Getting Started with JupyterLab: A Quick Notebook Example for Data Analysis

This quick-start example builds on our previous post about installing JupyterLab on Ubuntu 24.04, where we configured a secure, password-protected JupyterLab instance using a non-sudo user and systemd service.

Now that your JupyterLab server is up and running, let’s walk through a simple notebook to help you:

• Install Python packages inside a notebook
• Simulate temperature and humidity sensor data
• Visualize it using Matplotlib and Seaborn
• Document your analysis with Markdown cells

1. Launch a New Notebook

Open your browser and visit:

http://<your-server-ip>:8888

Log in with your password. On the Launcher tab, click Python 3 under Notebook to create a new notebook.

2. Install Required Packages

In the first cell, run:

import sys
!{sys.executable} -m pip install --quiet numpy pandas matplotlib seaborn

This ensures packages are installed inside your virtual environment

3. Import Libraries

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

4. Generate Sample Data

np.random.seed(42)

data = pd.DataFrame({
    'temperature': np.random.normal(loc=25, scale=5, size=100),
    'humidity': np.random.uniform(low=40, high=80, size=100)
})

data.head()

5. Visualize the Data

plt.figure(figsize=(10, 5))
sns.scatterplot(x='temperature', y='humidity', data=data)
plt.title('Temperature vs. Humidity')
plt.xlabel('Temperature (°C)')
plt.ylabel('Humidity (%)')
plt.grid(True)
plt.show()

6. Add Markdown Notes

Change a cell to Markdown, then paste:

# Temperature and Humidity Dataset

- Simulated using NumPy
- Visualized with Matplotlib and Seaborn
- Example for IoT, time-series, and telemetry workflows

Conclusion

You’re now ready to use JupyterLab interactively:

• Install packages
• Generate and explore data
• Build notebooks with code + markdown
• Expand into IoT analytics, machine learning, or big data pipelines