The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain
Its use includes:
What doesn't go on a Jupyter Notebook?
When running time and computational intensive models, particulrly making you of cloud-computing, you will often test your code on a notebook, but submit for running as a .py file.
.ipynb is really a JSON file¶At it's core, an Jupyter notebook is a JSON (JavaScript Object Notation) file.
Open a notebook with a text editor and you will see it!
Pros:
R). This makes Jupyter notebooks particularly friendly when you're first learning PythonCons:
There are two primary methods for initializing a notebook.
.ipynb notebook. cd /Users/me/Desktop/jupyter notebookQuit and Logout buttons on the page. Quit == close the local server (i.e. the web application connection)Logout == shut down the home page of the web application (but keep the server running)Control-C in the console. Jupyter lab
Instead of using the simple jupyter notebooks, you can also follow the same steps above using JupyterLab. Jupyter notebook only offers a very simple interface. Jupyter lab offers a more interactive interface that includes notebooks, consoles, terminals, CSV editors, markdown editors, interactive maps, and more.
To use Jupyter lab:
jupyter labIf you prefer point-and-click, you can start jupyter notebook through your anaconda distribution.
A kernel is a computational engine that executes the code contained in a notebook document. A cell (or "Chunk") is a container for text to be displayed in the notebook or code to be executed by the notebook's kernel.
Though we can only have one type of kernel running for any given notebook (we can't change between kernels in the middle of a notebook), we can use jupyter beyond just a python kernel.
Here is a list of all the kernels that you can use with a jupyter notebook. For example, we can easily employ an R kernel in a jupyter notebook.
This was always the notebooks original intent. Actually, "Jupyter" is a loose acronym meaning Julia, Python and R
Code chunks are what we use to execute Python (or whatever kernel we have running) code. In addition, we can write prose in a code chunk by altering the metadata regarding how the code should be run.
There are two states of a code chunk:
Esc or using the mouse to click outside a cell's editor area.y when on the cell in Command Mode to switch to a code chunk.m when on the cell in Command Mode to switch to a markdown chunkA code chunk will always reflect the behavior of the kernel that you're using (e.g. a Python code chunk will follow Python coding Syntax).
Best Practices
As with most user interfaces, Jupyter Notebooks have developed their own way of doing things. Thus there are a number of useful shortcuts that you can employ to help perform useful tasks.
We can access a full (searchable) list of keyboard shortcuts by pressing p when in Command Mode, or by clicking the keyboard icon in the tools.
Important ones while in Command Mode:
a: create a new code chunk above the current one.b: create a new code chunk below the current one.ii: interrupt the kernel (really useful when some code is running too long or you've accidentally initiated an infinite loop!y: code modem: markdown modeshift + m: merge cells (when more than one cell is highlighted)dd: delete cell.Important ones while in Edit Mode:
shit + ctrl + minus: split cellMagic commands, and are prefixed by the % character. These magic commands are designed to succinctly solve various common problems in standard data analysis.
Magic commands come in two flavors:
% prefix and operate on a single line of input, %% prefix and operate on multiple lines of input. List off all the available magic commands.
%lsmagic
Or consult the quick reference sheet of all available magic
%quickref
Here are some useful magic commands that come in handy as you're working with code.
"Come back here later"
%bookmark Home
See below
%cd ~/Dropbox
%pwd
Using the bookmark to return to where we were...
%cd -b Home
%pwd
Extremely useful when we develop some functionality that we'd like to utilize later on.
%%writefile my_fib_func.py
def fib(n):
'''Fibonacci Sequence'''
x = [0]*n
for i in range(n):
if i == 0:
x[i] = 0
elif i == 1:
x[i] = 1
else:
x[i] = x[i-2] + x[i-1]
return x
%ls # list files ( see our function)
# %load my_fib_func.py
def fib(n):
'''Fibonacci Sequence'''
x = [0]*n
for i in range(n):
if i == 0:
x[i] = 0
elif i == 1:
x[i] = 1
else:
x[i] = x[i-2] + x[i-1]
return x
%run my_fib_func.py
How fast does what we wrote run?
%time fib(10)
How long does many runs take (statistical sample)?
%timeit fib(10)
main_dat = [1,2,3,4]
main_key = ["a","b"]
x = 5
y = 6
%psearch main*
Whenever you encounter an error or exception, just open a new notebook cell, type %debug and run the cell. This will open a command line where you can test your code and inspect all variables right up to the line that threw the error. Type n and hit Enter to run the next line of code (The -> arrow shows you the current position). Use c to continue until the next breakpoint. q quits the debugger and code execution.
%%timeit?
Let's try
psearch main*
pwd
timeit fib(10)
answer: actually no. You can use the magin
We can expand the functionality of Jupyter notebooks through extensions. Extensions allow for use to create and use new features that better customize the notebook's user experience. For example, there are extensions for spell check, a table of contents to ease navigation, run code in parallel, and for viewing differences in notebooks when using Version control.
Download python module to install notebook extensions: https://github.com/ipython-contrib/jupyter_contrib_nbextensions
Using PyPi (module manager):
pip install jupyter_nbextensions_configurator jupyter_contrib_nbextensions
jupyter contrib nbextension install --user
jupyter nbextensions_configurator enable --userUsing Conda (Anaconda module manager):
conda install -c conda-forge jupyter_contrib_nbextensions
jupyter contrib nbextension install --user
jupyter nbextensions_configurator enable --userExtensions can be activated most easily on the home screen when you first activate your Jupyter notebook.
pip install nbdime!jupyter nbconvert _using-jupyter-notebooks.ipynb --to html --template classic