Matplotlib Customization with National Park Visitation Data (Exercise)

matplotlib
advanced
exercise
Published

February 26, 2024

Exercises

Matplotlib Customization with National Park Visitation Data

Solutions

These exercises use National Park visitation data from 1979–2024. For more context about the dataset, see the data essay.

Concepts covered:

  • Filtering data for a specific category
  • Line plots with custom colors and titles
  • Customizing x-axis tick intervals
  • Abbreviating y-axis labels (millions, thousands)
  • Adjusting axis limits to zoom into a time period

Load National Park Visitation data

Code 
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker

np_data = pd.read_csv("https://raw.githubusercontent.com/melaniewalsh/responsible-datasets-in-context/main/datasets/national-parks/US-National-Parks_RecreationVisits_1979-2024.csv")
np_data.head()
  • How have visits to a particular National Park changed over time?
  • What is the most interesting period of change?

Exercise 1

First, filter the dataframe for a park of your choice. Pick a National Park that you haven’t worked with yet, and filter the data for only that park.

Code 
# Your code here

Exercise 2

Now, make a line plot that shows the number of visits per year to that park from 1979 to 2022.

2a.

Choose a color for the line.

2b.

Give the plot a title that also functions as a kind of “headline” for the most interesting story of the plot.

2c.

Change the x-axis ticks so that they increase 5 years at a time.

2d.

Change the y-axis tick labels so that they abbreviate millions to M and thousands to K.

Code 
# Your code here

Exercise 3

Now, create a plot that zooms in on the most interesting time period for this particular National Park.

3a.

Change the x-axis limits so that it only shows the most interesting years.

3b.

Come up with a new title that describes this time period.

Code 
# Your code here