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Introduction to Numpy

In this post I am going to introduce Numpy.Only introductory information will be included in post further.Details regarding the installation and syntax is not included within this article.



Hello All,

Today we are going to take a look at what Numpy is and the post further will introduce Numpy in more detail.

Moving on lets see what numpy is really.

On searching Numpy on Google.com I get the following result:



First link is of NumPy.org and lets see what inside.

Next on the website the description is given as follows:

NumPy is the fundamental package for scientific computing with Python. It contains among other things:

  • a powerful N-dimensional array object
  • sophisticated (broadcasting) functions
  • tools for integrating C/C++ and Fortran code
  • useful linear algebra, Fourier transform, and random number capabilities[1]

Given the popularity of NumPy(one of the reason for writing this post), its extensive use in the Artificial Intelligence field specifically the Machine Learning and Deep Learning fields.

Both Machine Learning and Deep Learning have played an important role in increasing the popularity of NumPy with the help of the results they are able to produce for the real world problems and challenges.

Numpy efficiently abstracts all the complex matrix, array and n dimensional array computation and operations behind simple function calls.

For instance if we want to increment all the values inside an array which contains only number then it can be done within one line.The following code snippet exemplifies the ease of operation(code was executed using Jupyter Notebook installed using Anaconda):

 From the code snippet above we can see that arr_1 has all the values as integers and only one line of code was used to increment all the values.There is no need to iterate through all the array elements in a loop to just increment them.We can just use the "+= integer_value" way to increment every value inside the array.

 There are many more benefits of using NumPy when working with N dimensional arrays which will be explored further in other posts.

Using NumPy is much simpler then we can imagine!!

Thank you.

That's all for this post!!

Thank you for reading this post.
If you have any suggestions regarding the post contents or if you need some more details on any other topic, please post it in the comments section.
Your suggestions are too valuable so they should not be missed.

References:
1.http://www.numpy.org/
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