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Lab 1: DataFrames (Browser Version)

This version of Lab 1 runs entirely in your browser using JupyterLite - no installation required!

What is JupyterLite?

JupyterLite is a Jupyter distribution that runs completely in your web browser using WebAssembly. It includes pandas, numpy, and matplotlib pre-installed, making it perfect for getting started with data science without any setup.

Launch JupyterLite

Click the button below to open JupyterLite in a new tab:

Launch JupyterLite :material-rocket-launch:

Steps

Step 1: Create a New Notebook

  1. Click the Launch JupyterLite button above
  2. In JupyterLite, click File → New → Notebook
  3. Select Python (Pyodide) as the kernel

Step 2: Load the Healthcare Dataset

Copy and paste the following code into the first cell and press Shift+Enter to run it:

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# Import libraries for JupyterLite/Pyodide
import pandas as pd
from io import StringIO
from pyodide.http import pyfetch

# Fetch the CSV file from the course website
url = "https://dmccreary.github.io/data-science-course/labs/01-dataframe/healthcare-per-capita-2022.csv"
response = await pyfetch(url)
csv_text = await response.string()

# Load into a DataFrame
df = pd.read_csv(StringIO(csv_text))

# Display the first few rows
print("First 5 rows of the data:")
df.head()

How This Works

JupyterLite runs in your browser, which has security restrictions on fetching data from other websites. We use Pyodide's special pyfetch function to handle this. The await keyword is needed because fetching data over the network is asynchronous.

Step 3: Explore the DataFrame

Add a new cell and run this code to explore the data:

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# Count the number of rows
line_count = len(df)
print(f"Total number of rows: {line_count}")

# Display DataFrame shape and columns
print(f"\nDataFrame shape (rows, columns): {df.shape}")
print(f"\nColumn names: {list(df.columns)}")

Step 4: Basic Statistics

Add another cell to see summary statistics:

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# Get summary statistics for the numeric column
print("Summary Statistics for Health Expenditure Per Capita:")
df['Health_Exp_PerCapita_2022'].describe()

Step 5: Find Specific Countries

Let's find some interesting data points:

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# Find the country with highest healthcare spending
max_spending = df.loc[df['Health_Exp_PerCapita_2022'].idxmax()]
print(f"Highest spending: {max_spending['Country_Name']} - ${max_spending['Health_Exp_PerCapita_2022']:,}")

# Find the country with lowest healthcare spending
min_spending = df.loc[df['Health_Exp_PerCapita_2022'].idxmin()]
print(f"Lowest spending: {min_spending['Country_Name']} - ${min_spending['Health_Exp_PerCapita_2022']:,}")

# Find the United States
usa = df[df['Country_Code'] == 'USA']
print(f"\nUnited States: ${usa['Health_Exp_PerCapita_2022'].values[0]:,}")

Expected Output

After running all cells, you should see output similar to:

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First 5 rows of the data:
                  Country_Name Country_Code  Health_Exp_PerCapita_2022
0  Africa Eastern and Southern          AFE                        228
1                  Afghanistan          AFG                        383
2   Africa Western and Central          AFW                        201
3                       Angola          AGO                        217
4                      Albania          ALB                       1186

Total number of rows: 238

DataFrame shape (rows, columns): (238, 3)

Column names: ['Country_Name', 'Country_Code', 'Health_Exp_PerCapita_2022']

Troubleshooting

"CORS Error" or Data Won't Load

If you encounter a CORS error, you can use this alternative approach with the data embedded directly:

Click to expand: Embedded Data Alternative 
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import pandas as pd
from io import StringIO

# First few rows of data embedded directly
data = """Country_Name,Country_Code,Health_Exp_PerCapita_2022
United States,USA,12434
Switzerland,CHE,10668
Norway,NOR,9927
Germany,DEU,8454
Australia,AUS,7072
Canada,CAN,6991
France,FRA,6853
Japan,JPN,5387
United Kingdom,GBR,6449
Brazil,BRA,1696
China,CHN,1136
India,IND,273
"""

df = pd.read_csv(StringIO(data))
df.head()

Kernel Won't Start

  • Try refreshing the page
  • Clear browser cache and try again
  • Use a different browser (Chrome or Firefox work best)

Next Steps

Once you're comfortable with JupyterLite, consider setting up a local Python environment for more advanced work. See the Desktop Setup Guide for instructions.

Comparison: Browser vs Desktop

Feature JupyterLite (Browser) VS Code (Desktop)
Setup required None Python + VS Code installation
Works offline No Yes
File access URLs only Full filesystem
Library support Core data science libs Any Python package
Performance Good for small datasets Better for large datasets
Best for Quick experiments, learning Real projects, large data