groupby to convert a row values to their own columns with count

Asked Jun 02 '22 at 09:41

Active Jun 02 '22 at 09:41

Viewed 18 times

I have the following dataframe:

import pandas as pd
import numpy as np
df = pd.DataFrame({ 
     'Year': [2022,2022,2022,2022,2022,2022,2022,2022],
    'Quarter':[2,2,2,2,1,1,2,2],
    'Product':['Car','Bike','Car','Bus','Bike','Bus','Car','Bike'],
    })
df

I am trying to group by Year and Quarter whilst transposing the Product column to have all uniqiue values getting their own column.

My expected output is this, with just a basic count of the values. Any help would be much appreciated! Thanks!

asked Jun 02 '22 at 09:41

SOK

1,732
2
15
33

1

Use `pd.crosstab([df.Year, df.Quarter], df.Product)` – jezrael Jun 02 '22 at 09:44
Thanks! when i add this code to a bigger dataset, it only shows 1 row and does not group by the Year and Quarter. Any ideas? – SOK Jun 02 '22 at 09:56
Seems some data related problem, no idea – jezrael Jun 02 '22 at 09:57
Yes it was a data error sorry! thanks – SOK Jun 02 '22 at 10:01
Another question - is there a way to have the cross tab become a normal dataframe? So i could perform joins onto it? – SOK Jun 02 '22 at 10:18
1

Sure, add `.reset_index()` – jezrael Jun 02 '22 at 10:19

Pandas transpose / pivot / groupby to convert a row values to their own columns with count

0 Answers0