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  • Book Overview & Buying Applying Math with Python
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Applying Math with Python

Applying Math with Python

By : Sam Morley
4.3 (4)
Buy this Book
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Applying Math with Python

Applying Math with Python

4.3 (4)
By: Sam Morley
Buy this Book

Overview of this book

Python, one of the world's most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way. These core capabilities help programmers pave the way for building exciting applications in various domains, such as machine learning and data science, using knowledge in the computational mathematics domain. The book teaches you how to solve problems faced in a wide variety of mathematical fields, including calculus, probability, statistics and data science, graph theory, optimization, and geometry. You'll start by developing core skills and learning about packages covered in Python’s scientific stack, including NumPy, SciPy, and Matplotlib. As you advance, you'll get to grips with more advanced topics of calculus, probability, and networks (graph theory). After you gain a solid understanding of these topics, you'll discover Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code. By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.
Table of Contents (12 chapters)
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Preface
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Preface
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Who this book is for
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What this book covers
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To get the most out of this book
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Download the example code files
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Code in Action
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Conventions used
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Sections
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Getting ready
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How to do it…
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How it works…
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There's more…
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See also
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Get in touch
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Reviews
1
Basic Packages, Functions, and Concepts
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Basic Packages, Functions, and Concepts
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Technical requirements
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Python numerical types
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Basic mathematical functions
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NumPy arrays
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Matrices
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Summary
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Further reading
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2
Mathematical Plotting with Matplotlib
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Mathematical Plotting with Matplotlib
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Technical requirements
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Basic plotting with Matplotlib
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Changing the plotting style
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Adding labels and legends to plots
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Adding subplots
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Saving Matplotlib figures
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Surface and contour plots
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Customizing three-dimensional plots
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Further reading
3
Calculus and Differential Equations
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Calculus and Differential Equations
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Technical requirements
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Working with polynomials and calculus
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Differentiating and integrating symbolically using SymPy
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Solving equations
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Integrating functions numerically using SciPy
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Solving simple differential equations numerically
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Solving systems of differential equations
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Solving partial differential equations numerically
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Using discrete Fourier transforms for signal processing
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Further reading
4
Working with Randomness and Probability
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Working with Randomness and Probability
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Technical requirements
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Selecting items at random
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Generating random data
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Changing the random number generator
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Generating normally distributed random numbers
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Working with random processes
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Analyzing conversion rates with Bayesian techniques
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Estimating parameters with Monte Carlo simulations
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Further reading
5
Working with Trees and Networks
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Working with Trees and Networks
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Technical requirements
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Creating networks in Python
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Visualizing networks
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Getting the basic characteristics of networks
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Generating the adjacency matrix for a network
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Creating directed and weighted networks
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Finding the shortest paths in a network
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Quantifying clustering in a network
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Coloring a network
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Finding minimal spanning trees and dominating sets
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Further reading
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Working with Data and Statistics
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Working with Data and Statistics
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Technical requirements
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Creating Series and DataFrame objects
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Loading and storing data from a DataFrame
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Manipulating data in DataFrames
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Plotting data from a DataFrame
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Getting descriptive statistics from a DataFrame
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Understanding a population using sampling
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Testing hypotheses using t-tests
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Testing hypotheses using ANOVA
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Testing hypotheses for non-parametric data
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Creating interactive plots with Bokeh
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Further reading
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Regression and Forecasting
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Regression and Forecasting
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Technical requirements
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Using basic linear regression
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Using multilinear regression
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Classifying using logarithmic regression
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Modeling time series data with ARMA
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Forecasting from time series data using ARIMA
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Forecasting seasonal data using ARIMA
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Using Prophet to model time series data
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Further reading
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Geometric Problems
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Geometric Problems
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Technical requirements
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Visualizing two-dimensional geometric shapes
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Finding interior points
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Finding edges in an image
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Triangulating planar figures
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Computing convex hulls
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Constructing Bezier curves
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Further reading
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Finding Optimal Solutions
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Finding Optimal Solutions
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Technical requirements
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Minimizing a simple linear function
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Minimizing a non-linear function
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Using gradient descent methods in optimization
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Using least squares to fit a curve to data
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Analyzing simple two-player games
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Computing Nash equilibria
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Further reading
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Miscellaneous Topics
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Miscellaneous Topics
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Technical requirements
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Keeping track of units with Pint
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Getting ready
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How to do it...
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How it works...
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There's more...
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Accounting for uncertainty in calculations
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Getting ready
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How to do it...
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How it works...
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There's more...
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Loading and storing data from NetCDF files
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Getting ready
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How to do it...
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How it works...
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There's more...
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Working with geographical data
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Getting ready
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How to do it...
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How it works...
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Executing a Jupyter notebook as a script
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Getting ready
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How to do it...
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How it works...
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Validating data
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Getting ready
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How to do it...
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How it works...
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Working with data streams
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Getting ready
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How to do it...
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How it works...
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See also
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Accelerating code with Cython
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Getting ready
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How to do it...
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How it works...
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There's more...
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Distributing computing with Dask
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Getting ready
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How to do it...
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How it works...
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There's more...
11
Other Books You May Enjoy
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Other Books You May Enjoy
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Leave a review - let other readers know what you think

Exploring Python numerical types

Python provides basic numerical types such as arbitrarily sized integers and floating-point numbers (double precision) as standard, but it also provides several additional types that are useful in specific applications where precision is especially important. Python also provides (built-in) support for complex numbers, which is useful for some more advanced mathematical applications. Let’s take a look at some of these different numerical types, starting with the Decimal type.

Decimal type

For applications that require decimal digits with accurate arithmetic operations, use the Decimal type from the decimal module in the Python Standard Library:

from decimal import Decimal
num1 = Decimal('1.1')
num2 = Decimal('1.563')
num1 + num2  # Decimal('2.663')

Performing this calculation with float objects gives the result 2.6630000000000003, which includes a small error arising from the fact that certain...

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