In this section, you will start with the first step in statistical analysis by calculating the basic statistics of your dataset. Even though NumPy has limited built-in statistical functions, we can leverage its usage with SciPy. Before we start, let's describe how our analysis will flow. All of the feature columns and label columns are numerical, but you may have noticed that the Charles River dummy variable (CHAS) column has binary values (0,1), which means that it's actually encoded from categorical data. When you analyze your dataset, you can separate your columns into Categorical and Numerical. In order to analyze them all together, one type should be converted to another. If you have a categorical value and you want to convert it into a numeric value, you can do so by converting each category to a numerical value. This process is called...
Mastering Numerical Computing with NumPy
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Mastering Numerical Computing with NumPy
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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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Current Title:
Mastering Numerical Computing with NumPy
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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