[(scholar.google.com/scholar?hl=en&as_sdt.. ]

] he NumPy array is useful in several fundamental array concepts.

The NumPy array data structure and its associated metadata fields. Indexing an array with slices and steps. These operations return a ‘view’ of the original data. Indexing an array with masks, scalar coordinates or other arrays, so that it returns a ‘copy’ of the original data. In the bottom example, an array is indexed with other arrays; this broadcasts the indexing arguments before performing the lookup. Vectorization efficiently applies operations to groups of elements. Broadcasting in the multiplication of two-dimensional arrays. Reduction operations act along one or more axes. In this example, an array is summed along select axes to produce a vector, or along two axes consecutively to produce a scalar. Example NumPy code, illustrating some of these concepts.

The NumPy array is a data structure that efficiently stores and accesses multidimensional arrays17 (also known as tensors), and enables a wide variety of scientific computation. It consists of a pointer to memory, along with metadata used to interpret the data stored there, notably ‘data type’, ‘shape’ and ‘strides’

Two Python array packages existed before NumPy. The Numeric package was developed in the mid-1990s and provided array objects and array-aware functions in Python. It was written in C and linked to standard fast implementations of linear algebra3,4. One of its earliest uses was to steer C++ applications for inertial confinement fusion research at Lawrence Livermore National Laboratory5. To handle large astronomical images coming from the Hubble Space Telescope, a reimplementation of Numeric, called Numarray, added support for structured arrays, flexible indexing, memory mapping, byte-order variants, more efficient memory use, flexible IEEE 754-standard error-handling capabilities, and better type-casting rules6. Although Numpyarray was highly compatible with Numeric, the two packages had enough differences that it divided the community; however, in 2005 NumPy emerged as a ‘best of both worlds’ unification—combining the features of Numarray with the small-array performance of Numeric and its rich C API.

Now, 15 years later, NumPy underpins almost every Python library that does scientific or numerical computation including SciPy Matplotlib, pandas, scikit-learn and scikit-image. NumPy is a community-developed, open-source library, which provides a multidimensional Python array object along with array-aware functions that operate on it. Because of its inherent simplicity, the NumPy array is the de facto exchange format for array data in Python. The data type describes the nature of elements stored in an array. An array has a single data type, and each element of an array occupies the same number of bytes in memory. Examples of data types include real and complex numbers (of lower and higher precision), strings, timestamps and pointers to Python objects.

The shape of an array determines the number of elements along each axis, and the number of axes is the dimensionality of the array. For example, a vector of numbers can be stored as a one-dimensional array of shape N, whereas colour videos are four-dimensional arrays of shape (T, M, N, 3).

Strides are necessary to interpret computer memory, which stores elements linearly, as multidimensional arrays. They describe the number of bytes to move forward in memory to jump from row to row, column to column, and so forth. Consider, for example, a two-dimensional array of floating-point numbers with shape (4, 3), where each element occupies 8 bytes in memory. To move between consecutive columns, we need to jump forward 8 bytes in memory, and to access the next row, 3 × 8 = 24 bytes. The strides of that array are therefore (24, 8). NumPy can store arrays in either C or Fortran memory order, iterating first over either rows or columns. This allows external libraries written in those languages to access NumPy array data in memory directly.

Users interact with NumPy arrays using ‘indexing’ (to access subarrays or individual elements), ‘operators’ (for example, +, − and × for vectorized operations and also for matrix multiplication), as well as ‘array-aware functions’; together, these provide an easily readable, expressive, high-level API for array programming while NumPy deals with the underlying mechanics of making operations fast.

Indexing an array returns single elements, subarrays or elements that satisfy a specific condition (Fig. 1b). Arrays can even be indexed using other arrays (Fig. 1c). Wherever possible, indexing that retrieves a subarray returns a ‘view’ on the original array such that data are shared between the two arrays. This provides a powerful way to operate on subsets of array data while limiting memory usage.

To complement the array syntax, NumPy includes functions that perform vectorized calculations on arrays, including arithmetic, statistics and trigonometry. Vectorization—operating on entire arrays rather than their individual elements—is essential to array programming. This means that operations that would take many tens of lines to express in languages such as C can often be implemented as a single, clear Python expression. This results in concise code and frees users to focus on the details of their analysis, while NumPy handles looping over array elements near-optimally—for example, taking strides into consideration to best utilize the computer’s fast cache memory.

When performing a vectorized operation (such as addition) on two arrays with the same shape, it is clear what should happen. Through ‘broadcasting’ NumPy allows the dimensions to differ, and produces results that appeal to intuition. A trivial example is the addition of a scalar value to an array, but broadcasting also generalizes to more complex examples such as scaling each column of an array or generating a grid of coordinates. In broadcasting, one or both arrays are virtually duplicated (that is, without copying any data in memory), so that the shapes of the operands match.

NumPy also includes array-aware functions for creating, reshaping, concatenating and padding arrays; searching, sorting and counting data; and reading and writing files. It provides extensive support for generating pseudorandom numbers, includes an assortment of probability distributions, and performs accelerated linear algebra, using one of several backends such as OpenBLAS18,19 or Intel MKL optimized for the CPUs at hand (see Supplementary Methods for more details).

Altogether, the combination of a simple in-memory array representation, a syntax that closely mimics mathematics, and a variety of array-aware utility functions forms a productive and powerfully expressive array programming language.

Even though NumPy is not part of Python’s standard library, it benefits from a good relationship with the Python developers. Over the years, the Python language has added new features and special syntax so that NumPy would have a more succinct and easier-to-read array notation. However, because it is not part of the standard library, NumPy is able to dictate its own release policies and development patterns.

SciPy and Matplotlib are tightly coupled with NumPy in terms of history, development and use. SciPy provides fundamental algorithms for scientific computing, including mathematical, scientific and engineering routines. Matplotlib generates publication-ready figures and visualizations. The combination of NumPy, SciPy and Matplotlib, together with an advanced interactive environment such as IPython20 or Jupyter21, provides a solid foundation for array programming in Python. The scientific Python ecosystem builds on top of this foundation to provide several, widely used technique-specific libraries that in turn underlie numerous domain-specific projects. NumPy, at the base of the ecosystem of array-aware libraries, sets documentation standards, provides array testing infrastructure and adds build support for Fortran and other compilers.

Author: Abdulakeem Yusuf Ademola