Pandas – 10.1 聚合groupby-agg/aggreagte

可以与groupby一起使用的方法或函数

count / np.count_nonzero 统计频数（不包含NaN值）
size 统计频数 （包含NaN值）
mean / np.mean 求平均值
std / np.std 样本标准差
min /np.min 最小值
quantile(q=0.25) / np.percentile(q=0.25) 较小四分位数
quantile(q=0.5) / np.percentile(q=0.5) 中位数
quantile(q=0.75) / np.percentile(q=0.75) 较大四分位数
max / np.max 最大值
sum / np.sum 求和
var / np.var 无偏方差
sem / scipy.stats.sem 平均值的无偏方差
describe / scipy.stats.describe 统计信息描述
frist 返回第一行
last 返回最后一行
nth 返回第n行

import pandas as pd
df = pd.read_csv('data/gapminder.tsv', sep='/t')

continent_describe = df.groupby('continent').lifeExp.describe()
print(continent_describe)

'''
           count       mean        std     min       25%      50%       75%  /
continent                                                                     
Africa     624.0  48.865330   9.150210  23.599  42.37250  47.7920  54.41150   
Americas   300.0  64.658737   9.345088  37.579  58.41000  67.0480  71.69950   
Asia       396.0  60.064903  11.864532  28.801  51.42625  61.7915  69.50525   
Europe     360.0  71.903686   5.433178  43.585  69.57000  72.2410  75.45050   
Oceania     24.0  74.326208   3.795611  69.120  71.20500  73.6650  77.55250   

              max  
continent          
Africa     76.442  
Americas   80.653  
Asia       82.603  
Europe     81.757  
Oceania    81.235  
'''

聚合函数

除了上面列出的函数，可以调用agg或aggregate方法传入想用的聚合函数。

• 传入其他库的函数
• 传入自定义的函数

传入其他库的函数

import numpy as np

cont_le_agg = df.groupby('continent').lifeExp.agg(np.mean)
print(cont_le_agg)

'''
continent
Africa      48.865330
Americas    64.658737
Asia        60.064903
Europe      71.903686
Oceania     74.326208
Name: lifeExp, dtype: float64
'''

cont_le_agg2 = df.groupby('continent').lifeExp.aggregate(np.mean)
print(cont_le_agg2)

'''
continent
Africa      48.865330
Americas    64.658737
Asia        60.064903
Europe      71.903686
Oceania     74.326208
Name: lifeExp, dtype: float64
'''

自定义函数

def my_mean(values):
    n = len(values)
    sum = 0
    for value in values:
        sum += value
    return (sum/n)

agg_my_mean = df.groupby('continent').lifeExp.aggregate(my_mean)
print(agg_my_mean)

'''
continent
Africa      48.865330
Americas    64.658737
Asia        60.064903
Europe      71.903686
Oceania     74.326208
Name: lifeExp, dtype: float64
'''

带有多个参数的自定义聚合函数,第一个参数是值序列，其他参数作为关键字传入agg

def my_mean_diff(values, diff_value):
    n = len(values)
    sum =0
    for value in values:
        sum += value
    mean = sum/n
    return (mean - diff_value)

global_mean = df.lifeExp.mean()
print(global_mean) # 59.47443936619713

agg_mean_diff = df.groupby('year').lifeExp.agg(my_mean_diff, diff_value=global_mean)
print(agg_mean_diff)

'''
year
1952   -10.416820
1957    -7.967038
1962    -5.865190
1967    -3.796150
1972    -1.827053
1977     0.095718
1982     2.058758
1987     3.738173
1992     4.685899
1997     5.540237
2002     6.220483
2007     7.532983
Name: lifeExp, dtype: float64
'''

同时传入多个函数

• 对于一个序列计算多个聚合函数，将它们放入一个python列表，再将列表传入agg
• 对多个序列分别使用不同的聚合函数，将字典传入agg

一个序列计算多个聚合函数

gdf = df.groupby('year').lifeExp.agg([np.mean, np.std, np.count_nonzero])
print(gdf)

'''
           mean        std  count_nonzero
year                                     
1952  49.057620  12.225956          142.0
1957  51.507401  12.231286          142.0
1962  53.609249  12.097245          142.0
1967  55.678290  11.718858          142.0
1972  57.647386  11.381953          142.0
1977  59.570157  11.227229          142.0
1982  61.533197  10.770618          142.0
1987  63.212613  10.556285          142.0
1992  64.160338  11.227380          142.0
1997  65.014676  11.559439          142.0
2002  65.694923  12.279823          142.0
2007  67.007423  12.073021          142.0
'''

gdf = df.groupby('year').lifeExp./
    agg([np.mean, np.std, np.count_nonzero])./
    rename(columns={'mean':'avg',
                   'count_nonzero':'count',
                   'std':'std_dev'}).reset_index()

print(gdf)

'''
    year        avg    std_dev  count
0   1952  49.057620  12.225956  142.0
1   1957  51.507401  12.231286  142.0
2   1962  53.609249  12.097245  142.0
3   1967  55.678290  11.718858  142.0
4   1972  57.647386  11.381953  142.0
5   1977  59.570157  11.227229  142.0
6   1982  61.533197  10.770618  142.0
7   1987  63.212613  10.556285  142.0
8   1992  64.160338  11.227380  142.0
9   1997  65.014676  11.559439  142.0
10  2002  65.694923  12.279823  142.0
11  2007  67.007423  12.073021  142.0
'''

多个序列分别使用不同的聚合函数，针对DataFrame

gdf_dict = df.groupby('year').agg({
    'lifeExp':'mean',
    'pop':'median',
    'gdpPercap':'median'})
print(gdf_dict)

'''
        lifeExp         pop    gdpPercap
year                                    
1952  49.057620   3943953.0  1968.528344
1957  51.507401   4282942.0  2173.220291
1962  53.609249   4686039.5  2335.439533
1967  55.678290   5170175.5  2678.334741
1972  57.647386   5877996.5  3339.129407
1977  59.570157   6404036.5  3798.609244
1982  61.533197   7007320.0  4216.228428
1987  63.212613   7774861.5  4280.300366
1992  64.160338   8688686.5  4386.085502
1997  65.014676   9735063.5  4781.825478
2002  65.694923  10372918.5  5319.804524
2007  67.007423  10517531.0  6124.371109

'''