Pandas MultiIndex(多重索引)

创建多重索引

In [16]: df = pd.DataFrame(np.random.randn(3, 8), index=['A', 'B', 'C'], columns=index)

In [17]: df
Out[17]:
first        bar                 baz                 foo                 qux  \
second       one       two       one       two       one       two       one
A       0.895717  0.805244 -1.206412  2.565646  1.431256  1.340309 -1.170299
B       0.410835  0.813850  0.132003 -0.827317 -0.076467 -1.187678  1.130127
C      -1.413681  1.607920  1.024180  0.569605  0.875906 -2.211372  0.974466

first
second       two
A      -0.226169
B      -1.436737
C      -2.006747

获得索引信息

In [23]: index.get_level_values(0)
Out[23]: Index(['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'], dtype='object', name='first')

In [24]: index.get_level_values('second')
Out[24]: Index(['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two'], dtype='object', name='second')

基本索引

In [25]: df['bar']
Out[25]:
second       one       two
A       0.895717  0.805244
B       0.410835  0.813850
C      -1.413681  1.607920

In [26]: df['bar', 'one']
Out[26]:
A    0.895717
B    0.410835
C   -1.413681
Name: (bar, one), dtype: float64

In [27]: df['bar']['one']
Out[27]:
A    0.895717
B    0.410835
C   -1.413681
Name: one, dtype: float64

使用reindex对齐数据

In [11]: s = pd.Series(np.random.randn(8), index=arrays)

In [12]: s
Out[12]:
bar  one   -0.861849
two   -2.104569
baz  one   -0.494929
two    1.071804
foo  one    0.721555
two   -0.706771
qux  one   -1.039575
two    0.271860
dtype: float64

In [34]: s + s[:-2]
Out[34]:
bar  one   -1.723698
two   -4.209138
baz  one   -0.989859
two    2.143608
foo  one    1.443110
two   -1.413542
qux  one         NaN
two         NaN
dtype: float64

In [35]: s + s[::2]
Out[35]:
bar  one   -1.723698
two         NaN
baz  one   -0.989859
two         NaN
foo  one    1.443110
two         NaN
qux  one   -2.079150
two         NaN
dtype: float64