Table of Contents
Overview
== “Numeric Python”
– Open Source Python Library for array-based calculations
– First realeased in 1995 as Numeric (first implementation of a Python matrix package); 2006 as NumPy
– allows easy handling of vectors, matrices or generally large multidimensional arrays
– NumPys operators and functions are optimized for multidimensional array operations and evaluate particularly efficiently
– written in C
– compatible to various Python libraries (Matplotlib, Pandas, SciPy)
– SciPy extends the power of NumPy with other useful features, such as: minimization, regression, Fourier transform…
Numpy Applications
Python and Science
– The programming language Python is used very intensively in the application area of scientific research
– NumPy was designed for scientific calculations
The ndarray data structure
– Core functionality of NumPy is based on the data structure “ndarray
– Components: a pointer to a contiguous storage area together with metadata describing the data stored in it
– All elements of an array must be of the same data type
Technical terms
– shape == Defines the dimensions in each index value (“axis”) of the array and the number of axes
– strides == describe for each axis, how many bytes you have to jump in linear memory, if an index belonging to this axis is increased by 1
– reshaping == Altering the shape of a provided array
– slicing == Setting up smaller subarrays within a given larger array
– splitting + joining == Splitting one array into many and combining multiple arrays into one single array
– indexing == Setting the value of individual array elements
Multiplication
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