{"id":6053,"date":"2022-08-16T15:11:28","date_gmt":"2022-08-16T06:11:28","guid":{"rendered":"https:\/\/itips.krsw.biz\/?p=6053"},"modified":"2025-06-09T22:34:19","modified_gmt":"2025-06-09T13:34:19","slug":"numpy-pandas-valueerror-the-truth-value-of-is-ambiguous","status":"publish","type":"post","link":"https:\/\/itips.krsw.biz\/en\/numpy-pandas-valueerror-the-truth-value-of-is-ambiguous\/","title":{"rendered":"The reason of \"ValueError: The truth value of ... is ambiguous\" in NumPy or Pandas"},"content":{"rendered":"

\"The<\/p>\n

<\/div><\/div>
I tried to do logical operation with pandas. Then it showed ValueError<\/span> ...<\/div><\/div><\/div>\n

<\/br><\/p>\n

You might see such an error when you tried to do logical operation.<\/p>\n

\nValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().\n<\/div>\n

<\/br><\/p>\n

Why does such kind of error happen ?<\/p>\n

How can we solve it \uff1f<\/p>\n

So today I will introduce about "The reason of 'ValueError: The truth value of ... is ambiguous' in NumPy or Pandas<\/span>"<\/strong>.<\/p>\n

<\/i>Author<\/p>


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Mid-carieer engineer (AI, system). Good at Python and SQL.<\/div><\/div><\/div>
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<\/span>Advantage to read<\/span><\/p><\/div>\n

<\/i><\/div>

You can understand about the reason of 'ValueError: The truth value of ... is ambiguous' in NumPy or Pandas. Then you don't have to concern about programming with pandas.DataFrame or NumPy.ndarray.<\/p><\/div><\/div>\n

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The reason of "ValueError: The truth value of ... is ambiguous" in NumPy or Pandas<\/h2>\n

\"\"<\/p>\n

The reason of "ValueError: The truth value of ... is ambiguous" in NumPy or Pandas.<\/p>\n

It is because you used and<\/code>, or<\/code>, not<\/code> with NumPy or Pandas<\/span>.<\/p>\n

They are not for element-wise logical operation, but for object-wise logical operation.<\/p>\n

<\/br><\/p>\n

Let's see sample code.<\/p>\n

First, prepare data that contains boolean column.<\/p>\n

<\/span>DATA<\/span><\/p><\/div>\n

\nimport pandas as pd\n\ndata_list1 = [\n    ["a",True,True],\n    ["b",True,False],\n    ["c",False,True],\n    ["d",False,False]\n]\ncol_list1 = ["col1","col2","col3"]\ndf1 = pd.DataFrame(data=data_list1, columns=col_list1)\nprint(df1)\n\n#   col1   col2   col3\n# 0    a   True   True\n# 1    b   True  False\n# 2    c  False   True\n# 3    d  False  False\n<\/pre>\n
<\/br><\/p>\n
Then do logical operation by and<\/code>, or<\/code>, not<\/code>.<\/p>\n
You can see the error like below.<\/p>\n
<\/span>SAMPLE<\/span><\/p><\/div>\n
\ndf1["not_col2"] = not df1["col2"]\nprint(df1)\n# ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().\n\ndf1["and_col2_3"] = (df1["col2"] and df1["col3"])\nprint(df1)\n# ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().\n\ndf1["or_col2_3"] = (df1["col2"] or df1["col3"])\nprint(df1)\n# ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().\n<\/pre>\n
<\/br>
\nAll of them occur ValueError: The truth value of ... is ambiguous<\/code>.<\/p>\n
How can we do element-wise logical operetion ?<\/p>\n
<\/br><\/br><\/p>\n
Solution for "ValueError: The truth value of ... is ambiguous"<\/h2>\n
\"\"<\/p>\n
Object-wise operator and<\/code>, or<\/code>, not<\/code> cause the error.<\/p>\n
So what should we do for element-wise logical operetion ?<\/p>\n
<\/br><\/p>\n
The solution is that using &<\/code>, |<\/code>, ~<\/code>.<\/p>\n
For example, use &<\/code> instead of and<\/code>.<\/p>\n
Then you can get the result below.<\/p>\n
<\/span>SAMPLE<\/span><\/p><\/div>\n
\ndf1["and_col2_3"] = (df1["col2"] & df1["col3"])\nprint(df1)\n\n#   col1   col2   col3  and_col2_3\n# 0    a   True   True        True\n# 1    b   True  False       False\n# 2    c  False   True       False\n# 3    d  False  False       False\n<\/pre>\n
<\/br><\/p>\n
Same as &<\/code>, you can use |<\/code> instead of or<\/code>.<\/p>\n
<\/span>SAMPLE<\/span><\/p><\/div>\n
\ndf1["or_col2_3"] = (df1["col2"] | df1["col3"])\nprint(df1)\n\n#   col1   col2   col3  or_col2_3\n# 0    a   True   True       True\n# 1    b   True  False       True\n# 2    c  False   True       True\n# 3    d  False  False      False\n<\/pre>\n
<\/br><\/p>\n
Also you can use  ~<\/code> instead of not<\/code>.<\/p>\n
<\/span>SAMPLE<\/span><\/p><\/div>\n
\ndf1["not_col2"] = ~df1["col2"]\nprint(df1)\n\n#   col1   col2  not_col2\n# 0    a   True     False\n# 1    b   True     False\n# 2    c  False      True\n# 3    d   True     False\n<\/pre>\n
<\/br><\/p>\n
With using &<\/code>, |<\/code>, ~<\/code>, we can do element-wise logical operetion.<\/p>\n
<\/br><\/br><\/p>\n
Conclusion<\/h2>\n
\"\"<\/p>\n
Today I will explained about "The reason of 'ValueError: The truth value of ... is ambiguous' in NumPy or Pandas<\/span>"<\/strong>.<\/p>\n
Points are below.<\/p>\n
<\/span>Point<\/span><\/p><\/div>\n
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