Matplotlib.py

# Instructions

# print() the last item from both the year and the pop list to see what the predicted
# population for the year 2100 is. Use two print() functions.
# Before you can start, you should import matplotlib.pyplot as plt. pyplot is a 
# sub-package of matplotlib, hence the dot.
# Use plt.plot() to build a line plot. year should be mapped on the horizontal axis, 
# pop on the vertical axis. Don't forget to finish off with the plt.show() function 
# to actually display the plot.
# Print the last item from year and pop
print(year[-1]);print(pop[-1])


# Import matplotlib.pyplot as plt
import matplotlib.pyplot as plt

# Make a line plot: year on the x-axis, pop on the y-axis
plt.plot(year,pop)

# Display the plot with plt.show()
plt.show()

# Print the last item of gdp_cap and life_exp
print(gdp_cap[-1]);print(life_exp[-1])


# Make a line plot, gdp_cap on the x-axis, life_exp on the y-axis
plt.plot(gdp_cap,life_exp)

# Display the plot
plt.show()

# Change the line plot below to a scatter plot
plt.scatter(gdp_cap, life_exp)

# Put the x-axis on a logarithmic scale
plt.xscale('log')

# Show plot
plt.show()


# Build Scatter plot
plt.scatter(pop,life_exp)

# Show plot
plt.show()

# Create histogram of life_exp data
plt.hist(life_exp)

# Display histogram
plt.show()

# Build histogram with 5 bins
plt.hist(life_exp,bins=5)

# Show and clean up plot
plt.show()
plt.clf()

# Build histogram with 20 bins
plt.hist(life_exp,bins=20)

# Show and clean up again
plt.show()
plt.clf()

# Basic scatter plot, log scale
plt.scatter(gdp_cap, life_exp)
plt.xscale('log') 

# Strings
xlab = 'GDP per Capita [in USD]'
ylab = 'Life Expectancy [in years]'
title = 'World Development in 2007'

# Add axis labels
plt.xlabel(xlab)
plt.ylabel(ylab)

# Add title
plt.title(title)

# Definition of tick_val and tick_lab
tick_val = [1000, 10000, 100000]
tick_lab = ['1k', '10k', '100k']

# Adapt the ticks on the x-axis
plt.xticks(tick_val,tick_lab)

# After customizing, display the plot
plt.show()

# Import numpy as np
import numpy as np

# Store pop as a numpy array: np_pop
np_pop = np.array(pop)

# Double np_pop
np_pop = np_pop*2

# Update: set s argument to np_pop
plt.scatter(gdp_cap, life_exp, s = np_pop)

# 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'])

# Display the plot
plt.show()

# Specify c and alpha inside plt.scatter()
plt.scatter(x = gdp_cap, y = life_exp, s = np.array(pop) * 2, c= col, alpha=0.8)

# Display the plot
plt.show()

# Additional customizations
plt.text(1550, 71, 'India')
plt.text(5700, 80, 'China')

# Add grid() call
plt.grid(True)

# Show the plot
plt.show()