Home/ Questions/What is the best solution for: ''the truth value of a series is ambiguous. use a.empty, a.bool(), a.item(), a.any() or a.all().'' error?
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domenicalindgren
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Asked: 2022-06-08T16:02:41+00:00 In:

What is the best solution for: ”the truth value of a series is ambiguous. use a.empty, a.bool(), a.item(), a.any() or a.all().” error?

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Hi everyone. I’m learning about pandas DataFrame and I’m in trouble with ”the truth value of a series is ambiguous. use a.empty, a.bool(), a.item(), a.any() or a.all().” error. Here is the detail of program which I run:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'team': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],
'points': [18, 22, 19, 14, 14, 11, 20, 28],
'assists': [5, 7, 7, 9, 12, 9, 9, 4],
'rebounds': [11, 8, 10, 6, 6, 5, 9, 12]})

#view DataFrame
print(df)

team points assists rebounds
0 A 18 5 11
1 A 22 7 8
2 A 19 7 10
3 A 14 9 6
4 B 14 12 6
5 B 11 9 5
6 B 20 9 9
7 B 28 4 12

I receive this error when I try to filter for rows where the team is equal “A” and the points are less than 20:

#attempt to filter DataFrame

df[(df['team'] == 'A') and (df['points'] < 20)]

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(),
a.any() or a.all().

And when we I try to filter for rows where the team is equal to “A” or the points is less than 20, I get following error:

#attempt to filter DataFrame
df[(df['team'] == 'A') or (df['points'] < 20)]

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(),
a.any() or a.all().

I have read a lot of documents but I haven’t found the desired answer. Can you help me with this problem? Please write your suggestions below. Thanks!

the truth value of a series is ambiguous
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1 Answer

  1. Best Answer
    Expert

    The cause:

    This error happens because instead of using the & and | operators, you try to filter a pandas DataFrame with the words and and or.

    Solution:

    To solve this error, you have to use  the & and | instead of the words and and or.

    Below are the codes I suggest:

    #filter DataFrame
df[(df['team'] == 'A') & (df['points'] < 20)]

team points assists rebounds
0 A 18 5 11
2 A 19 7 10
3 A 14 9 6
    #filter DataFrame
df[(df['team'] == 'A') | (df['points'] < 20)]

team points assists rebounds
0 A 18 5 11
1 A 22 7 8
2 A 19 7 10
3 A 14 9 6
4 B 14 12 6
5 B 11 9 5
    • 8

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