By Cyrille Rossant

Learn IPython for interactive Python programming, high-performance numerical computing, and information visualization.

Overview

• a realistic step by step instructional that allows you to assist you to interchange the Python console with the strong IPython command-line interface;
• Use the IPython laptop to modernize how you have interaction with Python;
• practice hugely effective computations with NumPy and Pandas;
• Optimize your code utilizing parallel computing and Cython.

In Detail

You already use Python as a scripting language, yet do you know it's also more and more used for clinical computing and knowledge research? Interactive programming is vital in such exploratory projects and IPython is the ideal instrument for that. as soon as you’ve learnt it, you won't be capable of stay with out it.

Learning IPython for Interactive Computing and information Visualization is a realistic, hands-on, example-driven instructional to significantly enhance your productiveness in the course of interactive Python periods, and indicates you ways to successfully use IPython for interactive computing and knowledge analysis.

This ebook covers all elements of IPython, from the hugely robust interactive Python console to the numerical and visualization gains which are typically linked to IPython.

You will find out how IPython allows you to practice effective vectorized computations, via examples masking numerical simulations with NumPy, facts research with Pandas, and visualization with Matplotlib. additionally, you will realize how IPython will be with ease used to optimize your code utilizing parallel computing and dynamic compilation in C with Cython.

Learning IPython for Interactive Computing and knowledge Visualization will let you optimize your productiveness in interactive Python sessions.

What you'll study from this book

• Debug your code from the IPython console;
• Benchmark and profile your code from IPython;
• practice effective vectorized computations with NumPy;
• study facts tables with Pandas;
• Create visualizations with Matplotlib;
• Parallelize your code simply with IPython;
• customise IPython and create your individual magic commands;
• speed up your Python code utilizing dynamic C compilation with Cython.

Approach

A useful hands-on advisor which specializes in interactive programming, numerical computing, and knowledge research with IPython.

Who this ebook is written for

This ebook is for Python builders who use Python as a scripting language or for software program improvement, and have an interest in studying IPython for expanding their productiveness in the course of interactive periods within the console.

Knowledge of Python is needed, while no wisdom of IPython is necessary.

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Sample text

Since all three prices are increasing, the is_increasing_trend method should return True. To make this test pass, we need to first add support for storing price history. In the initializer, let us replace the price attribute with a price_history list. price_history = [] [ 30 ] Chapter 2 After making this change, all the tests will now fail, including the ones that were passing before. Only after we complete a few steps will we be able to have the tests pass again. When tests are passing, we can constantly run the tests to make sure we aren't breaking any functionality with our changes.

It checks that its parameter is None. If the parameter is not None, it raises an AssertionError and fails the test. Otherwise, execution continues to the next line. Since that is the last line of the method, the test completes and is marked as a pass. 6. The last segment checks if the module was executed directly from the command line. main() function. This function will scan the current file for all tests and execute them. The reason we need to wrap this function call inside the conditional is because this part does not get executed if the module is imported into another file.

Remember to put the @classmethod decorator on the setupClass method. exchange. [ 36 ] Chapter 2 Otherwise, the tests are simply a matter of constructing a rule and checking the matches method. A (very) quick primer on decorators Decorators are functions that take a function as an input and return another function as an output. Python has a shorthand syntax by which we can say @decorator above a function or method and it will apply the decorator to that function. For more details, check the python documentation or a tutorial.

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