Book Image

Mastering Numerical Computing with NumPy

By : Umit Mert Cakmak, Tiago Antao, Mert Cuhadaroglu
Book Image

Mastering Numerical Computing with NumPy

By: Umit Mert Cakmak, Tiago Antao, Mert Cuhadaroglu

Overview of this book

NumPy is one of the most important scientific computing libraries available for Python. Mastering Numerical Computing with NumPy teaches you how to achieve expert level competency to perform complex operations, with in-depth coverage of advanced concepts. Beginning with NumPy's arrays and functions, you will familiarize yourself with linear algebra concepts to perform vector and matrix math operations. You will thoroughly understand and practice data processing, exploratory data analysis (EDA), and predictive modeling. You will then move on to working on practical examples which will teach you how to use NumPy statistics in order to explore US housing data and develop a predictive model using simple and multiple linear regression techniques. Once you have got to grips with the basics, you will explore unsupervised learning and clustering algorithms, followed by understanding how to write better NumPy code while keeping advanced considerations in mind. The book also demonstrates the use of different high-performance numerical computing libraries and their relationship with NumPy. You will study how to benchmark the performance of different configurations and choose the best for your system. By the end of this book, you will have become an expert in handling and performing complex data manipulations.

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Table of Contents (11 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Working with NumPy Arrays
Working with NumPy Arrays
Technical requirements
Why do we need NumPy?
Who uses NumPy?
Introduction to vectors and matrices
Basics of NumPy array objects
NumPy array operations
Working with multidimensional arrays
Indexing, slicing, reshaping, resizing, and broadcasting
Summary
2
Linear Algebra with NumPy
Linear Algebra with NumPy
Vector and matrix mathematics
What's an eigenvalue and how do we compute it?
Computing the norm and determinant
Solving linear equations
Computing gradient
Summary
3
Exploratory Data Analysis of Boston Housing Data with NumPy Statistics
Exploratory Data Analysis of Boston Housing Data with NumPy Statistics
Loading and saving files
Exploring our dataset
Looking at basic statistics
Computing histograms
Explaining skewness and kurtosis
Trimmed statistics
Box plots
Computing correlations
Summary
4
Predicting Housing Prices Using Linear Regression
Predicting Housing Prices Using Linear Regression
Supervised learning and linear regression
Independent and dependent variables
Hyperparameters
Loss and error functions
Univariate linear regression with gradient descent
Using linear regression to model housing prices
Summary
5
Clustering Clients of a Wholesale Distributor Using NumPy
Clustering Clients of a Wholesale Distributor Using NumPy
Unsupervised learning and clustering
Hyperparameters
The loss function
Implementing our algorithm for a single variable
Modifying our algorithm
Summary
6
NumPy, SciPy, Pandas, and Scikit-Learn
NumPy, SciPy, Pandas, and Scikit-Learn
NumPy and SciPy
NumPy and pandas
SciPy and scikit-learn
Summary
7
Advanced Numpy
Advanced Numpy
NumPy internals
Summary
8
Overview of High-Performance Numerical Computing Libraries
Overview of High-Performance Numerical Computing Libraries
BLAS and LAPACK
ATLAS
Intel Math Kernel Library
OpenBLAS
Configuring NumPy with low-level libraries using AWS EC2
Compute-intensive tasks for benchmarking
Summary
9
Performance Benchmarks
Performance Benchmarks
Why do we need a benchmark?
Preparing for a performance benchmark
Results
Summary
10
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Loading and saving files

In this section, you will learn how to load/import your data and save it. There are many different ways of loading data, and the right way depends on your file type. You can load/import text files, SAS/Stata files, HDF5 files, and many others. HDF (Hierarchical Data Format) is one of the popular data formats which is used to store and organize large amounts of data and it is very useful while working with a multidimensional homogeneous arrays. For example, Pandas library has a very handy class named as HDFStore where you can easily work with HDF5 files. While working on data science projects, you will most likely see many of these types of files, but in this book, we will cover the most popular ones, such as NumPy binary files, text files (.txt), and comma-separated values (.csv) files.

If you have a large dataset in memory and on disk to manage, you can...

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