See how the output is printed directly on the notebook.
This is how we can do an interactive project by seeing the output at each step of the process.
Also notice that when you ran the cell, the first line which had an In [] next to it has now changed to In [1] .
The number inside the square brackets indicates the order in which the cell was ran; the first cell has a 1 because it was the first cell that we ran.
We can run each cell individually at anytime and those numbers will change.
Let’s take an example.
We’re going to set up 2 cells, each one with a different print statement.
We’ll run the second print statement first following by the first print statement.
Take a look at how the number inside the squared brackets changed as a result.
When you have multiple cells in your Notebook and you run the cells in order, you can share your variables and imports across cells.
This makes it easy to separate out your code into related sections without needing to re-create variable at every cell.
Just be sure that you run your cells in the proper order so that all your variables are created before usage.
Adding Descriptive Text to Your NotebookJupyter Notebooks come with a great set of tools for adding descriptive text to your notebooks.
Not only can you write comments, but you can also add titles, lists, bold, and italics.
All of this is done in the super easy Markdown format.
The first thing to do is to change the cell type — click the drop down menu that says “Code” on it and change it to “Markdown”.
This changes the type of cell we are working with.
Let’s try out a couple of the options.
We can create titles using the # symbol.
A single # will make the biggest title and adding more #s will create a smaller and smaller title.
We can italicise our text using a single star on either side or bold it using a double star.
Creating a list is easy with a simple dash – and space beside each list item.
Interactive Data ScienceLet’s do a quick running example of how to create an interactive Data Science project.
This notebook and code comes from an actual project I did.
I start out with a Markdown cell and put up a title with the biggest header by using a single # .
I then create a list and description of all the important libraries I’m about to import.
Next comes the first code cell which imports all of the relevant libraries.
This will be standard Python Data Science code except for 1 additional item: in-order to see your Matplotlib visualisations directly within the notebook, you’ll need to add the following line: %matplotlib inline .
Next I’m going to import a dataset from a CSV file and print out the first 10 items.
Notice in the screenshot below how Jupyter automatically shows the .
head() function’s output as a table — Jupyter works beautifully with the Pandas library!Now we’ll create a figure and plot it directly in our notebook.
Since we’ve added the line %matplotlib inline above, anytime we run a plt.
show() our figure will be displayed directly in our notebook!Also notice how all of the variables from previous cells, particularly the dataframe which we read from CSV, carries over to future cells as long as we pressed the “Run” button on those previous cells.
Voila!.That’s the easy way to create an interactive Data Science project!The MenusThe Jupyter server has several menus that you can use to get the most out of your project.
These menus enable you to interact with your notebook, as well as access documentation for popular Python libraries and export your project into a format for external presentation.
The File menu allows you to create, copy, rename, and save your notebooks to file.
The most notable item in the File menu is the Download as drop down menu which lets you download your notebook in a variety of formats including pdf, html, and slides — perfect for creating a presentation!The Edit menu lets you do the good’ol can cut, copy, and paste of code.
You can also reorder cells here, perhaps if you’re creating a notebook for an interactive presentation and want to show your audience things in a certain order.
The View menu lets you play around with things like displaying line numbers and modifying the toolbar.
The best feature in this menu is definitely the Cell Toolbar where you can add tags, notes, and attachments to each cell.
You can even select the formatting you would want for this cell if you turned the notebook into a slide show!The Insert menu is just for inserting cells above or below the currently selected cell.
The Cell menu is where you go to run your cells in a specific order or change the cell type.
Finally you have the Help menu which is one of my personal favourites!.The help menu gives you direct access to important documentation.
You’ll be able to learn about all the Jupyter Notebook shortcuts to speed up your workflow.
You also get convenient links to the documentation of some of the most important Python libraries including Numpy, Scipy, Matplotlib, and Pandas!Like to learn?Follow me on twitter where I post all about the latest and greatest AI, Technology, and Science!.Connect with me on LinkedIn too!Recommended ReadingWant to learn more about Data Science?.The Python Data Science Handbook book is the best resource out there for learning how to do real Data Science with Python!And just a heads up, I support this blog with Amazon affiliate links to great books, because sharing great books helps everyone!.As an Amazon Associate I earn from qualifying purchases.
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