ValueError: No axis named X for object type DataFrame

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# ValueError: No axis named X for object type DataFrame [Fix]

The Pandas error "ValueError: No axis named X for object type DataFrame" most commonly occurs when you pass multiple column names to DataFrame.groupby() but forget to wrap them in a list [].

To solve the error, wrap the column names in a list by surrounding them with square brackets when calling groupby().

Here is an example of how the error occurs.

main.py

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import pandas as pd

df = pd.DataFrame({
    'Animal': ['Cat', 'Cat', 'Cat', 'Dog', 'Dog', 'Dog'],
    'Max Speed': [25, 25, 40, 45, 45, 65]
})

# ⛔️ ValueError: No axis named Max Speed for object type DataFrame
print(df.groupby('Animal', 'Max Speed'))

value error no axis named for object type dataframe

We called the DataFrame.groupby method with multiple column names but forgot to wrap them in a list.

You can solve the error by wrapping the column names in a list when calling groupby().

main.py

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import pandas as pd

df = pd.DataFrame({
    'Animal': ['Cat', 'Cat', 'Cat', 'Dog', 'Dog', 'Dog'],
    'Max Speed': [25, 25, 40, 45, 45, 65]
})

# <pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f40c1f8d2a0>
print(df.groupby(['Animal', 'Max Speed']))

print('-' * 50)

# Animal  Max Speed
# Cat     25           2
#         40           1
# Dog     45           2
#         65           1
# Name: Animal, dtype: int64
print(df.groupby(['Animal', 'Max Speed'])['Animal'].count())

wrap column names in list when calling groupby

The code for this article is available on GitHub

The DataFrame.groupby() method groups the DataFrame on one or more columns.

The first argument the method takes is used to determine the groups for the groupby.

The argument can be a label or a list of labels (for multiple columns).

# Getting the error when setting the `axis` parameter incorrectly

You might also get the error if you set the axis parameter incorrectly.

main.py

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import pandas as pd
import numpy as np

df = pd.DataFrame(
    {'A': np.array([1, 2, 3, 4, 5, 6])}
)

# ⛔️ ValueError: No axis named -1 for object type DataFrame
print(np.mean(df, axis=-1))

We called the numpy.mean() method with a DataFrame and set the axis parameter to -1 which caused the error.

You can try to omit the axis parameter.

main.py

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import pandas as pd
import numpy as np

df = pd.DataFrame(
    {'A': np.array([1, 2, 3, 4, 5, 6])}
)

print(np.mean(df))  # 👉️  3.5

The code for this article is available on GitHub

The axis parameter can also be set if you pass a NumPy array to numpy.mean(), and not a DataFrame.

main.py

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import numpy as np

arr = np.array([[1, 2], [3, 4], [5, 6]])

print(np.mean(arr, axis=-1))  # 👉️ [1.5 3.5 5.5]
print(np.mean(arr, axis=1))  # 👉️ [1.5 3.5 5.5]

If you need to calculate the mean of the values over a specific axis in a DataFrame, use the DataFrame.mean() method.

main.py

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import pandas as pd
import numpy as np

df = pd.DataFrame(
    {'A': np.array([1, 2, 3, 4, 5, 6])}
)

# A    3.5
# dtype: float64
print(df.mean())

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ValueError: No axis named X for object type DataFrame

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