Pandas DataFrame Groupby：如何统计满足条件的分组行数

Question

I want to group rows by 'Age', and return a count of 1) how many rows make up each group, and 2) how many of those rows meet a condition.

Given a DataFrame that looks like this:

    Age     Died
0   26      0
1   26      0
2   27      1
3   28      0
4   28      1
5   28      1

I want to return a DataFrame that looks like this:

   Age     Count    Died_Count
   26        2        0
   27        1        1
   28        3        2

I have tried numerous combinations of various groupbys such as groupby(['Age', 'Died']) with different aggregators ( sum , count ) but can't seem to find a winning combination. Can someone point me in the right direction?

Answer 1

You can use namedagg:

(
    df.groupby('Age')
    .agg(Count=('Died', 'size'),
        Died_count=('Died', 'sum'))
    .reset_index()
)

Answer 2

Assume your dataframe is df

res=df.groupby("Age").agg({'Age': 'count', 'Died': 'sum'}).rename(columns={"Age":"Count"})

output

        Count  Died
Age             
26       2     0
27       1     1
28       3     2

you can reset index and set Age to a column as well.

res = res.reset_index(drop=False)

output

   Age  Count  Died
0   26      2     0
1   27      1     1
2   28      3     2