jupyters interface prerequisites for coding – python programming for finance

jupyters interface prerequisites for coding - python programming for finance
What is Jupyter, and why do you care? After all, Jupyter has never become a buzzword like data science, artificial intelligence, or Web 2.0. Unlike those big abstractions, Jupyter is very concrete. It’s an open source project, a piece of software, that does specific things.

But without attracting the hype, Jupyter Notebooks are revolutionizing the way engineers and data scientists work together. If all important work is collaborative, the most important tools we have are tools for collaboration, tools that make working together more productive.

That’s what Jupyter is, in a nutshell: it’s a tool for collaborating. It’s built for writing and sharing code and text, within the context of a web page. The code runs on a server, and the results are turned into HTML and incorporated into the page you’re writing. That server can be anywhere: on your laptop, behind your firewall, or on the public internet. Your page contains your thoughts, your code, and the results of running the code.


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Code is never just code. It’s part of a thought process, an argument, even an experiment. This is particularly true for data analysis, but it’s true for almost any application. Jupyter lets you build a “lab notebook” that shows your work: the code, the data, the results, along with your explanation and reasoning. As IBM puts it, Jupyter lets you build a “computational narrative that distills data into insights.” Data means nothing if you can’t turn it into insight, if you can’t explore it, share it, and discuss it. Data analysis means little if you can’t explore and experiment with someone else’s results. Jupyter is a tool for exploring, sharing, and discussing.

A notebook is easily shareable. You can save it and send it as an attachment, so someone else can open the notebook with Jupyter. You can put the notebook in a GitHub repository and let others read it there; GitHub automatically renders the notebook to a static web page. GitHub users can download (clone) their own copy of the notebook and any supporting files so they can expand on your work: they can inspect the results, modify the code, and see what happens. It’s a lot easier to maintain an up-to-date archive on GitHub than to hand distribute your code, data, supporting files, and results. You can go further by using container technology, such as Docker, to package your notebook, a notebook server, any libraries you need, your data, and a stripped-down operating system, into a single downloadable object.

Sharing can be as public as you want. You can run a Jupyter server on your laptop, largely inaccessible to anyone else. You can run a multi-user Jupyter server, JupyterHub, behind your corporate firewall. You can even push Jupyter Notebooks into the cloud. GitHub and GitLab (a host-it-yourself git server) automatically convert notebooks into static HTML for access over the web, and platforms like Binder allow others to run your code in the cloud. They can experiment with it and modify it, all within the context of a private instance.

While Jupyter’s roots are in Python (it evolved from IPython Notebooks), it is now multi-lingual. The name itself comes from three languages: Ju(lia) + Py(thon) + (e)R. Language support comes by way of modular kernels, and there are kernels in existence or under way for many programming languages. In addition to Julia, Python, and R, there are Jupyter kernels for JavaScript, Java, Scala, Go, C#, Ruby, Kotlin, and Haskell, and more than 50 other languages. The Toree kernel is especially useful: it supports the Scala programming language and the Spark distributed computing platform (plus Python and R), simplifying the task of building large, data-intensive distributed applications. O’Reilly has Orioles (cloud-based notebooks with video tracks added) demonstrating Scala and Go.

I’ve said that sharing becomes even easier when Jupyter is combined with Docker. One of the biggest problems facing developers in any programming language is installing the software and libraries you need to run someone else’s code. Version incompatibilities and operating system incompatibilities make your life painful; it can literally take days just to install the software needed to run a complex project. That pain can be eliminated by combining Jupyter with Docker. Docker lets you build a container that includes everything needed to run your notebook. So, when you share the container, which is as simple as sharing a link, you’re not just sharing your project: you’re sharing all the dependencies needed to run that project, in a form that’s known to work.

When you combine Jupyter with containers and a source management system like GitHub, you get a platform for collaboration: on coding, on data analysis, on visualization, on anything that can be done in most programming languages.

jupyters interface prerequisites for coding – python programming for finance