Comparing-GDP-Population-of-the-World-s-Most-Populous-Countries-in-2007-

Bar chart comparing populations in millions, with GDP in millions, formatted for Jupyter Notebook.

import pandas as pd from matplotlib import pyplot as plt

data = pd.read_csv('countries.csv') data.head()

Finding the 10 most populous countries:

data_2007 = data[data.year == 2007] top10 = data_2007.sort_values('population', ascending=False).head(10)

print(top10)

import numpy as np

Importing np for np.arange().

np.arange(10) is similar to range(10), and it allows us to shift

each value in it by the bar width as you can see below.

x = np.arange(10)

Creating subplots in order to overlay two bar plots

with proper axes on the left hand side and the right hand side.

fig, ax1 = plt.subplots()

width = 0.3 # This is the width of each bar in the bar plot. plt.xticks(x, top10.country, rotation=45) population = ax1.bar(x, top10.population / 10**6, width, color='lightgreen') plt.ylabel('Population', fontsize='14')

ax1.twinx() gives us the same x-axis with the y-axis on the right.

ax2 = ax1.twinx() gdp = ax2.bar(x + width, top10.gdpPerCapita * top10.population / 10**9, width, color='hotpink') plt.ylabel('GDP', fontsize='14') plt.legend([population, gdp], ['Population in Millions', 'GDP in Billions']) figure = plt.gcf() # get current figure

plt.title('Comparing GDP & Population of the World's Most Populous Countries in 2007', color='mediumseagreen', fontweight='bold', fontsize=15) plt.show()