Pandas: Split a Column of Lists into Multiple Columns

# Table of Contents

# Pandas: Split a Column of Lists into Multiple Columns

To split a column of lists (or tuples) into multiple columns in Pandas:

Use the DataFrame.tolist() to convert the column values to a list.
Use the pandas.DataFrame() constructor to create a new DataFrame.
Set the index argument to the index of the existing DataFrame.

main.py

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import pandas as pd

df = pd.DataFrame({
    'A': ['Alice', 'Bobby', 'Carl'],
    'B': [[1, 2], [3, 4], [5, 6]],
})

print(df)

print('-' * 50)

df[['B1', 'B2']] = pd.DataFrame(
  df['B'].tolist(),
  index=df.index
)

print(df)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

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       A       B
0  Alice  [1, 2]
1  Bobby  [3, 4]
2   Carl  [5, 6]
--------------------------------------------------
       A       B  B1  B2
0  Alice  [1, 2]   1   2
1  Bobby  [3, 4]   3   4
2   Carl  [5, 6]   5   6

pandas split column of lists into multiple columns

The code sample splits the B column into B1 and B2 columns.

You could use the same approach to split the B column into 3 columns if the lists contained 3 values.

We used the tolist() method to convert the Series to a list of lists.

main.py

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import pandas as pd

df = pd.DataFrame({
    'A': ['Alice', 'Bobby', 'Carl'],
    'B': [[1, 2], [3, 4], [5, 6]],
})

# 👇️ [[1, 2], [3, 4], [5, 6]]
print(df['B'].tolist())

convert series to list of lists

The code for this article is available on GitHub

main.py

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#        A       B  B1  B2
# 0  Alice  [1, 2]   1   2
# 1  Bobby  [3, 4]   3   4
# 2   Carl  [5, 6]   5   6

df[['B1', 'B2']] = pd.DataFrame(
  df['B'].tolist(),
  index=df.index
)

We passed the list of lists and the DataFrame index to the pandas.DataFrame constructor to create a new DataFrame that contains the split columns.

The row values for the new B1 and B2 columns come from each sublist.

If you want to store the results of splitting the column into a new DataFrame, declare a new variable.

main.py

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import pandas as pd

df = pd.DataFrame({
    'A': ['Alice', 'Bobby', 'Carl'],
    'B': [[1, 2], [3, 4], [5, 6]],
})

new_df = pd.DataFrame(df['B'].to_list(), columns=['B1', 'B2'])

#    B1  B2
# 0   1   2
# 1   3   4
# 2   5   6
print(new_df)

store results of splitting column into new dataframe

The new_df variable stores only the results of splitting the B column into B1 and B2.

The same approach can be used to add the split columns to the existing DataFrame.

main.py

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import pandas as pd

df = pd.DataFrame({
    'A': ['Alice', 'Bobby', 'Carl'],
    'B': [[1, 2], [3, 4], [5, 6]],
})


df[['B1', 'B2']] = pd.DataFrame(
    df['B'].tolist(),
    columns=['B1', 'B2']
)

#        A       B  B1  B2
# 0  Alice  [1, 2]   1   2
# 1  Bobby  [3, 4]   3   4
# 2   Carl  [5, 6]   5   6
print(df)

adding the split columns to the existing dataframe

The code for this article is available on GitHub

# Pandas: Split a Column of Lists into Multiple Columns using `apply()`

You can also use the DataFrame.apply() method to split a column of lists (or tuples) into multiple columns.

main.py

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import pandas as pd

df = pd.DataFrame({
    'A': ['Alice', 'Bobby', 'Carl'],
    'B': [[1, 2], [3, 4], [5, 6]],
})

print(df)

print('-' * 50)

df[['B1', 'B2']] = pd.DataFrame(
    df['B'].apply(pd.Series),
    index=df.index
)

print(df)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
       A       B
0  Alice  [1, 2]
1  Bobby  [3, 4]
2   Carl  [5, 6]
--------------------------------------------------
       A       B  B1  B2
0  Alice  [1, 2]   1   2
1  Bobby  [3, 4]   3   4
2   Carl  [5, 6]   5   6

split column of lists into multiple columns using apply

We used the DataFrame.apply() method to apply the pd.Series class to each column.

main.py

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import pandas as pd

df = pd.DataFrame({
    'A': ['Alice', 'Bobby', 'Carl'],
    'B': [[1, 2], [3, 4], [5, 6]],
})


#    0  1
# 0  1  2
# 1  3  4
# 2  5  6
print(df['B'].apply(pd.Series))

The last step is to use the pandas.DataFrame({}) constructor to add the B1 and B2 columns to the existing DataFrame.

main.py

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#        A       B  B1  B2
# 0  Alice  [1, 2]   1   2
# 1  Bobby  [3, 4]   3   4
# 2   Carl  [5, 6]   5   6
df[['B1', 'B2']] = pd.DataFrame(
    df['B'].apply(pd.Series),
    index=df.index
)

You can use the same approach to add the split columns to a new DataFrame.

main.py

Copied!
import pandas as pd

df = pd.DataFrame({
    'A': ['Alice', 'Bobby', 'Carl'],
    'B': [[1, 2], [3, 4], [5, 6]],
})

print(df)

print('-' * 50)

new_df = df['B'].apply(pd.Series)
new_df.columns = ['B1', 'B2']

print(new_df)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
       A       B
0  Alice  [1, 2]
1  Bobby  [3, 4]
2   Carl  [5, 6]
--------------------------------------------------
   B1  B2
0   1   2
1   3   4
2   5   6

adding the split columns to new dataframe using columns

# Pandas: Split a Column of Lists with different lengths into Multiple Columns

You can also use the tolist() method if you need to split a column of lists with different lengths into multiple columns.

main.py

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import pandas as pd

df = pd.DataFrame({
    'A': ['Alice', 'Bobby', 'Carl'],
    'B': [[1, 2], [3, 4, 5], [6, 7, 8, 9]],
})

new_df = pd.DataFrame(df['B'].tolist())

new_df.columns = ['B1', 'B2', 'B3', 'B4']

#    B1  B2   B3   B4
# 0   1   2  NaN  NaN
# 1   3   4  5.0  NaN
# 2   6   7  8.0  9.0
print(new_df)

split column of lists with different lengths into multiple columns

The code for this article is available on GitHub

The longest sublist in the example has a length of 4.

This is why we supplied 4 column names.

main.py

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import pandas as pd

df = pd.DataFrame({
    'A': ['Alice', 'Bobby', 'Carl'],
    'B': [[1, 2], [3, 4, 5], [6, 7, 8, 9]],
})

# 👇️ [[1, 2], [3, 4, 5], [6, 7, 8, 9]]
print(df['B'].tolist())

Since not all sublists contain 4 values, some of the rows contain NaN.

main.py

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new_df = pd.DataFrame(df['B'].tolist())

new_df.columns = ['B1', 'B2', 'B3', 'B4']

#    B1  B2   B3   B4
# 0   1   2  NaN  NaN
# 1   3   4  5.0  NaN
# 2   6   7  8.0  9.0
print(new_df)

You can use the same approach if you want to add the split columns to the existing DataFrame.

main.py

Copied!
import pandas as pd

df = pd.DataFrame({
    'A': ['Alice', 'Bobby', 'Carl'],
    'B': [[1, 2], [3, 4, 5], [6, 7, 8, 9]],
})

print(df)

print('-' * 50)

df[['B1', 'B2', 'B3', 'B4']] = pd.DataFrame(df['B'].tolist())

print(df)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
       A             B
0  Alice        [1, 2]
1  Bobby     [3, 4, 5]
2   Carl  [6, 7, 8, 9]
--------------------------------------------------
       A             B  B1  B2   B3   B4
0  Alice        [1, 2]   1   2  NaN  NaN
1  Bobby     [3, 4, 5]   3   4  5.0  NaN
2   Carl  [6, 7, 8, 9]   6   7  8.0  9.0

split column of lists with diff length into multiple columns

We specified 4 column names because the longest sublist has 4 values.

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Pandas: Split a Column of Lists into Multiple Columns

# Table of Contents

# Pandas: Split a Column of Lists into Multiple Columns

# Pandas: Split a Column of Lists into Multiple Columns using apply()

# Pandas: Split a Column of Lists with different lengths into Multiple Columns

# Additional Resources

Borislav Hadzhiev

# Pandas: Split a Column of Lists into Multiple Columns using `apply()`