python/pandas/numpy数据分析（七）-MultiIndex

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## python/pandas/numpy数据分析（七）-MultiIndex

``````data=Series(np.random.randn(10),index=[list('aaabbbccdd'),list('1231231223')])
data

a  1    0.198134
2    0.657700
3   -0.984464
b  1    0.105481
2   -1.587769
3    0.329646
c  1   -0.172460
2   -1.234518
d  2   -1.200264
3   -0.239958
dtype: float64

data.index
MultiIndex(levels=[['a', 'b', 'c', 'd'], ['1', '2', '3']],
labels=[[0, 0, 0, 1, 1, 1, 2, 2, 3, 3], [0, 1, 2, 0, 1, 2, 0, 1, 1, 2]])``````

``````data['b':'c']
b  1    0.105481
2   -1.587769
3    0.329646
c  1   -0.172460
2   -1.234518
dtype: float64

data.ix[['b','c']]

b  1    0.105481
2   -1.587769
3    0.329646
c  1   -0.172460
2   -1.234518
dtype: float64``````

``````data[:, '2']

a    0.657700
b   -1.587769
c   -1.234518
d   -1.200264
dtype: float64
``````

unstack: 将Series放到DataFrame中

``````data.unstack()
1   2   3
a   0.198134    0.657700    -0.984464
b   0.105481    -1.587769   0.329646
c   -0.172460   -1.234518   NaN
d   NaN -1.200264   -0.239958
``````

data.unstack().stack() 进行还原

``data.unstack().stack()``

``````frame=DataFrame(np.arange(12).reshape(4,3),
index=[list('aabb'),list('1212')],
frame
``````

``````frame.index.names=['key1','key2']
frame.columns.names=['state','color']
frame``````

``frame['ohio']``

## 根据级别汇总统计

``````frame.index
MultiIndex(levels=[['a', 'b'], ['1', '2']],
labels=[[0, 0, 1, 1], [0, 1, 0, 1]],
names=['key1', 'key2'])
``````

levels用于在指定在某条轴上进行求和的级别.

``````frame.sum(level='key2')

frame.sum(level='color',axis=1)
``````

## 将列转换为行索引

DataFrame的set_index函数会将一个或者多个列转换为行索引,并创建一个新的DataFrame

reset_index的功能跟set_index刚好相反，层次化索引的级别挥别转移到列里面

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