Code
import pandas as pd
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")These exercises use National Park visitation data from 1979–2024. For more context about the dataset, see the data essay.
Concepts covered:
- Groupby with aggregation (mean, count)
- Counting distinct values within groups
- Descriptive statistics by category
Load the data
Exercise 1
What is the average number of visits for each state?
Save as avg_state_visits and then view the resulting dataframe.
Code
avg_state_visits = np_data.groupby('State')['RecreationVisits'].mean().reset_index()
avg_state_visits| State | RecreationVisits | |
|---|---|---|
| 0 | AK | 1.419814e+05 |
| 1 | AR | 1.432561e+06 |
| 2 | AS | 1.531890e+04 |
| 3 | AZ | 1.862048e+06 |
| 4 | CA | 9.978256e+05 |
| 5 | CO | 1.061889e+06 |
| 6 | FL | 4.780917e+05 |
| 7 | HI | 1.295476e+06 |
| 8 | IN | 1.871786e+06 |
| 9 | KY | 1.281423e+06 |
| 10 | ME | 3.001627e+06 |
| 11 | MI | 2.031413e+04 |
| 12 | MN | 2.217869e+05 |
| 13 | MO | 2.419499e+06 |
| 14 | MT | 2.095725e+06 |
| 15 | ND | 5.365100e+05 |
| 16 | NM | 5.442810e+05 |
| 17 | NV | 8.682670e+04 |
| 18 | OH | 2.211138e+06 |
| 19 | OR | 4.866585e+05 |
| 20 | SC | 1.031458e+05 |
| 21 | SD | 8.171871e+05 |
| 22 | TN | 9.822658e+06 |
| 23 | TX | 2.545897e+05 |
| 24 | UT | 1.185455e+06 |
| 25 | VA | 1.524167e+06 |
| 26 | VI | 5.516879e+05 |
| 27 | WA | 1.502220e+06 |
| 28 | WV | 1.055089e+06 |
| 29 | WY | 2.821491e+06 |
Discuss/consider: What state has the most and least average visits? What patterns or surprises do you notice?
Exercise 2
What is the average number of visits for each National Park?
Save as avg_park_visits and then view the resulting dataframe.
Code
avg_park_visits = np_data.groupby('ParkName')['RecreationVisits'].mean().reset_index()
avg_park_visits| ParkName | RecreationVisits | |
|---|---|---|
| 0 | Acadia NP | 3.001627e+06 |
| 1 | Arches NP | 8.943829e+05 |
| 2 | Badlands NP | 1.022876e+06 |
| 3 | Big Bend NP | 3.269033e+05 |
| 4 | Biscayne NP | 4.636490e+05 |
| ... | ... | ... |
| 58 | Wind Cave NP | 6.114985e+05 |
| 59 | Wrangell-St. Elias NP & PRES | 4.934063e+04 |
| 60 | Yellowstone NP | 3.133457e+06 |
| 61 | Yosemite NP | 3.474583e+06 |
| 62 | Zion NP | 2.697032e+06 |
63 rows × 2 columns
Discuss/consider: Which National Park has the most and least average visits? What patterns or surprises do you notice?
Exercise 3:
How many National Parks are there in each state?
Save your answer as distinct_parks.
Code
distinct_parks = np_data.groupby('State')['ParkName'].nunique().reset_index(name='NumParks')
distinct_parks| State | NumParks | |
|---|---|---|
| 0 | AK | 8 |
| 1 | AR | 1 |
| 2 | AS | 1 |
| 3 | AZ | 3 |
| 4 | CA | 9 |
| 5 | CO | 4 |
| 6 | FL | 3 |
| 7 | HI | 2 |
| 8 | IN | 1 |
| 9 | KY | 1 |
| 10 | ME | 1 |
| 11 | MI | 1 |
| 12 | MN | 1 |
| 13 | MO | 1 |
| 14 | MT | 1 |
| 15 | ND | 1 |
| 16 | NM | 2 |
| 17 | NV | 1 |
| 18 | OH | 1 |
| 19 | OR | 1 |
| 20 | SC | 1 |
| 21 | SD | 2 |
| 22 | TN | 1 |
| 23 | TX | 2 |
| 24 | UT | 5 |
| 25 | VA | 1 |
| 26 | VI | 1 |
| 27 | WA | 3 |
| 28 | WV | 1 |
| 29 | WY | 2 |
Discuss/consider: What state has the most and least average visits? What patterns or surprises do you notice?