Jivan - 7 months ago 29

Python Question

Given this DataFrame:

`bowl cookie`

0 one chocolate

1 two chocolate

2 two chocolate

3 two vanilla

4 one vanilla

5 one vanilla

6 one vanilla

7 one vanilla

8 one vanilla

9 two chocolate

I'd like to obtain the following summarized DataFrame:

`vanilla chocolate`

one 5 1

two 1 3

Apart from proceeding manually with:

`vanilla_bowl1 = len(df_picks[(df_picks['bowl'] == 'one') & (df_picks['cookie'] == 'vanilla')])`

vanilla_bowl2 = len(df_picks[(df_picks['bowl'] == 'two') & (df_picks['cookie'] == 'vanilla')])

chocolate_bowl1 = ...

chocolate_bowl2 = ...

Is there a way to do that in a single operation with

`Pandas`

`df.pivot()`

`count`

`1`

`bowl cookie count`

0 one chocolate 1

1 two chocolate 1

2 two chocolate 1

3 two vanilla 1

4 one vanilla 1

5 one vanilla 1

6 one vanilla 1

7 one vanilla 1

8 one vanilla 1

9 two chocolate 1

And then

`df.pivot(index='bowl', columns='cookie', values='count')`

However, I'm wondering if there is a more direct method, that wouldn't require adding the

`count`

Answer

The most concise way is probably the `pandas.crosstab`

function:

```
>>> pandas.crosstab(d.bowl, d.cookie)
cookie chocolate vanilla
bowl
one 1 5
two 3 1
```

Source (Stackoverflow)