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。