Must have equal len keys and value when setting with iterable

# Must have equal len keys and value when setting with iterable

The Pandas "ValueError: Must have equal len keys and value when setting with an iterable" occurs when you incorrectly iterate over a DataFrame to try to set column values.

To solve the error, use the DataFrame.apply() method.

Here is an example of how the error occurs.

main.py

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import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'experience': [11, 14, 16, 18],
    'salary': [175.1, 180.2, 190.3, 210.4],
})

df['coords'] = ''

print(df)

for i in range(len(df)):
    # ⛔️ ValueError: Must have equal len keys and value when setting with an iterable
    df.loc[i, 'coords'] = ['X', 'Y']

value error must have equal len keys and value when setting with iterable

We tried to use a for loop to iterate over the DataFrame and set the values of the coords column to a list which caused the error.

# Use the `DataFrame.apply()` method to solve the error

You can solve the error by using the DataFrame.apply() method.

main.py

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import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'experience': [11, 14, 16, 18],
    'salary': [175.1, 180.2, 190.3, 210.4],
})

df['coords'] = ''

df['coords'] = df['coords'].apply(lambda x: ['X', 'Y'])

print(df)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

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    name  experience  salary  coords
0  Alice          11   175.1  [X, Y]
1  Bobby          14   180.2  [X, Y]
2   Carl          16   190.3  [X, Y]
3    Dan          18   210.4  [X, Y]

using dataframe apply method to solve the error

The DataFrame.apply() method applies a function along an axis of the DataFrame.

By default, the axis argument is set to 0 which means that the function is applied to each column.

If you need to apply the function to each row of the DataFrame, make sure to set the axis argument to 1.

main.py

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import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'experience': [11, 14, 16, 18],
    'salary': [175.1, 180.2, 190.3, 210.4],
})

df['salary'] = df.apply(
    lambda x: 1000000 if x['experience'] > 16
    else x['salary'],
    axis=1
)

print(df)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

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    name  experience     salary
0  Alice          11      175.1
1  Bobby          14      180.2
2   Carl          16      190.3
3    Dan          18  1000000.0

apply the function to each row with axis set to 1

# Using list multiplication to solve the error

You can also use the multiplication operator with lists to concatenate the lists and solve the error.

main.py

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import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'experience': [11, 14, 16, 18],
    'salary': [175.1, 180.2, 190.3, 210.4],
})

df['coords'] = [['X', 'Y']] * len(df)

#     name  experience  salary  coords
# 0  Alice          11   175.1  [X, Y]
# 1  Bobby          14   180.2  [X, Y]
# 2   Carl          16   190.3  [X, Y]
# 3    Dan          18   210.4  [X, Y]
print(df)

using multiplication operator to solve the error

The code for this article is available on GitHub

When the multiplication operator is used with a list, the values of the list get repeated N times.

main.py

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# [['X', 'Y', 'X', 'Y', 'X', 'Y', 'X', 'Y']]
print([['X', 'Y'] * 4])

# Using the `DataFrame.at()` method to solve the error

You can also use the DataFrame.at() method to solve the error.

main.py

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import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'experience': [11, 14, 16, 18],
    'salary': [175.1, 180.2, 190.3, 210.4],
})

df['coords'] = ''

for i in range(len(df)):
    df.at[i, 'coords'] = ['X', 'Y']

#     name  experience  salary  coords
# 0  Alice          11   175.1  [X, Y]
# 1  Bobby          14   180.2  [X, Y]
# 2   Carl          16   190.3  [X, Y]
# 3    Dan          18   210.4  [X, Y]
print(df)

using dataframe at method to solve the error

The code for this article is available on GitHub

The DataFrame.at() method enables you to access a single value for a row/column label pair.

main.py

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import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', 'Carl', 'Dan'],
    'experience': [11, 14, 16, 18],
    'salary': [175.1, 180.2, 190.3, 210.4],
})


print(df.at[0, 'name']) # 👉️ Alice

The method uses label-based lookups, like the .loc indexer.

# Additional Resources

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Must have equal len keys and value when setting with iterable