NumPy Recipes
NumPy Recipes
A practical introduction to NumPy
About the Book
NumPy Recipes takes practical approach to the basics of NumPy
This book is primarily aimed at developers who have at least a small amount of Python experience, who wish to use the NumPy library for data analysis, machine learning, image or sound processing, or any other mathematical or scientific application. It only requires a basic understanding of Python programming.
Detailed examples show how to create arrays to optimise storage different types of information, and how to use universal functions, vectorisation, broadcasting and slicing to process data efficiently. Also contains an introduction to file i/o and data visualisation with Matplotlib.
About the Author
Table of Contents
-
Foreword
- Who is this book for?
- About the author
- Keep in touch
-
1 Introduction to NumPy
- 1.1 Installing NumPy
- 1.2 What is NumPy?
- 1.3 NumPy vs Python lists
- 1.4 Advantages of NumPy
- 1.5 NumPy universal functions
- 1.6 Compatibility with other libraries
-
2 Anatomy of a NumPy array
- 2.1 NumPy arrays compared to lists
-
2.2 Printing the characteristics of an array
- 2.2.1 Rank
- 2.2.2 Shape
- 2.2.3 Size
- 2.2.4 Data type
- 2.2.5 Item size
- 2.2.6 Data location
- 2.3 Array rank examples
-
2.4 Data types
- 2.4.1 Integers
- 2.4.2 Unsigned integers
- 2.4.3 Floating point values
- 2.4.4 Complex number formats
- 2.4.5 Boolean
- 2.4.6 List of main data types
-
3 Creating arrays
- 3.1 Creating an array of zeroes
- 3.2 Creating other fixed content arrays
- 3.3 Choosing the data type
- 3.4 Creating multi-dimensional arrays
- 3.5 Creating like arrays
- 3.6 Creating an array from a Python list
- 3.7 Controlling the type with the array function
- 3.8 array function anti-patterns
- 3.9 Creating a value series with arange
- 3.10 Rounding error problem with arange
- 3.11 Create a sequence of a specific length with linspace
- 3.12 Making linspace more like arange using the endpoint parameter
- 3.13 Obtaining the linspace step size
- 3.14 Other sequence generators
- 3.15 Creating an identity matrix
- 3.16 Creating an eye matrix
- 3.17 Using vectorisation
-
4 Vectorisation
- 4.1 Performing simple maths on an array
- 4.2 Vectorisation with other data types
- 4.3 Vectorisation with multi-dimensional arrays
- 4.4 Expressions using two arrays
- 4.5 Expressions using two multi-dimensional arrays
- 4.6 More complex expressions
- 4.7 Using conditional operators
- 4.8 Combining conditional operators
-
5 Universal functions
- 5.1 Example universal function - sqrt
- 5.2 Example universal function of two arguments - power
-
5.3 Summary of ufuncs
- 5.3.1 Maths operations
- 5.3.2 Trigonometric functions
- 5.3.3 Bit manipulation
- 5.3.4 Comparison functions
- 5.3.5 Logical functions
- 5.3.6 Min and max
- 5.3.7 Float functions
-
5.4 ufunc methods
- 5.4.1 Reduce
- 5.4.2 Accumulation
-
5.5 Optional keyword arguments for ufuncs
- 5.5.1 out
- 5.5.2 where
-
6 Indexing, slicing and broadcasting
-
6.1 Indexing an array
- 6.1.1 Indexing in 1 dimension
- 6.1.2 Indexing in 2 dimensions
- 6.1.3 Picking a row or column in 2-dimensions
- 6.1.4 Indexing in 3 dimensions
- 6.1.5 Picking a row or column in a 3D array
- 6.1.6 Picking a matrix in a 3D array
-
6.2 Slicing an array
- 6.2.1 Slicing lists - a recap
- 6.2.2 Slicing 1D NumPy arrays
- 6.2.3 Slicing a 2D array
- 6.2.4 Slicing a 3D array
- 6.2.5 Full slices
- 6.3 Slices vs indexing
- 6.4 Views
-
6.5 Broadcasting
- 6.5.1 Broadcasting from 1 to 2 dimensions
- 6.5.2 Broadcasting 1 to 3 dimensions
- 6.5.3 Broadcasting 2 to 3 dimensions
- 6.6 Broadcasting rules
- 6.7 Broadcasting a column vector
- 6.8 Broadcasting a row vector and a column vector
- 6.9 Broadcasting scalars
- 6.10 Efficient broadcasting
- 6.11 Fancy indexing
-
6.1 Indexing an array
-
7 Array manipulation functions
- 7.1 Copying an array
- 7.2 Changing the type of an array
- 7.3 Changing the shape of an array
-
7.4 Splitting arrays
- 7.4.1 Splitting along different axes
- 7.4.2 Unequal splits
- 7.4.3 Alternative functions
-
7.5 Stacking arrays
- 7.5.1 Stacking 2-dimensional arrays
-
8 File input and output
- 8.1 CSV format
-
8.2 Writing CSV data
- 8.2.1 Adding a header or footer
- 8.2.2 Changing the line separator
- 8.2.3 Compressing the output file
-
8.3 Reading CSV data
- 8.3.1 Skipping header or footer
- 8.3.2 Reading compressing file
-
9 Using Matplotlib with NumPy
- 9.1 Installing Matplotlib
- 9.2 Plotting a histogram
- 9.3 Plotting functions
- 9.4 Plotting functions with NumPy
- 9.5 Creating a heatmap
-
10 Reference
-
10.1 Data types
- 10.1.1 Unsigned integer sizes and ranges
- 10.1.2 Signed integer sizes and ranges
- 10.1.3 Integer wrap-around
- 10.1.4 Float characteristics
- 10.1.5 Complex characteristics
-
10.1 Data types
The Leanpub 60 Day 100% Happiness Guarantee
Within 60 days of purchase you can get a 100% refund on any Leanpub purchase, in two clicks.
Now, this is technically risky for us, since you'll have the book or course files either way. But we're so confident in our products and services, and in our authors and readers, that we're happy to offer a full money back guarantee for everything we sell.
You can only find out how good something is by trying it, and because of our 100% money back guarantee there's literally no risk to do so!
So, there's no reason not to click the Add to Cart button, is there?
See full terms...
Earn $8 on a $10 Purchase, and $16 on a $20 Purchase
We pay 80% royalties on purchases of $7.99 or more, and 80% royalties minus a 50 cent flat fee on purchases between $0.99 and $7.98. You earn $8 on a $10 sale, and $16 on a $20 sale. So, if we sell 5000 non-refunded copies of your book for $20, you'll earn $80,000.
(Yes, some authors have already earned much more than that on Leanpub.)
In fact, authors have earnedover $14 millionwriting, publishing and selling on Leanpub.
Learn more about writing on Leanpub
Free Updates. DRM Free.
If you buy a Leanpub book, you get free updates for as long as the author updates the book! Many authors use Leanpub to publish their books in-progress, while they are writing them. All readers get free updates, regardless of when they bought the book or how much they paid (including free).
Most Leanpub books are available in PDF (for computers) and EPUB (for phones, tablets and Kindle). The formats that a book includes are shown at the top right corner of this page.
Finally, Leanpub books don't have any DRM copy-protection nonsense, so you can easily read them on any supported device.
Learn more about Leanpub's ebook formats and where to read them
