Pandas: split column into smaller ones based on another column's values

Question

I have the following Pandas dataframe:

import pandas

df = pandas.DataFrame({'Status': ['Draft', 
   'Active','Completed','Completed', 'Draft', 'Active'], 'Region': 
   ['Europe','Europe', 'Asia','Australia', 'Asia', 'Australia'],'Budget': [1000,
   2000,3000,4000,5000,6000]})

I am grouping and aggregating it as follows:

grouped_by_region = df.groupby('Region').agg({'Budget':sum})

So the output of print(grouped_by_region) is:

Region    Budget

Asia         8000
Australia   10000
Europe       3000

What I want to do is split the budget column into "Budget draft", "Budget active", "Budget completed" columns. So basically I need to create new columns based 'Status' column values and rename them accordingly.

How to achieve this?

https://stackoverflow.com/questions/47152691/how-to-pivot-a-dataframe — BENY, Jun 11 '18 at 16:56
In this case, `df.pivot_table('Budget', 'Region', 'Status', np.sum)` — hilberts_drinking_problem, Jun 11 '18 at 17:02
See the answer to question #4 https://stackoverflow.com/a/47152692/2336654 — piRSquared, Jun 11 '18 at 17:13

score 0 · Accepted Answer · answered Jun 11 '18 at 17:04

IIUC, you can just add Status to your groupby and unstack:

df.groupby(['Region', 'Status']).agg({'Budget':sum}).unstack(fill_value=0)

          Budget                
Status    Active Completed Draft
Region                          
Asia           0      3000  5000
Australia   6000      4000     0
Europe      2000         0  1000

Though the pivot solutions proposed in the comments work just as well

Pandas: split column into smaller ones based on another column's values

1 Answers1