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 -1.087401two -0.673690  0.113648 -1.478427  0.524988
baz one  0.404705  0.577046 -1.715002 -1.039268two -0.370647 -1.157892 -1.344312  0.844885
foo one  1.075770 -0.109050  1.643563 -1.469388two  0.357021 -0.674600 -1.776904 -0.968914
qux one -1.294524  0.413738  0.276662 -0.472035two -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.

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