import numpy as np
import pandas as pd
df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",
"bar", "bar", "bar", "bar"],
"B": ["one", "one", "one", "two", "two",
"one", "one", "two", "two"],
"C": ["small", "large", "large", "small",
"small", "large", "small", "small",
"large"],
"D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
"E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})
df
Out[38]:
A B C D E
0 foo one small 1 2
1 foo one large 2 4
2 foo one large 2 5
3 foo two small 3 5
4 foo two small 3 6
5 bar one large 4 6
6 bar one small 5 8
7 bar two small 6 9
8 bar two large 7 9
table = pd.pivot_table(df, values='D', index=['A', 'B'],
columns=['C'], aggfunc=np.sum)
Out[39]:
C large small
A B
bar one 4.0 5.0
two 7.0 6.0
foo one 4.0 1.0
two NaN 6.0
数据透视,就是对数据进行一次函数变换，将数据映射到一个新的空间，新空间的维度是A ,B,C，新空间的值是D,可以认为是高维空间的离散点
