I have a dataframe like this: df
| Country | Year | Area | count |
|---|---|---|---|
| Albania | 2018 | T | 250 |
| Albania | 2018 | NT | 150 |
| Albania | 2019 | T | 260 |
| Albania | 2019 | NT | 180 |
| USA | 2018 | T | 200 |
| USA | 2018 | NT | 120 |
| USA | 2019 | T | 280 |
| USA | 2019 | NT | 180 |
| Oman | 2018 | T | 260 |
| Oman | 2018 | NT | 160 |
| Oman | 2019 | T | 220 |
| Oman | 2019 | NT | 120 |
| UK | 2018 | T | 290 |
| UK | 2018 | NT | 140 |
| UK | 2019 | T | 290 |
| UK | 2019 | NT | 130 |
But I want these ouput
| Country | Year | T | NT |
|---|---|---|---|
| Albania | 2018 | 250 | 150 |
| Albania | 2019 | 260 | 180 |
| USA | 2018 | 200 | 120 |
| USA | 2019 | 280 | 180 |
| Oman | 2018 | 260 | 160 |
| Oman | 2019 | 220 | 120 |
| UK | 2018 | 290 | 140 |
| UK | 2019 | 290 | 130 |
Is there a way to put the column values into columns?
CodePudding user response：
You can use crosstab:
dfx=pd.crosstab([df['Country'],df['Year']], df['Area'],values=df['count'],aggfunc='first').reset_index()
Output:
Area Country Year NT T
0 Albania 2018 150 250
1 Albania 2019 180 260
2 Oman 2018 160 260
3 Oman 2019 120 220
4 UK 2018 140 290
5 UK 2019 130 290
6 USA 2018 120 200
7 USA 2019 180 280
CodePudding user response：
An alternative is to use the pivot() method. For example,
df.pivot(index=['Country', 'Year'], columns='Area', values='count').reset_index(level=1)
Output
Area Year NT T
Country
Albania 2018 150 250
Albania 2019 180 260
Oman 2018 160 260
Oman 2019 120 220
UK 2018 140 290
UK 2019 130 290
USA 2018 120 200
USA 2019 180 280