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SciPy Recipes
SciPy Recipes

SciPy Recipes: A cookbook with over 110 proven recipes for performing mathematical and scientific computations

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Profile Icon Martins Profile Icon Ruben Oliva Ramos Profile Icon V Kishore Ayyadevara
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NZ$56.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3 (3 Ratings)
Paperback Dec 2017 386 pages 1st Edition
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Arrow left icon
Profile Icon Martins Profile Icon Ruben Oliva Ramos Profile Icon V Kishore Ayyadevara
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NZ$56.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3 (3 Ratings)
Paperback Dec 2017 386 pages 1st Edition
eBook
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SciPy Recipes

Getting Started with NumPy

In this chapter, we will learn the following recipes:

  • Creating NumPy arrays
  • Querying and changing the shape of an array
  • Storing and retrieving NumPy arrays
  • Indexing
  • Operations on arrays
  • Using marked arrays to represent invalid data
  • Using object arrays to store heterogeneous data
  • Defining, symbolically, a function operating on arrays

Introduction

We are now ready to start exploring NumPy, the fundamental package upon which the whole scientific Python stack is built. This chapter presents an introduction to the essential features of NumPy that are used in day-to-day scientific and data computations.

Built-in Python data structures, such as lists and dictionaries, are ill suited for scientific and data-oriented computing and their use results in programs that are significantly slower than numerical code written in compiled languages such as C, C++, and Fortran. NumPy was created to address this problem, and solves it by defining specialized array-oriented objects and methods designed for efficient numerical and data computing. The main data structure defined in NumPy with this purpose is ndarray, which represents a multidimensional array of data.

Objects of ndarray type differ from the native Python data structures...

Creating NumPy arrays

There are several ways to create objects of ndarray type. The recipes in this chapter provide a comprehensive list of the possibilities.

How to do it…

Let's move on to learn how an array can be created from a list.

Creating an array from a list

To create an array from an explicit list, use the following code:

x = np.array([2, 3.5, 5.2, 7.3])

This will assign to x the following array object:

array([ 2. , 3.5, -1. , 7.3, 0. ])

Notice that integer array entries are converted to floating point values. NumPy arrays are homogeneous, that is...

Querying and changing the shape of an array

Since we have learnt various ways to create an array, we can now definitely learn how to query and change the shape of an array.

How to do it...

The shape of an array is stored in the shape field of the ndarray object, as shown in the following example:

x = np.array([[1,2,3,4,5,6],[7,8,9,10,11,12]])
x.shape

The shape field of an ndarray object contains a tuple with the size of each of the dimensions of the array, so the preceding code will produce the following output:

(2, 6)

It is possible to assign a to the field shape, which has the effect of reshaping the array, as shown in the following example:

x.shape = (4,3)

This statement will change the array to the following:

array...

Storing and retrieving NumPy arrays

Most realistic applications deal with large datasets and will require results to be stored in persistent media, such as a hard disk. NumPy arrays can be stored in either text or binary format and binary files can be optionally compressed. We start the section by showing you a recipe to store files in text format.

How to do it...

Let us proceed with getting our queries about storing and retrieving NumPy arrays resolved.

Storing a NumPy array in text format

To store a single NumPy array to the disk in text format, use the savetxt...

Indexing

In this section, we address the methods NumPy offers for access and modification of data in an array. Python itself provides a rich set of indexing modes, and NumPy extends these with a number of methods suitable for numerical computations.

To access items of an array a, NumPy, as Python, uses the a[...] bracket notation. In the background, NumPy defines the __getitem__, __setitem__, and __deleteitem__ methods to do the requested operations on the array items. The arguments inside the brackets are expressions that specify the locations of the items we want to access. For example, to access the element at position (1,2) of the two-dimensional array a, we use the expression a[1,2]. Since indexing starts at 0, the expression refers to the item in the second row and third column of the array.

In NumPy, it is common to use notation, as in the preceding example, to index items...

Operations on arrays

NumPy defines a rich set of operations and functions for the ndarray type. NumPy defines the notion of a universal function, abbreviated as ufunc, which is a function object that can be applied to arbitrary arrays. Universal functions are objects of ufunc type, and NumPy provides a vast collection of built-in ufunc functions, covering all computations needed in scientific and data applications.

A ufunc is specialized towards the element by element application of a function. That is, if x is an array object, and f is a ufunc, the f(x) expression will apply the function f to every element of array x, and return a new object with the resulting values.

A ufunc follows a strict functional protocol; applying the f function to x will never change the elements of the x arrays themselves, but return a new array with the values of f applied to each element of x. User...

Using masked arrays to represent invalid data

A common issue arising when working with data or doing computations is the presence of invalid values. Such values can arise either from data that is missing or as a result of operations that resulted in inconsistent data. We may also want to mask array elements that we know would raise errors in further computations.

Masked arrays in NumPy are supported by the masked_array class, which is defined in the numpy.ma module. To work with masked arrays, we need first to import this module, which can be done with the following code:

import numpy.ma as ma

How to do it...

In the following sections, we will get into the details of creating a masked array from the following:

  • An explicit...

Using object arrays to store heterogeneous data

Up to this point, we only considered arrays that contained native elementary data types.

How to do it...

If we need an array containing heterogeneous data, we can create an array with arbitrary Python objects as elements, as shown in the following code:

x = np.array([2.5, 'a string', [2,4], {'a':0, 'b':1}])

This will result in an array with the np.object ;data type, as indicated in the output line as follows:

array([2.5, 'string', [2, 4], {'a': 0, 'b': 1}], dtype=object)

If the objects to be contained in the array are not known at construction time, we can create an empty array of objects with the following code...

Defining, symbolically, a function operating on arrays

Anybody that has written numerical code will know that a common source of mistakes is the definition of functions that evaluate a complicated formula. One way around this problem is to use a package for symbolic computations, and we will take advantage of sympy, which is a compact Python symbolic package.

Getting ready

If you are using Anaconda, sympy is already installed on your system. Otherwise, you will have to install it by using pip3 install sympy.

To see the full results of the following recipe, we assume that the reader is running Jupyter. Before getting started, run the following code in a Jupyter cell:

from sympy import *
init_printing(use_latex=True)

This...

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Key benefits

  • Covers a wide range of data science tasks using SciPy, NumPy, pandas, and matplotlib
  • Effective recipes on advanced scientific computations, statistics, data wrangling, data visualization, and more
  • A must-have book if you're looking to solve your data-related problems using SciPy, on-the-go

Description

With the SciPy Stack, you get the power to effectively process, manipulate, and visualize your data using the popular Python language. Utilizing SciPy correctly can sometimes be a very tricky proposition. This book provides the right techniques so you can use SciPy to perform different data science tasks with ease. This book includes hands-on recipes for using the different components of the SciPy Stack such as NumPy, SciPy, matplotlib, and pandas, among others. You will use these libraries to solve real-world problems in linear algebra, numerical analysis, data visualization, and much more. The recipes included in the book will ensure you get a practical understanding not only of how a particular feature in SciPy Stack works, but also of its application to real-world problems. The independent nature of the recipes also ensure that you can pick up any one and learn about a particular feature of SciPy without reading through the other recipes, thus making the book a very handy and useful guide.

Who is this book for?

Python developers, aspiring data scientists, and analysts who want to get started with scientific computing using Python will find this book an indispensable resource. If you want to learn how to manipulate and visualize your data using the SciPy Stack, this book will also help you. A basic understanding of Python programming is all you need to get started.

What you will learn

  • • Get a solid foundation in scientific computing using Python
  • • Master common tasks related to SciPy and associated libraries such as NumPy, pandas, and matplotlib
  • • Perform mathematical operations such as linear algebra and work with the statistical and probability functions in SciPy
  • • Master advanced computing such as Discrete Fourier Transform and K-means with the SciPy Stack
  • • Implement data wrangling tasks efficiently using pandas
  • • Visualize your data through various graphs and charts using matplotlib
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Mastering Numerical Computing with NumPy
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Table of Contents

10 Chapters
Getting to Know the Tools Chevron down icon Chevron up icon
Getting Started with NumPy Chevron down icon Chevron up icon
Using Matplotlib to Create Graphs Chevron down icon Chevron up icon
Data Wrangling with pandas Chevron down icon Chevron up icon
Matrices and Linear Algebra Chevron down icon Chevron up icon
Solving Equations and Optimization Chevron down icon Chevron up icon
Constants and Special Functions Chevron down icon Chevron up icon
Calculus, Interpolation, and Differential Equations Chevron down icon Chevron up icon
Statistics and Probability Chevron down icon Chevron up icon
Advanced Computations with SciPy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3
(3 Ratings)
5 star 66.7%
4 star 0%
3 star 33.3%
2 star 0%
1 star 0%
sk Feb 21, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I found this book while trying to learn Python for Data Science and Analytics. As the name of the book suggests, SciPy 'Recipies', has a huge collection of data related operations, that can be performed in Python using NumPy, Pandas and SciPy libraries. Being someone who prefers hands-on approach to learn a particular topic, this book has been of huge help with tons of examples. Though this is not a theoretically heavy book, it gave enough context about the topic in hand, for me to clearly understand various functions used throughout the book. Few topics like Sparse Matrices with the example of how they are used in the real-world scenarios of Recommendation systems were excellently explained by the authors along with code implementation.Overall, its a highly recommended book for those who love the hands-on approach to learn Python for Data Science.
Amazon Verified review Amazon
Rakesh Feb 20, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great book !! clear explanation and runbook like instructions will help you to gets handson learning experience in analytics.
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roudan Aug 30, 2018
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
it is a scipy book, but 1/2 book talks about numpy,pandas, matplotlib. finally it starts scipy, the examples are not good, not much explanation on how to use, why use it, most of time, just list the functions and their input which I can find from scipy documents. so I was disappointed then go bought a different one, "mastering scipy" from Francisco J. Blanco-Silva. It is much better. I really love that " mastering scipy" book from Francisco.
Amazon Verified review Amazon
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