could use a simple
loc assignment with a condition:
df.loc[df['Col_A'].eq('TRUE'), ['Col_B', 'Col_C']] = [1, 'SD']
df.loc[df['Col_A'], ['Col_B', 'Col_C']] = [1, 'SD']
Col_A Col_B Col_C 0 TRUE 1.0 SD 1 FALSE 1001.0 Valid 2 TRUE 1.0 SD 3 TRUE 1.0 SD
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