Coding Colab

Exploring, Cleaning, and Analyzing National Parks Visitor Data with Pandas

Introduction

In this collaborative coding exercise, you’ll work with a partner to practice importing, cleaning, exploring, and analyzing DataFrames using pandas. You’ll be working with a dataset containing yearly visitor information about national parks in the United States.

Helpful class CheatSheets:

Pandas DataFrames

Pandas PDF Cheat Sheet

DataFrame Workflows

Setup

First, let’s import the necessary libraries and load our dataset.

Code 
import pandas as pd
import numpy as np

# Load the dataset
url = "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-09-17/national_parks.csv"
parks_df = pd.read_csv(url)

Task 1: Data Exploration and Cleaning

With your partner, explore the DataFrame and perform some initial cleaning. Create cells in your notebook that provide the following information

Tip

Use print() statements and/or f-strings to create your output in a way that makes it easy to understand your results.

  1. How many rows and columns does the DataFrame have?
  2. What are the column names?
  3. What data types are used in each column?
  4. Are there any missing values in the DataFrame?
  5. Remove the rows where year is Total (these are summary rows we don’t need for our analysis).
  6. Convert the year column to numeric type.

Task 2: Basic Filtering and Analysis

Now, let’s practice some basic filtering and analysis operations:

  1. Create a new DataFrame containing only data for the years 2000-2015 and only data for National Parks (unit_type is National Park)
  2. Find the total number of visitors across all National Parks for each year from 2000-2015.
  3. Calculate the average yearly visitors for each National Park during the 2000-2015 period.
  4. Identify the top 5 most visited National Parks (based on total visitors) during the 2000-2015 period.

Task 3: Thinking in DataFrames

  1. In 2016, a blog post from 538.com explored these data. Take a look at the graphics in the post that use our data and discuss with your partner what steps and functions you think would be necessary to filter, group, and aggregate your dataframe in order to make any of the plots. See if you can make β€œrough drafts” of any of them using the simple DataFrame.plot() function.

Conclusion

Great job working through these exercises! You’ve practiced importing data, cleaning a dataset, exploring DataFrames, and performing various filtering and analysis operations using pandas. These skills are fundamental to data analysis in Python and will be valuable as you continue to work with more complex datasets.