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.

Jupyter Notebook is a powerful tool for data analysis. Here are 28 tips, tricks, and shortcuts to turn you into a Jupyter notebooks power user!

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.

• Quando scrivo qualcosa nel campo markdown del taccuino jupyter, gli errori di battitura non vengono evidenziati e spesso mi sono ritrovato con qualcosa del genere: In quasi tutti gli IDE che ho.
• The free text converter for all your documents.

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

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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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. This topic covers the support offered through Python code files and demonstrates how to:

• Work with Jupyter-like code cells
• Run code in the Python Interactive Window
• View, inspect, and filter variables using the Variable explorer and data viewer
• Connect to a remote Jupyter server
• Debug a Jupyter notebook
• Export a Jupyter notebook

To work with Jupyter notebooks, you must activate an Anaconda environment in VS Code, or another Python environment in which you've installed the Jupyter package. To select an environment, use the Python: Select Interpreter command from the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)).

Once the appropriate environment is activated, you can create and run Jupyter-like code cells, connect to a remote Jupyter server for running code cells, and export Python files as Jupyter notebooks.

Jupyter code cells

You define Jupyter-like code cells within Python code using a # %% comment:

Note: Make sure to save the code shown above in a file with a .py extension.

Jupyter Notebook Markdown Latex

When the Python extension detects a code cell, it adds Run Cell and Debug Cell CodeLens adornments. The first cell also includes Run Below and all subsequent cells include Run Above:

Note: By default, Debug Cell just steps into user code. If you want to step into non-user code, you need to uncheck Data Science: Debug Just My Code in the Python extension settings (⌘, (Windows, Linux Ctrl+,)).

Run Cell applies to only the one code cell. Run Below, which appears on the first cell, runs all the code in the file. Run Above applies to all the code cells up to, but not including, the cell with the adornment. You would use Run Above, for example, to initialize the state of the runtime environment before running that specific cell.

Jupiter Shortcut For Markdown Free

Selecting a command starts Jupyter (if necessary, which might take a minute), then runs the appropriate cell(s) in the Python Interactive window:

You can also run code cells using (Ctrl+Enter) or the Python: Run Selection/Line in Python Terminal command (Shift+Enter). After using this command, the Python extension automatically moves the cursor to the next cell. If you're in the last cell in the file, the extension automatically inserts another # %% delimiter for a new cell, mimicking the behavior of a Jupyter notebook.

You can also click in the margin to the left of line numbers to set breakpoints. Then you can use Debug Cell to start a debugging session for that code cell. The debugger stops execution at breakpoints and allows you to step through code one line at a time and inspect variables (see Debugging for details).

Additional commands and keyboard shortcuts

The following table lists additional commands and keyboard shortcuts supported when working with code cells.

Python Interactive window

The Python Interactive window, mentioned in the previous section, can be used as a standalone console with arbitrary code (with or without code cells). To use the window as a console, open it with the Jupyter: Create Interactive Window command from the Command Palette. You can then type in code, using Enter to go to a new line and Shift+Enter to run the code.

To use the window with a file, use the Jupyter: Run Current File in Python Interactive Window command from the Command Palette.

IntelliSense

The Python Interactive window has full IntelliSense – code completions, member lists, quick info for methods, and parameter hints. You can be just as productive typing in the Python Interactive window as you are in the code editor.

Plot Viewer

The Plot Viewer gives you the ability to work more deeply with your plots. In the viewer you can pan, zoom, and navigate plots in the current session. You can also export plots to PDF, SVG, and PNG formats.

Within the Python Interactive window, double-click any plot to open it in the viewer, or select the expand button on the upper left corner of the plot.

Note: The Python Interactive window supports rendering plots created with matplotlib and Altair.

Live Share for Python Interactive

The Python Interactive window also supports Visual Studio Live Share for real-time collaboration. Live Share lets you co-edit and co-debug while sharing audio, servers, terminals, diffs, comments, and more.

This feature requires the Live Share extensions to be installed on both host and guest machines.

Variable explorer and data viewer

Within the Python Interactive window, it's possible to view, inspect, and filter the variables within your current Jupyter session. By expanding the Variables section after running code and cells, you'll see a list of the current variables, which will automatically update as variables are used in code.

For additional information about your variables, you can also double-click on a row or use the Show variable in data viewer button to see a more detailed view of a variable in the Data Viewer. Once open, you can filter the values by searching over the rows.

Note: Variable explorer is enabled by default, but can be turned off in settings (Python > Data Science: Show Jupyter Variable Explorer).

Connect to a remote Jupyter server

You can offload intensive computation in a Jupyter notebook to other computers by connecting to a remote Jupyter server. Once connected, code cells run on the remote server rather than the local computer.

To connect to a remote Jupyter server:

1. Run the Jupyter: Specify local or remote Jupyter server for connections command from the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)).

2. Select how you would like to connect to a Jupyter server.

3. If working remotely, provide the server's URI (hostname) with the authentication token included with a ?token= URL parameter when prompted. (If you start the server in the VS Code terminal with an authentication token enabled, the URL with the token typically appears in the terminal output from where you can copy it.) Alternatively, you can specify a username and password after providing the URI.

4. The Python Interactive window indicates where code is run by displaying the URI (which is blurred out in the image below):

Note: For added security, Microsoft recommends configuring your Jupyter server with security precautions such as SSL and token support. This helps ensure that requests sent to the Jupyter server are authenticated and connections to the remoter server are encrypted. For guidance about securing a notebook server, see the Jupyter docs.

Convert Jupyter notebooks to Python code file

When you've activated an environment with Jupyter installed, you can open a Jupyter notebook file (.ipynb) in VS Code and then convert it to Python code. Once you've converted the file, you can run the code as you would with any other Python file and also use the VS Code debugger. Opening and debugging notebooks in VS Code is a convenient way to find and resolve code bugs, which is difficult to do directly in a Jupyter notebook.

When you open a notebook file, Visual Studio Code will open it in the Notebook Editor automatically. Use the convert icon on the toolbar to convert the Notebook (.ipynb) file to a Python file (.py).

Jupyter Hotkey Markdown

Select the convert icon followed by 'Python Script', wait a few seconds, and then VS Code opens the converted notebook in an untitled file. The notebook's cells are delimited in the Python file with # %% comments; Markdown cells are converted wholly to comments preceded with # %% [markdown], and render as HTML in the interactive window alongside code and output such as graphs:

Note: The first time you run code cells in a Python file, the Python extension starts a Jupyter server. It may take some time for the server to start up and for the Python Interactive window to appear with the results of the code.

Debug a Jupyter notebook

The Visual Studio Code debugger lets you step through your code, set breakpoints, examine state, and analyze problems. Using the debugger is a helpful way to find and correct issues in notebook code.

1. In VS Code, activate a Python environment in which Jupyter is installed, as described at the beginning of this article.

2. Import the notebook's .ipynb file into VS Code as described in the previous section. (Download the file first if you're using a cloud-based Jupyter environment such as Azure Notebooks.)

3. To start the debugger, use one of the following options:

   • For the whole notebook, open the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)) and run the Jupyter: Debug Current File in Python Interactive Window command.
   • For an individual cell, use the Debug Cell adornment that appears above the cell. The debugger specifically starts on the code in that cell. By default, Debug Cell just steps into user code. If you want to step into non-user code, you need to uncheck Data Science: Debug Just My Code in the Python extension settings (⌘, (Windows, Linux Ctrl+,)).

4. To familiarize yourself with the general debugging features of VS Code, such as inspecting variables, setting breakpoints, and other activities, review VS Code debugging.

5. As you find issues, stop the debugger, correct your code, save the file, and start the debugger again.

6. When you're satisfied that all your code is correct. Save the file, then export the notebook as described in the following section. You can then upload the notebook to your normal Jupyter environment.

Export a Jupyter notebook

In addition to opening a Jupyter notebook, you can also use one of the following commands from the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)) to export content from a Python file in VS Code to a Jupyter notebook (with the .ipynb extension).

Shortcut For Markdown Cell In Jupyter

• Jupyter: Export Current Python File as Jupyter Notebook: creates a Jupyter notebook from the contents of the current file, using the # %% and # %% [markdown] delimiters to specify their respective cell types.
• Jupyter: Export Current Python File and Output as Jupyter Notebook: creates a Jupyter notebook from the contents of the current file and includes output from code cells.
• Jupyter: Export Interactive Window as Jupyter Notebook: creates a Jupyter notebook from the contents of the Python Interactive window.

Jupyter Markdown Code Block

After exporting the contents, VS Code displays a prompt through which you can open the notebook in a browser.