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 Applying Math with Python
  • Table Of Contents Toc
  • Feedback & Rating feedback
Applying Math with Python

Applying Math with Python

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

Applying Math with Python

4.3 (4)
By: Morley, 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)
close
close
close
Preface
Icon
Preface
Icon
Who this book is for
Icon
What this book covers
Icon
To get the most out of this book
Icon
Download the example code files
Icon
Code in Action
Icon
Conventions used
Icon
Sections
Icon
Getting ready
Icon
How to do it…
Icon
How it works…
Icon
There's more…
Icon
See also
Icon
Get in touch
Icon
Reviews
1
Basic Packages, Functions, and Concepts
Icon
Basic Packages, Functions, and Concepts
Icon
Technical requirements
Icon
Python numerical types
Icon
Basic mathematical functions
Icon
NumPy arrays
Icon
Matrices
Icon
Summary
Icon
Further reading
Lock Free Chapter
2
Mathematical Plotting with Matplotlib
Icon
Mathematical Plotting with Matplotlib
Icon
Technical requirements
Icon
Basic plotting with Matplotlib
Icon
Changing the plotting style
Icon
Adding labels and legends to plots
Icon
Adding subplots
Icon
Saving Matplotlib figures
Icon
Surface and contour plots
Icon
Customizing three-dimensional plots
Icon
Further reading
3
Calculus and Differential Equations
Icon
Calculus and Differential Equations
Icon
Technical requirements
Icon
Working with polynomials and calculus
Icon
Differentiating and integrating symbolically using SymPy
Icon
Solving equations
Icon
Integrating functions numerically using SciPy
Icon
Solving simple differential equations numerically
Icon
Solving systems of differential equations
Icon
Solving partial differential equations numerically
Icon
Using discrete Fourier transforms for signal processing
Icon
Further reading
4
Working with Randomness and Probability
Icon
Working with Randomness and Probability
Icon
Technical requirements
Icon
Selecting items at random
Icon
Generating random data
Icon
Changing the random number generator
Icon
Generating normally distributed random numbers
Icon
Working with random processes
Icon
Analyzing conversion rates with Bayesian techniques
Icon
Estimating parameters with Monte Carlo simulations
Icon
Further reading
5
Working with Trees and Networks
Icon
Working with Trees and Networks
Icon
Technical requirements
Icon
Creating networks in Python
Icon
Visualizing networks
Icon
Getting the basic characteristics of networks
Icon
Generating the adjacency matrix for a network
Icon
Creating directed and weighted networks
Icon
Finding the shortest paths in a network
Icon
Quantifying clustering in a network
Icon
Coloring a network
Icon
Finding minimal spanning trees and dominating sets
Icon
Further reading
6
Working with Data and Statistics
Icon
Working with Data and Statistics
Icon
Technical requirements
Icon
Creating Series and DataFrame objects
Icon
Loading and storing data from a DataFrame
Icon
Manipulating data in DataFrames
Icon
Plotting data from a DataFrame
Icon
Getting descriptive statistics from a DataFrame
Icon
Understanding a population using sampling
Icon
Testing hypotheses using t-tests
Icon
Testing hypotheses using ANOVA
Icon
Testing hypotheses for non-parametric data
Icon
Creating interactive plots with Bokeh
Icon
Further reading
7
Regression and Forecasting
Icon
Regression and Forecasting
Icon
Technical requirements
Icon
Using basic linear regression
Icon
Using multilinear regression
Icon
Classifying using logarithmic regression
Icon
Modeling time series data with ARMA
Icon
Forecasting from time series data using ARIMA
Icon
Forecasting seasonal data using ARIMA
Icon
Using Prophet to model time series data
Icon
Further reading
8
Geometric Problems
Icon
Geometric Problems
Icon
Technical requirements
Icon
Visualizing two-dimensional geometric shapes
Icon
Finding interior points
Icon
Finding edges in an image
Icon
Triangulating planar figures
Icon
Computing convex hulls
Icon
Constructing Bezier curves
Icon
Further reading
9
Finding Optimal Solutions
Icon
Finding Optimal Solutions
Icon
Technical requirements
Icon
Minimizing a simple linear function
Icon
Minimizing a non-linear function
Icon
Using gradient descent methods in optimization
Icon
Using least squares to fit a curve to data
Icon
Analyzing simple two-player games
Icon
Computing Nash equilibria
Icon
Further reading
10
Miscellaneous Topics
chevron up
Icon
Miscellaneous Topics
Icon
Technical requirements
Icon
Keeping track of units with Pint
Icon
Getting ready
Icon
How to do it...
Icon
How it works...
Icon
There's more...
Icon
Accounting for uncertainty in calculations
Icon
Getting ready
Icon
How to do it...
Icon
How it works...
Icon
There's more...
Icon
Loading and storing data from NetCDF files
Icon
Getting ready
Icon
How to do it...
Icon
How it works...
Icon
There's more...
Icon
Working with geographical data
Icon
Getting ready
Icon
How to do it...
Icon
How it works...
Icon
Executing a Jupyter notebook as a script
Icon
Getting ready
Icon
How to do it...
Icon
How it works...
Icon
There's more...
Icon
Validating data
Icon
Getting ready
Icon
How to do it...
Icon
How it works...
Icon
Working with data streams
Icon
Getting ready
Icon
How to do it...
Icon
How it works...
Icon
See also
Icon
Accelerating code with Cython
Icon
Getting ready
Icon
How to do it...
Icon
How it works...
Icon
There's more...
Icon
Distributing computing with Dask
Icon
Getting ready
Icon
How to do it...
Icon
How it works...
Icon
There's more...
11
Other Books You May Enjoy
Icon
Other Books You May Enjoy
Icon
Leave a review - let other readers know what you think

Keeping track of units with Pint

Correctly keeping track of units in calculations can be very difficult, particularly if there are places where different units can be used. For example, it is very easy to forget to convert between different units – feet/inches into meters – or metric prefixes – converting 1 km into 1,000 m, for instance.

In this recipe, we’ll learn how to use the Pint package to keep track of units of measurement in calculations.

Getting ready

For this recipe, we need the Pint package, which can be imported as follows:

import pint

How to do it...

The following steps show you how to use the Pint package to keep track of units in calculations:

  1. First, we need to create a UnitRegistry object:
    ureg = pint.UnitRegistry(system="mks")
  2. To create a quantity with a unit, we multiply the number by the appropriate attribute of the registry object:
    distance = 5280 * ureg.feet
  3. We can change the units of the quantity...
Visually different images
CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Applying Math with Python
End of Section 10
Next Section
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist download Download options
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
Modal Close icon
Modal Close icon