创建多重索引
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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 -1.170299B 0.410835 0.813850 0.132003 -0.827317 -0.076467 -1.187678 1.130127C -1.413681 1.607920 1.024180 0.569605 0.875906 -2.211372 0.974466first second two A -0.226169B -1.436737C -2.006747 |
获得索引信息
get_level_values
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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
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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
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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.
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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 |
以上这篇对Pandas MultiIndex(多重索引)详解就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/claroja/article/details/72547815
