Data analysis skills are now essential in scientific research, engineering, finance, economics, journalism, and many other domains. With its high accessibility and vibrant ecosystem, Python is one of the most appreciated open source languages for data science.
This book is a beginner-friendly introduction to the Python data analysis platform, focusing on IPython (Interactive Python) and its Notebook. While IPython is an enhanced interactive Python terminal specifically designed for scientific computing and data analysis, the Notebook is a graphical interface that combines code, text, equations, and plots in a unified interactive environment.
The first edition of Learning IPython for Interactive Computing and Data Visualization was published in April 2013, several months before the release of IPython 1.0. This new edition targets IPython 4.0, released in August 2015. In addition to reflecting the novelties of this new version of IPython, the present book is also more accessible to non-programmer beginners. The first chapter contains a brand new crash course on Python programming, as well as detailed installation instructions.
Since the first edition of this book, IPython's popularity has grown significantly, with an estimated user base of several millions of people and ongoing collaborations with large companies like Microsoft, Google, IBM, and others. The project itself has been subject to important changes, with a refactoring into a language-independent interface called the Jupyter Notebook, and a set of backend kernels in various languages. The Notebook is no longer reserved to Python; it can now also be used with R, Julia, Ruby, Haskell, and many more languages (50 at the time of this writing!).
The Jupyter project has received significant funding in 2015 from the Leona M. and Harry B. Helmsley Charitable Trust, the Gordon and Betty Moore Foundation, and the Alfred P. Sloan Foundation, which will allow the developers to focus on the growth and maturity of the project in the years to come.
Here are a few references:
Home page for the Jupyter project at http://jupyter.org/
Announcement of the funding for Jupyter at https://blog.jupyter.org/2015/07/07/jupyter-funding-2015/
Detail of the project's grant at https://blog.jupyter.org/2015/07/07/project-jupyter-computational-narratives-as-the-engine-of-collaborative-data-science/
Chapter 1, Getting Started with IPython, is a thorough and beginner-friendly introduction to Anaconda (a popular Python distribution), the Python language, the Jupyter Notebook, and IPython.
Chapter 2, Interactive Data Analysis with pandas, is a hands-on introduction to interactive data analysis and visualization in the Notebook with pandas, matplotlib, and seaborn.
Chapter 3, Numerical Computing with NumPy, details how to use NumPy for efficient computing on multidimensional numerical arrays.
Chapter 4, Interactive Plotting and Graphical Interfaces, explores many capabilities of Python for interactive plotting, graphics, image processing, and interactive graphical interfaces in the Jupyter Notebook.
Chapter 5, High-Performance and Parallel Computing, introduces the various techniques you can employ to accelerate your numerical computing code, namely parallel computing and compilation of Python code.
Chapter 6, Customizing IPython, shows how IPython and the Jupyter Notebook can be extended for customized use-cases.
The following software is required for the book:
Anaconda with Python 3
Windows, Linux, or OS X can be used as a platform
This book targets anyone who wants to analyze data or perform numerical simulations of mathematical models.
Since our world is becoming more and more data-driven, knowing how to analyze data effectively is an essential skill to learn. If you're used to spreadsheet programs like Microsoft Excel, you will appreciate Python for its much larger range of analysis and visualization possibilities. Knowing this general-purpose language will also let you share your data and analysis with other programs and libraries.
In conclusion, this book will be useful to students, scientists, engineers, analysts, journalists, statisticians, economists, hobbyists, and all data enthusiasts.
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "Run it with a command like bash Anaconda3-2.3.0-Linux-x86_64.sh (if necessary, replace the filename by the one you downloaded)."
A block of code is set as follows:
def load_ipython_extension(ipython):
"""This function is called when the extension is loaded.
It accepts an IPython InteractiveShell instance.
We can register the magic with the `register_magic_function`
method of the shell instance."""
ipython.register_magic_function(cpp, 'cell')Any command-line input or output is written as follows:
$ python Python 3.4.3 |Anaconda 2.3.0 (64-bit)| (default, Jun 4 2015, 15:29:08) [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux Type "help", "copyright", "credits" or "license" for more information. >>>
New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "To create a new notebook, click on the New button, and select Notebook (Python 3)."
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