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Learning IPython for Interactive Computing and Data Visualization, Second Edition

Learning IPython for Interactive Computing and Data Visualization, Second Edition

By : Cyrille Rossant
4.5 (12)
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Learning IPython for Interactive Computing and Data Visualization, Second Edition

Learning IPython for Interactive Computing and Data Visualization, Second Edition

4.5 (12)
By: Cyrille Rossant

Overview of this book

Python is a user-friendly and powerful programming language. IPython offers a convenient interface to the language and its analysis libraries, while the Jupyter Notebook is a rich environment well-adapted to data science and visualization. Together, these open source tools are widely used by beginners and experts around the world, and in a huge variety of fields and endeavors. This book is a beginner-friendly guide to the Python data analysis platform. After an introduction to the Python language, IPython, and the Jupyter Notebook, you will learn how to analyze and visualize data on real-world examples, how to create graphical user interfaces for image processing in the Notebook, and how to perform fast numerical computations for scientific simulations with NumPy, Numba, Cython, and ipyparallel. By the end of this book, you will be able to perform in-depth analyses of all sorts of data.
Table of Contents (8 chapters)
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Image processing

Several libraries bring image processing capabilities to Python. SciPy, the main scientific Python library, contains a few image processing routines. scikit-image is another library dedicated to image processing. We will show an example in this section, inspired by the one at http://scikit-image.org/docs/dev/auto_examples/plot_equalize.html.

When using the Anaconda distribution, scikit-image can be installed with conda install scikit-image.

Let's import some packages.

In [1]: import numpy as np
        import skimage
        from skimage import img_as_float
        import skimage.filters as skif
        from skimage.color import rgb2gray
        import skimage.data as skid
        import skimage.exposure as skie
        from ipywidgets import interact
        import matplotlib.pyplot as plt
        import seaborn
        %matplotlib inline

There are a few test images in scikit-image. Here is one:

In [2]: chelsea = skid.chelsea()
In [3]: chelsea.shape, chelsea.dtype
Out...

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