层次化索引
层次化也就是在一个轴上拥有多个索引级别
Series的层次化索引
data=Series(np.random.randn(10),index=[
['a','a','a','b','b','b','c','c','d','d'],
[1,2,3,1,2,3,1,2,2,3]
])
data
a 1 0.965999
2 -0.271733
3 0.133910
b 1 -0.806885
2 -0.622905
3 -0.355330
c 1 -0.659194
2 -1.082872
d 2 -0.043984
3 -1.125324
dtype: float64
# 选取数据子集
data['b']
1 -0.806885
2 -0.622905
3 -0.355330
dtype: float64
data['b':'c'] # 在pandas中顾头也顾尾
b 1 -0.806885
2 -0.622905
3 -0.355330
c 1 -0.659194
2 -1.082872
dtype: float64
data.ix[['b','d']] # 按行索引名称选择
b 1 -0.806885
2 -0.622905
3 -0.355330
d 2 -0.043984
3 -1.125324
dtype: float64
# 在内层中进行选取，选择所有的行索引中的2这一行
data[:,2]
a -0.271733
b -0.622905
c -1.082872
d -0.043984
dtype: float64
# 层次化索引在数据重塑和基于分组的操作中扮演着重要的角色
# 这个函数会把层次化索引转为DataFrame格式，最外层的行索引作为DataFrame的行索引，内层的索引作为列索引
data.unstack()
1 2 3
a 0.965999 -0.271733 0.133910
b -0.806885 -0.622905 -0.355330
c -0.659194 -1.082872 NaN
d NaN -0.043984 -1.125324
# unstack()的逆运算，转回来
data.unstack().stack()
a 1 0.965999
2 -0.271733
3 0.133910
b 1 -0.806885
2 -0.622905
3 -0.355330
c 1 -0.659194
2 -1.082872
d 2 -0.043984
3 -1.125324
dtype: float64
DataFrame的层次化索引
frame = pd.DataFrame(np.arange(12).reshape(4,3),index=[['a','a','b','b'],[1,2,1,2]],
columns=[['ohio','ohio','color'],['green','red','green']]
)
frame
ohio color
green red green
a 1 0 1 2
2 3 4 5
b 1 6 7 8
2 9 10 11
# 给层级行索引加名字
frame.index.names = ['key1','key2']
# 给层级列索引加名字
frame.columns.names = ['state','color']
frame
state ohio color
color green red green
key1 key2
a 1 0 1 2
2 3 4 5
b 1 6 7 8
2 9 10 11
frame['ohio']
color green red
key1 key2
a 1 0 1
2 3 4
b 1 6 7
2 9 10
重排分级顺序
frame
state ohio color
color green red green
key1 key2
a 1 0 1 2
2 3 4 5
b 1 6 7 8
2 9 10 11
# 这里sortlevel()括号里的0指把key2和key1交换后按key2排序
frame.swaplevel(0,1).sortlevel(0)
state ohio color
color green red green
key2 key1
1 a 0 1 2
b 6 7 8
2 a 3 4 5
b 9 10 11
# 1指按key1排序
frame.swaplevel(0,1).sortlevel(1)
state ohio color
color green red green
key2 key1
1 a 0 1 2
2 a 3 4 5
1 b 6 7 8
2 b 9 10 11
根据层次索引级别汇总统计
frame
state ohio color
color green red green
key1 key2
a 1 0 1 2
2 3 4 5
b 1 6 7 8
2 9 10 11
# 以key2的1和1相加，2和2索引相加
frame.sum(level='key2')
state ohio color
color green red green
key2
1 6 8 10
2 12 14 16
# 以行索引的green索引相加，red没有不做改变
frame.sum(level='color',axis=1)
color green red
key1 key2
a 1 2 1
2 8 4
b 1 14 7
2 20 10
使用DataFrame的列
frame1 = pd.DataFrame({'a':range(7),'b':range(7,0,-1),
'c':['one','one','one','two','two','two','two'],
'd':[0,1,2,0,1,2,3]
})
frame1
a b c d
0 0 7 one 0
1 1 6 one 1
2 2 5 one 2
3 3 4 two 0
4 4 3 two 1
5 5 2 two 2
6 6 1 two 3
#把c/d设置为行索引，默认会删除这两列，如果不想删除，可以吧drop=False开启
frame1.set_index(['c','d'])
a b
c d
one 0 0 7
1 1 6
2 2 5
two 0 3 4
1 4 3
2 5 2
3 6 1
# reset_index会把cd设置为列索引,了解就行
frame2.reset_index()
index a b c d
0 0 0 7 one 0
1 1 1 6 one 1
2 2 2 5 one 2
3 3 3 4 two 0
4 4 4 3 two 1
5 5 5 2 two 2
6 6 6 1 two 3
