Intermediate Python for Data Science learning 1

Basic plots with matplotlib

from:https://campus.datacamp.com/courses/intermediate-python-for-data-science/matplotlib?ex=1

• Line plot (1)

With matplotlib, you can create a bunch of different plots in Python. The most basic plot is the line plot. A general recipe is given here.

import matplotlib.pyplot as plt
plt.plot(x,y)
plt.show()

# 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 (2): Interpretation

Have another look at the plot you created in the previous exercise; it's shown on the right. Based on the plot, in approximately what year will there be more than ten billion human beings on this planet?

pop[year.index(2060)]

You can check the population for a particular year by checking out the plot. If you want the exact result, use pop[year.index(2030)], to get the population for 2030, for example.

• Line plot (3)

Now that you've built your first line plot, let's start working on the data that professor Hans Rosling used to build his beautiful bubble chart. It was collected in 2007. Two lists are available for you:

• life_exp which contains the life expectancy for each country and
• gdp_cap, which contains the GDP per capita (i.e. per person) for each country expressed in US Dollars.

# 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
import matplotlib.pyplot as plt
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()

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

# Put the x-axis on a logarithmic scale. A correlation will become clear when you display the GDP per capita on a logarithmic scale. Add the line plt.xscale('log')
plt.xscale('log')

# Show plot
plt.show()

• Scatter plot (2)

In the previous exercise, you saw that that the higher GDP usually corresponds to a higher life expectancy. In other words, there is a positive correlation.

Do you think there's a relationship between population and life expectancy of a country? The list life_exp from the previous exercise is already available. In addition, now also pop is available, listing the corresponding populations for the countries in 2007. The populations are in millions of people.

# Import package
import matplotlib.pyplot as plt

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

# Show plot
plt.show()