Searching Dataframe Using Two Criteria-CodePudding

I have a dataframe called test_df (below) and I am trying to search for the ID based on two criteria: the Factor and the name.

Here's my code:

test_id = test_df.loc[test_df['Name'].str.contains(test_name, case=False) & test_df['Factor'].str.contains(test_factor, case=False), 'ID'].item()

But I get the following error:

Traceback (most recent call last):
  File "C:/Users/v3.py", line 508, in <module>
    test_id = test_df.loc[test_df['Name'].str.contains(test_name, case=False) & test_df['Factor'].str.contains(test_factor, case=False), 'ID'].item()
  File "C:\Users\AppData\Local\Programs\Python\Python37\lib\site-packages\pandas\core\base.py", line 331, in item
    raise ValueError("can only convert an array of size 1 to a Python scalar")
ValueError: can only convert an array of size 1 to a Python scalar

I thought the issue was the Factor column being a float format but I have converted it to a string with the same result.

Can anyone see what the problem is?

FIELD1	ID	Factor	Status	Speed	Name
0	49513622	-4	ACTIVE		II
1	11193741	4	ACTIVE		AP
2	49513622	-3.75	ACTIVE		II
3	11193741	3.75	ACTIVE		AP
4	49513622	-3.5	ACTIVE		II
5	11193741	3.5	ACTIVE		AP
6	49513622	-3.25	ACTIVE		II
7	11193741	3.25	ACTIVE		AP
8	49513622	-3	ACTIVE		II
9	11193741	3	ACTIVE		AP
10	49513622	-2.75	ACTIVE		II
11	11193741	2.75	ACTIVE		AP
12	49513622	-2.5	ACTIVE		II
13	11193741	2.5	ACTIVE		AP
14	49513622	-2.25	ACTIVE		II
15	11193741	2.25	ACTIVE		AP
16	49513622	-2	ACTIVE		II
17	11193741	2	ACTIVE		AP
18	49513622	-1.75	ACTIVE		II
19	11193741	1.75	ACTIVE		AP
20	49513622	-1.5	ACTIVE		II
21	11193741	1.5	ACTIVE		AP
22	49513622	-1.25	ACTIVE		II
23	11193741	1.25	ACTIVE		AP
24	49513622	-1	ACTIVE		II
25	11193741	1	ACTIVE		AP
26	49513622	-0.75	ACTIVE		II
27	11193741	0.75	ACTIVE	1.02	AP
28	49513622	-0.5	ACTIVE		II
29	11193741	0.5	ACTIVE		AP
30	49513622	-0.25	ACTIVE		II
31	11193741	0.25	ACTIVE		AP
32	49513622	0	ACTIVE	2.68	II
33	11193741	0	ACTIVE	1.03	AP
34	49513622	0.25	ACTIVE		II
35	11193741	-0.25	ACTIVE	1.99	AP
36	49513622	0.5	ACTIVE		II
37	11193741	-0.5	ACTIVE	2.3	AP
38	49513622	0.75	ACTIVE		II
39	11193741	-0.75	ACTIVE		AP
40	49513622	1	ACTIVE		II
41	11193741	-1	ACTIVE		AP
42	49513622	1.25	ACTIVE		II
43	11193741	-1.25	ACTIVE		AP
44	49513622	1.5	ACTIVE		II
45	11193741	-1.5	ACTIVE		AP
46	49513622	1.75	ACTIVE		II
47	11193741	-1.75	ACTIVE		AP
48	49513622	2	ACTIVE		II
49	11193741	-2	ACTIVE		AP
50	49513622	2.25	ACTIVE		II
51	11193741	-2.25	ACTIVE		AP
52	49513622	2.5	ACTIVE		II
53	11193741	-2.5	ACTIVE		AP
54	49513622	2.75	ACTIVE		II
55	11193741	-2.75	ACTIVE		AP
56	49513622	3	ACTIVE		II
57	11193741	-3	ACTIVE		AP
58	49513622	3.25	ACTIVE		II
59	11193741	-3.25	ACTIVE		AP
60	49513622	3.5	ACTIVE		II
61	11193741	-3.5	ACTIVE		AP
62	49513622	3.75	ACTIVE		II
63	11193741	-3.75	ACTIVE		AP
64	49513622	4	ACTIVE		II
65	11193741	-4	ACTIVE		AP

CodePudding user response：

Problem is no match any value, so DataFrame.loc return empty Series.

Possible solution is use next iter for assign default value if no match:

mask = test_df['Name'].str.contains(test_name, case=False) & 
       test_df['Factor'].str.contains(test_factor, case=False)

#if no match assign 'no match'
test_id = next(iter(test_df.loc[mask, 'ID']), 'no match')
#if no match assign None
test_id = next(iter(test_df.loc[mask, 'ID']),  None)

Or use if-else with test if at least one value match:

test_id = test_df.loc[mask, 'ID'].item() if m.any() else 'no match'

Or:

if m.any():
    test_id = test_df.loc[mask, 'ID'].item()

EDIT: For testing si possible create helper columns:

m1 = test_df['Name'].str.contains(test_name, case=False)
m2 = test_df['Factor'].str.contains(test_factor, case=False)

test_df = test_df.assign(name_mask = m1, factor_mask = m1, both = m1 & m2)