Python的熊猫数据框中的Groupby函数似乎不起作用

Question

我有一张表格，其中包含 15 个国家/地区的各种信息（例如能源供应、可再生能源供应比例）。我必须创建一个数据框，其中包含有关大陆级别的信息，包括每个大陆上的国家数量以及这些大陆上各个国家的人口的平均值、标准差和总和。数据框由上述表格的数据组成。我的问题是，在将 15 个国家映射到各自的大陆之后，我似乎无法汇总大陆级别的数据。我必须使用预定义的字典来解决这个任务。你能帮我解决这个问题吗？请在下面找到我的代码：

def answer_eleven():

import numpy as np
import pandas as pd

Top15 = answer_one()
Top15['Country Name'] = Top15.index

ContinentDict  = {'China':'Asia', 
                  'United States':'North America', 
                  'Japan':'Asia', 
                  'United Kingdom':'Europe', 
                  'Russian Federation':'Europe', 
                  'Canada':'North America', 
                  'Germany':'Europe', 
                  'India':'Asia',
                  'France':'Europe', 
                  'South Korea':'Asia', 
                  'Italy':'Europe', 
                  'Spain':'Europe', 
                  'Iran':'Asia',
                  'Australia':'Australia', 
                  'Brazil':'South America'}

Top15['Continent'] = pd.Series(ContinentDict)
#Top15['size'] = Top15['Country'].count()
Top15['Population'] = (Top15['Energy Supply'] / Top15['Energy Supply per Capita'])
#columns_to_keep = ['Continent', 'Population']
#Top15 = Top15[columns_to_keep]
#Top15 = Top15.set_index('Continent').groupby(level=0)['Population'].agg({'sum': np.sum})
Top15.set_index(['Continent'], inplace = True)
Top15['size'] = Top15.groupby(['Continent'])['Country Name'].count()
Top15['sum'] = Top15.groupby(['Continent'])['Population'].sum()
Top15['mean'] = Top15.groupby(['Continent'])['Population'].mean()
Top15['std'] = Top15.groupby(['Continent'])['Population'].std()
columns_to_keep = ['size', 'sum', 'mean', 'std']
Top15 = Top15[columns_to_keep]
#Top15['Continent Name'] = Top15.index
#Top15.groupby(['Continent'], level = 0, sort = True)['size'].count()

return Top15.iloc[:5]
answer_eleven()

Answer 1

我相信您需要agg 来按字典聚合：

def answer_eleven():

    Top15 = answer_one()
    ContinentDict  = {'China':'Asia',
                      'United States':'North America',
                      'Japan':'Asia',
                      'United Kingdom':'Europe',
                      'Russian Federation':'Europe',
                      'Canada':'North America',
                      'Germany':'Europe',
                      'India':'Asia',
                      'France':'Europe',
                      'South Korea':'Asia',
                      'Italy':'Europe',
                      'Spain':'Europe',
                      'Iran':'Asia',
                      'Australia':'Australia',
                      'Brazil':'South America'}

    Top15['Population'] = (Top15['Energy Supply'] / Top15['Energy Supply per Capita'])
    Top15 = Top15.groupby(ContinentDict)['Population'].agg(['size','sum','mean','std'])
    return Top15

---

df = answer_eleven()
print (df)

                        sum          mean           std  size
Country Name                                                 
Asia           2.771785e+09  9.239284e+08  6.913019e+08     3
Australia      2.331602e+07  2.331602e+07           NaN     1
Europe         4.579297e+08  7.632161e+07  3.464767e+07     6
North America  3.528552e+08  1.764276e+08  1.996696e+08     2
South America  2.059153e+08  2.059153e+08           NaN     1