Python Notebook Markdown

Posted on 01-05-2021 by admin

Jupyter Tutorial

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Ipython Notebook Markdown Table
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Jupyter Notebook - Markdown Cells - 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. Numerical Python: A Practical Techniques Approach for Industry Robert Johansson download Z-Library. Download books for free. Jupyter (formerly IPython Notebook) is an open-source project that lets you easily combine Markdown text and executable Python source code on one canvas called a notebook. Visual Studio Code supports working with Jupyter Notebooks natively, as well as through Python code files.

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Selected Reading

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. The In[] prompt before cell disappears.

Header cell

A markdown cell can display header text of 6 sizes, similar to HTML headers. Start the text in markdown cell by # symbol. Use as many # symbols corresponding to level of header you want. It means single # will render biggest header line, and six # symbols renders header of smallest font size. The rendering will take place when you run the cell either from cell menu or run button of toolbar.

Following screenshot shows markdown cells in edit mode with headers of three different levels.

When cells are run, the output is as follows −

Note that Jupyter notebook markdown doesn’t support WYSWYG feature. The effect of formatting will be rendered only after the markdown cell is run.

Ordered Lists

To render a numbered list as is done by <ol> tag of HTML, the First item in the list should be numbered as 1. Subsequent items may be given any number. It will be rendered serially when the markdown cell is run. To show an indented list, press tab key and start first item in each sublist with 1.

If you give the following data for markdown −

It will display the following list −

Bullet lists

Each item in the list will display a solid circle if it starts with – symbol where as solid square symbol will be displayed if list starts with * symbol. The following example explains this feature −

The rendered markdown shows up as below −

Hyperlinks

Markdown text starting with http or https automatically renders hyperlink. To attach link to text, place text in square brackets [] and link in parentheses () optionally including hovering text. Following screenshot will explain this.

The rendered markdown appears as shown below −

Bold and Italics

To show a text in bold face, put it in between double underscores or two asterisks. To show in italics, put it between single underscores or single asterisks.

The result is as shown below −

Images

To display image in a markdown cell, choose ‘Insert image’ option from Edit menu and browse to desired image file. The markdown cell shows its syntax as follows −

Image will be rendered on the notebook as shown below −

Table

In a markdown cell, a table can be constructed using | (pipe symbol) and – (dash) to mark columns and rows. Note that the symbols need not be exactly aligned while typing. It should only take respective place of column borders and row border. Notebook will automatically resize according to content. A table is constructed as shown below −

The output table will be rendered as shown below −

In the following tutorial you will be guided through the process of installing Jupyter Notebook. Furthermore we’ll explore the basic functionality of Jupyter Notebook and you’ll be able to try out first examples.

This is at the same time the beginning of a series of Python-related tutorial on CodingTheSmartWay.com. From the very beginning you’ll learn everything to need to know to use Python for scientific computing and machine learning use cases.

Jupyter Notebook is a web application that allows you to create and share documents that contain:

live code (e.g. Python code)
visualizations
explanatory text (written in markdown syntax)

Jupyter Notebook is great for the following use cases:

learn and try out Python
data processing / transformation
numeric simulation
statistical modeling
machine learning

Let’s get started and install Jupyter Notebook on your computer …

Setting Up Jupyter Notebook

The first step to get started is to visit the project’s website at http://www.jupyter.org:

Here you’ll find two options:

Try it in your browser
Install the Notebook

With the first option Try it in your browser you can access a hosted version of Jupyter Notebook. This will get you direct access without needing to install it on your computer.

The second option Install the Notebook will take you to another page which gives you detailed instruction for the installation. There are two different ways:

Installing Jupyter Notebook by using the Python’s package manager pip
Installing Jupyter Notebook by installing the Anaconda distribution

Especially if you’re new to Python and would like to set up your development environment from scratch using the Anaconda distribution is a great choice. If you follow the link (https://www.anaconda.com/download/) to the Anaconda download page you can choose between installers for Windows, macOS, and Linux:

Download and execute the installer of your choice. Having installed the Anaconda distribution we can now start Jupyter Notebook by using the following command:

$ jupyter notebook

You’ll see the following response on the command line:

The web server is started and the Jupyter Notebook application is opened in your default browser automatically. You should be able to see a browser output which is similar to the following screenshot:

As you can see the user interface of Jupyter Notebook is split up into three sections (tabs):

Files
Running
Clusters

The default view is the Files tab from where you can open or create notebooks.

Creating A New Notebook

Creating a new Jupyter Notebook is easy. Just use the New dropdown menu and you’ll see the following options:

Select option Python 3 to open a new Jupyter Notebook for Python. The notebook is created and you should be able to see something similar to:

The notebook is created but still untitled. By clicking into the text “Untitled” on the top you can give it a name. By giving it a name the notebook will also be saved as a file of the same name with extension .ipynb. E.g. name the notebook notebook01:

Switching back to the Files tab you’ll be able to see a new file notebook01.ipynb:

Because this notebook file is opened right now the file is marked with status Running. From here you can decided to shutdown this notebook by clicking on button Shutdown.

However before shutting down the notebook let’s switch back to the notebook view and try out a few things to get familiar with the notebook concept.

Working With The Notebook

The notebook itself consists of cells. A first empty cell is already available after having created the new notebook:

This cell is of type “Code” and you can start typing in Python code directly. Executing code in this cell can be done by either clicking on the run cell button or hitting Shift + Return keys:

The resulting output becomes visible right underneath the cell.

The next empty code cell is created automatically and you can continue to add further code to that cell. Just another example:

You can change the cell type from Code to Markdown to include explanatory text in your notebook. To change the type you can use the dropdown input control:

Once switched the type to Markdown you can start typing in markdown code:

After having entered the markdown code you can compile the cell by hitting Shift + Return once again. The markdown editor cell is then replaced with the output:

If you want to change the markdown code again you can simply click into the compiled result and the editor mode opens again.

Edit And Command Mode

If a cell is active two modes distinguished:

edit mode
command mode

Ipython Notebook Examples

If you just click in one cell the cell is opened in command mode which is indicated by a blue border on the left:

The edit mode is entered if you click into the code area of that cell. This mode is indicated by a green border on the left side of the cell:

If you’d like to leave edit mode and return to command mode again you just need to hit ESC.

To get an overview of functions which are available in command and in edit mode you can open up the overview of key shortcuts by using menu entry Help → Keyboard Shortcuts:

Checkpoints

Another cool function of Jupyter Notebook is the ability to create checkpoint. By creating a checkpoint you’re storing the current state of the notebook so that you can later on go back to this checkpoint and revert changes which have been made to the notebook in the meantime.

To create a new checkpoint for your notebook select menu item Save and Checkpoint from the File menu. The checkpoint is created and the notebook file is saved. If you want to go back to that checkpoint at a later point in time you need to select the corresponding checkpoint entry from menu File → Revert to Checkpoint.

Exporting The Notebook

Jupyter Notebook gives you several options to export your notebook. Those options can be found in menu File → Download as:

What’s Next?

Now that you have a sound understanding of Jupyter Notebook you can start creating your own notebook and use it for your next Python project. Jupyter Notebook is perfect for using Python for scientific computing and data analysis with libraries like numpy, pandas, and matplotlib.

However, if you’re totally new to Python, just wait for our next tutorial in which you’ll learn Python basics by using Jupyter Notebook from the very beginning. Stay tuned …

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