#### 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.
Table of Contents (11 chapters)
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
Advanced Numpy
Overview of High-Performance Numerical Computing Libraries
Performance Benchmarks
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# Computing histograms

A histogram is a visual representation of the distribution of numerical data. Karl Pearson first introduced this concept more than a century ago. A histogram is a kind of bar chart that is used for continuous data, while a bar chart visually represents categorical variables. As a first step, you need to divide your entire range of values into a series of intervals (bins). Each bin has to be adjacent and none of them can overlap. In general, bin sizes are equal, and the rule of thumb for the number of bins to include is 5–20. This means that if you have more than 20 bins, your graph will be hard to read. On the contrary, if you have fewer than 5 bins, then your graph will give very little insight into the distribution of your data:

`In [48]: %matplotlib notebook         %matplotlib notebook         import matplotlib.pyplot as plt         NOX = samples...`