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]: dfOut[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

get_level_values

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       twoA       0.895717  0.805244B       0.410835  0.813850C      -1.413681  1.607920In [26]: df['bar', 'one']Out[26]: A    0.895717B    0.410835C   -1.413681Name: (bar, one), dtype: float64In [27]: df['bar']['one']Out[27]: A    0.895717B    0.410835C   -1.413681Name: one, dtype: float64

使用reindex对齐数据

数据准备

In [11]: s = pd.Series(np.random.randn(8), index=arrays)In [12]: sOut[12]: bar  one   -0.861849     two   -2.104569baz  one   -0.494929     two    1.071804foo  one    0.721555     two   -0.706771qux  one   -1.039575     two    0.271860dtype: float64

s序列加(0~-2)索引的值,因为s[:-2]没有最后两个的索引,所以为NaN.s[::2]意思是步长为1.

In [34]: s + s[:-2]Out[34]: bar  one   -1.723698     two   -4.209138baz  one   -0.989859     two    2.143608foo  one    1.443110     two   -1.413542qux  one         NaN     two         NaNdtype: float64In [35]: s + s[::2]Out[35]: bar  one   -1.723698     two         NaNbaz  one   -0.989859     two         NaNfoo  one    1.443110     two         NaNqux  one   -2.079150     two         NaNdtype: float64