user3471881 - 1 year ago 151

Python Question

I have a

`Pandas`

`DataFrame`

`df`

`n`

`columns`

`columns`

`COUNT`

`A`

`A`

`1`

`column`

`COUNT`

`A B C D E COUNT`

id1 cat1 1 a 15 1

id2 cat2 2 b 14 1

id3 cat2 2 c 14 1

id4 cat1 1 d 15 1

id5 cat3 2 e 14 1

.....

Now I want to transform my

`df`

`14 15`

cat1_tot NaN 2

cat1_share NaN 1

cat2_tot 2 NaN

cat2_share 0.6666 NaN

cat3_tot 1 NaN

cat3_share 0.3333 NaN

All 3 2

I can get

`catx_tot`

`pd.pivot_table`

`pd.pivot_table(`

df,

values='COUNT',

index=['B'],

columns=['E'],

margins=True,

aggfunc=np.sum

)

But how do I add share to this?

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Answer Source

combine `groupby.size`

with `groupby.transform`

```
size = df.groupby(['B', 'E']).size()
sums = size.groupby(level='E').transform(np.sum)
aggd = pd.concat([size, size / sums], axis=1, keys=['total', 'share'])
aggd.unstack().stack(0)
```

to get the `All`

row

```
all_ = aggd.groupby(level='E').sum().total.rename(('All', 'total'))
aggd.unstack().stack(0).append(all_)
```

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