Python Pandas 按 mean() 分组一定数量的行

Question

I need to group by mean() for the first 2 values of each category, how I define that. df like

category    value
-> a    2
-> a    5
a   4
a   8
-> b    6
-> b    3
b   1
-> c    2
-> c    2
c   7

by reading only the arrowed data where the output be like

category    mean
a   3.5
b   4.5
c   2

how can I do this I am trying but do not know where to define the to get only 1st 2 observation from each categrory

output = df.groupby(['category'])['value'].mean().reset_index()

your help is appreciated, thanks in advance

Answer 1

Try apply on each group of values and use head(2) to just get the first 2 values then mean :

import pandas as pd

df = pd.DataFrame({
    'category': {0: 'a', 1: 'a', 2: 'a', 3: 'a', 4: 'b', 5: 'b',
                 6: 'b', 7: 'c', 8: 'c', 9: 'c'},
    'value': {0: 2, 1: 5, 2: 4, 3: 8, 4: 6, 5: 3, 6: 1, 7: 2,
              8: 2, 9: 7}
})

output = df.groupby('category', as_index=False)['value'] \
    .apply(lambda a: a.head(2).mean())

print(output)

output :

  category  value
0        a    3.5
1        b    4.5
2        c    2.0

---

Or create a boolean index to filter df with:

m = df.groupby('category').cumcount().lt(2)
output = df[m].groupby('category')['value'].mean().reset_index()
print(output)

  category  value
0        a    3.5
1        b    4.5
2        c    2.0

Answer 2

You can also do this via groupby() and agg() :

out=df.groupby('category',as_index=False)['value'].agg(lambda x:x.head(2).mean())