Book Image

NumPy Essentials

By : Leo (Liang-Huan) Chin, Tanmay Dutta, Shane Holloway
Book Image

NumPy Essentials

By: Leo (Liang-Huan) Chin, Tanmay Dutta, Shane Holloway

Overview of this book

In today’s world of science and technology, it’s all about speed and flexibility. When it comes to scientific computing, NumPy tops the list. NumPy gives you both the speed and high productivity you need. This book will walk you through NumPy using clear, step-by-step examples and just the right amount of theory. We will guide you through wider applications of NumPy in scientific computing and will then focus on the fundamentals of NumPy, including array objects, functions, and matrices, each of them explained with practical examples. You will then learn about different NumPy modules while performing mathematical operations such as calculating the Fourier Transform; solving linear systems of equations, interpolation, extrapolation, regression, and curve fitting; and evaluating integrals and derivatives. We will also introduce you to using Cython with NumPy arrays and writing extension modules for NumPy code using the C API. This book will give you exposure to the vast NumPy library and help you build efficient, high-speed programs using a wide range of mathematical features.
Table of Contents (16 chapters)
NumPy Essentials
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface

pandas


pandas is, by far, the most preferable data preprocessing module in Python. The way it handles data is very similar to R. Its data frame not only gives you visually appealing printouts of tables, but also allows you to access data in a more instinctive way. If you are not familiar with R, try to think of using a spreadsheet software such as Microsoft Excel or SQL tables but in a programmatic way. This covers a lot of that what pandas does.

You can download and install pandas from its official site at http://pandas.pydata.org/. A more preferable way is to use pip or install Python scientific distributions, such as Anaconda.

Remember how we used numpy.genfromtxt() to read the csv data in Chapter 4NumPy Core and Libs Submodules? Actually, using pandas to read tables and pass pre-processed data to ndarray (simply performing np.array(data_frame) will transfer a data frame into a multidimensional ndarray) would be a more preferable workflow for analytics. In this section, we are going to...