Practitioner of data science is less about memorizing the tool orĬommand you should use for every possible situation, and more about Topic you’ve found yourself searching before. Such as online documentation, mailing-list threads, and Stack OverflowĪnswers contain a wealth of information, even (especially?) if it is a In data science it’s the same: searchable web resources Member, or colleague with a computer problem, most of the time it’s lessĪ matter of knowing the answer as much as knowing how to quickly find an When a technologically minded person is asked to help a friend, family Tools discussed here to be the most transformative contributions of If you read no other section in this chapter, read this one: I find the Window and manually open this address ( in this If the browser does not open automatically, you can open a Navigate to the listed local URL the exact address will depend on your Upon issuing the command, your default browser should automatically open and The IPython Notebook is running at: Use Control-C to stop this server and shut down all kernels. Serving notebooks from local directory: /Users/jakevdp/. That log will look something like this: $ jupyter notebook It immediately spits out a log showing what it is doing This command will launch a local web server that will be visible to To start this process (known as a “kernel”), run the following command in your system shell: $ jupyter notebook Though the IPython notebook is viewed and edited through your webīrowser window, it must connect to a running Python process in order toĮxecute code. People open them and execute the code on their own systems. Furthermore, these documents can be saved in a way that lets other Visualizations, mathematical equations, JavaScript widgets, and much Notebook allows the user to include formatted text, static and dynamic As well as executing Python/IPython statements, the IPython shell, and builds on it a rich set of dynamic displayĬapabilities. The Jupyter notebook is a browser-based graphical interface to the The features of the notebook that make it useful in understanding data The more useful “magic commands” that can speed up common tasks inĬreating and using data science code. Next, we will go into a bit more depth on some of Science, focusing especially on the syntax it offers beyond the standardįeatures of Python. Some of the IPython features that are useful to the practice of data This chapter will start by stepping through IPython is about using Python effectively for interactive scientific andĭata-intensive computing. Manuscript for this book was composed as a set of IPython notebooks. As an example of the usefulness of the notebookįormat, look no further than the page you are reading: the entire Notebook structure, which encompasses notebooks for Julia, R, and other The IPython notebook is actually a special case of the broader Jupyter Provides a browser-based notebook that is useful for development,Ĭollaboration, sharing, and even publication of data science results. In addition, IPython isĬlosely tied with the Jupyter project, which Provides a number of useful syntactic additions to the language we’llĬover the most useful of these additions here. The engine of our data science task, you might think of IPython as theĪs well as being a useful interactive interface to Python, IPython also “Tools for the entire lifecycle of research computing.” If Python is Has since grown into a project aiming to provide, in Perez’s words, Started in 2001 by Fernando Perez as an enhanced Python interpreter, and IPython (short for Interactive Python) was IPython plus a text editor (in my case, Emacs orĪtom depending on my mood). Surprises people: my preferred environment is Often asked which one I use in my own work. There are many options for development environments for Python, and I’m
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