对Pandas MultiIndex(多重索引)详解

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

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