To check Pandas Dataframe column for TRUE/FALSE, if TRUE check another column for condition to satisfy and generate new column with values PASS/FAIL

user_7818560 0
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Input Explained: I have a dataframe 'df', which holds columns 'Space' and 'Threshold'.

Space   Threshold

TRUE    0.1

TRUE    0.25

FALSE   0.5

FALSE   0.6

Scenario to consider: When df['Space'] is TRUE, check df['Threshold']<=0.2 and if both condition satisfies generate a new column called df['Space_Test'] with value PASS/FAIL. If df['Space'] value is FALSE, put 'FALSE' as value to newly generated column df['Space_Test'].

Expected Output:

Space   Threshold   Space_Test

TRUE    0.1         PASS

TRUE    0.25        FAIL

FALSE   0.5         FALSE   

FALSE   0.6         FALSE

Tried Code: Have tried out the below mentioned codeline for above mentioned scenario but doesn't works.

df['Space_Test'] = np.where(df['Space'] == 'TRUE',np.where(df['Threshold'] <= 0.2, 'Pass', 'Fail'),'FALSE')

In need of help to resolve this. Thanks in Advance!

pandas dataframe boolean numpy python
2 Answers

Another solution

from pandas import DataFrame

names = {
    'Space': ['TRUE','TRUE','FALSE','FALSE'],
    'Threshold': [0.1, 0.25, 1, 2]
         }
df = DataFrame(names,columns=['Space','Threshold'])

df.loc[(df['Space'] == 'TRUE') & (df['Threshold'] <= 0.2), 'Space_Test'] = 'Pass'
df.loc[(df['Space'] != 'TRUE') | (df['Threshold'] > 0.2), 'Space_Test'] = 'Fail'

print (df)
user_23507190

If TRUE are boolean your solution is simplify by compare by df['Space'] only:

df['Space_Test'] = np.where(df['Space'],
                   np.where(df['Threshold'] <= 0.2, 'Pass', 'Fail'),'FALSE')
print (df)
   Space  Threshold Space_Test
0   True       0.10       Pass
1   True       0.25       Fail
2  False       0.50      FALSE
3  False       0.60      FALSE

Alternative with numpy.select:

m1 = df['Space']
m2 = df['Threshold'] <= 0.2
df['Space_Test'] = np.select([m1 & m2, m1 & ~m2], ['Pass', 'Fail'],'FALSE')
print (df)
   Space  Threshold Space_Test
0   True       0.10       Pass
1   True       0.25       Fail
2  False       0.50      FALSE
3  False       0.60      FALSE
user_29010020
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