Introduction

Python is a popular programming language for its simplicity and readability. When it is combined with Jupyter Notebook, it offers interactive experimentation, documentation of code and data. This article discusses Python tricks in Jupyter Notebook to enhance coding experience, productivity, and understanding. Keyboard shortcuts, magic commands, interactive widgets, and visualization tools can streamline workflow and improve coding skills. These tricks are suitable for beginners and experienced coders, making the coding journey more enjoyable and productive.

What is Jupyter Notebook?

Jupyter Notebook is an online application that is available for free. Users can use it to create and share documents with narrative text, mathematics, live code, and visualizations. It supports Python and enables data analysis and interactive, iterative coding. Markdown cells can also include formatted text, equations, and images in the notebook. Because of its adaptability, it is frequently used in machine learning, scientific computing, data analysis, and teaching. The ability to export Jupyter Notebooks to HTML, PDF, and presentations makes it simple to share work with people who do not have Jupyter installed.

Click here to access the Jupyter Notebook.

Why is Learning Python Tricks Tricks in Jupyter Notebook Important?

Learning Python tricks in Jupyter Notebook can be important for several reasons:

• Interactive Learning: Jupyter Notebook offers interactive learning by allowing users to execute code cells individually, enhancing their understanding of Python concepts and tricks in real-time.
• Visualizations: Jupyter Notebook facilitates Python data manipulation, plotting, and visualization using libraries like Matplotlib, Seaborn, or Plotly, aiding comprehension and retention through visual representations.
• Documentation and Explanation: Code and Markdown cells are combined in Jupyter Notebook to provide thorough documentation and explanations of Python tricks, improving comprehension of their meaning, applicability, and subtleties.
• Sharing and Collaboration: With Jupyter Notebooks, users may easily share information and skills with others by creating tutorials, educational materials, and collaborative projects that others can access and participate to.
• Reproducibility: Because Jupyter Notebooks capture the complete workflow in a single document—code, explanations, visualizations, and results, they facilitate replication.

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Best Python Tricks in Jupyter Notebook

We have many python tricks and shortcuts for Jupyter notebook. Let us explore them one by one:

Keyboard Shortcuts

Keyboard Shortcuts saves time with shortcuts like Esc for command mode, A to insert a cell above, B to insert below, M to change a cell to Markdown, and Y to change it back to code.

Shortcuts:

• Esc: Switch to command mode.
• A: Insert a cell above.
• B: Insert a cell below.
• M: Change a cell to Markdown.
• Y: Change it back to code.

This code is written in a Markdown cell and describes the keyboard shortcuts in Jupyter Notebook. You can copy and paste it into a Markdown cell in your Jupyter Notebook to create a reference for the keyboard shortcuts.

Magic Commands

Magic Commands is used for a variety of tasks

• %lsmagic shows all available magic commands
• %timeit: measures the execution time of a simple loop.
• %matplotlib inline: displays plots inline
• %matplotlib inline: following command displays a plot inline.

Implementation with code

 %lsmagic
# The following command lists all available magic commands.
%lsmagic

### %timeit
# The following command measures the execution time of a simple loop.
import timeit
%timeit for i in range(1000): i * 2

### %matplotlib inline
# The following command displays a plot inline.
import matplotlib.pyplot as plt
import numpy as np

%matplotlib inline

x = np.linspace(0, 10, 100)
y = np.sin(x)

plt.plot(x, y)
plt.xlabel('x')
plt.ylabel('sin(x)')
plt.title('Plot of sin(x)')
plt.show()

# The plot is displayed inline within the Jupyter Notebook.

This code snippet demonstrates how to use magic commands %lsmagic, %timeit, and %matplotlib inline in a Jupyter Notebook. You can copy and paste it into a code cell in your Jupyter Notebook to execute and see the results.

Interactive Widgets

These widgets enhance interactivity with widgets using ipywidgets. They help in creating sliders, buttons, and other interactive elements to manipulate real-time data.

import ipywidgets as widgets
from IPython.display import display

# Create a slider widget
slider = widgets.FloatSlider(
    value=5.0,
    min=0.0,
    max=10.0,
    step=0.1,
    description='Slider:',
    continuous_update=True
)

# Create a button widget
button = widgets.Button(
    description='Click Me!',
    button_style='success'  # 'success', 'info', 'warning', 'danger' or ''
)

# Define a function to be called when the button is clicked
def on_button_clicked(b):
    print("Button clicked!")

# Register the function to be called when the button is clicked
button.on_click(on_button_clicked)

# Create a text widget to display output
output = widgets.Output()

# Display the widgets
display(slider)
display(button)
display(output)

# Define a function to be called when the slider value changes
def on_slider_value_change(change):
    with output:
        output.clear_output()
        print("Slider value changed to:", change.new)

# Register the function to be called when the slider value changes
slider.observe(on_slider_value_change, names='value')

Code Explanation

• We import the necessary modules: ipywidgets for creating interactive widgets and IPython.display for displaying output.
• We create a slider widget using FloatSlider and a button widget using Button.
• We define a function on_button_clicked to be called when the button is clicked.
• We register the function using on_click the method of the button widget.
• We create a text widget to display output.
• We display the widgets using the display function.
• We define a function on_slider_value_change to be called when the slider value changes.
• We register the function using the observe slider widget method.

You can copy and paste this code into a Jupyter Notebook cell and run it to see the interactive widgets in action.

Extensions

To utilize extensions in Jupyter Notebook, you’ll first need to install the jupyter_contrib_nbextensions package. Then you can enable the extensions you want to use. Below is the code to install and enable extensions:

# Install jupyter_contrib_nbextensions package
pip install jupyter_contrib_nbextensions

# Install JavaScript and CSS files for the extensions
jupyter contrib nbextension install --user

# Enable the extensions you want to use
jupyter nbextension enable <extension_name>

Replace <extension_name> with the name of the extension you want to enable. For example:
# Enable Table of Contents (toc) extension
jupyter nbextension enable toc

You can find a list of available extensions by running:

jupyter nbextension list

The extensions can be used directly in the Jupyter Notebook interface once they have been installed and activated. Turn on the Table of Contents (TOC) extension, for instance. The Jupyter toolbar now has a button for creating and displaying a table of contents for your notebook. After enabling extensions, restart the Jupyter Notebook server to observe the changes.

You can do this by stopping and starting the server, or by using the restart option in the Jupyter interface. Remember that instead of running the aforementioned instructions inside a Jupyter Notebook cell, you should usually do so at your terminal or command prompt.

Version Control

Using version control systems, like Git, with Jupyter Notebooks requires storing your notebooks in a plain text format, like.ipynb. This is a short guide to utilizing Jupyter Notebooks with Git:

• Initialize Git Repository: If you haven’t already, initialize a Git repository in your project directory.

  git init

• Add Jupyter Notebooks: Add your Jupyter Notebooks to the repository.

  git add notebook.ipynb

• Commit Changes: Commit your changes to the repository.

  git commit -m "Add notebook.ipynb"

• Ignore Output Cells: Ignoring output cells and other transient data that may change between notebook runs is a good practice. Create a .gitignore file if you haven’t already, and include patterns to ignore output files:

  *.ipynb_checkpoints/
  *.pyc
  *.pyo

   __pycache__/

• Collaboration: When collaborating with others, ensure everyone is working on the latest version of the notebook. Pull changes from the remote repository before making modifications.

  git pull origin main

• Resolve Conflicts: Manually resolve conflicts in merges using Git diffing and merging tools, as Jupyter Notebooks’ complex structure can be challenging to resolve.
• Push Changes: Once you’ve made your modifications and worked out any kinks, push them to the remote repository.

  git push origin main

Following these steps, you can effectively use Git to control version with Jupyter Notebooks. Additionally, there are tools and extensions available that provide better integration between Jupyter Notebooks and version control systems, such as Jupytext, which allows you to work with notebooks in a plain text format (e.g., Markdown or Julia), making them easier to diff and merge with Git.

Cell Execution Tricks

Cell execution tricks are used to execute cells.

• Shift + Enter: Run and move to the next cell
• Alt + Enter: Run and insert a new cell below
• Ctrl + Enter: Run and stay in the same cell

This code is written in a Markdown cell and describes the cell execution tricks in Jupyter Notebook. You can copy and paste it into a Markdown cell in your Jupyter Notebook to create a reference for the cell execution shortcuts.

Visualization Tools

Here’s an example code that demonstrates the utilization of matplotlib, seaborn, and plotly for creating visualizations in a Jupyter Notebook:

Visualization Tools utilize libraries like matplotlib, seaborn, or plotly for creating interactive and publication-quality visualizations directly in your notebook.

import matplotlib.pyplot as plt
import seaborn as sns
import plotly.graph_objs as go
import numpy as np

# Example 1: Matplotlib
# Create a simple line plot
x = np.linspace(0, 10, 100)
y = np.sin(x)

plt.figure(figsize=(8, 4))
plt.plot(x, y)
plt.title('Matplotlib Line Plot')
plt.xlabel('x')
plt.ylabel('sin(x)')
plt.grid(True)
plt.show()

# Example 2: Seaborn
# Create a histogram using Seaborn
data = np.random.randn(1000)
plt.figure(figsize=(8, 4))
sns.histplot(data, kde=True, color='skyblue')
plt.title('Seaborn Histogram')
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.show()

# Example 3: Plotly
# Create an interactive scatter plot using Plotly
x = np.random.randn(1000)
y = np.random.randn(1000)

trace = go.Scatter(x=x, y=y, mode='markers', marker=dict(color='red', size=5))

layout = go.Layout(title='Plotly Scatter Plot', xaxis=dict(title='X'), yaxis=dict(title='Y'))

fig = go.Figure(data=[trace], layout=layout)
fig.show()

Code Explanation

• We first import the necessary libraries (matplotlib, seaborn, and plotly.graph_objs).
• We create sample data for visualization.
• We use each library to create different types of plots:
• Matplotlib: Line plot
• Seaborn: Histogram
• Plotly: Scatter plot
• Finally, we display the plots using the respective methods (plt.show() for Matplotlib, sns.histplot() for Seaborn, and fig.show() for Plotly).

You can copy and paste this code into a code cell in your Jupyter Notebook and execute it to see the visualizations. Make sure you have the necessary libraries installed (matplotlib, seaborn, and plotly) to run the code successfully.

Direct Shell Access

You can run shell commands directly in Jupyter Notebook cells by prefixing the command with an exclamation mark !. Here’s an example code demonstrating direct shell access:

Direct Shell Access runs shell commands directly in Jupyter cells by prefixing the command with !

You can copy and paste this code into a code cell in your Jupyter Notebook and execute it to run the shell commands directly within the notebook. Make sure you have the necessary permissions to execute the shell commands.

Linking Code and Markdown

Certainly! Below is an example code demonstrating how to link code with Markdown explanations in a Jupyter Notebook.

In this notebook, we’ll demonstrate how to link code with Markdown explanations to create more readable notebooks.

Example1: Adding Narrative Text

Let’s start by adding narrative text to explain our code. 

We’ll create a simple Python function to calculate the square of a number.

def square(x):
"""
This function calculates the square of a given number.
Args:
x (int or float): The number to be squared.

Returns:
int or float: The square of the input number.
"""
return x ** 2

Now, let’s use the function to calculate the square of 5:

Calculate the square of 5
result = square(5)
print("The square of 5 is:", result)

Inline Documentation

The Jupyter Notebook environment supports inline documentation, which allows you to view the docstring for any function or object quickly without leaving the notebook. Here’s how you can use Shift + Tab to access documentation:

# Let's define a simple function with a docstring
def square(x):
    """
    This function calculates the square of a given number.

    Args:
    x (int or float): The number to be squared.

    Returns:
    int or float: The square of the input number.
    """
    return x ** 2


# Now, let's call the function and use Shift + Tab to view the docstring
# Place your cursor inside the parentheses of the function call (square), then press Shift + Tab
# A tooltip will appear with the docstring of the function, providing information about its usage and parameters
square(5)  # Place your cursor inside the parentheses and press Shift + Tab to view the docstring

Code Explanation

• We define a simple function square that calculates the square of a number and includes a docstring explaining its usage and parameters.
• We then call the square function and use the Shift + Tab to view its docstring without leaving the notebook. This keyboard shortcut brings up a tooltip with the docstring information.
• Using the Shift + Tab within the function call, you can quickly access documentation for any function or object in your Jupyter Notebook. Thus, making it easier to understand and use the code.

Conclusion

Mastering Python tricks in Jupyter Notebook can significantly enhance your coding experience and productivity. By leveraging keyboard shortcuts, magic commands, interactive widgets, visualization tools, and other features offered by Jupyter Notebook, you can streamline your workflow, create more interactive and informative notebooks, and ultimately become a more efficient and effective Python programmer.

As you continue to explore and experiment with Python in Jupyter Notebook, remember that the journey of learning and discovery never ends. Embrace the power of experimentation, curiosity, and continuous improvement, and let Jupyter Notebook be your trusted companion in your coding adventures.