text: import pandas as pd
  import numpy as np
  from matplotlib import pyplot as plt
  globe =  pd.read_csv("gapminder.csv", skipinitialspace=True, usecols=['life_exp', 'gdp_cap', 'population','cont'])
  life_exp = list(globe.life_exp)
  gdp_cap = list(globe.gdp_cap)
  pop = list(globe.population)
  continent = list(globe.cont)
  
  
  dict = {
      'Asia':'red',
      'Europe':'green',
      'Africa':'blue',
      'Americas':'yellow',
      'Oceania':'black'
  }
  
  col = [dict[x] for x in continent] # here we can assing colors to each continent
  
  
  
  # Store pop as a numpy array: np_pop
  np_pop = np.array(pop)
  np_pop = np_pop / 1000000 #resize the data into millons
  
  
  
  
  # Double np_pop
  
  
  # Update: set s argument to np_pop
  plt.scatter(gdp_cap, life_exp, s = np_pop * 2, c = col, alpha=0.8 )
  
  # Previous customizations
  plt.xscale('log')
  plt.xlabel('GDP per Capita [in USD]')
  plt.ylabel('Life Expectancy [in years]')
  plt.title('World Development in 2007')
  plt.xticks([1000, 10000, 100000],['1k', '10k', '100k'])
  
  
  
  # Additional customizations
  plt.text(1550, 71, 'India')
  plt.text(5700, 80, 'China')
  
  
  # Add grid() call
  
  plt.grid(True)
  
  # Display the plot
  plt.show()