熊猫：如何分组和总和MultiIndex

Question

I have a dataframe with categorical attributes where the index contains duplicates. I am trying to find the sum of each possible combination of index and attribute.

x = pd.DataFrame({'x':[1,1,3,3],'y':[3,3,5,5]},index=[11,11,12,12])
y = x.stack()
print(y)
print(y.groupby(level=[0,1]).sum())

output

11  x    1
    y    3
    x    1
    y    3
12  x    3
    y    5
    x    3
    y    5
dtype: int64
11  x    1
    y    3
    x    1
    y    3
12  x    3
    y    5
    x    3
    y    5
dtype: int64

The stack and group by sum are just the same.

However, the one I expect is

11  x    2
11  y    6
12  x    6
12  y    10

EDIT 2:

x = pd.DataFrame({'x':[1,1,3,3],'y':[3,3,5,5]},index=[11,11,12,12])
y = x.stack().groupby(level=[0,1]).sum()
print(y.groupby(level=[0,1]).sum())

output:

11  x    1
    y    3
    x    1
    y    3
12  x    3
    y    5
    x    3
    y    5
dtype: int64

EDIT3: An issue has been logged https://github.com/pydata/pandas/issues/10417

Answer 1

Using Pandas 0.15.2, you just need one more iteration of groupby

x = pd.DataFrame({'x':[1,1,3,3],'y':[3,3,5,5]},index=[11,11,12,12])
y = x.stack().groupby(level=[0,1]).sum()
print(y.groupby(level=[0,1]).sum())

prints

11  x     2
    y     6
12  x     6
    y    10

Answer 2

With pandas 0.16.2 and Python 3, I was able to get the correct result via:

x.stack().reset_index().groupby(['level_0','level_1']).sum()

Which produces:

                    0
level_0 level_1 
     11       x     2
              y     6
     12       x     6
              y     10

You can then change the index and column names to more desirable ones using reindex() and columns .

Based on my research, I agree that the failure of the original approach appears to be a bug. I think the bug is on Series , which is what x.stack() produces. My workaround is to turn the Series into a DataFrame via reset_index() . In this case the DataFrame does not have a MultiIndex anymore - I'm just grouping on labeled columns.

To make sure that grouping and summing works on a DataFrame with a MultiIndex , you can try this to get the same correct output:

x.stack().reset_index().set_index(['level_0','level_1'],drop=True).\
groupby(level=[0,1]).sum()

Either of these workarounds should take care of things until the bug is resolved.

I wonder if the bug has something to do with the MultiIndex instances that are created on a Series vs. a DataFrame . For example:

In[1]: obj = x.stack()
       type(obj)
Out[1]: pandas.core.series.Series

In[2]: obj.index
Out[2]: MultiIndex(levels=[[11, 11, 12, 12], ['x', 'y']],
           labels=[[0, 0, 1, 1, 2, 2, 3, 3], [0, 1, 0, 1, 0, 1, 0, 1]])

vs.

In[3]: obj = x.stack().reset_index().set_index(['level_0','level_1'],drop=True)
       type(obj)
Out[3]: pandas.core.frame.DataFrame

In[4]: obj.index
Out[4]: MultiIndex(levels=[[11, 12], ['x', 'y']],
           labels=[[0, 0, 0, 0, 1, 1, 1, 1], [0, 1, 0, 1, 0, 1, 0, 1]],
           names=['level_0', 'level_1'])

Notice how the MultiIndex on the DataFrame describes the levels more correctly.

Answer 3

sum allows you to specify the levels to sum over in a MultiIndex data frame.

x = pd.DataFrame({'x':[1,1,3,3],'y':[3,3,5,5]},index=[11,11,12,12])
y = x.stack()

y.sum(level=[0,1])

11  x     2
    y     6
12  x     6
    y    10