ValueError: Grouper for 'X' not 1-dimensional [Solved]

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# ValueError: Grouper for 'X' not 1-dimensional [Solved]

The Pandas "ValueError: Grouper for 'X' not 1-dimensional" occurs when you have duplicate column names in your DataFrame and try to call the groupby() method.

To solve the error, make sure you don't have any duplicate column names before calling groupby().

Here is an example of how the error occurs.

main.py

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import pandas as pd

df = pd.DataFrame({
    'first': [10, 15, 20],
    'second': [10, 15, 20],
})

df.rename(columns={'first': 'second'}, inplace=True)

print(df)

# ⛔️ ValueError: Grouper for 'salary' not 1-dimensional
print(df.groupby('second'))

value error grouper for salary not 1 dimensional

The error message "ValueError: Grouper for 'X' not 1-dimensional" basically means that your DataFrame has more than 1 column named "X".

We renamed the first column to second, so the DataFrame has 2 columns with the same name (second).

main.py

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import pandas as pd

df = pd.DataFrame({
    'first': [10, 15, 20],
    'second': [10, 15, 20],
})

df.rename(columns={'first': 'second'}, inplace=True)

#    second  second
# 0      10      10
# 1      15      15
# 2      20      20
print(df)

# ⛔️ ValueError: Grouper for 'salary' not 1-dimensional
print(df.groupby('second'))

dataframe has duplicate column names

Calling the DataFrame.groupby method when your DataFrame has duplicate column names causes the error.

You can solve the error by renaming the columns in your DataFrame, so that there are no duplicate names.

main.py

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import pandas as pd

df = pd.DataFrame({
    'first': [10, 15, 20],
    'second': [10, 15, 20],
})

df.columns = ['first', 'second']

#    first  second
# 0     10      10
# 1     15      15
# 2     20      20
print(df)

# <pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f8a62579270>
print(df.groupby('second'))

rename columns so there are no duplicates

The code for this article is available on GitHub

Make sure to specify exactly as many columns between the square brackets as there are in the DataFrame, otherwise, you'd get the Length mismatch: Expected axis has X elements, new values have Y elements error.

# Creating MultiIndex columns by mistake

The error also occurs when you create MultiIndex columns by mistake.

main.py

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import pandas as pd
import numpy as np

values = np.array([[2, 2], [4, 4], [6, 6]])

df = pd.DataFrame(values, columns=[['first', 'second']])

print(df)

# ⛔️ ValueError: Grouper for 'first' not 1-dimensional
print(df.groupby('first'))

creating multi index columns by mistake

Notice that we used 2 sets of square brackets for the columns argument when instantiating the pandas.DataFrame() class.

Instead, only use one set of square brackets to not create MultiIndex columns.

main.py

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import pandas as pd
import numpy as np

values = np.array([[2, 2], [4, 4], [6, 6]])

# 👇️ not creating MultiIndex columns
df = pd.DataFrame(values, columns=['first', 'second'])

print(df)

print(df.groupby('first'))

only use one set of square brackets when instantiating the dataframe

You can also use the DataFrame.columns.get_level_values() method to remove the MultiIndex.

main.py

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import pandas as pd


df1 = pd.DataFrame()


df2 = pd.DataFrame({
    'first': [10, 15, 20],
    'second': [10, 15, 20],
})

df1[['first', 'second']] = df2[['first', 'second']]

# 👇️ Remove MultiIndex
df1.columns = df1.columns.get_level_values(0)

print(df1)

remove multiindex using get level values

The code for this article is available on GitHub

# Calling `pandas.pivot_table()` incorrectly

The error also occurs when you call the pandas.pivot_table method incorrectly.

Here is an example.

main.py

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import numpy as np
import pandas as pd


df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",
                         "bar", "bar", "bar", "bar"],
                   "B": ["one", "one", "one", "two", "two",
                         "one", "one", "two", "two"],
                   "C": ["small", "large", "large", "small",
                         "small", "large", "small", "small",
                         "large"],
                   "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
                   "E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})

table = pd.pivot_table(df, values='C', index=['A', 'B'],
                       columns=['C'], aggfunc=np.sum)

# ⛔️ ValueError: Grouper for 'C' not 1-dimensional
print(table)

calling pandas pivot table incorrectly

Notice that we have the C column in values and columns which caused the error.

The column cannot be in both values and columns.

main.py

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import numpy as np
import pandas as pd


df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",
                         "bar", "bar", "bar", "bar"],
                   "B": ["one", "one", "one", "two", "two",
                         "one", "one", "two", "two"],
                   "C": ["small", "large", "large", "small",
                         "small", "large", "small", "small",
                         "large"],
                   "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
                   "E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})

table = pd.pivot_table(df, values='D', index=['A', 'B'],
                       columns=['C'], aggfunc=np.sum)

# C        large  small
# A   B
# bar one    4.0    5.0
#     two    7.0    6.0
# foo one    4.0    1.0
#     two    NaN    6.0
print(table)

dont use same column in values and columns

The code for this article is available on GitHub

Also, make sure to call the pivot_table method on the pandas module and not the DataFrame object.

The following is correct.

main.py

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# ✅ Correct
import pandas as pd
import numpy as np

table = pd.pivot_table(df, values='D', index=['A', 'B'],
                       columns=['C'], aggfunc=np.sum)

And the following is incorrect.

main.py

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# ⛔️ Incorrect. Calling pivot_table on `df`
table = df.pivot_table(df, values='D', index=['A', 'B'],
                       columns=['C'], aggfunc=np.sum)

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ValueError: Grouper for 'X' not 1-dimensional [Solved]

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