I want expanding mean of col2 based on groupby('col1'), but I want the mean to not include the row itself (just the rows above it)
dummy = pd.DataFrame({"col1": ["a",'a','a','b','b','b','c','c'],"col2":['1','2','3','4','5','6','7','8'] }, index=list(range(8)))
print(dummy)
dummy['one_liner'] = dummy.groupby('col1').col2.shift().expanding().mean().reset_index(level=0, drop=True)
dummy['two_liner'] = dummy.groupby('col1').col2.shift()
dummy['two_liner'] = dummy.groupby('col1').two_liner.expanding().mean().reset_index(level=0, drop=True)
print(dummy)
---------------------------
here is result of first print statement:
col1 col2
0 a 1
1 a 2
2 a 3
3 b 4
4 b 5
5 b 6
6 c 7
7 c 8
here is result of the second print:
col1 col2 one_liner two_liner
0 a 1 NaN NaN
1 a 2 1.000000 1.0
2 a 3 1.500000 1.5
3 b 4 1.500000 NaN
4 b 5 2.333333 4.0
5 b 6 3.000000 4.5
6 c 7 3.000000 NaN
7 c 8 3.800000 7.0
I would have thought their results would be identical. two_liner is the expected result. one_liner mixes numbers in between groups.
It took a long time to figure out this solution, can anyone explain the logic? Why does one_liner not give expected results?
You are looking for expanding().mean() and shift() within the groupby():
groups = df.groupby('col1')
df['one_liner'] = groups.col2.apply(lambda x: x.expanding().mean().shift())
df['two_liner'] = groups.one_liner.apply(lambda x: x.expanding().mean().shift())
Output:
col1 col2 one_liner two_liner
0 a 1 NaN NaN
1 a 2 1.0 NaN
2 a 3 1.5 1.0
3 b 4 NaN NaN
4 b 5 4.0 NaN
5 b 6 4.5 4.0
6 c 7 NaN NaN
7 c 8 7.0 NaN
Explanation:
(dummy.groupby('col1').col2.shift() # this shifts col2 within the groups
.expanding().mean() # this ignores the grouping and expanding on the whole series
.reset_index(level=0, drop=True) # this is not really important
)
So that the above chained command is equivalent to
s1 = dummy.groupby('col1').col2.shift()
s2 = s1.expanding.mean()
s3 = s2.reset_index(level=0, drop=True)
As you can see, only s1 considers the grouping by col1.