Pandas Multiindex dataframe remove rows

问题

I have Multiiindex DF as follows:

tuples = list(zip(*[['a', 'a', 'b', 'b'], ['c', 'd', 'c', 'd']]))
index = pd.MultiIndex.from_tuples(tuples, names=['i1', 'i2'])
df = pd.DataFrame([5, 6, 7, 8], index=index[:4], columns=['col'])

       col
i1 i2     
a  c     5
   d     6
b  c     7
   d     8

Would like to keep rows whose index (level 0) is in

idx_to_keep = ['a']

Should be a straightforward task, but I can't think of any other way than

idx_to_drop = np.setdiff1d(pd.unique(df.index.levels[0]), idx_to_keep)
df.drop(idx_to_drop, inplace = True)

       col
i1 i2     
a  c     5
   d     6

Can I do better?

回答1:

One way is to use the index method get_level_values():

df
       col
i1 i2     
a  c     5
   d     6
b  c     7
   d     8

df[df.index.get_level_values(0).isin(idx_to_keep)]
       col
i1 i2     
a  c     5
   d     6

回答2:

You are looking for .xs:

df.xs('a', axis=0, level=0, drop_level=False)

Which gives:

       col
i1 i2     
a  c     5
   d     6

回答3:

You can just use loc:

df.loc[['a']]

The resulting output:

       col
i1 i2     
a  c     5
   d     6

回答4:

Let's use slice

idx_to_keep = ['a']
df.loc[slice(*idx_to_keep,)]

Output:

       col
i1 i2     
a  c     5
   d     6

来源：https://stackoverflow.com/questions/45333949/pandas-multiindex-dataframe-remove-rows