pandas series truth value giving a ambiguous result-CodePudding

Hello i have a series of bool values where i have performed logical operations on them since they are series i have used & instead of and here are the series

z = ~(df['HOME ZIP'].isin(zip_series['zipcd_ZIP_CD']))

here is type of z

type(z)

pandas.core.series.Series

0     False
1     False
2     False
3     False
4     False
5     False
6     False
7     False
8     False
9     False
10    False
11    False
12    False
13    False
14    False
15    False
16    False
17    False
18    False
19    False
20    False
21    False
22    False
23    False
24    False
25    False
26    False
27    False
28    False
29    False
30    False
Name: HOME ZIP, dtype: bool

similarly

y = df['HOME ZIP'].astype(str).str.len() != 0
x = (df['HOME ZIP'].isnull() == False)

values of x and y are both True

0     True
1     True
2     True
3     True
4     True
5     True
6     True
7     True
8     True
9     True
10    True
11    True
12    True
13    True
14    True
15    True
16    True
17    True
18    True
19    True
20    True
21    True
22    True
23    True
24    True
25    True
26    True
27    True
28    True
29    True
30    True
Name: HOME ZIP, dtype: bool

x & y & z values are here

0     False
1     False
2     False
3     False
4     False
5     False
6     False
7     False
8     False
9     False
10    False
11    False
12    False
13    False
14    False
15    False
16    False
17    False
18    False
19    False
20    False
21    False
22    False
23    False
24    False
25    False
26    False
27    False
28    False
29    False
30    False
Name: HOME ZIP, dtype: bool

but when i keep all x y,z as single statements i am getting different output

(df['HOME ZIP'].isnull() == False) & df['HOME ZIP'].astype(str).str.len() != 0 & ~(df['HOME ZIP'].isin(zip_series['zipcd_ZIP_CD']))

0     True
1     True
2     True
3     True
4     True
5     True
6     True
7     True
8     True
9     True
10    True
11    True
12    True
13    True
14    True
15    True
16    True
17    True
18    True
19    True
20    True
21    True
22    True
23    True
24    True
25    True
26    True
27    True
28    True
29    True
30    True
Name: HOME ZIP, dtype: bool

CodePudding user response：

You should add () for each condition

(df['HOME ZIP'].isnull() == False) & 
(df['HOME ZIP'].astype(str).str.len() != 0) & 
(~(df['HOME ZIP'].isin(zip_series['zipcd_ZIP_CD'])))