Data visualization involves exploring data through visual representations. It’s closely associated with data mining, which uses code to explore the patterns and connections in a data set. A data set can be just a small list of numbers that fits in one line of code or many gigabytes of data.

TRY IT YOURSELF #1

15-1. Cubes: A number raised to the third power is a cube. Plot the first five cubic numbers, and then plot the first 5000 cubic numbers.

15-2. Colored Cubes: Apply a colormap to your cubes plot.

TRY IT YOURSELF #2

15-3. Molecular Motion: Modify rw_visual.py by replacing plt.scatter() with plt.plot(). To simulate the path of a pollen grain on the surface of a drop of water, pass in the rw.x_values and rw.y_values, and include a linewidth argument. Use 5000 instead of 50,000 points.

15-4. Modified Random Walks: In the class RandomWalk, x_step and y_step are generated from the same set of conditions. The direction is chosen randomly from the list [1, -1] and the distance from the list [0, 1, 2, 3, 4]. Modify the values in these lists to see what happens to the overall shape of your walks. Try a longer list of choices for the distance, such as 0 through 8, or remove the -1 from the x or y direction list.

15-5. Refactoring: The method fill_walk() is lengthy. Create a new method called get_step() to determine the direction and distance for each step, and then calculate the step. You should end up with two calls to get_step() in fill_walk():

x_step = get_step()
y_step = get_step()

This refactoring should reduce the size of fill_walk() and make the method easier to read and understand.

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TRY IT YOURSELF #3

15-6. Automatic Labels: Modify die_visual.py and dice_visual.py by replacing the list we used to set the value of hist.x_labels with a loop to generate this list automatically. If you’re comfortable with list comprehensions, try replacing the other for loops in die_visual.py and dice_visual.py with comprehensions as well.

15-7. Two D8s: Create a simulation showing what happens if you roll two eight-sided dice 1000 times. Increase the number of rolls gradually until you start to see the limits of your system’s capabilities.

15-8. Three Dice: If you roll three D6 dice, the smallest number you can roll is 3 and the largest number is 18. Create a visualization that shows what happens when you roll three D6 dice.

15-9. Multiplication: When you roll two dice, you usually add the two numbers together to get the result. Create a visualization that shows what happens if you multiply these numbers instead.

15-10. Practicing with Both Libraries: Try using matplotlib to make a die-rolling visualization, and use Pygal to make the visualization for a random walk.

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