Chapter 1 : A Tour of Interactive Computing with Jupyter and IPython
1.1. Introducing IPython and the Jupyter Notebook
1.2. Getting started with exploratory data analysis in the Jupyter Notebook
1.3. Introducing the multidimensional array in NumPy for fast array computations
1.4. Creating an IPython extension with custom magic commands
1.5. Mastering IPython's configuration system
1.6. Creating a simple kernel for Jupyter
Chapter 2 : Best practices in Interactive Computing
2.1. Learning the basics of the Unix shell
2.2. Using the latest features of Python 3
2.3. Learning the basics of the distributed version control system Git
2.4. A typical workflow with Git branching
2.5. Efficient interactive computing workflows with IPython
2.6. Ten tips for conducting reproducible interactive computing experiments
2.7. Writing high-quality Python code
2.8. Writing unit tests with py.test
2.9. Debugging code with IPython *
Chapter 3 : Mastering the Jupyter Notebook
3.1. Teaching programming in the Notebook with IPython blocks
3.2. Converting a Jupyter notebook to other formats with nbconvert
3.3. Mastering widgets in the Jupyter Notebook
3.4. Creating custom Jupyter Notebook widgets in Python, HTML, and JavaScript
3.5. Configuring the Jupyter Notebook *
3.6. Introducing JupyterLab
Chapter 4 : Profiling and Optimization
4.1. Evaluating the time taken by a command in IPython *
4.2. Profiling your code easily with cProfile and IPython
4.3. Profiling your code line-by-line with line_profiler
4.4. Profiling the memory usage of your code with memory_profiler
4.5. Understanding the internals of NumPy to avoid unnecessary array copying
4.6. Using stride tricks with NumPy
4.7. Implementing an efficient rolling average algorithm with stride tricks
4.8. Processing large NumPy arrays with memory mapping
4.9. Manipulating large arrays with HDF5 *
Chapter 5 : High-Performance Computing
5.1. Knowing Python to write faster code
5.2. Accelerating pure Python code with Numba and just-in-time compilation
5.3. Accelerating array computations with Numexpr
5.4. Wrapping a C library in Python with ctypes
5.5. Accelerating Python code with Cython
5.6. Optimizing Cython code by writing less Python and more C
5.7. Releasing the GIL to take advantage of multi-core processors with Cython and OpenMP
5.8. Writing massively parallel code for NVIDIA graphics cards (GPUs) with CUDA
5.9. Distributing Python code across multiple cores with IPython
5.10. Interacting with asynchronous parallel tasks in IPython
5.11. Performing out-of-core computations on large arrays with Dask
5.12. Trying the Julia programming language in the Jupyter Notebook *
Chapter 6 : Data Visualization
6.1. Using matplotlib styles
6.2. Creating statistical plots easily with seaborn
6.3. Creating interactive Web visualizations with Bokeh and HoloViews
6.4. Visualizing a NetworkX graph in the Notebook with D3.js
6.5. Discovering interactive visualization libraries in the Notebook *
6.6. Creating plots with Altair and the Vega-Lite specification
Chapter 7 : Statistical Data Analysis
7.1. Exploring a dataset with pandas and matplotlib
7.2. Getting started with statistical hypothesis testing — a simple z-test
7.3. Getting started with Bayesian methods
7.4. Estimating the correlation between two variables with a contingency table and a chi-squared test
7.5. Fitting a probability distribution to data with the maximum likelihood method
7.6. Estimating a probability distribution nonparametrically with a kernel density estimation
7.7. Fitting a Bayesian model by sampling from a posterior distribution with a Markov Chain Monte Carlo method
7.8. Analyzing data with the R programming language in the Jupyter Notebook *
Chapter 8 : Machine Learning
8.1. Getting started with scikit-learn
8.2. Predicting who will survive on the Titanic with logistic regression *
8.3. Learning to recognize handwritten digits with a K-nearest neighbors classifier
8.4. Learning from text — Naive Bayes for Natural Language Processing
8.5. Using support vector machines for classification tasks
8.6. Using a random forest to select important features for regression
8.7. Reducing the dimensionality of a dataset with a principal component analysis *
8.8. Detecting hidden structures in a dataset with clustering
Chapter 9 : Numerical Optimization
9.1. Finding the root of a mathematical function *
9.2. Minimizing a mathematical function
9.3. Fitting a function to data with nonlinear least squares
9.4. Finding the equilibrium state of a physical system by minimizing its potential energy
Chapter 10 : Signal Processing
10.1. Analyzing the frequency components of a signal with a Fast Fourier Transform
10.2. Applying a linear filter to a digital signal
10.3. Computing the autocorrelation of a time series
Chapter 11 : Image and Audio Processing
11.1. Manipulating the exposure of an image
11.2. Applying filters on an image
11.3. Segmenting an image
11.4. Finding points of interest in an image
11.5. Detecting faces in an image with OpenCV *
11.6. Applying digital filters to speech sounds
11.7. Creating a sound synthesizer in the Notebook
Chapter 12 : Deterministic Dynamical Systems
12.1. Plotting the bifurcation diagram of a chaotic dynamical system
12.2. Simulating an elementary cellular automaton
12.3. Simulating an ordinary differential equation with SciPy
12.4. Simulating a partial differential equation — reaction-diffusion systems and Turing patterns
Chapter 13 : Stochastic Dynamical Systems
13.1. Simulating a discrete-time Markov chain
13.2. Simulating a Poisson process *
13.3. Simulating a Brownian motion
13.4. Simulating a stochastic differential equation
Chapter 14 : Graphs, Geometry, and Geographic Information Systems
14.1. Manipulating and visualizing graphs with NetworkX *
14.2. Drawing flight routes with NetworkX
14.3. Resolving dependencies in a directed acyclic graph with a topological sort
14.4. Computing connected components in an image
14.5. Computing the Voronoi diagram of a set of points
14.6. Manipulating geospatial data with Cartopy
14.7. Creating a route planner for a road network
Chapter 15 : Symbolic and Numerical Mathematics
15.1. Diving into symbolic computing with SymPy
15.2. Solving equations and inequalities
15.3. Analyzing real-valued functions
15.4. Computing exact probabilities and manipulating random variables
15.5. A bit of number theory with SymPy
15.6. Finding a Boolean propositional formula from a truth table
15.7. Analyzing a nonlinear differential system — Lotka-Volterra (predator-prey) equations
15.8. Getting started with Sage *
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