Markdown Shortcut Jupyter

  1. Markdown cell displays text which can be formatted using markdown language. In order to enter a text which should not be treated as code by Notebook server, it must be first converted as markdown cell either from cell menu or by using keyboard shortcut M while in command mode.
  2. Hi, I need some help setting up Jupyter notebooks on my system I tried following the instructions given here.

TL;DR My new workflow for writing academic papers involves Jupyter Notebook for data analysis and generating the figures, Markdown for writing the paper, and Pandoc for generating the final output. We will also discuss jupyter functionality in detail. Click the specific option which says, Run cell or the shortcut key for the same is Shift + Enter.

Learning Objectives

  • Explain what the Markdown format is.
  • Describe the role of Markdown for documentation of earth data science workflows.
  • Use Markdown syntax in Jupyter Notebook to:
    • Create headers and lists
    • Bold and italicize bold text
    • Render images and create hyperlinks to web pages

What is Markdown?

Markdown is a human readable syntax (also referred to as a markup language) for formatting text documents. Markdown can be used to produce nicely formatted documents including PDFs and web pages.

When you format text using Markdown in a document, it is similar to using the format tools (e.g. bold, heading 1, heading 2) in a word processing tool like Microsoft Word or Google Docs. However, instead of using buttons to apply formatting, you use syntax such as **this syntax bolds text in markdown** or # Here is a heading.

Markdown syntax allows you to format text in many ways, such as making headings, bolding and italicizing words, creating bulleted lists, adding links, formatting mathematical symbols and making tables. These options allow you to format text in visually appealing and organized ways to present your ideas.

You can use Markdown to format text in many different tools including GitHub.com, R using RMarkdown, and Jupyter Notebook, which you will learn more about this page.

Data Tip: Learn more about how you can use Markdown to format text and document workflows in a variety of tools.

Markdown in Jupyter Notebook

A great benefit of Jupyter Notebook is that it allows you to combine both code (e.g. Python) and Markdown in one document, so that you can easily document your workflows.

A Jupyter Notebook file uses cells to organize content, and it can contain both cells that render text written using the Markdown syntax as well as cells that contain and run Python code.

Thus, you can use a combination of Markdown and Python code cells to organize and document your Jupyter Notebook for others to easily read and follow your workflow.

Data Tip: Learn more about Markdown for Jupyter Notebook.

If you render your Jupyter Notebook file to HTML or PDF, this Markdown will appear as formatted text in the output document.

Data Tip: In fact, this web page that you are reading right now is generated from a Markdown document! On this page, you will learn the basic syntax of Markdown.

Benefits of Markdown for Earth Data Science

Being able to include both Markdown and code (e.g. Python) cells in a Jupyter Notebook file supports reproducible science by allowing you to:

  • Document your workflow: You can add text to the document that describes the steps of your processing workflow (e.g. how data is being processed and what results are produced).
  • Describe your data: You can describe the data that you are using (e.g. source, pre-processing, metadata).
  • Interpret code outputs: You can add some text that interprets or discusses the outputs.

all in one document!

When used effectively, Markdown documentation can help anyone who opens your Jupyter Notebook to follow, understand and even reproduce your workflow.

Format Text in Jupyter Notebook with Markdown

Markdown Cells in Jupyter Notebook

In the previous chapter on Jupyter Notebook, you learned how to add new Markdown cells to your Jupyter Notebook files using Menu tools and Keyboard Shortcuts to create new cells.

FunctionKeyboard ShortcutMenu Tools
Create new cellEsc + a (above), Esc + b (below)Insert→ Insert Cell Above OR Insert → Insert Cell Below
Copy CellcCopy Key
Paste CellvPaste Key

You also learned how to change the default type of the cell by clicking in the cell and selecting a new cell type (e.g. Markdown) in the cell type menu in the toolbar. Furthermore, you learned that in a Jupyter Notebook file, you can double-click in any Markdown cell to see the syntax, and then run the cell again to see the Markdown formatting.

Note: if you type text in a Markdown cell with no additional syntax, the text will appear as regular paragraph text. You can add additional syntax to that text to format it in different ways.

On this page, you will learn basic Markdown syntax that you can use to format text in Jupyter Notebook files.

Section Headers

You can create a heading using the pound (#) sign. For the headers to render properly, there must be a space between the # and the header text.

Heading one is denoted using one # sign, heading two is denoted using two ## signs, etc, as follows:

Here is a sample of the rendered Markdown:

Heading Three

Heading Four

Note: the titles on this page are actually formatted using Markdown (e.g. the words Section Headers above are formatted as a heading two).

Lists

You can also use Markdown to create lists using the following syntax:

It will render as follows:

  • This is a bullet list
  • This is a bullet list
  • This is a bullet list
  1. And you can also create ordered lists
  2. by using numbers
  3. and listing new items in the lists
  4. on their own lines

Notice that you have space between the * or 1. and the text. The space triggers the action to create the list using Markdown.

Bold and Italicize

You can also use ** to bold or * to italicize words. To bold and italicize words, the symbols have to be touching the word and have to be repeated before and after the word using the following syntax:

It will render as follows:

These are italicized words, not a bullet listThese are bold words, not a bullet list

  • This is a bullet item with bold words
  • This is a bullet item with italicized words

Highlight Code

If you want to highlight a function or some code within a plain text paragraph, you can use one backtick on each side of the text like this:

which renders like this:

Here is some code!

The symbol used is the backtick, or grave; not an apostrophe (on most US keyboards, it is on the same key as the tilde (~)).

Horizontal Lines (Rules)

You can also create a horizontal line or rule to highlight a block of Markdown syntax (similar to the highlighting a block of code using the backticks):

which renders like this:

Here is some important text!

Hyperlinks

You can also use HTML in Markdown cells to create hyperlinks to websites using the following syntax:

<a href='url' target='_blank'>hyperlinked words</a>

You can identify the words that will be hyperlinked (i.e. prompt a web page to open when clicked) by replacing hyperlinked words in the example above.

For example, the following syntax:

Make Jupyter Notebook Markdown Cell

Our program website can be found at <a href='http://earthdatascience.org' target='_blank'>this link</a>.

will render as follows with this link as the hyperlinked words:

Our program website can be found at this link.

Render Images

You can also use Markdown to link to images on the web using the following syntax:

![alt text here](url-to-image-here)

The alt text is the alternative text that appears if an image fails to load on webpage; it is also used by screen-reading tools to identify the image to users of the screen-reading tools.

For example, the following syntax:

![Markdown Logo is here.](https://www.fullstackpython.com/img/logos/markdown.png)

will render as follows with an alt text of Markdown Logo is here.:

Local Images Using Relative Computer Paths

You can also add images to a Markdown cell using relative paths to files in your directory structure using:

![alt text here](path-to-image-here)

For relative paths (images stored on your computer) to work in Jupyter Notebook, you need to place the image in a location on your computer that is RELATIVE to your .ipynb file. This is where good file management becomes extremely important.

For a simple example of using relative paths, imagine that you have a subdirectory named images in your earth-analytics directory (i.e. earth-analytics/images/).

If your Jupyter Notebook file (.ipynb) is located in root of this directory (i.e. earth-analytics/notebook.ipynb), and all images that you want to include in your report are located in the images subdirectory (i.e. earth-analytics/images/), then the path that you would use for each image is:

images/image-name.png

If all of your images are in the images subdirectory, then you will be able to easily find them. This also follows good file management practices because all of the images that you use in your report are contained within your project directory.

Data tip: There are many free Markdown editors out there! The atom.io editor is a powerful text editor package by GitHub, that also has a Markdown renderer that allows you to preview the rendered Markdown as you write.

Additional Resources

Practice Your Markdown Skills

  1. Open or create a new Jupyter Notebook file.

  2. Add a new Markdown cell and include:
    • A title for the notebook (e.g. Intro to Earth Analytics - Chapter Four)
    • A bullet list with:
      • A bold word for Author: and then add text for your name.
      • A bold word for Date: and then add text for today’s date.
  3. Add another Markdown cell and include:
    • A list of your top three favorite foods (e.g. blueberries, chocolate bars, avocados).
      • Italicize the first item in your list.
      • Add a hyperlink (i.e. webpages) for the second item in your list (include the name of the food in the title of the hyperlink).
      • Add an image for the last item in your list (include the name in the alt text of the image).

Introduction

Interactive notebooks are experiencing a rise in popularity. How do we know? They’re replacing PowerPoint in presentations, shared around organizations, and they’re even taking workload away from BI suites. Today there are many notebooks to choose from Jupyter, R Markdown, Apache Zeppelin, Spark Notebook and more. There are kernels/backends to multiple languages, such as Python, Julia, Scala, SQL, and others. Notebooks are typically used by data scientists for quick exploration tasks.

Markdown

In this blog, we are going to learn about Jupyter notebooks and Google colab. We will learn about writing code in the notebooks and will focus on the basic features of notebooks. Before diving directly into writing code, let us familiarise ourselves with writing the code notebook style!

The Notebook way

Traditionally, notebooks have been used to document research and make results reproducible, simply by rerunning the notebook on source data. But why would one want to choose to use a notebook instead of a favorite IDE or command line? There are many limitations in the current browser-based notebook implementations, but what they do offer is an environment for exploration, collaboration, and visualization. Notebooks are typically used by data scientists for quick exploration tasks. In that regard, they offer a number of advantages over any local scripts or tools. Notebooks also tend to be set up in a cluster environment, allowing the data scientist to take advantage of computational resources beyond what is available on her laptop, and operate on the full data set without having to download a local copy.

Jupyter Notebooks

The Jupyter Notebook is an open source web application that you can use to create and share documents that contain live code, equations, visualizations, and text. Jupyter Notebook is maintained by the people at Project Jupyter.

Jupyter Notebooks are a spin-off project from the IPython project, which used to have an IPython Notebook project itself. The name, Jupyter, comes from the core supported programming languages that it supports: Julia, Python, and R. Jupyter ships with the IPython kernel, which allows you to write your programs in Python, but there are currently over 100 other kernels that you can also use.

Why Jupyter Notebooks

Jupyter notebooks are particularly useful as scientific lab books when you are doing computational physics and/or lots of data analysis using computational tools. This is because, with Jupyter notebooks, you can:

  • Record the code you write in a notebook as you manipulate your data. This is useful to remember what you’ve done, repeat it if necessary, etc.
  • Graphs and other figures are rendered directly in the notebook so there’s no more printing to paper, cutting and pasting as you would have with paper notebooks or copying and pasting as you would have with other electronic notebooks.
  • You can have dynamic data visualizations, e.g. animations, which is simply not possible with a paper lab book.
  • One can update the notebook (or parts thereof) with new data by re-running cells. You could also copy the cell and re-run the copy only if you want to retain a record of the previous attempt.

Google Colab

Colaboratory is a free Jupyter notebook environment that requires no setup and runs entirely in the cloud. With Colaboratory you can write and execute code, save and share your analyses, and access powerful computing resources, all for free from your browser.

Why Google Colab

As the name suggests, Google Colab comes with collaboration backed in the product. In fact, it is a Jupyter notebook that leverages Google Docs collaboration features. It also runs on Google servers and you don’t need to install anything. Moreover, the notebooks are saved to your Google Drive account.

Some Extra Features

1. System Aliases

Jupyter includes shortcuts for common operations, such as ls and others.

2. Tab-Completion and Exploring Code

Colab provides tab completion to explore attributes of Python objects, as well as to quickly view documentation strings.

3. Exception Formatting

Exceptions are formatted nicely in Colab outputs

4. Rich, Interactive Outputs

Until now all of the generated outputs have been text, but they can be more interesting.

5. Integration with Drive

Colaboratory is integrated with Google Drive. It allows you to share, comment, and collaborate on the same document with multiple people:

Differences between Google Colab and Jupyter notebooks

1. Infrastructure
Google Colab runs on Google Cloud Platform ( GCP ). Hence it’s robust, flexible

2. Hardware
Google Colab recently added support for Tensor Processing Unit ( TPU ) apart from its existing GPU and CPU instances. So, it’s a big deal for all deep learning people.

3. Pricing
Despite being so good at hardware, the services provided by Google Colab are completely free. This makes it even more awesome.

4. Integration with Google Drive
Yes, this seems interesting as you can use your google drive as an interactive file system with Google Colab. This makes it easy to deal with larger files while computing your stuff.

5. Boon for Research and Startup Community
Perhaps this is the only tool available in the market which provides such a good PaaS for free to users. This is overwhelmingly helpful for startups, the research community and students in deep learning space

Working with Notebooks — The Cells Based Method

Jupyter Notebook supports adding rich content to its cells. In this section, you will get an overview of just some of the things you can do with your cells using Markup and Code.

Cell Types

There are technically four cell types: Code, Markdown, Raw NBConvert, and Heading.

The Heading cell type is no longer supported and will display a dialogue that says as much. Instead, you are supposed to use Markdown for your Headings.

The Raw NBConvert cell type is only intended for special use cases when using the nbconvert command line tool. Basically, it allows you to control the formatting in a very specific way when converting from a Notebook to another format.

The primary cell types that you will use are the Code and Markdown cell types. You have already learned how code cells work, so let’s learn how to style your text with Markdown.

Styling Your Text

Jupyter Notebook supports Markdown, which is a markup language that is a superset of HTML. This tutorial will cover some of the basics of what you can do with Markdown.

Set a new cell to Markdown and then add the following text to the cell:

When you run the cell, the output should look like this:

If you would prefer to bold your text, use a double underscore or double asterisk.

Headers

Creating headers in Markdown is also quite simple. You just have to use the humble pound sign. The more pound signs you use, the smaller the header. Jupyter Notebook even kind of previews it for you:

Then when you run the cell, you will end up with a nicely formatted header:

Creating Lists

You can create a list (bullet points) by using dashes, plus signs, or asterisks. Here is an example:

Code and Syntax Highlighting

If you want to insert a code example that you don’t want your end user to actually run, you can use Markdown to insert it. For inline code highlighting, just surround the code with backticks. If you want to insert a block of code, you can use triple backticks and also specify the programming language:

Useful Jupyter Notebook Extensions

Extensions are a very productive way of enhancing your productivity on Jupyter Notebooks. Surfshark netflix vpn. One of the best tools to install and use extensions I have found is ‘Nbextensions’. It takes two simple steps to install it on your machine (there are other methods as well but I found this the most convenient):

Step 1: Install it from pip:

Keyboard Shortcut Markdown Jupyter

Step 2: Install the associated JavaScript and CSS files:

Once you’re done with this, you’ll see a ‘Nbextensions’ tab on the top of your Jupyter Notebook home. And voila! There are a collection of awesome extensions you can use for your projects.

Multi-user Notebooks

There is a thing called JupyterHub which is the proper way to host a multi-user notebook server which might be useful for collaboration and could potentially be used for teaching. However, I have not investigated this in detail as there is no need for it yet. If lots of people start using jupyter notebooks, then we could look into whether JupyterHub would be of benefit. Work is also ongoing to facilitate real-time live collaboration by multiple users on the same notebook — more information is available here and here.

Summary

Jupyter notebooks are useful as a scientific research record, especially when you are digging about in your data using computational tools. In this lesson, we learned about Jupyter notebooks. To add, in Jupyter notebooks, we can either be in insert mode or escape mode. While in insert mode, we can edit the cells and undo changes within that cell with cmd + z on a mac or ctl + z on windows. In escape mode, we can add cells with b, delete a cell with x, and undo deletion of a cell with z. We can also change the type of a cell to markdown with m and to Python code with y. Furthermore, we can have our code in a cell executed, we need to press shift + enter. If we do not do this, then the variables that we assigned in Python are not going to be recognized by Python later on in our Jupyter notebook.

Jupyter notebooks/Google colab are more focused on making work reproducible and easier to understand. These notebooks find the usage in cases where you need story telling with your code!

Follow this link, if you are looking to learn more about data science online!

You can follow this link for our Big Data course!

Additionally, if you are having an interest in learning Data Science, click here to start

Furthermore, if you want to read more about data science, you can read our blogs here

Also, the following are some blogs you may like to read