Cannot subset columns with tuple with more than one element

# Cannot subset columns with tuple with more than one element

The article addresses the following 2 equivalent errors:

ValueError: Cannot subset columns with a tuple with more than one element. Use a list instead.
Future Warning: Indexing with multiple keys

If you use a Pandas version less than 2.0.0, you will get the "Future Warning: Indexing with multiple keys".

If your Pandas version is greater than 2.0.0, you will get a ValueError exception.

The "ValueError: Cannot subset columns with a tuple with more than one element. Use a list instead." occurs when you use single, instead of double square brackets when selecting multiple columns.

To solve the error, make sure to use two sets of square brackets when selecting multiple columns.

Here is an example of how the error occurs.

main.py

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import pandas as pd

df = pd.DataFrame({
    'ID': [1, 1, 1, 2, 2, 2],
    'Animal': ['Cat', 'Cat', 'Cat', 'Dog', 'Dog', 'Dog'],
    'Max Speed': [25, 35, 45, 55, 65, 75]
})


# ⛔️ Future Warning: Indexing with multiple keys
# ⛔️ ValueError: Cannot subset columns with a tuple with more than one element. Use a list instead.
print(df.groupby('Animal')['Animal', 'Max Speed'].apply(lambda x: x))

cannot subset columns with tuple with more than one element

Notice that we used a single set of square brackets when selecting the Animal and Max Speed columns.

Instead, use two sets of square brackets to solve the error.

main.py

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import pandas as pd

df = pd.DataFrame({
    'ID': [1, 1, 1, 2, 2, 2],
    'Animal': ['Cat', 'Cat', 'Cat', 'Dog', 'Dog', 'Dog'],
    'Max Speed': [25, 35, 45, 55, 65, 75]
})


print(
    df.groupby('Animal')[['Animal', 'Max Speed']].apply(lambda x: x)
)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

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         Animal  Max Speed
Animal
Cat    0    Cat         25
       1    Cat         35
       2    Cat         45
Dog    3    Dog         55
       4    Dog         65
       5    Dog         75

use two sets of square brackets when selecting multiple columns

If you need to groupby using multiple columns, pass a list of column names to the DataFrame.groupby() method.

main.py

Copied!
import pandas as pd

df = pd.DataFrame({
    'ID': [1, 1, 1, 2, 2, 2],
    'Animal': ['Cat', 'Cat', 'Cat', 'Dog', 'Dog', 'Dog'],
    'Max Speed': [25, 35, 45, 55, 65, 75]
})


print(
    df.groupby(
        ['ID', 'Animal']
    )[['Animal', 'Max Speed']].apply(lambda x: x)
)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
            Animal  Max Speed
ID Animal
1  Cat    0    Cat         25
          1    Cat         35
          2    Cat         45
2  Dog    3    Dog         55
          4    Dog         65
          5    Dog         75

groupby with multiple columns correctly

Always make sure to use two sets of square brackets when selecting multiple columns after calling DataFrame.groupby().

For example, the following is correct.

main.py

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# ✅ Correct used 2 sets of square brackets [[]]
print(
    df.groupby('Animal')[['Animal', 'Max Speed']].apply(lambda x: x)
)

And the following is incorrect and causes the error.

main.py

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# ⛔️ Incorrect only used 1 set of square brackets [] after groupby
print(
  df.groupby('Animal')['Animal', 'Max Speed'].apply(lambda x: x)
)

Two sets of square brackets [[]] are used to output a DataFrame, whereas one set of square brackets [] is used to output a Series.

# Additional Resources

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Cannot subset columns with tuple with more than one element

# Cannot subset columns with tuple with more than one element

# Additional Resources

Borislav Hadzhiev