Numpy Tutorial

NumPy is a Python library used to work with the Python programming language. It is open-source software, having many contributors to its development and working. It provides support for large, multi-dimensional arrays and matrices. It also supports a massive collection of higher-level mathematical functions to apply functions on the arrays. Numeric was used previously in place of NumPy. It was designed and developed by Jim Hugunin along with other developers in the year 2005. NumPy was an invention by Travis Oliphant. He incorporated characteristics of the competing Numarray into numeric along with a few other extensive alterations. NumFOCUS fiscally sponsored the NumPy project. 

NumPy focuses on the CPython reference implementation of Python. It is a non-optimizing bytecode interpreter. Mathematical algorithms that are programmed for CPython are run and executed slower than other compiled equivalents. NumPy addresses this limitation of slow execution problems partly. It provides multidimensional arrays, functions, and operators which can operate efficiently on arrays. These operations require alterations of some of the code, such as inner loops, using NumPy. 

NumPy gives Python the same functionality as that of MATLAB as both of these libraries are interpreted, and both these libraries allow the developer to write faster-compiling programs. This holds right as far as most operations functions that are coded are on arrays or matrices and not scalars. In comparison to NumPy, MATLAB boasts a high number of additional toolboxes, such as Simulink. On the other hand, NumPy is intrinsically integrated with Python programming. It is a modern and complete programming language that is most popularly used. NumPy also includes complimentary Python packages such as SciPy and MAtplotlib libraries. SciPy adds additional MATLAB-like functionalities, while Matplotlib works as a plotting package to provide MATLAB-like plotting functionalities. Both NumPy and MATLAB rely on BLAS and LAPACK internally for computing linear algebra efficiently. 

Computer vision is a Python binding widely used library that OpenCV uses NumPy arrays to store and functio on the data.  Images with multiple channels are represented in a simple three-dimensional arrays, indexing, slicing or masking with different arrays are really efficient ways to access particular pixels of an image. The NumPy array is considered the universal data structure in OpenCV for images, extracted feature points, filter kernels and many more hugely simplifies the workflow of programming and decoding. 

The ndarray data structure “ndarray” is the core functionality of the NumPy for an n-dimensional array data structure. These arrays have stridden views on memory. On the contrary to Python’s built-in list data structure, these arrays are homogenously typed, which means that all the elements in the single array must be of the same type. The type of arrays can be viewed in-memory buffers allocated by C/C++, Cython, and Fortan extensions to the CPython interpreter without having the need to copy data always, which gives it a degree of compatibility with the pre-existing numerical libraries. SciPy package exploits this functionality, wrapping a number of similar kinds of libraries, notably BLAS and LAPACK. Memory-mapped ndarrays have NumPy’s built-in support. 

Limitations:

Appending or inserting entries into an array is not possible trivially since it is with Python's lists. The np. pad() routine is used to extend arrays and actually creates newer arrays of required shape and padding values, copies the existing array into the new array, and returns it. np. concatenate([a1, a2]) operation of NumPy will not actually link two arrays, but it returns a newer array which is filled with the entities from both the given arrays in sequence. np. reshape() reshapes the dimensionality of the given array. It is only possible as long as the total number of elements in the array remains constant. These are the circumstances occurring from the fact that NumPy's arrays should be views on a contiguous memory buffer. Blaze is the replacement package that attempts to overcome this limitation. 

Algorithms that cannot be expressed as vectorize operations are executed slowly since they should be implemented in pure Python. The vectorization of the array can increase the memory complexity of a few operations from constant to linear since the temporary arrays should be created that have a size of the inputs. Several groups implement runtime compilation of numerical code to avoid these kinds of problems. Numba, numexpr, and scipy. the weave is the open-source solutions that are incorporated with NumPy. Cython and Pythian are the alternative static compilers to these open-source solutions. 

Many recent and in-trend large-scale scientific computing applications have requirements that exceed the capabilities of the NumPy arrays, for example, NumPy arrays are generally loaded into the computer’s memory can have insufficient capacity to analyze the large set of data. Furthermore, NumPy operations are executed on a single CPU. However, many linear algebra operations are accelerated through executing these operations on clusters of CPUs or of specialized hardware, such as, GPUs and TPUs. These are mostly used in many deep learning applications.

Many alternative array implementations have been developed in the scientific Python ecosystem over a few recent years as a result. These include Dask (used for distributed arrays) and TensorFlow or JAX (used for computations on GPUs). These usually implement a subset of NumPy’s API or they mimic it because of its popularity, and also so that the developers can change their array implementation with minimal changes to their required piece of code. CUPy is a recently introduced library, accelerated by Nvidia’s CUDA framework, which has shown potential for faster computing of arrays. It is considered a drop-in replacement of NumPy.

Great Learning gives you an opportunity to learn Python’s popular mathematical operations library, NumPy certification tutorial for free online. This NumPy tutorial will take you through various topics like packages, support, operations, alternate solutions, and much more. You will have a deep understanding of the complete library and you can potentially apply them to deep learning and other such computing domains. Enroll in Great Learning Academy today and avial the NumPy certification tutorial for free. Happy Learning.