NumPy is a package(library) for python, This package consists of Array, Objects, Multi-Dimensional array, and various functions to manipulate and handle these arrays. With the help of Its functions we can perform various mathematical and logical operations. suppose you have an array and you wanted to check the dimension of the array , dimension means n*m matrix. So with the help of the NumPy function called ndim, we can get it easily. suppose we have some array and we want to know the size of our array than we can use the size function of NumPy. Even if we want to know the data type of array attributes that we can know with a function called dtype.
In python, it has list which can handle array but still, we should learn NumPy because it has a better and optimized way to use Array. Generally an array can be of heterogeneous type in python , like [12,”ranjan”,”ajay”,78] . But if we want to have a homogeneous array than we should only use NumPy.Because many times in real application we store similar types of data in the one array.
There are several important reasons why we should learn NumPy they are listed below.
suppose you have a very big size of the array and you want to store them in such a way that they take less memory than NumPy will do it very efficiently as they will take less memory to store. Because if any one of us is writing code for NLP or for algorithms than he/she will need to write code in a more optimized way to store data with taking less memory.
If some of you are trying to learn NLP, then you will see that the n-dimension matrix are used for storing data, So it will be a great option to use NumPy to store data as it saves memory.
Let me tell you one example, if we have any 2 * 5 matrix array than we can convert it into 5 * 2 and if we have any 1 * 4 matrix array than we can convert it into 2 * 2. All of these can be simply done by using NumPy.reshape(...).
We will face situations where we need to find any specific element from arrays so to handle these things in a better way NumPy provides function they are where, nonzero and count_nonzero. These functions are optimized internally for faster search.
Because of better array storage and great mathematical routines availability in Numpy, it has mostly used in NLP and scientific applications where more algorithms are used. Because in these cases we needed to calculate and manage very big floating and double numbers with exact precision.
You should have a basic understanding of programming languages and a basic understanding of Python along with we should know arrays and operations of the array .
This is useful for python developers, especially those who are more keen on learning algorithms and complex data manipulations.