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Intermediate Python
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    • Intermediate Python

    Run the hidden code cell below to import the data used in this course.

    Take Notes

    Add notes about the concepts you've learned and code cells with code you want to keep.

    Add your notes here

    # Add your code snippets here

    Explore Datasets

    Use the DataFrames imported in the first cell to explore the data and practice your skills!

    • Create a loop that iterates through the brics DataFrame and prints "The population of {country} is {population} million!".
    • Create a histogram of the life expectancies for countries in Africa in the gapminder DataFrame. Make sure your plot has a title, axis labels, and has an appropriate number of bins.
    • Simulate 10 rolls of two six-sided dice. If the two dice add up to 7 or 11, print "A win!". If the two dice add up to 2, 3, or 12, print "A loss!". If the two dice add up to any other number, print "Roll again!".
    import matplotlib.pyplot as plt
year=[1950,1970,1990,2010]
pop=[2.519,3.692,5.263,6.972]
plt.plot(year,pop)
plt.show()
    import matplotlib.pyplot as plt
year=[1950,1970,1990,2010]
pop=[2.519,3.692,5.263,6.972]
plt.scatter(year,pop)
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()
    # 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()
    # Import matplotlib.pyplot as plt
import matplotlib.pyplot as plt

# Define the variables gdp_cap and life_exp
gdp_cap = [1000, 2000, 3000, 4000, 5000]
life_exp = [50, 60, 70, 80, 90]

# 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()
    # Import package
import matplotlib.pyplot as plt

# 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()
    import matplotlib.pyplot as plt
year = [1950, 1951, 1952, ..., 2100] 
pop = [2.538, 2.57, 2.62, ..., 10.85]
# Add more data
year = [1800, 1850, 1900] + year
pop = [1.0, 1.262, 1.650] + pop
plt.plot(year, pop)
plt.xlabel('Year')
plt.ylabel('Population')
plt.title('World Population Projections')
plt.yticks([0, 2, 4, 6, 8, 10],['0', '2B', '4B', '6B', '8B', '10B'])
plt.show()
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