Ipynb Markdown

Posted on  by admin

Converting notebooks to other formats¶. Newly added in the 1.0 release of IPython is the nbconvert tool, which allows you to convert an.ipynb notebook document file into various static formats. Currently, nbconvert is provided as a command line tool, run as a script using IPython. A direct export capability from within the IPython Notebook web app is planned. What is Markdown? Colab has two types of cells: text and code. Text cells are formatted using a simple markup language called Markdown. To see the Markdown source, double-click a text cell, showing both the Markdown source and the rendered version. Above the Markdown. Read a Jupyter/IPython notebook file (.ipynb) via jsonlite::fromJSON, convert its code cells to R Markdown code chunks, preserve Markdown cells, and write out the results to an Rmd file. Notebooks with Markdown can be read in, executed, and cached by Jupyter Book (see Execute and cache your pages for information on how to cache pages). This allows you to store all of your notebook content in a text format that is much nicer for version control software, while still having all the functionality of a Jupyter notebook.

Info to help convert the Jupyter notebooks we use in class into blog posts for your github blog. Manual conversion instructions are given for one-off posts, as well as detials to make a bash alias to automate the conversion process, move the files, and update your blog in one fell swoop.

Ipynb MarkdownCheatsheet

Manual instructions

Run the following command in terminal to convert the file named notebook.ipynb from Jupyter notebook format to a markdown file suitable for your Jekyll blog. Note this must be run in the terminal inside the directory that includes the file you intend to convert.

That command will run the translation utility provided by Jupyter and create two new items:

  • notebook.md is a new file containing the markdown for your new blog post
  • notebook_files is a new folder containing all the images for your new blog post

Ipynb Markdown Table

Move the notebook.md file to your blog’s _posts folder and rename it to include the date and title for your post. You will also want to make sure the first few lines contain the appropriate front matter (YAML block in Jekyll parlance). You will also need to change any image links to point to your blog’s image folder. Read below to see my tool for automating most of these tasks.

Move the notebook_files folder to your blog’s “images” folder (this is also automated in my tool below).

Add these files to the git repository and commit. Then push the repo up to the server. Congratulations, your updated site should be live within a few minutes!

Automated instructions

Once you have installed the tool below, the instructions for uploading a notebook as a new post on your blog are:
  • Run new_post notebook.ipynb (where notebook.ipynb is the name of the file you wish to convert and post)
  • Start impatiently refreshing your blog page to see the new post appear.
Please note that certain items in the notebook file may not come through to the final blog post:
  • LaTeX equations will not be interpreted properly
  • Manually inserted images will need to be fixed by hand
  • YAML metadata (e.g. title, date, category, etc.) will only work properly if the first cell type is set to Raw NBConvert
Things my new_post function does:
  • Convert to markdown
  • Fix inline plot image addresses
  • Move post file and images folder to appropriate area of blog directory
  • Add and commit new post files to git repo
  • Push repo changes to server

There are currently no sanity checks in place in this function, so please take care to understand how it works before you run it, and be careful what you use it on. I have made an example post

Latex In Google Colab

Automation setup

This process is very similar to the tool I created in the first week of the class. Basically we are adding a shortcut to our .bash_profile script that the CLI loads everytime we open a new terminal window. I call my shortcut new_post but you can change this to whatever command you want to type when you post a notebook to your blog. The installation is easy, just copy and paste the code below into your ~/.bash_profile file (you can use the command subl ~/.bash_profile to open this file in Sublime Text directly from the CLI). Do note that you will need to change the info on the first few lines to reflect how your personal computer is setup.

Setup pretty tables

When you set up your system for translating the blog posts, you will need to add some CSS code to your html template file to tell Jekyll to format the Pandas DataFrame tables appropraitely. Below is the relevant section of my <username>.github.io/_layouts/default.html file that has the CSS code included at the end of the <head> section. You should edit the your defualts file to be similar.

Jupyter Notebook Markdown Commands

Here is a quick overview of the features in the added CSS code. You can change any of these settings to suit your fancy.

Ipynb Markdown Cheat Sheet

  • Set the table size to be the full width and 240px tall
  • Enable scroll bars for accessing larger tables
  • Set the font to Arial size 13, and align it to the center of each cell
  • Make the table headers (both column and row) bold
  • Give each cell 4px of padding
  • Highlight the row your mouse hovers over in light blue (#b8d1f3)

Ipython Notebook Markdown New Line

I am taking a course that uses ipython notebook. When I try to download the notebook (through File -> Download as -> ipython notebook), I get a file that ends with '.ipynb.json'. It doesn't open as an ipython notebook but as a .json file so something like this:

I've tried deleting the '.json' in the file name and it doesn't work. How can I convert this file back to something that can be opened and run as an ipython notebook? Thank you very much!

Convert Jupyter Notebook To Markdown

See also questions close to this topic

  • Filter items in a listbox that have been added with json

    Well I'm trying to list items that are from listbox created with UserControl and the text properties are defined with a json file.

    This is my .json file:

    This is the code to load the games as items in the listbox:

    This is the code I used in a textbox to try to filter:

    But it didn't work, the item names are always GameUserContro.Can someone help me?

  • Python - How can I convert S3 folders into JSON hierarchy?

    I have created a list using boto3 that contains all the subfolders in my S3 bucket. The list is sorted as below:

    I'm trying to convert this list into JSON hierarchy structure:

    Whats the best way/libraries to get this going in Python?

  • Get separate dataframes according to differents json files

    I have three .json files,

    I would like to get 3 separate data.frames corresponding to the 3 files, with the filename corresponding to each data.frame. For the moment, I can get a list with the data.

  • How to interactively update IPywidgets container (VBox, HBox)?

    When I try to update a container that contains other dynamically created container it doesn't change.I tried different options but in the current one I defined the variables holding the containers before the update function and call this function whenever a change occurs:

    later in the script I change the containers globally like:

    I tried to display the containers before the function only changed the container without display() and tried many other options but it still only shows an empty list.

    I put the notebook on github IpySci.ipynb shows the current state with the describet probelm IpySci-Copy1.ipynb my earliest version that show that the basic function are working as intended.

  • Jupyter notebook autocomplete showing duplicate options

    Jupyter notebook's autocomplete seems working, but somehow it will show duplicate options for the method. For example below:

    For each possible options, drop down menu will show 2 identical choices. Why this happen and how to fix it?

  • how to predict test rmse in mxnet graphlab? model.evaluate(dataiter) does not work

    Here is my code in the given image.

    I have written few lines of code to predict a parameter in my dataset, but it only gives RMSE for train data as shown in picture. What would be a way around that it shows RMSE for test data? model.evaluate(dataiter) does not work, help needed