Statload255

1. Jupiter Notebook Md Reviews
2. Jupyter Notebook Windows
3. Installing Jupyter Notebooks On Windows
4. Jupiter Notebook Mdx
5. Jupyter Notebook Module Not Found
6. Jupyter Notebook Download

In the last post, I tested out the functionality of Jupyter Notebook, a platform that I am just starting to get acquainted with. I’m pleased with how that experimental post turned out, although there are still things to modify and improve. In this post, I explain how I was able to automate the process of converting Jupyter Notebook into markdown using shell script and python commands.

This blog runs on GitHub pages using Jekyll, a static website generator engine. When I conceived of the idea of starting a personal blog about a month ago, I considered various hosting options, such as Wordpress or Wix, but I eventually settled on Jekyll, convinced by its utility as well as customizability. I also considered other options such as Hugo or Gatsby, but decided that Jekyll was more starter-friendly and offered to do things right out of the box.

Jekyll makes use of .md files to display posts. For the most part, uploading posts is a painless process as markdown files are very easy to create. However, one drawback of using this file format with Jekyll is that embedding images in posts requires me to download and place images in the correct path, namely assets/images in the GitHub repository. Because most of the images I use are graphs and plots generated from executing blocks of code on Python, this manual reference protocol requires that I follow a rather roundabout routine:

Jupiter Notebook Md Reviews

• Write python code on Sublime or PyCharm
• Call the plt.savefig() method to save generated figures on local drive
• Move image file to assets/images directory
• Copy and paste relevant code manually onto the .md file
• Delete the plt.savefig() line
• Reference image file in assets/images/ in .md file

While this is not a complicated process, it is a time-consuming one indeed, especially if the post gets longer. Another downside of this workflow is that it is prone to human error because most of the steps are performed manually by me. Copy and pasting, writing links, and moving files across different directories are all tasks that require human input. For those reasons, I found the my workflow to be inefficient. Now enters automation and Jupyter Notebooks into the picture.

The biggest advantage of using Jupyter Notebooks is that plots and figures are generated on the document directly. In other words, there is no need to write code in one location and copy and paste it to another, neither do I have to save a figure as an image file and link it in the post separately—code, figures, and texts all stay nicely in one package. After all, the whole idea behind Jupyter Notebook is to present information in a readable, accessible fashion. This is why I found Jupyter Notebook to be a very convenient option: it streamlined the process of generating figures and writing explanations for my blog posts, specifically into the following three steps:

The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning,. The Jupyter Notebook provides a powerful interface for accessing interactively the power of the Python ecosystem on Azure. It covers a wide range of usage cases including simple exploration and learning Python, data analysis and visualization, simulation and parallel computing.

• Write Jupyter Notebook
• Run each code block
• Save file by generating a checkpoint

And now I’m done! Notice how the complicated, inefficient workflow above has shrunk into three, nice and simple steps. The Jupyter Notebook UI is also highly intuitive, which is why it did not take a lot of time for me to start writing code and generating figures after fidgeting around with some options for a bit.

However, Jupyter Notebook is not without its problems. For one, I had to find a method to integrate Jupyter Notebooks into Jekyll. Jupyter Notebook comes in .ipynb file format, which is not presentable on GitHub pages by default. This meant that I had to convert Jupyter Notebook into something like .html or .md, the two most commonly used file types in Jekyll. Fortunately, I found a command line conversion tool, named nbconverter that performed this task quite painlessly. After some experimentation, I was able to generate a .md file of the Jupyter Notebook.

So I should be done, right?

Well, unfortunately not. Examining the end product, I realized two glaring problems. First, the converted file did not include any YAML front matter. Basically, all posts on Jekyll include some header that contains information on their title, tag, category, and date information. The converted file, however, did not include this header because it was converted from Jupyter Notebooks which inherently does not have the notion of YAML front matter. This meant that I had to manually type in these information again, which is, to be fair, not such a time consuming process, but nonetheless additional work on my part.

A more serious issue with this conversion was that all the image links were broken. This was certainly bad news: when I compiled and built the website on GitHub, I found that none of the images were properly displayed on the web page. Upon examination, I found that all the dysfunctional links took the following form:

Proper image links, on the other hand, look like

On a quick side note, those of you who know .html will easily recognize that this conforms to .html syntax. Anyhow, to make this work, I realized that I would have to walk through every image link in the converted file and restructure them correctly into standard .html tag format. This sounded intimidating, but I decided to try it nonetheless. After performing this menial task for some time, however, I concluded that this manual handwork was not the way to go.

This is where the fun part comes in. I decided to implement a simple automation system that would solve all of my problems and worries. Specifically, this script would have to perform the following list of tasks:

• Convert Jupyter Notebook to markdown
• Add YAML front matter to the converted file
• Fix broken image links
• Move the converted file to _posts directory
• Move image files to /assets/images/ directory

This might seem a lot to handle, but some simple scripting can do the trick. First, we begin by creating a .sh script that will form the basis of this automating process. I used the hash bang #!/bin/sh to make this an executable. The script itself is very short—it goes as follows.

The first line of the function invokes a nbconvert command, the result of which is the creation of a .md converted file. Then, the script runs another a Python script, but I will get more into that in a second. After Python does its portion of the work, I use two mv commands to move the .md file to the _posts directory and the associated image files to the /assets/images/ directory, as specified above. Then, the script echoes a short message to tell the user that the process is complete. Pretty simple, yet it does its trick quite nicely.

Now onto the Python part. If you take a look back at the task list delineated above, you will see that there are two bullet point that has not been ticked off by the convert.sh script: adding YAML front matter and fixing broken image links—and that is exactly what our edit.py script will do! Let’s take a look at the code:

In a nutshell, the script opens and reads the converted .md file, finds and replaces broken image links with correct ones, and adds the default YAML front matter, defined as the string yaml in the code above. When all the necessary edits are performed, the script then rewrites the .md file with the edited content.

This script works fine, but I feel like all of this might have been implemented a lot more efficiently if I were proficient with regular expressions. The task of locating and editing broken image links into html tags is a classic regexp problem, but my knowledge of Python and regular expressions were not quite enough to implement the most efficient mechanism to tackle this problem. Nonetheless, my solution still produces the desired result, which is why I am very content with my first attempt at automation with Python and script.

Now, I have a completely revised workflow that suits my purposes the best: Jupyter Notebooks to write code and generate figures alongside text explanations, and an automation system that converts these Jupyter Notebooks to markdown efficiently. All I have to do is to edit the default YAML front matter, such as coming up with good titles and categorizing my post to appropriate categories.

This automation script is by no means perfect, which is why I plan on incremental updates as I go. Specifically, I might add additional features, such as the ability to auto-generate figure captions. I might also streamline the process of find and replace that occurs within the Python script after learning more about regular expressions. But for now, the project seems solid as it is, and I am happy with what I have.

Having completed this little script, I am thinking of starting another automation project that will help me with my work at the PMO. This is going to be a much tougher challenge that involves reading in .pdf files and outputting .xlsx files, but hopefully I can streamline my workflow at the office with Python’s help. I am excited to see where this project will go, and I will definitely keep this posted. In the meanwhile, happy thanksgiving!

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 native support available for Jupyter Notebooks and demonstrates how to:

• Create, open, and save Jupyter Notebooks
• Work with Jupyter code cells
• View, inspect, and filter variables using the Variable explorer and Data viewer
• Connect to a remote Jupyter server
• Debug a Jupyter notebook

Setting up your environment

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 open a Jupyter Notebook, connect to a remote Jupyter server for running code cells, and export a Jupyter Notebook as a Python file.

Note: By default, the Visual Studio Code Python extension will open a Jupyter Notebook (.ipynb) in the Notebook Editor. If you want to disable this behavior you can turn it off in settings. (Python > Data Science: Use Notebook Editor).

Create or open a Jupyter Notebook

You can create a Jupyter Notebook by running the Jupyter: Create Blank New Jupyter Notebook command from the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)) or by creating a new .ipynb file in your workspace. When you select the file, the Notebook Editor is launched allowing you to edit and run code cells.

If you have an existing Jupyter Notebook, you can open it in the Notebook Editor by double-clicking on the file and opening with Visual Studio Code, through the Visual Studio Code, or using the Command Palette Jupyter: Open in Notebook Editor command.

Jupyter Notebook Windows

Once you have a Notebook created, you can run a code cell using the green run icon above the cell and the output will appear directly below the code cell.

Trusted Notebooks

It's possible for malicious source code to be contained in a Jupyter Notebook. With that in mind, to help protect you, any Notebook that's not created with VS Code on your local machine (or explicitly set to Trusted by you) is considered Not Trusted. When a Notebook is Not Trusted, VS Code will not render Markdown cells or display the output of code cells within the Notebook. Instead, just the source of Markdown and code cells will be shown. The Notebook is essentially in read-only mode, with toolbars disabled and no ability to edit the file, until you set it as Trusted.

Note: Before setting a Notebook as Trusted, it is up to you to verify that the source code and Markdown are safe to run. VS Code does not perform any sanitizing of Markdown, it merely prevents it from being rendered until a Notebook is marked as Trusted to help protect you from malicious code.

When you first open a Notebook that's Not Trusted, the following notification prompt is displayed.

If you select Trust, the Notebook will be trusted going forward. If you opt not to trust the Notebook, then Not Trusted will be displayed in the toolbar and the Notebook will remain in a read-only state as described previously. If you select Trust all notebooks, you will be taken to settings, where you can specify that all Notebooks opened in VS Code be trusted. That means you will no longer be prompted to trust individual notebooks and harmful code could automatically run.

You can relaunch the trust notification prompt after reviewing the Notebook by clicking on the Not Trusted status.

Save your Jupyter Notebook

You can save your Jupyter Notebook using the keyboard combo Ctrl+S or through the save icon on the Notebook Editor toolbar.

Note: At present, you must use the methods discussed above to save your Notebook. The File>Save menu does not save your Notebook, just the toolbar icon or keyboard command.

Export your Jupyter Notebook

You can export a Jupyter Notebook as a Python file (.py), a PDF, or an HTML file. To export, just click the convert icon on the main toolbar. You'll then be presented with file options from the Command Palette.

Note: For PDF export, you must have TeX installed. If you don't, you will be notified that you need to install it when you select the PDF option. Also, be aware that if you have SVG-only output in your Notebook, they will not be displayed in the PDF. To have SVG graphics in a PDF, either ensure that your output includes a non-SVG image format or else you can first export to HTML and then save as PDF using your browser.

Work with code cells in the Notebook Editor

The Notebook Editor makes it easy to create, edit, and run code cells within your Jupyter Notebook.

Create a code cell

By default, a blank Notebook will have an empty code cell for you to start with and an existing Notebook will place one at the bottom. Add your code to the empty code cell to get started.

Code cell modes

While working with code cells a cell can be in three states, unselected, command mode, and edit mode. The current state of a cell is indicated by a vertical bar to the left of a code cell. When no bar is visible, the cell is unselected.

An unselected cell isn't editable, but you can hover over it to reveal additional cell specific toolbar options. These additional toolbar options appear directly below and to the left of the cell. You'll also see when hovering over a cell that an empty vertical bar is present to the left.

When a cell is selected, it can be in two different modes. It can be in command mode or in edit mode. When the cell is in command mode, it can be operated on and accept keyboard commands. When the cell is in edit mode, the cell's contents (code or Markdown) can be modified.

When a cell is in command mode, the vertical bar to the left of the cell will be solid to indicate it's selected.

When you're in edit mode, the vertical bar will have diagonal lines.

To move from edit mode to command mode, press the ESC key. To move from command mode to edit mode, press the Enter key. You can also use the mouse to change the mode by clicking the vertical bar to the left of the cell or out of the code/Markdown region in the code cell.

Add additional code cells

Code cells can be added to a Notebook using the main toolbar, a code cell's vertical toolbar, the add code cell icon at the bottom of the Notebook, the add code cell icon at the top of the Notebook (visible with hover), and through keyboard commands.

Using the plus icon in the main toolbar will add a new cell directly below the currently selected cell. Using the add cell icons at the top and bottom of the Jupyter Notebook, will add a code cell at the top and bottom respectively. And using the add icon in the code cell's toolbar, will add a new code cell directly below it.

When a code cell is in command mode, the A key can be used to add a cell above and the B can be used to add a cell below the selected cell.

Select a code cell

The selected code cell can be changed using the mouse, the up/down arrow keys on the keyboard, and the J (down) and K (up) keys. To use the keyboard, the cell must be in command mode.

Run a single code cell

Once your code is added, you can run a cell using the green run arrow and the output will be displayed below the code cell.

You can also use key combos to run a selected code cell.

• Ctrl+Enter runs the currently selected cell
• Shift+Enter runs the currently selected cell and, if a cell is not already present, inserts a new cell immediately below (focus moves to the below cell in command mode)
• Alt+Enter runs the currently selected cell and inserts a new cell immediately below (focus moves to new cell in edit mode).

These keyboard combos can be used in both command and edit modes.

Run multiple code cells

Running multiple code cells can be accomplished in a number of ways. You can use the double arrow in the toolbar of the Notebook Editor to run all cells within the Notebook or the run icons with directional arrows to run all cells above or below the current code cell.

Run code by line

To help diagnose issues with your Notebook code, run-by-line lets you step through the code of a cell in a line-by-line fashion. While stepping through code you can view the state of variables at each step via the variable explorer or hover your mouse over variables to see data tips.

To start a session, just click the run-by-line icon to the right of the run cell icon on the cell's toolbar.

Once in a run-by-line session, you can run the currently highlighted line of code by pressing the icon again. To exit, just click the stop button next to the run-by-line icon in the cell.

Move a code cell

Moving code cells up or down within a Notebook can be accomplished using the vertical arrows beside each code cell. Hover over the code cell and then click the up arrow to move the cell up and the down arrow to move the cell down.

Delete a code cell

Deleting a code cell can be accomplished by hovering over a code cell and using the delete icon in the code cell toolbar or through the keyboard combo dd when the selected code cell is in command mode.

Undo your last change

You can use the z key to undo your previous change, for example, if you've made an accidental edit you can undo it to the previous correct state, or if you've deleted a cell accidentally you can recover it.

Switch between code and Markdown

The Notebook Editor allows you to easily change code cells between Markdown and code. By default a code cell is set for code, but just click the Markdown icon (or the code icon, if Markdown was previously set) in the code cell's toolbar to change it.

Once Markdown is set, you can enter Markdown formatted content to the code cell. Once you select another cell or toggle out of the content selection, the Markdown content is rendered in the Notebook Editor.

You can also use the keyboard to change the cell type. When a cell is selected and in command mode, the M key switches the cell type to Markdown and the Y key switches the cell type to code.

Clear output or restart/interrupt the kernel

If you'd like to clear the code cell output or restart/interrupt the kernel, you can accomplish that using the main Notebook Editor toolbar.

Enable/Disable line numbers

You can enable or disable line numbering within a code cell using the L key.

IntelliSense support in the Jupyter Notebook Editor

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

Installing Jupyter Notebooks On Windows

Variable explorer and data viewer

Within the Python Notebook Editor, it's possible to view, inspect, and filter the variables within your current Jupyter session. By clicking the Variables icon in the top toolbar 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 next to the variable 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).

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 Notebook Editor window, double-click any plot to open it in the viewer, or select the plot viewer button on the upper left corner of the plot (visible on hover).

Jupiter Notebook Mdx

Note: There is support for rendering plots created with matplotlib and Altair.

Debug a Jupyter Notebook

Jupyter Notebook Module Not Found

If you need additional debug support in order to diagnose an issue in your code cells, you can export it as a Python file. Once exported as a Python file, 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. To debug your Python file:

1. In VS Code, if you haven't already, activate a Python environment in which Jupyter is installed.

2. From your Jupyter Notebook (.ipynb) select the convert button in the main toolbar.

   Once exported, you'll have a .py file with your code that you can use for debugging.

3. After saving the .py file, 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, use the Python Interactive window to export the Python file as a Jupyter Notebook (.ipynb).

Jupyter Notebook Download

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. When prompted to Pick how to connect to Jupyter, select Existing: Specify the URI of an existing server.

3. When prompted to Enter the URI of a Jupyter server, provide the server's URI (hostname) with the authentication token included with a ?token= URL parameter. (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.

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.