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Numpy Beginner's Guide (Update)

Numpy Beginner's Guide (Update)

By : Ivan Idris
2 (1)
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Numpy Beginner's Guide (Update)

Numpy Beginner's Guide (Update)

2 (1)
By: Ivan Idris

Overview of this book

This book is for the scientists, engineers, programmers, or analysts looking for a high-quality, open source mathematical library. Knowledge of Python is assumed. Also, some affinity, or at least interest, in mathematics and statistics is required. However, I have provided brief explanations and pointers to learning resources.
Table of Contents (16 chapters)
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14
C. NumPy Functions' References
15
Index

Preface

Scientists, engineers, and quantitative data analysts face many challenges nowadays. Data scientists want to be able to perform numerical analysis on large datasets with minimal programming effort. They also want to write readable, efficient, and fast code that is as close as possible to the mathematical language they are used to. A number of accepted solutions are available in the scientific computing world.

The C, C++, and Fortran programming languages have their benefits, but they are not interactive and considered too complex by many. The common commercial alternatives, such as MATLAB, Maple, and Mathematica, provide powerful scripting languages that are even more limited than any general-purpose programming language. Other open source tools similar to MATLAB exist, such as R, GNU Octave, and Scilab. Obviously, they too lack the power of a language such as Python.

Python is a popular general-purpose programming language that is widely used in the scientific community. You can access legacy C, Fortran, or R code easily from Python. It is object-oriented and considered to be of a higher level than C or Fortran. It allows you to write readable and clean code with minimal fuss. However, it lacks an out-of-the-box MATLAB equivalent. That's where NumPy comes in. This book is about NumPy and related Python libraries, such as SciPy and matplotlib.

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