You can use the pandas function to transform each element in a list to a row in a DataFrame.

This function uses the following basic syntax:

df.explode('variable_to_explode')

The following example shows how to use this syntax in practice.

Example: Use explode() Function with Pandas DataFrame

Suppose we have the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'team': [['A', 'B', 'C'], ['D', 'E', 'F'], ['G', 'H', 'I']],
                   'position':['Guard', 'Forward', 'Center'],
                   'points': [7, 14, 19]})

#view DataFrame
df

	team	        position  points
0	[A, B, C]	Guard	  7
1	[D, E, F]	Forward	  14
2	[G, H, I]	Center	  19

Notice that the team column contains lists of team names.

We can use the explode() function to explode each element in each list into a row:

#explode team column
df.explode('team')

        team	position  points
0	A	Guard	  7
0	B	Guard	  7
0	C	Guard	  7
1	D	Forward	  14
1	E	Forward	  14
1	F	Forward	  14
2	G	Center	  19
2	H	Center	  19
2	I	Center	  19

Notice that the team column no longer contains lists. We “exploded” each element of each list into a row.

Also notice that some rows now have the same index value.

We can use the reset_index() function to reset the index when exploding the team column:

#explode team column and reset index of resulting dataFrame
df.explode('team').reset_index(drop=True)

	team	position  points
0	A	Guard	  7
1	B	Guard	  7
2	C	Guard	  7
3	D	Forward	  14
4	E	Forward	  14
5	F	Forward	  14
6	G	Center	  19
7	H	Center	  19
8	I	Center	  19

Notice that each row now has a unique index value.

Additional Resources

The following tutorials explain how to perform other common operations in pandas: