How to convert the column with lists into one hot encoded columns?

Assume, there is one DataFrame such as following

import pandas as pd 
import numpy as np 

df = pd.DataFrame({'id':range(1,4), 
                   'items':[['A', 'B'], ['A', 'B', 'C'], ['A', 'C']]})
df
        id  items
        1   [A, B]
        2   [A, B, C]
        3   [A, C]

Is there an efficient way to convert above DataFrame into the following (one-hot encoded columns)? Many Thanks in advance!

   id   items       A   B   C
    1   [A, B]      1   1   0
    2   [A, B, C]   1   1   1
    3   [A, C]      1   0   1

>Solution :

Another possible solution, whose steps are:

• First, the explode function is used to transform each item of a list-like to a row, replicating the index values.

• Then, the pivot_table function is applied to reshape the data based on the unique values in the items column, aggregating the count of each id for every item. The fill_value=0 ensures that any missing combinations are filled with zeros.

• The rename_axis method is used to remove the axis name for the columns.

• Finally, reset_index is called to reset the index of the dataframe, turning the index into a column.

• The original dataframe df is then merged with this transformed dataframe using the merge function.

df.merge(
    df.explode('items')
    .pivot_table(index='id', columns='items', values='id', aggfunc=len, 
                 fill_value=0)
    .rename_axis(None, axis=1).reset_index())

Output:

   id      items  A  B  C
0   1     [A, B]  1  1  0
1   2  [A, B, C]  1  1  1
2   3     [A, C]  1  0  1