Chapter
Chapter 1: Getting to Know the Tools
Installing Anaconda on Windows
Installing Anaconda on macOS
Installing Anaconda on Linux
Checking the Anaconda installation
Installing SciPy from a binary distribution on Windows
Installing the SciPy stack
Installing SciPy from a binary distribution on macOS
Installing the Xcode command-line tools
Installing the SciPy stack
Installing SciPy from source on Linux
Installing the SciPy stack
Installing optional packages with conda
Installing packages with pip
Setting up a virtual environment with conda
Creating a virtual environment for development with conda
Creating a conda environment with a different version of a package
Using conda environments to run different versions of Python
Creating virtual environments with venv
Running SciPy in a script
Chapter 2: Getting Started with NumPy
Creating an array from a list
Specifying the data type for elements in an array
Creating an empty array with a given shape
Creating arrays of zeros and ones with a single value
Creating arrays with equally spaced values
Creating an array by repeating elements
Creating an array by tiling another array
Creating an array with the same shape as another array
Using object arrays to store heterogeneous data
Querying and changing the shape of an array
Storing and retrieving NumPy arrays
Storing a NumPy array in text format
Storing a NumPy array in CSV format
Loading an array from a text file
Storing a single array in binary format
Storing several arrays in binary format
Loading arrays stored in NPY binary format
Accessing sub arrays using slices
Selecting subarrays using an index list
Indexing with Boolean arrays
Computing a function for all elements of an array
Computing matrix products
Using masked arrays to represent invalid data
Creating a masked array from an explicit mask
Creating a masked array from a condition
Using object arrays to store heterogeneous data
Defining, symbolically, a function operating on arrays
Chapter 3: Using Matplotlib to Create Graphs
Creating two-dimensional plots of functions and data
Generating multiple plots in a single figure
Setting line styles and markers
Using different backends to display graphs
Generating histograms and box plots
Creating three-dimensional plots
Generating interactive displays in the Jupyter Notebook
Object-oriented graph creation using Artist objects
Creating a map with cartopy
Chapter 4: Data Wrangling with pandas
Creating DataFrame objects
Inserting and deleting columns to a DataFrame
Inserting and deleting rows to a DataFrame
Selecting items by row indexes and column labels
Selecting items by integer location
Selecting items using mixed indexing
Accessing, selecting, and modifying data
Selecting rows using Boolean selection
Reading and storing data in different formats
Working with CSV, text/tabular, and format data
Reading a CSV file into a DataFrame
Specifying the index column when reading a CSV file
Reading and writing data in Excel format
Reading and writing JSON files
Reading HTML data from the web
Accessing CSV data on the web
Reading and writing from/to SQL databases
Data displays employing different kinds of visual representation
How to apply numerical functions and operations to Series and DataFrame objects
Computing statistical functions on Series and DataFrame objects
Retrieving summary descriptive statistics
Calculating variance and standard deviation
How to sort data in Series and DataFrame objects
Performing merging, joins, concatenation, and grouping
Merging data from multiple pandas objects
Chapter 5: Matrices and Linear Algebra
Matrix operations and functions on two-dimensional arrays
Solving linear systems using matrices
Calculating the null space of a matrix
Calculating the LU decompositions of a matrix
Calculating the QR decomposition of a matrix
Calculating the eigenvalue and eigenvector of a matrix
Calculating the Jordan form of a matrix
Calculating the singular value decomposition of a matrix
Computations on top of a sparse matrix
Chapter 6: Solving Equations and Optimization
Non-linear equations and systems
System of equations and how to solve it
Choosing the solver used to find the solution of equations
Solving constrained non-linear optimization problems in several variables
Solving one-dimensional optimization problems
Solving multidimensional non-linear equations using the Newton-Krylov method
Solving multidimensional non-linear equations using the Anderson method
Finding the best linear fit for a set of data
Doing non-linear regression for a set of data
Chapter 7: Constants and Special Functions
Physical and mathematical constants available in SciPy
Using constants in the CODATA database
Orthogonal polynomials functions
The Riemann zeta function
The Bessel and Struve functions
Chapter 8: Calculus, Interpolation, and Differential Equations
Computing integrals using the Newton-Cotes method
Computing integrals using a Gaussian quadrature
Computing integrals with weighting functions
Computing multiple integrals
Computing a polynomial interpolation for a set of data points
Finding a cubic spline that interpolates a set of data
Defining a B-spline for a given set of control points
Solving a one-dimensional ordinary differential equation
Solving a system of ordinary differential equations
Solving differential equations and systems with parameters
Using ode and the objected-oriented interface to solve differential equations
Chapter 9: Statistics and Probability
Computing the probability mass function of a discrete random variable
Binomial discrete distribution
Multinomial discrete distribution
Visualizing the probability mass function
Computing the probability density function of a continuous random variable
Computing the cumulative distribution function for a random variable
Computing the values of inverse probabilities associated with a random variable
Computing the average, standard deviation, and higher moments of a distribution
Average and standard deviation of a distribution
Calculating the moments of a distribution
Computing probabilities associated with the multivariate Gaussian distribution
Getting started with simulation
Computing the summary statistics of a dataset
Chapter 10: Advanced Computations with SciPy
Discrete Fourier transforms
Computing the discrete Fourier transform (DFT) of a data series using the FFT algorithm
Computing the inverse DFT of a data series
Computing signal construction
Getting started with filters
Computing the DFT for two-dimensional data
How to find the DFT of the derivative of a function
Computing the convolution of two functions
Computing pairwise distances from a dataset, using different distance metrics
How to identify neighborhoods and nearest neighbors for a dataset and a given metric
Nearest neighbors regression
SciPy Recipes
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