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
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 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
数据准备
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
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 -4.209138 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
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找一找教程网。
