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Demo 0: Getting Started with Jupyter Notebooks 📓¶

Goal¶

Learn to use Jupyter notebooks for interactive data exploration and analysis.

Setup¶

1. Select or Create a Python Environment¶

In VS Code¶

  1. Open the Command Palette (Ctrl+Shift+P or Cmd+Shift+P)
  2. Click on "Select Kernel" -> "Python Environments"
  3. Choose "Create Environment" to create a new virtual environment or "".venv" (if available) to select an existing one
  4. Select "Venv" as the environment type
  5. Choose Python version (3.8+ recommended)
  6. VS Code will create and activate the environment automatically

(Alternative) Launch Jupyter from the Command Line¶

  1. Create and activate a virtual environment using
  2. python3 -m venv .venv
  3. source .venv/bin/activate
  4. Install Jupyter pip install jupyter
  5. Launch Jupyter: jupyter notebook
  6. Create a new notebook
  7. In the top-right corner, click on the kernel selector
  8. Choose "Select kernel" > "Python Environments"
  9. Select your environment or create a new one

2. Install Required Packages¶

You can install packages directly in the notebook using magic commands (see Task 2.1 below).

Tasks¶

1. Using Magic Commands¶

  1. Install packages with %pip:
%pip install pandas numpy matplotlib
  1. Measure execution time with %timeit:
# Create a list of numbers
numbers = list(range(1000000))

# Measure time to calculate sum
%timeit sum(numbers)
  1. Display plots inline with %matplotlib:
%matplotlib inline
import matplotlib.pyplot as plt

# Create a simple plot
plt.figure(figsize=(10, 6))
plt.plot([0, 1, 2, 3, 4], [0, 3, 1, 5, 2])
plt.title("Sample Patient Data")
plt.xlabel("Time (hours)")
plt.ylabel("Pain Level")
plt.grid(True)
plt.show()

2. Notebook Operations¶

  1. Create cells and run code:
  2. Create a new code cell
  3. Enter a simple Python statement: print("Hello, nerds!")
  4. Run the cell with Shift+Enter or the Run button

  5. In the next cell, try display("Hello, beautiful nerds!")

  6. Use markdown for documentation:

  7. Create a markdown cell (change cell type to "Markdown")
  8. Add a title, description, and bullet points:
# Patient Data Analysis

This notebook explores patient vital signs data.

Key metrics:
- Heart rate
- Blood pressure
- Oxygen saturation
  1. Use markdown for output:
  2. Display formatted text as output:
from IPython.display import display, Markdown
display(Markdown("Hello, gorgeous nerds!"))
  • Or show tables in a nicer format:
%pip install pandas --quiet
import pandas as pd
df = pd.DataFrame({
  'Column 1': ['Hello'],
  'Column 2': ['gorgeous'],
  'Column 3': ['nerds']
})
print(df)
display(df)

3. Using Shell Commands¶

  1. List files in the current directory:
!ls
  1. Check Python version:
!python --version
  1. Create a directory and check it exists:
!mkdir -p data
!ls -la

4. Working with Data¶

  1. Create sample patient data:
import pandas as pd
import numpy as np

# Create sample patient data
np.random.seed(42)  # For reproducibility

# Generate 100 patient records
n_patients = 100

data = {
    'patient_id': range(1, n_patients + 1),
    'age': np.random.randint(18, 90, size=n_patients),
    'heart_rate': np.random.normal(75, 15, size=n_patients).round().astype(int),
    'systolic_bp': np.random.normal(120, 20, size=n_patients).round().astype(int),
    'diastolic_bp': np.random.normal(80, 10, size=n_patients).round().astype(int),
    'temperature': np.random.normal(98.6, 1, size=n_patients).round(1),
    'o2_saturation': np.random.normal(97, 2, size=n_patients).round().astype(int)
}

# Create DataFrame
patients_df = pd.DataFrame(data)

# Display first few rows
patients_df.head()
  1. Explore the data:
# Basic statistics
patients_df.describe()

# Check for missing values
patients_df.isna().sum()

# Count patients by age group
patients_df['age_group'] = pd.cut(patients_df['age'], 
                                 bins=[0, 30, 50, 70, 100], 
                                 labels=['<30', '30-50', '50-70', '>70'])
patients_df['age_group'].value_counts()
  1. Visualize the data:
# Create a histogram of heart rates
plt.figure(figsize=(10, 6))
plt.hist(patients_df['heart_rate'], bins=15, alpha=0.7)
plt.title('Distribution of Patient Heart Rates')
plt.xlabel('Heart Rate (bpm)')
plt.ylabel('Number of Patients')
plt.grid(True, alpha=0.3)
plt.show()

# Create a scatter plot
plt.figure(figsize=(10, 6))
plt.scatter(patients_df['age'], patients_df['systolic_bp'], alpha=0.7)
plt.title('Age vs. Systolic Blood Pressure')
plt.xlabel('Age (years)')
plt.ylabel('Systolic BP (mmHg)')
plt.grid(True, alpha=0.3)
plt.show()
  1. Save the data:
# Save to CSV
patients_df.to_csv('data/patient_vitals.csv', index=False)

# Verify the file was created
!ls -la data/

Expected Outcomes¶

  • Students can create and run Jupyter notebooks
  • Students can use markdown for documentation
  • Students can use magic commands and shell commands
  • Students can create, explore, and visualize simple datasets
  • Students understand the interactive nature of notebooks

Notes¶