text: # -*- coding: utf-8 -*-
  """
  Created on Tue May 14 16:51:31 2019
  
  @author: Shibo
  """
  import pandas as pd
  import numpy as np
  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=answer_one()
  i=0
  top15["population"]=top15["Energy Supply"]/top15["Energy Supply per Capita"]
  a=pd.DataFrame(columns=["c","p"])
  for key,value in ContinentDict.items():
      a.loc[i,"c"]=value
      a.loc[i,"p"]=top15.loc[key,"population"]
      i=i+1
  ans=pd.DataFrame({"size":[a[a["c"]=='Asia']["c"].count(),a[a["c"]=='Australia']["c"].count(),a[a["c"]=='Europe']["c"].count(),a[a["c"]=='North America']["c"].count(),a[a["c"]=='South America']["c"].count()],"sum":[a[a["c"]=='Asia']["p"].sum(),a[a["c"]=='Australia']["p"].sum(),a[a["c"]=='Europe']["p"].sum(),a[a["c"]=='North America']["p"].sum(),a[a["c"]=='South America']["p"].sum()],"mean":[a[a["c"]=='Asia']["p"].mean(),a[a["c"]=='Australia']["p"].mean(),a[a["c"]=='Europe']["p"].mean(),a[a["c"]=='North America']["p"].mean(),a[a["c"]=='South America']["p"].mean()],"std":[a[a["c"]=='Asia']["p"].std(),a[a["c"]=='Australia']["p"].std(),a[a["c"]=='Europe']["p"].std(),a[a["c"]=='North America']["p"].std(),a[a["c"]=='South America']["p"].std()]},index=['Asia', 'Australia', 'Europe', 'North America', 'South America']).astype('float')
  def answer_eleven():
      return ans