NumPy stands for numeric python. Today we will learn about the first advanced library of python. It was developed in 2005 by Travis Oliphant. All the data science libraries depend on NumPy as it is a lingua Franca for all others. Its base is nd array (n-dimensional array). Let’s learn about Numpy array Python.

## Benefits of Numpy Array Python:

With the help of a NumPy array python, we can do false operations on an array. If we do addition, or subtraction on arrays in python, we need to run a loop. But in NumPy, we can do it directly. Another benefit is the solutions of linear algebra through NumPy. Datastores in NumPy are a contiguous block of memory. That makes it fast.

We can run complex computations on the entire array. And can perform array-oriented operations on arrays. For example, with the help of the “where” function, we can find anything from an array.

### How To Create Array In Numpy:

Numpy store arrays in the ndarray form. All the arrays even the one-dimensional, two-dimensional, or multidimensional arrays are stored in n-dimensional array form in Numpy.

** ndarray is a fast, flexible container for large datasets in python. **

** It contains Homogenius data, All the data should be of the same type. **

we can create desired shape ndarray in NumPy python. Let’s create NumPy array in jupyter notebook.

As we know, first of all, we need to Import the NumPy library in the jupyter notebook by “import numpy as np”. Then we create a variable “x”. In this variable, we store a Numpy array that should contain all the zeros in it (through np.zeros). The in the brackets we gave the size of that array in the form of a tuple (4,4).

In the end, that gave the output containing a NumPy array of 4 by 4 that contains all the zeros in it.

### Arithmetic With ndarray:

As we discussed earlier, we can run direct operations on the NumPy array python without running a loop. This method is also called vectorization. Any two or more arrays that must be of equal size can run arithmetic operations on it, that follow an element-wise sequence to run operation.

We can also run the operation of a scaler on the whole array. For example, we are multiplying the whole array with 2. All we need to do is to write code as (array * 2). Each element of the array will be multiplicated with 2 individually.