问题
I would like to call those row with same index.
so this is the example data frame,
arrays = [np.array(['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux']),
np.array(['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two'])]
df = pd.DataFrame(np.random.randn(8, 4), index=arrays)
In [16]: df
Out[16]:
0 1 2 3
bar one -0.424972 0.567020 0.276232 -1.087401
two -0.673690 0.113648 -1.478427 0.524988
baz one 0.404705 0.577046 -1.715002 -1.039268
two -0.370647 -1.157892 -1.344312 0.844885
foo one 1.075770 -0.109050 1.643563 -1.469388
two 0.357021 -0.674600 -1.776904 -0.968914
qux one -1.294524 0.413738 0.276662 -0.472035
two -0.013960 -0.362543 -0.006154 -0.923061
I would like to select
0 1 2 3
bar one -0.424972 0.567020 0.276232 -1.087401
baz one 0.404705 0.577046 -1.715002 -1.039268
foo one 1.075770 -0.109050 1.643563 -1.469388
qux one -1.294524 0.413738 0.276662 -0.472035
or even as this format
0 1 2 3
one -0.424972 0.567020 0.276232 -1.087401
one 0.404705 0.577046 -1.715002 -1.039268
one 1.075770 -0.109050 1.643563 -1.469388
one -1.294524 0.413738 0.276662 -0.472035
I have tried df['bar','one] and it's not working. I am now sure how should I access the multi-level index.
回答1:
Use DataFrame.xs and if need both levels add drop_level=False:
df1 = df.xs('one', level=1, drop_level=False)
print (df1)
bar one -0.424972 0.567020 0.276232 -1.087401
baz one 0.404705 0.577046 -1.715002 -1.039268
foo one 1.075770 -0.109050 1.643563 -1.469388
qux one -1.294524 0.413738 0.276662 -0.472035
For second remove first level by DataFrame.reset_index with drop=True, so possible select by label with DataFrame.loc:
df2 = df.reset_index(level=0, drop=True).loc['one']
#alternative
#df2 = df.xs('one', level=1, drop_level=False).reset_index(level=0, drop=True)
print (df2)
0 1 2 3
one -0.424972 0.567020 0.276232 -1.087401
one 0.404705 0.577046 -1.715002 -1.039268
one 1.075770 -0.109050 1.643563 -1.469388
one -1.294524 0.413738 0.276662 -0.472035
More common is used xs without duplicated levels - so after select one is removed this level:
df3 = df.xs('one', level=1)
print (df3)
0 1 2 3
bar -0.424972 0.567020 0.276232 -1.087401
baz 0.404705 0.577046 -1.715002 -1.039268
foo 1.075770 -0.109050 1.643563 -1.469388
qux -1.294524 0.413738 0.276662 -0.472035
回答2:
You can use MultiIndex slicing (use slice(None) instead of colon):
df = df.loc[(slice(None), 'one'), :]
Result:
0 1 2 3
bar one -0.424972 0.567020 0.276232 -1.087401
baz one 0.404705 0.577046 -1.715002 -1.039268
foo one 1.075770 -0.109050 1.643563 -1.469388
qux one -1.294524 0.413738 0.276662 -0.472035
Finally you can drop the first index column:
df.index = df.index.droplevel(0)
Result:
0 1 2 3
one -0.424972 0.567020 0.276232 -1.087401
one 0.404705 0.577046 -1.715002 -1.039268
one 1.075770 -0.109050 1.643563 -1.469388
one -1.294524 0.413738 0.276662 -0.472035
来源：https://stackoverflow.com/questions/56594197/how-to-access-multi-level-index-in-pandas-data-frame