If you’re dealing with parallel large NumPy arrays, you should be aware of this simple approach to speeding up your code.

First, a quick primer on some terminology.

In Python, if we want to take full advantage of the processing power of your CPU, you need need to use *multiprocessing* (typically achieved via the `multiprocessing`

library). This library is therefore well suited for CPU intensive tasks. If we wish to efficiently do many things at once using a single processor, i.e. achieve *concurrency*, we can use Python’s libraries for asynchronous work — namely `threading`

or `asyncio`

. In both cases, a common programming practice for sharing information safely between the processes/threads is via the use of a Queue.

Learn about NumPy Array, NumPy Array creation, various array functions, array indexing & Slicing, array operations, methods and dimensions,It also includes array splitting, reshaping, and joining of arrays. Even the other external libraries in Python relate to NumPy arrays.

The content present in the NumPy arrays can be made accessible, and also we can make changes thorough indexing as we got to know in the previous module. Another way of data manipulation in arrays in NumPy is though slicing through the arrays. We can also try changing the position of the elements in the array with the help of their index number. Slicing is the extension of python’s basic concept of changing position in the arrays of N-d dimensions.

NumPy Releases First Review Paper On Fundamental Array Concepts. The library adds support for large, multi-dimensional arrays as well as matrices, and brings the computational power of languages like C and Fortran to Python.

Numpy is a python library used for computing scientific/mathematical data.

Learn to create arrays using NumPy in Python. The Numpy Array Creation of different dimensions has been illustrated with the help of examples. NumPy focuses on working with a multi-dimensional array, and these are those arrays that have more than two dimensions. These multidimensional arrays also are known as matrices. The functions that we can implement on these are traverse, insertion, deletion, search and update.