A huge debate is going on about how Python is great for data science. Also, it is being quite interesting as to how Python as a language and its numerous libraries are being used in various applications such as data science and machine learning. NumPy is one of the python libraries that used for the implementation of data science. Let’s know what exactly NumPy Library is and how to learn it practically to utilize its benefits

NumPy is one of the best suitable libraries of Python for the data science. NumPy is the fundamental library of Python for computing. It has tools for merging with the C or C++ code and the FORTRAN code. Also, it is a sophisticated function. NumPy is useful in calculations and is a powerful N-dimensional array object.

This library is mostly used to implement mathematical concepts such as linear algebra, random number capabilities, and Fourier Transforms. Also, NumPy is an excellent library to seamlessly combine with a lot of databases.

**Why is Learning NumPy Important for Data Science?**

**Why is Learning NumPy Important for Data Science?**

NumPy is a huge and powerful library for processing arrays and also computing Trigonometry, Statistics Matrices, and manipulation.

As Python is in the trend for data science, it becomes crucial to undertake a **Data Science Certification** that is useful for tech professionals and developers to get clear insights about languages to be effectively used in data science programming.

The most important aspect of Numpy arrays is that they are optimized for speed. So we’re going to do a demo where I prove to you that using a Numpy vectorized operation is faster than using a Python list. Then we’ll look at some more complicated matrix operations, like products, inverses, determinants, and solving linear systems

For now, take a rundown below at Python’s great Library: NumPy

*The NumPy Library Download*

*The NumPy Library Download*

NumPy is easy to download and install in our PC or laptop. All you have to do is to go to the authentic websites like anaconda and download all the significant python libraries from there.

The NumPy works on almost all the operating systems like Windows, Mac, and Linux. So, using it would be super easy. If you know how NumPy works, you can right away start working on it.

But if you do not know or if you do not want to download any of the libraries on your computer, then you can do it with tools such as Google Colab. You have free Jupyter Notebooks hosted on the cloud.

*NumPy and its Robust Functions*

*NumPy and its Robust Functions*

Once you are done downloading and installing NumPy in your laptop, you have to know a little bit of background and history. NumPy is widely used to handle multidimensional arrays, unlike Python’s array class which can handle only unidimensional array.

Also, you call the dimensions as axes in the world of NumPy. NumPy’s multidimensional array is known as *ndarray. *Its purpose is to increase more functionality and compute the things arranged in the multi-dimensional array. Sometimes, you also call the dimensions as the rank of that particular matrix.

Now, you have reached a certain situation where you need NumPy’s help to solve your problem. Here is how you are going to do it:

`import numpy as np`

Also, while using numpy function, it is pretty important to create arrays as they form almost most of the codes. So here is a way where you have to know how to call for an array using the numpy library:

```
a = np.array([1 ,2 ,3])
print (a)
```

*Output: [1 2 3]*

*Output: [1 2 3]*

#### This is just one way of creating an array in NumPy by calling the array function.

There are special functions like zeros and ones which form an array consisting of numbers like 0 and 1. Below is the code of NumPy that generates only binary output.

- You have to just specify the length and the shape (if it is multi-dimensional) and then, the array is created.

- You also have another method called arrange which creates arrays with even spaces where the values are given. The NumPy arrays are known for their speed. They work really fast and efficient. You can check for yourself where you do a mathematical vector operation using a Python list and a NumPy array. You can also check which performs faster operations when you have matric products, determinants, and inverses etc.
- There is a specific way of indexing these arrays. You can also learn the indexing and slicing of arrays through some special methods.

- To know more about NumPy and learn about it you should know how vectors, matrices, and other basic mathematical operations are computed using NumPy.
- Now, in Machine learning and applications of that level, a 1-D array is known as vector and a 2-D array is known as matrix.
- If the dimensions are more than 2 or even higher ranked, you should call it an n-dimensional tensor.

This is important because all the data which is going to be analyzed using NumPy will be in the form of matrices and tensors. Thus it is vital to know all about the notations and what you call them.

Here is a small code of how you do a transpose of a certain matrix:

`print (b)`

**#output**

**[[1 5 7]**

**[2 4 6]]**

`b. shape`

**#output**

**(2, 3)**

```
transpose = b.T
print (b_transpose)
```

**#output**

**[[1 5]**

**[7 2]**

**[4 6]]**

`b_transpose. shape`

**#output**

**(3, 2)**

*Broadcasting of NumPy Arrays*

*Broadcasting of NumPy Arrays*

Broadcasting is also another practical function to know about NumPy. It is basically adding two matrices of different shapes. Arrays of different sizes cannot be functioned arithmetically. That is, same sized arrays cannot be added, subtracted, or be used in to perform any of the arithmetic operations.

Broadcasting allows you to do any arithmetic operations between two matrices of different sizes. It can do this by duplicating the smaller array of the two and then getting it along with the last mismatching dimension.

The code below explains the broadcasting of arrays in NumPy:

*a = [a1, a2, a3]*

```
# scalar and one-dimensional
from numpy import array
a = array([1, 2, 3])
print(a)
b = 2
print(b)
c = a + b
print(c)
```

```
Output
[1 2 3]
2
[3 4 5]
```

However, NumPy is used for statistical operations. These functions come to help when you want to create some weight in your deep learning model. Also, using NumPy you can calculate the mean, median, mode of the input data.

*Final Considerations*

*Final Considerations*

NumPy is a very basic library for scientific and mathematical computations while you need them for your machine learning or data science models. Also, if you learn how to use NumPy, then you can easily sail through the journey of data science and machine learning.

In a nutshell, NumPy is an extension of Python programming language but it is not the other programming language. It is helpful in providing fast and quick efficient operations on multidimensional arrays and homogenous data. It is used to manipulate data of numerical origin and it is also quite similar to Matlab.

NumPy isn’t a regular framework or language. It is not like the Java and C++ programming. The arrays created in NumPy are much advanced. You can do a lot of linear arithmetic operations. Also, you can do vector and matrix addition, subtraction, multiplication etc.

Note: *This is a Guest Post contributed by* **Danish Wadhwa**