01_Matplotlib

INTERMEDIATE PYTHON: Matplotlib
------------Line plot (1)
# 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()

-----------Line plot (3)
# Print the last item of gdp_cap and life_exp
print(life_exp[-1], gdp_cap[-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()

-----------Scatter Plot (1)
When you have a time scale along the horizontal axis, the line plot is 
your friend. But in many other cases, when you're trying to assess if 
there's a correlation between two variables, for example, the scatter 
plot is the better choice. Below is an example of how to build a scatter 
plot.

import matplotlib.pyplot as plt
plt.scatter(x,y)
plt.show()
Let's continue with the gdp_cap versus life_exp plot, the GDP and life 
expectancy data for different countries in 2007. Maybe a scatter plot will 
be a better alternative?

Again, the matplotlib.pyplot package is available as plt.

-------Instructions

Change the line plot that's coded in the script to a scatter plot.
A correlation will become clear when you display the GDP per capita on 
a logarithmic scale. Add the line plt.xscale('log').
Finish off your script with plt.show() to display the plot.

-------Solution
# 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()

================Scatter plot (2)
# Import package
import matplotlib.pyplot as plt

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

# Show plot
plt.show()

---------------Build a histogram (1)
# Create histogram of life_exp data
plt.hist(life_exp)

# Display histogram
plt.show()

--------------Build a histogram (2): bins
# 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()
-------------Build a histogram (3): compare
# Histogram of life_exp, 15 bins
plt.hist(life_exp, bins = 15)

# Show and clear plot
plt.show()
plt.clf()

# Histogram of life_exp1950, 15 bins
plt.hist(life_exp1950, bins = 15)

# Show and clear plot again
plt.show()
plt.clf()

-------------Labels
# 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)

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

----------------Ticks
# Scatter plot
plt.scatter(gdp_cap, life_exp)

# Previous customizations
plt.xscale('log') 
plt.xlabel('GDP per Capita [in USD]')
plt.ylabel('Life Expectancy [in years]')
plt.title('World Development in 2007')

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

# Adapt the ticks on the x-axis (Use tick_val 
and tick_lab as inputs to the xticks() function 
to make the the plot more readable.)

plt.xticks(tick_val, tick_lab)

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

---------------Sizes
(the scatter plot is just a cloud of blue dots, indistinguishable 
from each other. Let's change this.)

# 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()

==================Colors
# 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)

# 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'])

# Show the plot
plt.show()
-------------------Additional Customizations
# Scatter plot
plt.scatter(x = gdp_cap, y = life_exp, s = np.array(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)

# Show the plot
plt.show()
--------------------