Python: How to group a List or an Array with numpy?

Question

im stuck.

i have a huge dataframe that looks like this:

| Index| Field|
| -------- | -------------- |
| 1| A|
| 1| B|
| 1| C|
| 2| A|
| 2| C|
| 3| A|
| 3| B|

At first i was grouping the Index and concatenated the column "field" with pandas and the groupby command.

Now my dataframe looks like this:

| Index| Field|
| -------- | -------------- |
| 1| [A, B, C]
| 2| [B, C] 
| 3| [A, B]

The next step is, that i want to count, how many times [A, B, C] exist in the whole dataset. The solution should be like this:

 | Field | Counts|
| -------- | -------------- |
| [A, B, C]| 222
| [B, C] | 530 
| [A, B] | 400

because i put it in an list (or np array), i don't know how to achieve the next output. Because i now have an list / array. i can not do another pd.groupby or im to blind to see.

can anybody give some hint or has an solution how to solve this?

thanks!

edit: sorry for the bad editing, stackoverflow didn't let me use the table formatting without saying this is some code

Your counts do not make sense based on your sample data.

It_is_Chris
– It_is_Chris

2021-08-24 20:10:07 +00:00
Commented Aug 24, 2021 at 20:10 — It_is_Chris
– It_is_Chris, Commented Aug 24, 2021 at 20:10

BENY · Accepted Answer · 2021-08-24 20:10:26Z

1

Try with tuple not list , then we can do value_counts

s = df.groupby('Index')['Field'].agg(tuple)
s.value_counts()
Out[642]: 
(A, B)       1
(A, B, C)    1
(A, C)       1
Name: Field, dtype: int64

answered Aug 24, 2021 at 20:10

BENY

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1 Comment

MKay Over a year ago

this is exactly what i was looking for. Thank you!

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