Pandas dataframe column split

Question

I have a pandas dataframe containing 3 columns.

This is what it looks like:

User History New
101  [X,Y,Z] [A-0,B-1] 
102  [Q,M,N] [A-1,B-0]

I would like to modify my dataframe to be represented this way:

User History New 0or1
101  [X,Y,Z] A   0
101  [X,Y,Z] B   1
102  [Q,M,N] A   1
102  [Q,M,N] B   0

How can I do so?

Basically, the reason I’m doing this is because I’m trying to create a model which predicts 0 or 1 for each element in new based on the the history. Hence, I thought splitting them this way would make sense to train the model based on the three columns.

Though I was looking for ways to split the dataframe as described, I’m open to suggestions if there’s any other efficient way I can use the data provided (first table) to create a model to predict 1 or 0 for each element in array ‘new’ for the respective history.

Thanks in advance.

1st explode using df.explode then use pd.Series.str.split — Ch3steR
– Ch3steR, Commented Jul 28, 2020 at 19:11
Yes, set expand parameter to True i.e pd.Series.str.split('-', expand=True) — Ch3steR
– Ch3steR, Commented Jul 28, 2020 at 19:16

Onyambu · Accepted Answer · 2020-07-28 19:26:34Z

1

import pandas as pd 
df1 = df.explode('New')
pd.concat([df1,df1.New.str.split('-', expand = True)],axis=1)
   User    History  New  0  1
0   101  [X, Y, Z]  A-0  A  0
0   101  [X, Y, Z]  B-1  B  1
1   102  [Q, M, N]  A-1  A  1
1   102  [Q, M, N]  B-0  B  0

answered Jul 28, 2020 at 19:26

Onyambu

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3 Comments

Ric S Over a year ago

Damn it, you beat me on time ;) just add a .drop(columns='New') to remove the initial column

Shahnawaz Over a year ago

Works well! Also, I can rename the two new columns right?

Ch3steR Over a year ago

df1.assign(**df1['New'].str.split('-',expand=True).rename(columns={0:'New', 1:'0or'}))

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