Data Science Hands on with Open source Tools – Getting started with Jupyter Notebooks


Welcome! In this video we’ll dive into Jupyter notebooks
on Data Scientist Workbench. To open a Jupyter Notebook from the Data Scientist
Workbench workspace, simply click Jupyter Notebooks. You can create new, empty notebooks by clicking
on the blue button in the top-right corner, and select which language you want to use
in your notebook. You can rename your notebook, or share your
notebook. Notebooks are made up of cells. Cells can contain code, or contain text and
html. Code cells look special because they’re
gray. You can run a code cell by clicking within
the gray area, and pressing the “Play” button. In this example, we’ve successfully imported
the “pandas” library, as no error message appeared. You can also create new code cells. To create a new, empty code cell in your notebook,
click on an existing cell, then in the menu, click on “Insert”, then “Insert a cell
above” or “below” where you clicked. Then you can type-in something like 1+1, press
“Play” and see the resulting output. Of course, you can also create graphs and
show your plots throughout your notebook. You can see that this notebook uses Python
2, because it says Python 2 here. But you can also use R or Scala or Python
3. To change the interpreter language, you can
go to Kernel>Change Kernel, and then change it to R, for example. For now, I’ll change it back to Python. Aside from code cells, there’s also a special
type of cell where you can write stylized text or even embed HTML. These text cells are called “Markdown”
cells, and they’re meant to help you document your code and make your notebook more organized. They’re called “Markdown” because that’s
the name of the syntax used to stylize the text. To create a Markdown cell, click on an existing
code cell, go to Cell>Cell Type>Markdown. This converts it into a Markdown cell that
you can edit. When you’re done, hit “Play” to finalize
it. You can double-click the cell to edit as needed. Jupyter notebooks automatically save every
two minutes, but you can manually save by going to File>Save and Checkpoint. If you click on the arrow to the left of your
notebook name, you can see a number of options, including. Rename. Clone, and Download (where you download as
a notebook). You also have the option to Delete, and Submit
to Spark Cluster. If you click Submit to Spark Cluster it will
show you how to submit to a local Spark cluster. You can also add tags to your notebook, and
create categories for them. And, of course, you can stop your notebooks. However, please remember that when you stop
a notebook, Data Scientist Workbench closes it and you lose any saved variables. So if you open it again by clicking on its
name, you’ll have to run your code again. It’s good practice to stop notebooks that
you know you won’t be using for a while. Always keep in mind that any active notebooks
will continually hog memory on your account, which is hosted in the cloud, so this may
make your other notebooks run a bit more slowly. On the Jupyter page, you can also see. A list of tutorials in the main area,
A sidebar to the right showing your Recent Notebooks, and
Your Recent Data. In this main area, clicking on any of these
tutorials will open up a notebook that has been prepared for you. For example, “Tutorial #1 – Get Data”,
available here in Python. As you click on it, you’ll see the tutorial
also appears under your “Recent Notebooks” on the right-hand side. So, go ahead and create Jupyter Notebooks,
and read through the tutorials to learn all that you can do with Jupyter Notebooks. Thanks for watching!

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