Length mismatch: Expected axis has X elements, new values have Y elements

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# ValueError: Length mismatch: Expected axis has X elements, new values have Y elements

The Pandas "ValueError: Length mismatch: Expected axis has X elements, new values have Y elements" occurs when the length of the column names you are assigning to the DataFrame doesn't match the length of the columns in the DataFrame.

To solve the error, make sure the length of the column names in the DataFrame matches the length of the column names.

value error length mismatch expected axis has elements

Here is an example of how the error occurs.

main.py

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import pandas as pd


df = pd.DataFrame(
    {
        'id': [112, 113, 114, 115],
        'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    }
)

# ⛔️ ValueError: Length mismatch: Expected axis has 2 elements, new values have 3 elements
df.columns = ['id', 'first_name', 'salary']

The DataFrame in the example has 2 columns - id and name, however, we've passed 3 column names to the columns list.

The expected axis has 2 elements (id and name) and the new values have 3 elements (id, first_name and salary).

One way to solve the error is to ensure the number of columns in the DataFrame matches the number of columns in the list on the right.

main.py

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import pandas as pd


df = pd.DataFrame(
    {
        'id': [112, 113, 114, 115],
        'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    }
)

df.columns = ['id', 'first_name']

#     id first_name
# 0  112      Alice
# 1  113      Bobby
# 2  114       Carl
# 3  115        Dan
print(df)

make sure number of columns matches number of column names

The code for this article is available on GitHub

The DataFrame has 2 columns and the list on the right-hand side also has 2 columns, so the error is no longer raised.

You can use the len() function to check that the length of the columns in the DataFrame matches the length of the column names.

main.py

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import pandas as pd


df = pd.DataFrame(
    {
        'id': [112, 113, 114, 115],
        'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    }
)

# ✅ Print the length of columns in DataFrame
print(len(df.columns))  # 👉️ 2

column_names = ['id', 'first_name']

# ✅ Print the length of the column names
print(len(column_names))  # 👉️ 2

df.columns = ['id', 'first_name']

#     id first_name
# 0  112      Alice
# 1  113      Bobby
# 2  114       Carl
# 3  115        Dan
print(df)

The code for this article is available on GitHub

There has to be a match between the following 2 calls to len().

main.py

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# ✅ Print the length of columns in DataFrame
print(len(df.columns))  # 👉️ 2

# ✅ Print the length of the column names
print(len(column_names))  # 👉️ 2

You can use the df.columns attribute if you need to print the columns in the DataFrame.

main.py

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import pandas as pd


df = pd.DataFrame(
    {
        'id': [112, 113, 114, 115],
        'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    }
)

# Index(['id', 'name'], dtype='object')
print(df.columns)

The DataFrame has id and name columns.

You might have excluded one or more of your DataFrame columns somewhere in your code.

main.py

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import pandas as pd


df = pd.DataFrame(
    {
        'id': [112, 113, 114, 115],
        'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    }
)

df = df.drop(['id'], axis=1)

#     name
# 0  Alice
# 1  Bobby
# 2   Carl
# 3    Dan
print(df)

# 👇️ Index(['name'], dtype='object')
print(df.columns)

print(len(df.columns)) # 👉️ 1

df.columns = ['first_name']

#   first_name
# 0      Alice
# 1      Bobby
# 2       Carl
# 3        Dan
print(df)

The code for this article is available on GitHub

The code sample uses the DataFrame.drop() method to remove the id column from the DataFrame.

After the column is removed, the DataFrame has only 1 column remaining.

Make sure the number of columns in the DataFrame matches the number of column names in the list on the right.

# Assigning column names to an empty DataFrame

The error is also raised when you try to assign column names to an empty DataFrame.

main.py

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import pandas as pd


df = pd.DataFrame()

print(len(df.columns))  # 👉️ 0

# ⛔️ ValueError: Length mismatch: Expected axis has 0 elements, new values have 2 elements
df.columns = ['id', 'name']

We initialized the DataFrame as empty, so it has 0 columns.

Trying to overwrite its column names with a list containing 2 items causes the error.

One way to solve the error is to initialize the DataFrame with 2 columns using numpy.empty().

main.py

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import pandas as pd
import numpy as np

df = pd.DataFrame(np.empty((0, 2)))

print(len(df.columns))  # 👉️ 2

df.columns = ['id', 'name']

print(df.columns)  # 👉️ Index(['id', 'name'], dtype='object')
print(len(df.columns))  # 👉️ 2

The code for this article is available on GitHub

Make sure to initialize the DataFrame with the correct number of columns (the example uses 2 columns).

The numpy.empty() method returns a new array of the specified shape and type, without initializing entries.

If you don't want to use NumPy, use the range class instead.

main.py

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import pandas as pd
df = pd.DataFrame(columns=range(2))

print(len(df.columns))  # 👉️ 2

df.columns = ['id', 'name']

print(df.columns)  # 👉️ Index(['id', 'name'], dtype='object')
print(len(df.columns))  # 👉️ 2

# Solving the error when reading CSV files

You might also get the error when setting the index_col argument to 0 when reading CSV files with pandas.read_csv.

main.py

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df = pd.read_csv(
  'data.csv',
  index_col=0, # 👈️
  header=None,
  skiprows=[0, 1, 2]
)

The index_col argument determines the column(s) to use as the row labels of the DataFrame.

Try to set the index_col argument to None instead and see if the error resolves.

main.py

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df = pd.read_csv(
  'data.csv',
  index_col=None, # 👈️
  header=None,
  skiprows=[0, 1, 2]
)

When index_col is set to None, a separate numerical index is assigned.

None is the default value for the index_col keyword argument.

# Additional Resources

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Length mismatch: Expected axis has X elements, new values have Y elements

# Table of Contents

# ValueError: Length mismatch: Expected axis has X elements, new values have Y elements

# Assigning column names to an empty DataFrame

# Solving the error when reading CSV files

# Additional Resources

Borislav Hadzhiev