How do i split a column into distinct strings

I wish to split the 'genres' column into multiple columns, where each of the new column will only contain a particular genre( example: action | adventure| horror| Comedy etc). There are 20 different different genres so I should have 20 new columns.

I tried to do this :

new_column=df['genres'].str.split(pat='|',expand=True)

But all it did was create only 5 new columns where the genres are not separated by column.

You could use str.get_dummies to split the values and create new columns.

Something like this can work.

import pandas as pd
  
# Creating dataframe
data = ["action | adventure| horror| Comedy | action1 | adventure1 | horror1 | Comedy1 | action2 | adventure2 | horror2 | Comedy2 "]
df = pd.DataFrame(data, columns=['genres'])
  
# Define genres columns
new_columns = df['genres'].str.get_dummies(sep='|')

# Split the columns
new_columns.columns = new_columns.columns.str.strip()

# Recreating the dataframe with the new structure
df = pd.concat([df, new_columns], axis=1)

Like this:

splt = lambda x: pd.Series([i for i in x.split('|')])
new_columns = df['genres'].apply(splt)

Then you can concat:

df_concat = pd.concat([df, new_columns], axis=1)

If you want to split the genres into their own columns, a better approach would be to use one-hot encoding or get_dummies function in pandas.

This would create a new column for each unique genre, and place a 1 in the corresponding column for each row where that genre appears

df = pd.concat([df, df['genres'].str.get_dummies(sep='|')], axis=1)