#### 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.
Preface
Free Chapter
Working with NumPy Arrays
Linear Algebra with NumPy
Exploratory Data Analysis of Boston Housing Data with NumPy Statistics
Predicting Housing Prices Using Linear Regression
Clustering Clients of a Wholesale Distributor Using NumPy
NumPy, SciPy, Pandas, and Scikit-Learn
Overview of High-Performance Numerical Computing Libraries
Performance Benchmarks
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# Preparing for a performance benchmark

For each instance and configuration, navigate to your Home directory and create a folder named py_scripts:

Create a file named linalg_benchmark.py with the following command and paste the contents:

After pasting the contents, type :, then type wq!, and Enter to save and quit:

Now you can run this file with the following command:

For Anaconda distribution, you will run the script with the following command:

# Performance with BLAS and LAPACK

Here, you will run the linalg_benchmark.py script with BLAS and LAPACK. Connect to the t2.micro instance where you have this configuration, and run the script as shown in the previous section.

The following are the results of the run with dim =...