Book Image

IPython Interactive Computing and Visualization Cookbook - Second Edition

By : Cyrille Rossant
Book Image

IPython Interactive Computing and Visualization Cookbook - Second Edition

By: Cyrille Rossant

Overview of this book

Python is one of the leading open source platforms for data science and numerical computing. IPython and the associated Jupyter Notebook offer efficient interfaces to Python for data analysis and interactive visualization, and they constitute an ideal gateway to the platform. IPython Interactive Computing and Visualization Cookbook, Second Edition contains many ready-to-use, focused recipes for high-performance scientific computing and data analysis, from the latest IPython/Jupyter features to the most advanced tricks, to help you write better and faster code. You will apply these state-of-the-art methods to various real-world examples, illustrating topics in applied mathematics, scientific modeling, and machine learning. The first part of the book covers programming techniques: code quality and reproducibility, code optimization, high-performance computing through just-in-time compilation, parallel computing, and graphics card programming. The second part tackles data science, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics.
Table of Contents (19 chapters)
IPython Interactive Computing and Visualization CookbookSecond Edition
Contributors
Preface
Index

Creating statistical plots easily with seaborn


seaborn is a library that builds on top of Matplotlib and Pandas to provide easy-to-use statistical plotting routines. In this recipe, we give a few examples, adapted from the official documentation, of the types of statistical plot that can be created with seaborn.

How to do it...

  1. Let's import NumPy, Matplotlib, and seaborn:

    >>> import numpy as np
        from scipy import stats
        import matplotlib.pyplot as plt
        import seaborn as sns
        %matplotlib inline
  2. seaborn comes with built-in datasets, which are useful when making demos. The tips dataset contains bills and tips for taxi journeys:

    >>> tips = sns.load_dataset('tips')
        tips
  3. seaborn implements easy-to-use functions to visualize the distribution of datasets. Here, we plot the histogram, Kernel Density Estimation (KDE), and a gamma distribution fit for our dataset:

    >>> # We create two subplots sharing the same y axis.
        f, (ax1, ax2) = plt.subplots(1, 2,
         ...