Intermediate Python for Data Science learning 3 - Customization

Customization

from:https://campus.datacamp.com/courses/intermediate-python-for-data-science/matplotlib?ex=13

 

• Labels

It's time to customize your own plot. This is the fun part, you will see your plot come to life!

# 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

Control the y-ticks by specifying two arguments:

plt.yticks([0,1,2], ["one","two","three"])

In this example, the ticks corresponding to the numbers 0, 1 and 2 will be replaced by one, two and three, respectively.

 

# 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
plt.xticks(tick_val,tick_lab)

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

 

• Sizes

Right now, the scatter plot is just a cloud of blue dots, indistinguishable from each other. Let's change this. Wouldn't it be nice if the size of the dots corresponds to the population?

To accomplish this, there is a list pop loaded in your workspace. It contains population numbers for each country expressed in millions. You can see that this list is added to the scatter method, as the argument s, for size.

 

Instructions

• Run the script to see how the plot changes.
• Looks good, but increasing the size of the bubbles will make things stand out more.
  • Import the numpy package as np.
  • Use np.array() to create a numpy array from the list pop. Call this Numpy array np_pop.
  • Double the values in np_pop by assigningnp_pop * 2 to np_pop again. Becausenp_pop is a Numpy array, each array element will be doubled.
  • Change the s argument insideplt.scatter() to be np_pop instead ofpop.

# 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

The code you've written up to now is available in the script on the right.

The next step is making the plot more colorful! 

 

# 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

If you have another look at the script, under # Additional Customizations, you'll see that there are two plt.text()functions now. They add the words "India" and "China" in the plot.

# 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()