Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Advanced Search Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying IPython Interactive Computing and Visualization Cookbook
  • Table Of Contents Toc
  • Feedback & Rating feedback
IPython Interactive Computing and Visualization Cookbook

IPython Interactive Computing and Visualization Cookbook

By : Cyrille Rossant
4.5 (13)
Buy this Book
close
close
IPython Interactive Computing and Visualization Cookbook

IPython Interactive Computing and Visualization Cookbook

4.5 (13)
By: Cyrille Rossant
Buy this Book

Overview of this book

Intended to anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, hobbyists... Basic knowledge of Python/NumPy is recommended. Some skills in mathematics will help you understand the theory behind the computational methods.
Table of Contents (22 chapters)
close
close
close
IPython Interactive Computing and Visualization Cookbook
Icon
IPython Interactive Computing and Visualization Cookbook
Credits
Icon
Credits
About the Author
Icon
About the Author
About the Reviewers
Icon
About the Reviewers
www.PacktPub.com
Icon
www.PacktPub.com
Preface
Icon
Preface
Free Chapter
1
A Tour of Interactive Computing with IPython
Icon
A Tour of Interactive Computing with IPython
Icon
Introduction
Icon
Introducing the IPython notebook
Icon
Getting started with exploratory data analysis in IPython
Icon
Introducing the multidimensional array in NumPy for fast array computations
Icon
Creating an IPython extension with custom magic commands
Icon
Mastering IPython's configuration system
Icon
Creating a simple kernel for IPython
2
Best Practices in Interactive Computing
Icon
Best Practices in Interactive Computing
Icon
Introduction
Icon
Choosing (or not) between Python 2 and Python 3
Icon
Efficient interactive computing workflows with IPython
Icon
Learning the basics of the distributed version control system Git
Icon
A typical workflow with Git branching
Icon
Ten tips for conducting reproducible interactive computing experiments
Icon
Writing high-quality Python code
Icon
Writing unit tests with nose
Icon
Debugging your code with IPython
3
Mastering the Notebook
Icon
Mastering the Notebook
Icon
Introduction
Icon
Teaching programming in the notebook with IPython blocks
Icon
Converting an IPython notebook to other formats with nbconvert
Icon
Adding custom controls in the notebook toolbar
Icon
Customizing the CSS style in the notebook
Icon
Using interactive widgets – a piano in the notebook
Icon
Creating a custom JavaScript widget in the notebook – a spreadsheet editor for pandas
Icon
Processing webcam images in real time from the notebook
4
Profiling and Optimization
Icon
Profiling and Optimization
Icon
Introduction
Icon
Evaluating the time taken by a statement in IPython
Icon
Profiling your code easily with cProfile and IPython
Icon
Profiling your code line-by-line with line_profiler
Icon
Profiling the memory usage of your code with memory_profiler
Icon
Understanding the internals of NumPy to avoid unnecessary array copying
Icon
Using stride tricks with NumPy
Icon
Implementing an efficient rolling average algorithm with stride tricks
Icon
Making efficient array selections in NumPy
Icon
Processing huge NumPy arrays with memory mapping
Icon
Manipulating large arrays with HDF5 and PyTables
Icon
Manipulating large heterogeneous tables with HDF5 and PyTables
5
High-performance Computing
Icon
High-performance Computing
Icon
Introduction
Icon
Accelerating pure Python code with Numba and just-in-time compilation
Icon
Accelerating array computations with Numexpr
Icon
Wrapping a C library in Python with ctypes
Icon
Accelerating Python code with Cython
Icon
Optimizing Cython code by writing less Python and more C
Icon
Releasing the GIL to take advantage of multicore processors with Cython and OpenMP
Icon
Writing massively parallel code for NVIDIA graphics cards (GPUs) with CUDA
Icon
Writing massively parallel code for heterogeneous platforms with OpenCL
Icon
Distributing Python code across multiple cores with IPython
Icon
Interacting with asynchronous parallel tasks in IPython
Icon
Parallelizing code with MPI in IPython
Icon
Trying the Julia language in the notebook
6
Advanced Visualization
chevron up
Icon
Advanced Visualization
Icon
Introduction
Icon
Making nicer matplotlib figures with prettyplotlib
Icon
Creating beautiful statistical plots with seaborn
Icon
Creating interactive web visualizations with Bokeh
Icon
Visualizing a NetworkX graph in the IPython notebook with D3.js
Icon
Converting matplotlib figures to D3.js visualizations with mpld3
Icon
Getting started with Vispy for high-performance interactive data visualizations
7
Statistical Data Analysis
Icon
Statistical Data Analysis
Icon
Introduction
Icon
Exploring a dataset with pandas and matplotlib
Icon
Getting started with statistical hypothesis testing – a simple z-test
Icon
Getting started with Bayesian methods
Icon
Estimating the correlation between two variables with a contingency table and a chi-squared test
Icon
Fitting a probability distribution to data with the maximum likelihood method
Icon
Estimating a probability distribution nonparametrically with a kernel density estimation
Icon
Fitting a Bayesian model by sampling from a posterior distribution with a Markov chain Monte Carlo method
Icon
Analyzing data with the R programming language in the IPython notebook
8
Machine Learning
Icon
Machine Learning
Icon
Introduction
Icon
Getting started with scikit-learn
Icon
Predicting who will survive on the Titanic with logistic regression
Icon
Learning to recognize handwritten digits with a K-nearest neighbors classifier
Icon
Learning from text – Naive Bayes for Natural Language Processing
Icon
Using support vector machines for classification tasks
Icon
Using a random forest to select important features for regression
Icon
Reducing the dimensionality of a dataset with a principal component analysis
Icon
Detecting hidden structures in a dataset with clustering
9
Numerical Optimization
Icon
Numerical Optimization
Icon
Introduction
Icon
Finding the root of a mathematical function
Icon
Minimizing a mathematical function
Icon
Fitting a function to data with nonlinear least squares
Icon
Finding the equilibrium state of a physical system by minimizing its potential energy
10
Signal Processing
Icon
Signal Processing
Icon
Introduction
Icon
Analyzing the frequency components of a signal with a Fast Fourier Transform
Icon
Applying a linear filter to a digital signal
Icon
Computing the autocorrelation of a time series
11
Image and Audio Processing
Icon
Image and Audio Processing
Icon
Introduction
Icon
Manipulating the exposure of an image
Icon
Applying filters on an image
Icon
Segmenting an image
Icon
Finding points of interest in an image
Icon
Detecting faces in an image with OpenCV
Icon
Applying digital filters to speech sounds
Icon
Creating a sound synthesizer in the notebook
12
Deterministic Dynamical Systems
Icon
Deterministic Dynamical Systems
Icon
Introduction
Icon
Plotting the bifurcation diagram of a chaotic dynamical system
Icon
Simulating an elementary cellular automaton
Icon
Simulating an ordinary differential equation with SciPy
Icon
Simulating a partial differential equation – reaction-diffusion systems and Turing patterns
13
Stochastic Dynamical Systems
Icon
Stochastic Dynamical Systems
Icon
Introduction
Icon
Simulating a discrete-time Markov chain
Icon
Simulating a Poisson process
Icon
Simulating a Brownian motion
Icon
Simulating a stochastic differential equation
14
Graphs, Geometry, and Geographic Information Systems
Icon
Graphs, Geometry, and Geographic Information Systems
Icon
Introduction
Icon
Manipulating and visualizing graphs with NetworkX
Icon
Analyzing a social network with NetworkX
Icon
Resolving dependencies in a directed acyclic graph with a topological sort
Icon
Computing connected components in an image
Icon
Computing the Voronoi diagram of a set of points
Icon
Manipulating geospatial data with Shapely and basemap
Icon
Creating a route planner for a road network
15
Symbolic and Numerical Mathematics
Icon
Symbolic and Numerical Mathematics
Icon
Introduction
Icon
Diving into symbolic computing with SymPy
Icon
Solving equations and inequalities
Icon
Analyzing real-valued functions
Icon
Computing exact probabilities and manipulating random variables
Icon
A bit of number theory with SymPy
Icon
Finding a Boolean propositional formula from a truth table
Icon
Analyzing a nonlinear differential system – Lotka-Volterra (predator-prey) equations
Icon
Getting started with Sage
Index
Icon
Index

Converting matplotlib figures to D3.js visualizations with mpld3

The mpld3 library automatically converts matplotlib figures to interactive D3.js visualizations. In this recipe, we will see how to use this library in the notebook.

Getting ready

To install the mpld3 library, you can just type pip install mpld3 in a terminal. See also the main website at http://mpld3.github.io.

How to do it…

  1. First, we load NumPy and matplotlib as usual:

    In [1]: import numpy as np
        import matplotlib.pyplot as plt
        %matplotlib inline

  2. Then, we enable the mpld3 figures in the notebook with a single function call:

    In [2]: from mpld3 import enable_notebook
        enable_notebook()

  3. Now, let's create a scatter plot with matplotlib:

    In [3]: X = np.random.normal(0, 1, (100, 3))
        color = np.random.random(100)
        size = 500 * np.random.random(100)
        plt.scatter(X[:,0], X[:,1], c=color,
                    s=size, alpha=0.5, linewidths=2)
        plt.grid(color='lightgray', alpha=0.7)

    The matplotlib...

Limited Time Offer

$10p/m for 3 months

Get online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech and supported with AI assistants
Claim Now Or Sign In
End of Section 7
Next Section
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful
Order Page Read Now

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon

Create a Note

Modal Close icon
You need to login to use this feature.

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Delete Note

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Edit Note

Modal Close icon
Write a note (max 255 characters)
Cancel
Update Note