Jupyter Notebooks¶. Any notebooks that you create will need front matter for hugo to know how to render the content. Once you edit the name of the jupyter notebook to something other than Untitled.ipynb, hugo-jupyter will automatically edit the notebook’s metadata to enable rendering with jupyter. Jupyter Notebook IntroJupyter Notebook TutorialData ScienceSconsJupyter Notebook SecurityPython BasicsIntro to Data AnalysisWhat is AnacondaJupyter Notebook Tips and TricksMultiple Python. A simple, retro theme for Hugo.
- Jupyter Tutorial
- IPython
Learn about how Cacher renders Jupyter Notebooks. Cacher empowers you and your team to write more code, faster. In this short guide, I'll show you how to add R to Jupyter Notebook. I will review the complete steps to add R from scratch.
- Jupyter
- QtConsole
- JupyterLab
- Jupyter Resources
- Selected Reading
While the menu bar and toolbar lets you perform various operations on notebook, it is desirable to be able to use keyboard shortcuts to perform them quickly.
Jupyter Notebooks have two different keyboard input modes −
Command Mode − Binds the keyboard to notebook level actions. Indicated by a grey cell border with a blue left margin.
Edit Mode − When you are typing in a cell. Indicated by a green cell border.
Command Mode (press Esc to enable)
F | find and replace | 1 | change cell to heading 1 |
Ctrl-Shift-F | open the command palette | 2 | change cell to heading 2 |
Ctrl-Shift-P | open the command palette | 3 | change cell to heading 3 |
Enter | enter edit mode | 4 | change cell to heading 4 |
P | open the command palette | 5 | change cell to heading 5 |
Shift-Enter | run cell, select below | 6 | change cell to heading 6 |
Ctrl-Enter | run selected cells | A | insert cell above |
Alt-Enter | run cell and insert below | B | insert cell below |
Y | change cell to code | X | cut selected cells |
M | change cell to markdown | C | copy selected cells |
R | change cell to raw | Drivers asix network & wireless cards. V | paste cells below |
K | select cell above | Z | undo cell deletion |
Up | select cell above | D,D | delete selected cells |
Down | select cell below | Shift-M | merge selected cells, or current cell with cell below if only one cell is selected |
J | select cell below | Shift-V | paste cells above |
Shift-K | extend selected cells above | L | toggle line numbers |
Shift-Up | extend selected cells above | O | toggle output of selected cells |
Shift-Down | extend selected cells below | Shift-O | toggle output scrolling of selected cells |
Shift-J | extend selected cells below | I,I | interrupt the kernel |
Ctrl-S | Save and Checkpoint | 0,0 | restart the kernel (with dialog) |
S | Save and Checkpoint | Esc | close the pager |
Shift-L | toggles line numbers in all cells, and persist the setting | Q | close the pager |
Shift-Space | scroll notebook up | Space | scroll notebook down |
Edit Mode (press Enter to enable)
Tab | code completion or indent | Ctrl-Home | go to cell start |
Shift-Tab | tooltip | Ctrl-Up | go to cell start |
Ctrl-] | indent | Ctrl-End | go to cell end |
Ctrl-[ | dedent | Ctrl-Down | go to cell end |
Ctrl-A | select all | Ctrl-Left | go one word left |
Ctrl-Z | undo | Ctrl-Right | go one word right |
Ctrl-/ | comment | Ctrl-M | enter command mode |
Ctrl-D | delete whole line | Ctrl-Shift-F | open the command palette |
Ctrl-U | undo selection | Ctrl-Shift-P | open the command palette |
Insert | toggle overwrite flag | Esc | enter command mode |
Ctrl-Backspace | delete word before | Ctrl-Y | redo |
Ctrl-Delete | delete word after | Alt-U | redo selection |
Shift-Enter | run cell, select below | Ctrl-Shift-Minus | split cell at cursor |
Ctrl-Enter | run selected cells | Down | move cursor down |
Alt-Enter | run cell and insert below | Up | move cursor up |
Ctrl-S | Save and Checkpoint |
iPython and Jupyter Notebook with Embedded D3.js
There have been several tries to incorporate D3 into IPython:
Though quite progresses have been made in those approaches, they were kind of hacks. Now, we have language agnostic Jupyter which was forked from IPython, we can take the D3 into Notebook without lots of effeorts.
We can start implement D3 into Jupyter from this repo: PyGoogle/PyD3.
The repo is based on this presentation:
The primary idea looks like this:
- Jupyter reads in HTML DOM as a string via IPython.core.display So, Jupyter simply imports D3 using HTML API
- DOM elements manipulation using Python's string.Template.substitute
- Data format - Panda's JSON
Before we use Jupyter, we'll use the following D3 animation.
Here is the pure D3 animation.
Here is the code for D3 animaition:
We're using the same animation showed in the previous section.
The code for Notebook looks like this: Download atrack driver.
Hugo Jupiter Notebook Book
Notebook is available at Github:
PyD3/D3-Circle-Animation.ipynb
Hugo Jupiter Notebooks
Note: This is an email I got from a reader:
hi K Hong,
I was just reading your post regarding embedding d3 in jupyter notebooks:
http://www.bogotobogo.com/python/IPython/iPython_Jupyter_Notebook_with_Embedded_D3.php
It seems there is an issue importing d3 as an external library:
see https://github.com/mpld3/mpld3/issues/33#issuecomment-32101013
(I had the same issue)
the solution was to place the usage of d3 within a require:
this worked for me.
you can se an example for how to get your code to work with this fix here:
https://github.com/fensterheim/DataProjects/blob/master/D3_example/D3Test.ipynb
according to the issue that was opened it seems that this is a problem with newer versions of d3, therefore it might be worth while noting this in your blog post.