In 2011, ten years after the first release of IPython, the IPython Notebook was introduced. IPython is a Python library that was originally meant to improve the default interactive console provided by Python, and to make it scientist-friendly. This makes the transition from research to production much easier. Today, the main advantage of Python, and one of the main reasons why it is so popular, is that it brings scientific computing features to a general-purpose language that is used in many research areas and industries. However, a wide ecosystem of tools has been developed to bring Python to the level of these other scientific computing systems. These tools are dedicated to scientific computing, whereas Python is a general-purpose programming language that was not initially designed for scientific computing. Competing open-source platforms include Julia, R, Octave, and Scilab.
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Other competing platforms include commercial software such as MATLAB, Maple, Mathematica, Excel, SPSS, SAS, and others. In the last twenty years, Python has been increasingly used for scientific computing and data analysis as well.
In addition, Python comes with an extremely rich standard library (the batteries included philosophy), which covers string processing, Internet Protocols, operating system interfaces, and many other domains. It is also a robust language for large-scale software development. This easy-to-use language is commonly used by system administrators as a glue language, linking various system components together. Python is a high-level, open-source, general-purpose programming language originally conceived by Guido van Rossum in the late 1980s (the name was inspired by the British comedy Monty Python's Flying Circus). In this introduction, we will give a broad overview of Python, IPython, Jupyter, and the scientific Python ecosystem. Creating an IPython extension with custom magic commands Introducing the multidimensional array in NumPy for fast array computations Getting started with exploratory data analysis in the Jupyter Notebook Introducing IPython and the Jupyter Notebook In this chapter, we will cover the following topics: The reason for this is that changing the default python3 to python3.5 broke the apt-get updating process.Chapter 1 : A Tour of Interactive Computing with Jupyter and IPython *Edit: Reverted the default python3 to python3.4 but created a python3.5 kernel entry for the notebook. sys.version_info(major=3, minor=5, micro=0, releaselevel='final', serial=0)
#Install ipython all install#
install Personal Package Archive: ppa:fkrull/deadsnakes.
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If you haven't already made the switch by installing from the source, and you want to install Python 3.5 system wide, you can use the following steps to upgrade to Python using the fkrull/deadsnakes ppa. Since this site is primarily here to provide help in the use of IPython, and host discussions about current and future features, make sure that it is clear how comments are relevant to the original post or the previous comment. The visitors to /r/IPython come from very different backgrounds and some even have little programming experience.
Help chat room (The new official help line).With new additions like the IPython Notebook, which runs in a browser, and the Notebook Viewer, IPython is a scientist's best friend. Since then it has grown in popularity, and gaining to the ability to make XKCD styled plots using matplotlib hasn't hurt. IPython (now Jupyter) was originally started by Fernando Perez as a way to improve the Python work flow for scientific computing.
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