30. Pandas的数据分组-aggregate聚合
在对数据进行分组之后，可以对分组后的数据进行聚合处理统计。
- agg函数，agg的形参是一个函数会对分组后每列都应用这个函数。
import pandas as pd
import numpy as np
idx = [101,101,101,102,102,102,103,103,103]
idx += [101,102,103]
name = ["apple","pearl","orange", "apple","pearl","orange","apple","pearl","orange"]
name += ["apple"] * 3
price = [1.0,2.0,3.0,4.00,5.0,6.0,7.0,8.0,9.0]
price += [4] * 3
df0 = pd.DataFrame({ "fruit": name, "price" : price, "supplier" :idx})
print "*" * 30
print df0
print "*" * 30
dg1 = df0.groupby(["fruit", "supplier"])
for n, g in dg1:
print "multiGroup on:", n, "\n|",g ,"|"
print "*" * 30
print dg1.agg(np.mean)
程序的执行结果：
******************************
fruit price supplier
0 apple 1 101
1 pearl 2 101
2 orange 3 101
3 apple 4 102
4 pearl 5 102
5 orange 6 102
6 apple 7 103
7 pearl 8 103
8 orange 9 103
9 apple 4 101
10 apple 4 102
11 apple 4 103
******************************
multiGroup on: ('apple', 101)
| fruit price supplier
0 apple 1 101
9 apple 4 101 |
...
multiGroup on: ('pearl', 103)
| fruit price supplier
7 pearl 8 103 |
******************************
price
fruit supplier
apple 101 2.5
102 4.0
103 5.5
orange 101 3.0
102 6.0
103 9.0
pearl 101 2.0
102 5.0
103 8.0
请注意水果apple的输出。
- agg应用均值、求和、最大等示例。
import pandas as pd
import numpy as np
idx = [101,101,101,102,102,102,103,103,103]
idx += [101,102,103] * 3
name = ["apple","pearl","orange", "apple","pearl","orange","apple","pearl","orange"]
name += ["apple"] * 3 + ["pearl"] * 3 + ["orange"] * 3
price = [4.1,5.3,6.3,4.20,5.4,6.0,4.5,5.5,6.8]
price += [4] * 3 + [5] * 3 + [6] * 3
df0 = pd.DataFrame({ "fruit": name, "price" : price, "supplier" :idx})
print "*" * 30
print df0
print "*" * 30
dg1 = df0.groupby(["fruit", "supplier"])
print dg1.agg(np.mean)
print "*" * 30
print dg1.agg([np.mean, np.std, np.min, np.sum])
程序执行结果：
******************************
fruit price supplier
0 apple 4.1 101
...
17 orange 6.0 103
******************************
price
fruit supplier
apple 101 4.05
102 4.10
103 4.25
orange 101 6.15
102 6.00
103 6.40
pearl 101 5.15
102 5.20
103 5.25
******************************
price
mean std amin sum
fruit supplier
apple 101 4.05 0.070711 4 8.1
102 4.10 0.141421 4 8.2
103 4.25 0.353553 4 8.5
orange 101 6.15 0.212132 6 12.3
102 6.00 0.000000 6 12.0
103 6.40 0.565685 6 12.8
pearl 101 5.15 0.212132 5 10.3
102 5.20 0.282843 5 10.4
103 5.25 0.353553 5 10.5
- 各列用不同的处理函数。需要在agg函数里以字典的形式给出，分组后的那列用那个函数处理。
import pandas as pd
import numpy as np
idx = [101,101,101,102,102,102,103,103,103]
idx += [101,102,103] * 3
name = ["apple","pearl","orange", "apple","pearl","orange","apple","pearl","orange"]
name += ["apple"] * 3 + ["pearl"] * 3 + ["orange"] * 3
price = [4.1,5.3,6.3,4.20,5.4,6.0,4.5,5.5,6.8]
price += [4] * 3 + [5] * 3 + [6] * 3
df0 = pd.DataFrame({ "fruit": name, "price" : price, "supplier" :idx})
print "*" * 30
print df0
print "*" * 30
dg1 = df0.groupby(["fruit"])
print dg1.agg(np.mean)
print "*" * 30
print dg1.agg([np.mean, np.std, np.min, np.sum])
print "*" * 30
print dg1.agg({"price" : np.mean, "supplier" : np.max})
程序的执行结果：
******************************
fruit price supplier
0 apple 4.1 101
1 pearl 5.3 101
2 orange 6.3 101
3 apple 4.2 102
4 pearl 5.4 102
5 orange 6.0 102
6 apple 4.5 103
7 pearl 5.5 103
8 orange 6.8 103
9 apple 4.0 101
10 apple 4.0 102
11 apple 4.0 103
12 pearl 5.0 101
13 pearl 5.0 102
14 pearl 5.0 103
15 orange 6.0 101
16 orange 6.0 102
17 orange 6.0 103
******************************
price supplier
fruit
apple 4.133333 102
orange 6.183333 102
pearl 5.200000 102
******************************
price supplier
mean std amin sum mean std amin sum
fruit
apple 4.133333 0.196638 4 24.8 102 0.894427 101 612
orange 6.183333 0.325064 6 37.1 102 0.894427 101 612
pearl 5.200000 0.228035 5 31.2 102 0.894427 101 612
******************************
supplier price
fruit
apple 103 4.133333
orange 103 6.183333
pearl 103 5.200000
agg函数是对列而言的，如果打算对分组后列的数据进行处理可以使用tranform函数，见下一章。
感谢Klang(金浪)智能数据看板klang.org.cn鼎力支持！