Pandas分组与聚合

分组 (groupby)

• 对数据集进行分组，然后对每组进行统计分析

• SQL能够对数据进行过滤，分组聚合

• pandas能利用groupby进行更加复杂的分组运算

• 分组运算过程：split->apply->combine

  1. 拆分：进行分组的根据

  2. 应用：每个分组运行的计算规则

  3. 合并：把每个分组的计算结果合并起来

示例代码：

import pandas as pd import numpy as np dict_obj = {‘key1‘ : [‘a‘,‘b‘,‘a‘,‘a‘],‘key2‘ : [‘one‘,‘one‘,‘two‘,‘three‘,‘three‘],‘data1‘: np.random.randn(8),‘data2‘: np.random.randn(8)} df_obj = pd.DataFrame(dict_obj) print(df_obj) 

运行结果：

data1     data2 key1   key2
0 0.974685 -0.672494 a one 1 -0.214324 0.758372 b one 2 1.508838 0.392787 a two 3 0.522911 0.630814 b three 4 1.347359 -0.177858 a two 5 -0.264616 1.017155 b two 6 -0.624708 0.450885 a one 7 -1.019229 -1.143825 a three 

一、GroupBy对象：DataFrameGroupBy，SeriesGroupBy

1. 分组操作

groupby()进行分组，GroupBy对象没有进行实际运算，只是包含分组的中间数据

按列名分组：obj.groupby(‘label’)

示例代码：

# dataframe根据key1进行分组 print(type(df_obj.groupby(‘key1‘))) # dataframe的 data1 列根据 key1 进行分组 print(type(df_obj[‘data1‘].groupby(df_obj[‘key1‘]))) 

运行结果：

<class ‘pandas.core.groupby.DataFrameGroupBy‘>
<class ‘pandas.core.groupby.SeriesGroupBy‘>

2. 分组运算

对GroupBy对象进行分组运算/多重分组运算，如mean()

非数值数据不进行分组运算

示例代码：

# 分组运算 grouped1 = df_obj.groupby(‘key1‘) print(grouped1.mean()) grouped2 = df_obj[‘data1‘].groupby(df_obj[‘key1‘]) print(grouped2.mean()) 

运行结果：

data1     data2
key1                    
a     0.437389 -0.230101 b 0.014657 0.802114 key1 a 0.437389 b 0.014657 Name: data1,dtype: float64 

size() 返回每个分组的元素个数

示例代码：

# size print(grouped1.size()) print(grouped2.size()) 

运行结果：

key1
a    5 b 3 dtype: int64 key1 a 5 b 3 dtype: int64 

3. 按自定义的key分组

obj.groupby(self_def_key)

自定义的key可为列表或多层列表

obj.groupby([‘label1’,‘label2’])->多层dataframe

示例代码：

# 按自定义key分组，列表 self_def_key = [0,1,2,3,4,5,7] print(df_obj.groupby(self_def_key).size()) # 按自定义key分组，多层列表 print(df_obj.groupby([df_obj[‘key1‘],df_obj[‘key2‘]]).size()) # 按多个列多层分组 grouped2 = df_obj.groupby([‘key1‘,‘key2‘]) print(grouped2.size()) # 多层分组按key的顺序进行 grouped3 = df_obj.groupby([‘key2‘,‘key1‘]) print(grouped3.mean()) # unstack可以将多层索引的结果转换成单层的dataframe print(grouped3.mean().unstack()) 

运行结果：

0 1 1 1 2 1 3 2 4 1 5 1 7 1 dtype: int64 key1 key2 a one 2 three 1 two 2 b one 1 three 1 two 1 dtype: int64 key1 key2 a one 2 three 1 two 2 b one 1 three 1 two 1 dtype: int64 data1 data2 key2 key1 one a 0.174988 -0.110804 b -0.214324 0.758372 three a -1.019229 -1.143825 b 0.522911 0.630814 two a 1.428099 0.107465 b -0.264616 1.017155 data1 data2 key1 a b a b key2 one 0.174988 -0.214324 -0.110804 0.758372 three -1.019229 0.522911 -1.143825 0.630814 two 1.428099 -0.264616 0.107465 1.017155 

二、GroupBy对象支持迭代操作

每次迭代返回一个元组 (group_name,group_data)

可用于分组数据的具体运算

1. 单层分组

示例代码：

# 单层分组，根据key1 for group_name,group_data in grouped1: print(group_name) print(group_data) 

运行结果：

a
      data1     data2 key1   key2
0 0.974685 -0.672494 a one 2 1.508838 0.392787 a two 4 1.347359 -0.177858 a two 6 -0.624708 0.450885 a one 7 -1.019229 -1.143825 a three b data1 data2 key1 key2 1 -0.214324 0.758372 b one 3 0.522911 0.630814 b three 5 -0.264616 1.017155 b two 

2. 多层分组

示例代码：

# 多层分组，根据key1 和 key2 for group_name,group_data in grouped2: print(group_name) print(group_data) 

运行结果：

(‘a‘,‘one‘) data1 data2 key1 key2 0 0.974685 -0.672494 a one 6 -0.624708 0.450885 a one (‘a‘,‘three‘) data1 data2 key1 key2 7 -1.019229 -1.143825 a three (‘a‘,‘two‘) data1 data2 key1 key2 2 1.508838 0.392787 a two 4 1.347359 -0.177858 a two (‘b‘,‘one‘) data1 data2 key1 key2 1 -0.214324 0.758372 b one (‘b‘,‘three‘) data1 data2 key1 key2 3 0.522911 0.630814 b three (‘b‘,‘two‘) data1 data2 key1 key2 5 -0.264616 1.017155 b two 

三、GroupBy对象可以转换成列表或字典

示例代码：

# GroupBy对象转换list print(list(grouped1)) # GroupBy对象转换dict print(dict(list(grouped1))) 

运行结果：

[(‘a‘,data1 data2 key1 key2 0 0.974685 -0.672494 a one 2 1.508838 0.392787 a two 4 1.347359 -0.177858 a two 6 -0.624708 0.450885 a one 7 -1.019229 -1.143825 a three),(‘b‘,data1 data2 key1 key2 1 -0.214324 0.758372 b one 3 0.522911 0.630814 b three 5 -0.264616 1.017155 b two)] {‘a‘: data1 data2 key1 key2 0 0.974685 -0.672494 a one 2 1.508838 0.392787 a two 4 1.347359 -0.177858 a two 6 -0.624708 0.450885 a one 7 -1.019229 -1.143825 a three,‘b‘: data1 data2 key1 key2 1 -0.214324 0.758372 b one 3 0.522911 0.630814 b three 5 -0.264616 1.017155 b two} 

1. 按列分组、按数据类型分组

示例代码：

# 按列分组 print(df_obj.dtypes) # 按数据类型分组 print(df_obj.groupby(df_obj.dtypes,axis=1).size()) print(df_obj.groupby(df_obj.dtypes,axis=1).sum()) 

运行结果：

data1    float64
data2    float64
key1      object
key2      object
dtype: object

float64    2 object 2 dtype: int64 float64 object 0 0.302191 a one 1 0.544048 b one 2 1.901626 a two 3 1.153725 b three 4 1.169501 a two 5 0.752539 b two 6 -0.173823 a one 7 -2.163054 a three 

2. 其他分组方法

示例代码：

df_obj2 = pd.DataFrame(np.random.randint(1,10,(5,5)),columns=[‘a‘,‘c‘,‘d‘,‘e‘],index=[‘A‘,‘B‘,‘C‘,‘D‘,‘E‘]) df_obj2.ix[1,1:4] = np.NaN print(df_obj2) 

运行结果：

a    b    c    d  e
A  7 2.0 4.0 5.0 8 B 4 NaN NaN NaN 1 C 3 2.0 5.0 4.0 6 D 3 1.0 9.0 7.0 3 E 6 1.0 6.0 8.0 1 

3. 通过字典分组

示例代码：

# 通过字典分组 mapping_dict = {‘a‘:‘Python‘,‘b‘:‘Python‘,‘c‘:‘Java‘,‘d‘:‘C‘,‘e‘:‘Java‘} print(df_obj2.groupby(mapping_dict,axis=1).size()) print(df_obj2.groupby(mapping_dict,axis=1).count()) # 非NaN的个数 print(df_obj2.groupby(mapping_dict,axis=1).sum()) 

运行结果：

C         1 Java 2 Python 2 dtype: int64 C Java Python A 1 2 2 B 0 1 1 C 1 2 2 D 1 2 2 E 1 2 2 C Java Python A 5.0 12.0 9.0 B NaN 1.0 4.0 C 4.0 11.0 5.0 D 7.0 12.0 4.0 E 8.0 7.0 7.0 

4. 通过函数分组，函数传入的参数为行索引或列索引

示例代码：

# 通过函数分组 df_obj3 = pd.DataFrame(np.random.randint(1,index=[‘AA‘,‘BBB‘,‘CC‘,‘EE‘]) #df_obj3 def group_key(idx): """ idx 为列索引或行索引 """ #return idx return len(idx) print(df_obj3.groupby(group_key).size()) # 以上自定义函数等价于 #df_obj3.groupby(len).size() 

运行结果：

1 1 2 3 3 1 dtype: int64 

5. 通过索引级别分组

示例代码：

# 通过索引级别分组 columns = pd.MultiIndex.from_arrays([[‘Python‘,‘Java‘,‘Python‘,‘Python‘],[‘A‘,‘A‘,‘B‘]],names=[‘language‘,‘index‘]) df_obj4 = pd.DataFrame(np.random.randint(1,columns=columns) print(df_obj4) # 根据language进行分组 print(df_obj4.groupby(level=‘language‘,axis=1).sum()) # 根据index进行分组 print(df_obj4.groupby(level=‘index‘,axis=1).sum()) 

运行结果：

language Python Java Python Java Python
index         A    A      B    C      B
0 2 7 8 4 3 1 5 2 6 1 2 2 6 4 4 5 2 3 4 7 4 3 1 4 7 4 3 4 8 language Java Python 0 11 13 1 3 13 2 9 12 3 10 9 4 8 18 index A B C 0 9 11 4 1 7 8 1 2 10 6 5 3 11 5 3 4 11 11 4 

聚合 (aggregation)

• 数组产生标量的过程，如mean()、count()等

• 常用于对分组后的数据进行计算

示例代码：

dict_obj = {‘key1‘ : [‘a‘,‘data1‘: np.random.randint(1,8),‘data2‘: np.random.randint(1,8)} df_obj5 = pd.DataFrame(dict_obj) print(df_obj5) 

运行结果：

data1  data2 key1   key2
0 3 7 a one 1 1 5 b one 2 7 4 a two 3 2 4 b three 4 6 4 a two 5 9 9 b two 6 3 5 a one 7 8 4 a three 

1. 内置的聚合函数

sum(),mean(),max(),min(),count(),size(),describe()

示例代码：

print(df_obj5.groupby(‘key1‘).sum()) print(df_obj5.groupby(‘key1‘).max()) print(df_obj5.groupby(‘key1‘).min()) print(df_obj5.groupby(‘key1‘).mean()) print(df_obj5.groupby(‘key1‘).size()) print(df_obj5.groupby(‘key1‘).count()) print(df_obj5.groupby(‘key1‘).describe()) 

运行结果：

data1  data2
key1              
a        27 24 b 12 18 data1 data2 key2 key1 a 8 7 two b 9 9 two data1 data2 key2 key1 a 3 4 one b 1 4 one data1 data2 key1 a 5.4 4.8 b 4.0 6.0 key1 a 5 b 3 dtype: int64 data1 data2 key2 key1 a 5 5 5 b 3 3 3 data1 data2 key1 a count 5.000000 5.000000 mean 5.400000 4.800000 std 2.302173 1.303840 min 3.000000 4.000000 25% 3.000000 4.000000 50% 6.000000 4.000000 75% 7.000000 5.000000 max 8.000000 7.000000 b count 3.000000 3.000000 mean 4.000000 6.000000 std 4.358899 2.645751 min 1.000000 4.000000 25% 1.500000 4.500000 50% 2.000000 5.000000 75% 5.500000 7.000000 max 9.000000 9.000000 

2. 可自定义函数，传入agg方法中

grouped.agg(func)

func的参数为groupby索引对应的记录

示例代码：

# 自定义聚合函数 def peak_range(df): """ 返回数值范围 """ #print type(df) #参数为索引所对应的记录 return df.max() - df.min() print(df_obj5.groupby(‘key1‘).agg(peak_range)) print(df_obj.groupby(‘key1‘).agg(lambda df : df.max() - df.min())) 

运行结果：

data1  data2
key1              
a         5 3 b 8 5 data1 data2 key1 a 2.528067 1.594711 b 0.787527 0.386341 In [25]: 

3. 应用多个聚合函数

同时应用多个函数进行聚合操作，使用函数列表

示例代码：

# 应用多个聚合函数 # 同时应用多个聚合函数 print(df_obj.groupby(‘key1‘).agg([‘mean‘,‘std‘,‘count‘,peak_range])) # 默认列名为函数名 print(df_obj.groupby(‘key1‘).agg([‘mean‘,(‘range‘,peak_range)])) # 通过元组提供新的列名 

运行结果：

data1                                data2                           
          mean       std count peak_range      mean       std count peak_range
key1                                                                          
a     0.437389 1.174151 5 2.528067 -0.230101 0.686488 5 1.594711 b 0.014657 0.440878 3 0.787527 0.802114 0.196850 3 0.386341 data1 data2 mean std count range mean std count range key1 a 0.437389 1.174151 5 2.528067 -0.230101 0.686488 5 1.594711 b 0.014657 0.440878 3 0.787527 0.802114 0.196850 3 0.386341 

4. 对不同的列分别作用不同的聚合函数，使用dict

示例代码：

# 每列作用不同的聚合函数 dict_mapping = {‘data1‘:‘mean‘,‘data2‘:‘sum‘} print(df_obj.groupby(‘key1‘).agg(dict_mapping)) dict_mapping = {‘data1‘:[‘mean‘,‘max‘],‘data2‘:‘sum‘} print(df_obj.groupby(‘key1‘).agg(dict_mapping)) 

运行结果：

data1     data2
key1                    
a     0.437389 -1.150505 b 0.014657 2.406341 data1 data2 mean max sum key1 a 0.437389 1.508838 -1.150505 b 0.014657 0.522911 2.406341 

5. 常用的内置聚合函数

数据的分组运算

示例代码：

import pandas as pd import numpy as np dict_obj = {‘key1‘ : [‘a‘,8)} df_obj = pd.DataFrame(dict_obj) print(df_obj) # 按key1分组后，计算data1，data2的统计信息并附加到原始表格中，并添加表头前缀 k1_sum = df_obj.groupby(‘key1‘).sum().add_prefix(‘sum_‘) print(k1_sum) 

运行结果：

data1  data2 key1   key2
0 5 1 a one 1 7 8 b one 2 1 9 a two 3 2 6 b three 4 9 8 a two 5 8 3 b two 6 3 5 a one 7 8 3 a three sum_data1 sum_data2 key1 a 26 26 b 17 17 

聚合运算后会改变原始数据的形状，

如何保持原始数据的形状?

1. merge

使用merge的外连接，比较复杂

示例代码：

# 方法1，使用merge k1_sum_merge = pd.merge(df_obj,k1_sum,left_on=‘key1‘,right_index=True) print(k1_sum_merge) 

运行结果：

data1  data2 key1   key2  sum_data1  sum_data2
0 5 1 a one 26 26 2 1 9 a two 26 26 4 9 8 a two 26 26 6 3 5 a one 26 26 7 8 3 a three 26 26 1 7 8 b one 17 17 3 2 6 b three 17 17 5 8 3 b two 17 17 

2. transform

transform的计算结果和原始数据的形状保持一致，

如：grouped.transform(np.sum)

示例代码：

# 方法2，使用transform k1_sum_tf = df_obj.groupby(‘key1‘).transform(np.sum).add_prefix(‘sum_‘) df_obj[k1_sum_tf.columns] = k1_sum_tf print(df_obj) 

运行结果：

data1  data2 key1   key2 sum_data1 sum_data2           sum_key2
0 5 1 a one 26 26 onetwotwoonethree 1 7 8 b one 17 17 onethreetwo 2 1 9 a two 26 26 onetwotwoonethree 3 2 6 b three 17 17 onethreetwo 4 9 8 a two 26 26 onetwotwoonethree 5 8 3 b two 17 17 onethreetwo 6 3 5 a one 26 26 onetwotwoonethree 7 8 3 a three 26 26 onetwotwoonethree 

也可传入自定义函数，

示例代码：

# 自定义函数传入transform def diff_mean(s): """ 返回数据与均值的差值 """ return s - s.mean() print(df_obj.groupby(‘key1‘).transform(diff_mean)) 

运行结果：

data1     data2 sum_data1 sum_data2
0 -0.200000 -4.200000 0 0 1 1.333333 2.333333 0 0 2 -4.200000 3.800000 0 0 3 -3.666667 0.333333 0 0 4 3.800000 2.800000 0 0 5 2.333333 -2.666667 0 0 6 -2.200000 -0.200000 0 0 7 2.800000 -2.200000 0 0 

groupby.apply(func)

func函数也可以在各分组上分别调用，最后结果通过pd.concat组装到一起（数据合并）

示例代码：

import pandas as pd import numpy as np dataset_path = ‘./starcraft.csv‘ df_data = pd.read_csv(dataset_path,usecols=[‘LeagueIndex‘,‘Age‘,‘HoursPerWeek‘,‘TotalHours‘,‘APM‘]) def top_n(df,n=3,column=‘APM‘): """ 返回每个分组按 column 的 top n 数据 """ return df.sort_values(by=column,ascending=False)[:n] print(df_data.groupby(‘LeagueIndex‘).apply(top_n)) 

运行结果：

LeagueIndex   Age  HoursPerWeek  TotalHours       APM
LeagueIndex                                                            
1 2214 1 20.0 12.0 730.0 172.9530 2246 1 27.0 8.0 250.0 141.6282 1753 1 20.0 28.0 100.0 139.6362 2 3062 2 20.0 6.0 100.0 179.6250 3229 2 16.0 24.0 110.0 156.7380 1520 2 29.0 6.0 250.0 151.6470 3 1557 3 22.0 6.0 200.0 226.6554 484 3 19.0 42.0 450.0 220.0692 2883 3 16.0 8.0 800.0 208.9500 4 2688 4 26.0 24.0 990.0 249.0210 1759 4 16.0 6.0 75.0 229.9122 2637 4 23.0 24.0 650.0 227.2272 5 3277 5 18.0 16.0 950.0 372.6426 93 5 17.0 36.0 720.0 335.4990 202 5 37.0 14.0 800.0 327.7218 6 734 6 16.0 28.0 730.0 389.8314 2746 6 16.0 28.0 4000.0 350.4114 1810 6 21.0 14.0 730.0 323.2506 7 3127 7 23.0 42.0 2000.0 298.7952 104 7 21.0 24.0 1000.0 286.4538 1654 7 18.0 98.0 700.0 236.0316 8 3393 8 NaN NaN NaN 375.8664 3373 8 NaN NaN NaN 364.8504 3372 8 NaN NaN NaN 355.3518 

1. 产生层级索引：外层索引是分组名，内层索引是df_obj的行索引

示例代码：

# apply函数接收的参数会传入自定义的函数中 print(df_data.groupby(‘LeagueIndex‘).apply(top_n,n=2,column=‘Age‘)) 

运行结果：

LeagueIndex   Age  HoursPerWeek  TotalHours       APM
LeagueIndex                                                            
1 3146 1 40.0 12.0 150.0 38.5590 3040 1 39.0 10.0 500.0 29.8764 2 920 2 43.0 10.0 730.0 86.0586 2437 2 41.0 4.0 200.0 54.2166 3 1258 3 41.0 14.0 800.0 77.6472 2972 3 40.0 10.0 500.0 60.5970 4 1696 4 44.0 6.0 500.0 89.5266 1729 4 39.0 8.0 500.0 86.7246 5 202 5 37.0 14.0 800.0 327.7218 2745 5 37.0 18.0 1000.0 123.4098 6 3069 6 31.0 8.0 800.0 133.1790 2706 6 31.0 8.0 700.0 66.9918 7 2813 7 26.0 36.0 1300.0 188.5512 1992 7 26.0 24.0 1000.0 219.6690 8 3340 8 NaN NaN NaN 189.7404 3341 8 NaN NaN NaN 287.8128 

2. 禁止层级索引,group_keys=False

示例代码：

print(df_data.groupby(‘LeagueIndex‘,group_keys=False).apply(top_n)) 

运行结果：

LeagueIndex   Age  HoursPerWeek  TotalHours       APM
2214 1 20.0 12.0 730.0 172.9530 2246 1 27.0 8.0 250.0 141.6282 1753 1 20.0 28.0 100.0 139.6362 3062 2 20.0 6.0 100.0 179.6250 3229 2 16.0 24.0 110.0 156.7380 1520 2 29.0 6.0 250.0 151.6470 1557 3 22.0 6.0 200.0 226.6554 484 3 19.0 42.0 450.0 220.0692 2883 3 16.0 8.0 800.0 208.9500 2688 4 26.0 24.0 990.0 249.0210 1759 4 16.0 6.0 75.0 229.9122 2637 4 23.0 24.0 650.0 227.2272 3277 5 18.0 16.0 950.0 372.6426 93 5 17.0 36.0 720.0 335.4990 202 5 37.0 14.0 800.0 327.7218 734 6 16.0 28.0 730.0 389.8314 2746 6 16.0 28.0 4000.0 350.4114 1810 6 21.0 14.0 730.0 323.2506 3127 7 23.0 42.0 2000.0 298.7952 104 7 21.0 24.0 1000.0 286.4538 1654 7 18.0 98.0 700.0 236.0316 3393 8 NaN NaN NaN 375.8664 3373 8 NaN NaN NaN 364.8504 3372 8 NaN NaN NaN 355.3518 

apply可以用来处理不同分组内的缺失数据填充，填充该分组的均值。