Pandas聚合

数据聚合

import pandas as pd
from pandas import Series
import numpy as np

# 准备数据

df = pd.DataFrame([[-0.204708,1.393406,'a','one'],
                   [0.478943,0.092908,'a','two'],
                   [-0.519439,0.281746,'b','one'],
                   [-0.555730,0.769023,'b','two'],
                   [1.965781,1.246435,'a','one']],columns=['data1','data2','key1','key2'])
df

    data1	      data2	   key1	key2
0	-0.204708	1.393406	a	one
1	0.478943	0.092908	a	two
2	-0.519439	0.281746	b	one
3	-0.555730	0.769023	b	two
4	1.965781	1.246435	a	one

# 先生成分组对象
grouped = df.groupby('key1')
# 执行具体的运算
grouped.quantile(0.9)

0.9	  data1	      data2
key1		
a	 1.668413	 1.364012
b	-0.523068	 0.720295

# 自定义的方法(agg接收自定义函数)这种方式速度要慢的多，不太推荐。
def peak_to_peak(arr):
    return arr.max() - arr.min()
grouped.agg(peak_to_peak)

       data1	  data2
key1		
a	2.170489	1.300498
b	0.036291	0.487277

# 聚合运算也可以用describe()
grouped.describe().T

         key1	    a	       b
data1	count	3.000000	 2.000000
         mean	0.746672	-0.537584
          std	1.109736	 0.025662
          min	-0.204708	-0.555730
          25%	0.137118	-0.546657
          50%	0.478943	-0.537584
          75%	1.222362	-0.528512
          max	1.965781	-0.519439
data2	count	3.000000	 2.000000
         mean	0.910916	 0.525385
          std	0.712217	 0.344557
          min	0.092908	 0.281746
          25%	0.669671	 0.403565
          50%	1.246435	 0.525385
          75%	1.319920	 0.647204
          max	1.393406	 0.769023

经过优化的groupby方法

函数名	说明
count	分组中非NA的数量
sum	非NA的和
mean	非NA值的平均值
median	非NA的算术中位数
std、var	无偏（分母n-1）标准差和方差
min、max	非NA值的最小值和最大值
prod	非NA值的积
first、last	第一个和最后一个非NA值

餐馆小费的数据

tips = pd.read_csv('C:/Users/1/Desktop/tips.csv')
tips[:6]

  total_bill tip	 sex smoker day   time	      size
0	16.99	1.01	Female	No	Sun	  Dinner	    2
1	10.34	1.66	Male	No	Sun	  Dinner	    3
2	21.01	3.50	Male	No	Sun	  Dinner	    3
3	23.68	3.31	Male	No	Sun	  Dinner	    2
4	24.59	3.61	Female	No	Sun	  Dinner	    4
5	25.29	4.71	Male	No	Sun	  Dinner	    4

# 小费占总额百分比的列
tips['tip_pct'] = tips['tip']/tips['total_bill']
tips[:6]

  total_bill	tip	sex	  smoker	day	time	size	tip_pct
0	16.99	   1.01	Female	No	    Sun	Dinner	 2	   0.059447
1	10.34	   1.66	Male	No	    Sun	Dinner	 3	   0.160542
2	21.01	   3.50	Male	No	    Sun	Dinner	 3	   0.166587
3	23.68	   3.31	Male	No	    Sun	Dinner	 2	   0.139780
4	24.59	   3.61	Female	No	    Sun	Dinner	 4	   0.146808
5	25.29	   4.71	Male	No	    Sun	Dinner	 4	   0.186240

# 根据sex和smoker对tips进行分组
grouped1 = tips.groupby(['sex','smoker'])
# 直接将讲过优化的groupby方法传入
grouped1['tip_pct'].agg('mean')

sex     smoker
Female  No        0.156921
        Yes       0.182150
Male    No        0.160669
        Yes       0.152771
Name: tip_pct, dtype: float64

# 如果传入一组函数或函数名，对应的列名就是函数名
grouped1['tip_pct'].agg(['mean','std',peak_to_peak])

                  mean	       std	  peak_to_peak
sex	    smoker			
Female	  No	0.156921	0.036421	0.195876
         Yes	0.182150	0.071595	0.360233
Male	  No	0.160669	0.041849	0.220186
         Yes	0.152771	0.090588	0.674707

# 传入的是一个由(name,function)元组组成的列表，则元组的第一个元素会被当做列名
grouped1['tip_pct'].agg([('foo','mean'),('bar',np.std)])


	              foo	     bar
sex	 smoker		
Female	No  	0.156921	0.036421
        Yes	    0.182150	0.071595
Male	No 	    0.160669	0.041849
        Yes	    0.152771	0.090588

grouped['tip_pct','total_bill'].agg(['count','mean','max'])

               tip_pct	                          total_bill
                count	mean	     max	       count	mean	 max
sex	    smoker						
Female	  No	  54	0.156921	0.252672	   54	18.105185	35.83
          Yes	  33	0.182150	0.416667	   33	17.977879	44.30
Male	  No	  97	0.160669	0.291990	   97	19.791237	48.33
          Yes	  60	0.152771	0.710345	   60	22.284500	50.81

# 这里也可以传入带有自定义名称的元组列表,
ftuples = [('new1','mean'),('new2',np.var)]
grouped['tip_pct','total_bill'].agg(ftuples)

                     tip_pct	total_bill
                new1	  new2	      new1	     new2
sex	  smoker				
Female	No	   0.156921	  0.001327	18.105185	53.092422
        Yes	   0.182150	  0.005126	17.977879	84.451517
Male	No	   0.160669	  0.001751	19.791237	76.152961
        Yes	   0.152771   0.008206	22.284500	98.244673

#传入字典
grouped.agg({'tip':np.max,'size':'sum'})

	        tip	    size
sex	 smoker		
Female	No	5.2	    140
        Yes	6.5	    74
Male	No	9.0	    263
        Yes	10.0	150

# 只有将多个函数应用到至少一列时，DataFrame才会拥有层次化的列
grouped.agg({'tip_pct':['min','max','mean','std'],'size':'sum'})

	                        tip_pct	                         size
                min	         max	    mean	     std	  sum
sex	smoker					
Female	No	  0.056797	  0.252672	  0.156921	  0.036421	  140
        Yes	  0.056433	  0.416667	  0.182150	  0.071595	  74
Male	No	  0.071804	  0.291990	  0.160669	  0.041849	  263
        Yes	  0.035638	  0.710345	  0.152771	  0.090588	  150

# 以''无索引''的形式返回聚合数据
# 不需要的时候可以关闭所有的索引，包括层次化索引
tips.groupby(['sex','smoker'],as_index=False).mean()

     sex	smoker	total_bill	  tip	       size	     tip_pct
0	Female	No	    18.105185	2.773519	2.592593	0.156921
1	Female	Yes	    17.977879	2.931515	2.242424	0.182150
2	Male	No	    19.791237	3.113402	2.711340	0.160669
3	Male	Yes	    22.284500	3.051167	2.500000	0.152771