something wrong in pandas.Series.mean() or .apply() - python-2.7

Question

I'm practicing the function of .apply() in pandas, but something goes wrong when I use .Series.mean() in the function.
here is my code:
In[1]: column = ['UserInfo_2', 'UserInfo_4','info_1', 'info_2', 'info_3','target']
value = [['a', 'b', 'a', 'c', 'b', 'a'],
['a', 'c', 'b', 'c', 'b', 'b'],
range(0, 11, 2),
range(1, 12, 2),
range(15, 21),
[0, 0, 1, 0, 1, 0]
]
master_train = pd.DataFrame(dict(zip(column, value)))
In[2]: def f(group):
return pd.DataFrame({'original': group,'demand':group-group.mean()})
In[3]: master_train.groupby('UserInfo_2')['info_1'].apply(f)
Out[3]:
demand original
0 -4.666667 0
1 -3.000000 2
2 -0.666667 4
3 0.000000 6
4 3.000000 8
5 5.333333 10
I am confused because the mean of info_1 is actually 5, but from the result abrove, the mean changes from 4.666667 to 7.
What's wrong??

Answer 1

I think now it is clear - you count mean of column info_1 (or original) by groups from column UserInfo_2:
def f(group):
return pd.DataFrame({'original': group,
'groups': group.name,
'demand':group - group.mean() ,
'mean':group.mean()})
print (master_train.groupby('UserInfo_2')['info_1'].apply(f))
demand groups mean original
0 -4.666667 a 4.666667 0
1 -3.000000 b 5.000000 2
2 -0.666667 a 4.666667 4
3 0.000000 c 6.000000 6
4 3.000000 b 5.000000 8
5 5.333333 a 4.666667 10
I think you want mean of column info_1:
def f(group):
return pd.DataFrame({'original': group,
'demand':group - master_train['info_1'].mean(),
'mean':master_train['info_1'].mean()})
print (master_train.groupby('UserInfo_2')['info_1'].apply(f))
demand mean original
0 -5.0 5.0 0
1 -3.0 5.0 2
2 -1.0 5.0 4
3 1.0 5.0 6
4 3.0 5.0 8
5 5.0 5.0 10
EDIT:
For testing is possible add print(group) to function f - it returns Series from column info_1 by groups from column UserInfo_2:
def f(group):
print (group)
return pd.DataFrame({'original': group,
'groups': group.name,
'demand':group - group.mean() ,
'mean':group.mean()})
print (master_train.groupby('UserInfo_2')['info_1'].apply(f))
0 0
2 4
5 10
Name: a, dtype: int32
1 2
4 8
Name: b, dtype: int32
3 6
Name: c, dtype: int32
demand groups mean original
0 -4.666667 a 4.666667 0
1 -3.000000 b 5.000000 2
2 -0.666667 a 4.666667 4
3 0.000000 c 6.000000 6
4 3.000000 b 5.000000 8
5 5.333333 a 4.666667 10
And if you need mean of all column info_1:
print (master_train['info_1'])
0 0
1 2
2 4
3 6
4 8
5 10
Name: info_1, dtype: int32
print (master_train['info_1'].mean())
5.0