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I am trying to update a column in my dataframe based on the testing and results of the values within three columns (3 tests).

Some sample code:

df_test = pd.DataFrame([('?',2.0,1,0,0,0), (None,2.0,1,0,0,0), 
 (None,2.0,0,0,0,0),(None,2.0,0,1,0,0), 
 ('?',2.0,0,0,0,0)], columns=['a','b','c','d','e','f'])
df_test.head()

When I try the following df_test['g'] = np.where(np.logical_and(df_test['a'] != 'None', df_test['c'] == 0, df_test['d'] == 0), True, False).astype(int)
I receive the error TypeError: return arrays must be of ArrayType So I try the following:

df_test = pd.DataFrame([('?',2.0,1,0,0,0), (None,2.0,1,0,0,0), 
 (None,2.0,0,0,0,0),(None,2.0,0,1,0,0), 
 ('?',2.0,0,0,0,0)], columns=['a','b','c','d','e','f'])
df_test['g'] = np.where(np.logical_and(df_test['a'] != None, 
 np.logical_and(df_test['c'] == 0, 
 df_test['d'] == 0)), 
 True, False).astype(int)
df_test.head()

And on row 2, where I would expect to see a 0 I see a 1 while row 4 appears correct. Test 1 (a), should be False while the second (c) and third (d) tests should be True, True. False == True == True is False.

 a b c d e f g
0 ? 2.0 1 0 0 0 0
1 None 2.0 1 0 0 0 0
2 None 2.0 0 0 0 0 1
3 None 2.0 0 1 0 0 0
4 ? 2.0 0 0 0 0 1

I need a way to evaluate 3 tests and return true or false as an int.

For 3 or more conditions, use np.logical_and.reduce and pass a list of masks;

mask = np.logical_and.reduce([
 df_test['a'].notna(), df_test['c'].eq(0), df_test['d'].eq(0)])
df_test['g'] = mask.astype(int)

print(df_test)
 a b c d e f g
0 ? 2.0 1 0 0 0 0
1 None 2.0 1 0 0 0 0
2 None 2.0 0 0 0 0 0
3 None 2.0 0 1 0 0 0
4 ? 2.0 0 0 0 0 1

The subsequent np.where is superfluous here.

Try:

df_test['htest' ] = (df_test['a'].isna() & (df_test['c'] == 0) & (df_test['d'] == 0)).astype(int)

df_test['htest' ] = (df_test['a'].notna() & (df_test['c'] == 0) & (df_test['d'] == 0)).astype(int)

Output:

 a b c d e f htest
0 ? 2.0 1 0 0 0 0
1 None 2.0 1 0 0 0 0
2 None 2.0 0 0 0 0 0
3 None 2.0 0 1 0 0 0
4 ? 2.0 0 0 0 0 1