You probably ended up here because you just had Excel crash, again, and you’re sick of it. You’ve probably also heard about R, Python, Jupyter Notebooks and probably have decided on starting your new data analysis journey into Jupyter.

Get Jupyter or die tryin’

-absolutely no one said this.

After about a week of absolutely struggling with some really simple operations and the plethora of tutorials which are just terrible, I wanted to give you the first steps to help you convert to Python/Jupyter from Excel.

Installation through Anaconda

Download and install Anaconda:

You could install Jupyter manually but don’t. Just install the Anaconda package and let them worry about all the gory details, don’t worry, you’ll get to use the command line later on you masochist.

Plus, Anaconda comes with a bunch of other applications that you’ll want to try out at some point.

Enable extensions

Open up your terminal (or windows command prompt) and run this command:

conda install -c conda-forge jupyter_contrib_nbextensions jupyter_nbextensions_configurator

Note on common Windows issue: Conda not recognized on Windows? See here

Extensions will save you, like Hinterland which enables a decent autocomplete for Jupyter. I’m not sure why the default docstring shift + tab doesn’t work properly but who cares. You’re enabling this pretty extension tab and you’ll be able to enable extensions using a friendly graphical user interface.

Once you’ve executed this command, open up Anaconda and launch Jupyter Notebooks, notice you have a new tab called Nbextensions. Ok, there’s a lot here, let me explain.

  1. Hinterland: autocompletion with each keystroke. Definitely enable this.
  2. Variable inspector: Find out what variables are consuming crazy memory, what’s in the variable, and a list with their names.
  3. Autopep8: prettifies your code in cells. Get it.
  4. Freeze: disable a cell block immediately, basically make them read-only.
  5. Gist-it: ship your notebook to a Github gist pronto
  6. Hide input all: hide the code input so you can examine the output in peace.
  7. Execute time: this is nice to know. How long your operations took and if they’re queued up or still executing. Trust me, the bare bones Jupyter running of cells isn’t super noticeable, this makes it easier to determine what’s running, been run, and how long it takes so you can optimize later.

There’s a bunch of other extensions that you can find across the web which you can enable and disable from this handy tab.

That’s it for now, the next part of this series will be exploring common operations and getting your head transferred from Excel. For now, please, please get to grips with the keyboard shortcuts