The reason of "ValueError: The truth value of ... is ambiguous" in NumPy or Pandas

I tried to do logical operation with pandas. Then it showed ValueError ...

You might see such an error when you tried to do logical operation.

Why does such kind of error happen ?

How can we solve it ？

So today I will introduce about "The reason of 'ValueError: The truth value of ... is ambiguous' in NumPy or Pandas".

Author

Mid-carieer engineer (AI, system). Good at Python and SQL.

The reason of "ValueError: The truth value of ... is ambiguous" in NumPy or Pandas

The reason of "ValueError: The truth value of ... is ambiguous" in NumPy or Pandas.

It is because you used and, or, not with NumPy or Pandas.

They are not for element-wise logical operation, but for object-wise logical operation.

Let's see sample code.

First, prepare data that contains boolean column.

import pandas as pd

data_list1 = [
    ["a",True,True],
    ["b",True,False],
    ["c",False,True],
    ["d",False,False]
]
col_list1 = ["col1","col2","col3"]
df1 = pd.DataFrame(data=data_list1, columns=col_list1)
print(df1)

#   col1   col2   col3
# 0    a   True   True
# 1    b   True  False
# 2    c  False   True
# 3    d  False  False

Then do logical operation by and, or, not.

You can see the error like below.

df1["not_col2"] = not df1["col2"]
print(df1)
# ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

df1["and_col2_3"] = (df1["col2"] and df1["col3"])
print(df1)
# ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

df1["or_col2_3"] = (df1["col2"] or df1["col3"])
print(df1)
# ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

All of them occur ValueError: The truth value of ... is ambiguous.

How can we do element-wise logical operetion ?

Solution for "ValueError: The truth value of ... is ambiguous"

Object-wise operator and, or, not cause the error.

So what should we do for element-wise logical operetion ?

The solution is that using &, |, ~.

For example, use & instead of and.

Then you can get the result below.

df1["and_col2_3"] = (df1["col2"] & df1["col3"])
print(df1)

#   col1   col2   col3  and_col2_3
# 0    a   True   True        True
# 1    b   True  False       False
# 2    c  False   True       False
# 3    d  False  False       False

Same as &, you can use | instead of or.

df1["or_col2_3"] = (df1["col2"] | df1["col3"])
print(df1)

#   col1   col2   col3  or_col2_3
# 0    a   True   True       True
# 1    b   True  False       True
# 2    c  False   True       True
# 3    d  False  False      False

Also you can use ~ instead of not.

df1["not_col2"] = ~df1["col2"]
print(df1)

#   col1   col2  not_col2
# 0    a   True     False
# 1    b   True     False
# 2    c  False      True
# 3    d   True     False

With using &, |, ~, we can do element-wise logical operetion.

Conclusion

Today I will explained about "The reason of 'ValueError: The truth value of ... is ambiguous' in NumPy or Pandas".

Points are below.

Remember it. So it'll be easy to use.

There are some other articles about pandas.Dataframe or NumPy.ndarray.

If you interested in them, please read them.