Now that you’ve seen the REPL in iPython, from now on in this class you will code in Jupyter Notebooks. Jupyter is an incredibly awesome and user-friendly integrated development environment (IDE). An IDE provides a place for data scientists to see and work with a bunch of different aspects of their work in a nice, organized interface.
Always remember: Python is the programming language. Jupyter Lab is an IDE.
JupyterLab provides a nice user interface for data science, development, reporting, and collaboration (all of which you’ll learn about throughout the MEDS program) in one place.
Interactive Computing: JupyterLab is designed primarily for interactive computing and data analysis. It supports live code execution, data visualization, and interactive widgets, which are key features of modern IDEs.
Multi-language Support: While originally developed for Python, JupyterLab supports many other programming languages through the use of kernels, making it versatile for various programming tasks.
Rich Text Editing: It provides a rich text editor for creating and editing Jupyter Notebooks, which can contain both code and narrative text (Markdown), allowing for documentation and code to coexist.
Code Execution: JupyterLab allows you to execute code cells and see the output immediately, making it suitable for testing and iterating on code quickly.
File Management: It includes a file manager for browsing and managing project files, similar to the file explorers found in traditional IDEs.
Extensions and Customization: JupyterLab supports numerous extensions that can enhance its capabilities, such as version control integration, terminal access, and enhanced visualizations.
Integrated Tools: It has an integrated terminal, variable inspector, and other tools that are typically part of an IDE, providing a comprehensive environment for development.
Focus on Notebooks: Unlike many traditional IDEs that focus on scripting and full-scale software development, JupyterLab emphasizes the use of notebooks for exploratory data analysis and research.
Non-linear Workflow: JupyterLab allows for a non-linear workflow, where users can execute cells out of order and iteratively modify and test code.
You can check out the JupyterLab User Guide for tons of information and helpful tips!
JupyterLab is a powerful interactive development environment (IDE) that allows you to work with Jupyter Notebooks, text editors, terminals, and other components in a single, integrated environment. It’s widely used in data science, scientific computing, and education.
Open JupyterLab: Once JupyterLab is running, you’ll see the JupyterLab interface with the file browser on the left.
Create a New Notebook:
+ button in the file browser to open a new Launcher tab.Rename the Notebook:
The Jupyter Notebook interface is divided into cells. There are two main types of cells:
Let’s start by writing some simple Python code in a code cell.
# Simple Python code
print("Hello, Jupyter!")Shift + Enter to execute the code.Markdown cells allow you to write formatted text. You can use Markdown to create headings, lists, links, and more.
# My First Markdown Cell
This is a simple example of a Markdown cell in JupyterLab.
## Features of Markdown
- **Bold Text**: Use `**text**` for **bold**.
- **Italic Text**: Use `*text*` for *italic*.
- **Lists**: Create bullet points using `-` or `*`.
- **Links**: [JupyterLab Documentation](https://jupyterlab.readthedocs.io/)Shift + Enter to render the Markdown text.Jupyter Notebooks are powerful because they allow you to combine code and markdown in a single document. This is useful for creating interactive tutorials, reports, and data analyses.
Jupyter Notebooks can render images directly in the output cells, which is particularly useful for data visualization.
from IPython.display import Image, display
# Display an image
img_path = 'https://jupyterlab.readthedocs.io/en/stable/_images/interface-jupyterlab.png'
display(Image(url=img_path, width=700))
Jupyter Notebooks support interactive features, such as widgets, which enhance the interactivity of your notebooks.
Widgets allow users to interact with your code and visualize results dynamically.
import ipywidgets as widgets
# Create a simple slider widget
slider = widgets.IntSlider(value=50, min=0, max=100, step=1, description='Value:')
display(slider)Ctrl + S (Cmd + S on macOS) to save your work.File > Export Notebook As and choose your desired format.Keyboard Shortcuts: Familiarize yourself with keyboard shortcuts to speed up your workflow. You can view shortcuts by clicking Help > Keyboard Shortcuts. You can also refer to our class Jupyter Keyboard Shortcuts Cheatsheet
Using the File Browser: Drag and drop files into the file browser to upload them to your workspace.
Using the Variable Inspector: The variable inspector shows variable names, types, values/shapes, and counts (for collections). Open the Variable Inspector using Menu: View > Activate Command Palette, then type “variable inspector.” Or use the keyboard shortcut: Ctrl + Shift + I (Windows/Linux) or Cmd + Shift + I (Mac)
JupyterLab is a versatile tool that makes it easy to combine code, text, and visualizations in a single document. By mastering the basic functionality of Jupyter Notebooks, you can create powerful and interactive documents that enhance your data analysis and scientific computing tasks.
Feel free to experiment with the code and markdown examples provided in this guide to deepen your understanding of JupyterLab. Happy coding!
We will get to know Jupyter Notebooks very well during the rest of this course, but here are even more resources you can use to learn and revisit:
There are many, many examples of textbooks, academic articles, journalism, analyses, and reports written in Jupyter Notebooks. Here is a link to a curated gallery containing many such examples. It’s worth exploring some of these just to get a sense of the diversity of applications and opportunities available using python and jupyter in data science!
pandas snippets.End interactive session 1B