将两个 pandas 列的计数分组到 dataframe

Question

I have the following data frame:

Sport	AgeGroup
Baseball	20s
Football	20s
Baseball	30s
Baseball	20s
Football	20s
Football	20s
Football	30s

And the goal here is to get the counts between the two columns such that it gets the counts of each column into a list of lists in the following format: [['baseball', '20s', 2], ['baseball', '30s', 1], ['football', '20s', 3], ['football', '30s', 1]] where each list consists of the format [sport, ageGroup, count where both exist in the table] .

Thus far, I've gotten what I needed in a way by doing: sport_age_count = df.groupby(["sport", "ageGroup"]).size() . The problem here though is that the result isn't indexable and only treats the count like a singular list. I get the following result with the simple code I have above:

Sport	AgeGroup
Baseball	20s	2
	30s	1
Football	20s	3
	30s	1

The problem is, when I try to use baseball, football, 20s, or 30s, it won't let me. I can only access the counts. Also, besides the fact, I'd really like this to be formatted into the bold list of lists like above as well. I wasn't sure if there's any alternative python tricks out there besides using extensive for loops to build this list myself.

Answer 1

Another solution, using .agg() :

print(
    df.groupby(["Sport", "AgeGroup"], as_index=False)
    .size()
    .agg(list, axis=1)
    .tolist()
)

Prints:

[['Baseball', '20s', 2], ['Baseball', '30s', 1], ['Football', '20s', 3], ['Football', '30s', 1]]

Answer 2

If you want an array (makes for easier indexing) you can reset the index and use to_numpy() after your groupby

sport_age_count.reset_index().to_numpy()

array([['Baseball', '20s', 2],
       ['Baseball', '30s', 1],
       ['Football', '20s', 3],
       ['Football', '30s', 1]], dtype=object)

But if you want a list of lists, you can use numpy's tolist()

sport_age_count.reset_index().to_numpy().tolist()

[['Baseball', '20s', 2],
 ['Baseball', '30s', 1],
 ['Football', '20s', 3],
 ['Football', '30s', 1]]