Pandas ValueError: Cannot index with multidimensional key

# Pandas ValueError: Cannot index with multidimensional key

The Pandas "ValueError: Cannot index with multidimensional key" occurs when you use the DataFrame.loc indexer with a multidimensional key.

To solve the error, access the specific DataFrame column when using DataFrame.loc.

Here is an example of how the error occurs.

main.py

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import pandas as pd

df = pd.DataFrame({
    'first_name': ['Alice', 'Bobby', 'Carl'],
    'salary': [175.1, 180.2, 190.3],
    'experience': [10, 15, 20]
})

df2 = pd.DataFrame({
    'a': [0, 2]
})

# ⛔️ ValueError: Cannot index with multidimensional key
print(df.loc[df2])

value error cannot index with multidimensional key

We tried to pass a DataFrame to the DataFrame.loc indexer which caused the error.

Instead, access the specific column of the DataFrame when using df.loc.

main.py

Copied!
import pandas as pd

df = pd.DataFrame({
    'first_name': ['Alice', 'Bobby', 'Carl'],
    'salary': [175.1, 180.2, 190.3],
    'experience': [10, 15, 20]
})

df2 = pd.DataFrame({
    'a': [0, 2]
})

#   first_name  salary  experience
# 0      Alice   175.1          10
# 2       Carl   190.3          20
print(df.loc[df2['a']])

access specific dataframe column when using df loc

The code for this article is available on GitHub

You can also use a one-dimensional boolean indexer when using DataFrame.loc.

main.py

Copied!
import pandas as pd

df = pd.DataFrame({
    'first_name': ['Alice', 'Bobby', 'Carl'],
    'salary': [175.1, 180.2, 190.3],
    'experience': [10, 15, 20]
})

df2 = pd.DataFrame({
    'a': [True, False, False]
})

#   first_name  salary  experience
# 0      Alice   175.1          10
print(df.loc[df2['a']])

using one dimensional boolean array indexer with df loc

You can also access specific rows directly, using two sets of square brackets.

main.py

Copied!
import pandas as pd

df = pd.DataFrame({
    'first_name': ['Alice', 'Bobby', 'Carl'],
    'salary': [175.1, 180.2, 190.3],
    'experience': [10, 15, 20]
})

#   first_name  salary  experience
# 0      Alice   175.1          10
# 2       Carl   190.3          20
print(df.loc[[0, 2]])

access specific rows directly using two sets of square brackets

The code for this article is available on GitHub

The code sample selects the rows at index 0 and 2.

This also works with Series.

main.py

Copied!
import pandas as pd

df = pd.DataFrame({
    'first_name': ['Alice', 'Bobby', 'Carl'],
    'salary': [175.1, 180.2, 190.3],
    'experience': [10, 15, 20]
})

df2 = pd.DataFrame({
    'a': pd.Series([0, 2])
})

#   first_name  salary  experience
# 0      Alice   175.1          10
# 2       Carl   190.3          20
print(df.loc[df2['a']])

also works with series

# Incorrectly renaming the columns in a DataFrame

You might also get the error when you try to incorrectly rename the columns in a DataFrame.

Here is an example.

main.py

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import pandas as pd

df = pd.DataFrame({
    'first_name': ['Alice', 'Bobby', 'Carl'],
    'salary': [175.1, 180.2, 190.3],
    'experience': [10, 15, 20]
})

# ⛔️ Incorrect
df.columns = [['name', 'salary', 'experience']]

Notice that we have 2 sets of square brackets on the right-hand side.

Instead, specify the column names in 1 set of square brackets.

main.py

Copied!
import pandas as pd

df = pd.DataFrame({
    'first_name': ['Alice', 'Bobby', 'Carl'],
    'salary': [175.1, 180.2, 190.3],
    'experience': [10, 15, 20]
})

# ✅ only using one set of square brackets
df.columns = ['name', 'salary', 'experience']

#     name  salary  experience
# 0  Alice   175.1          10
# 1  Bobby   180.2          15
# 2   Carl   190.3          20
print(df)

specify column names in one set of square brackets

The code for this article is available on GitHub

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Pandas ValueError: Cannot index with multidimensional key