One of the most important and fundamental libraries in Python is undoubtedly the numpy library. In the continuation of this series, I will first continue with numpy from the pandas library now. In general, its functional structure with library-based features is based on a more robust infrastructure than other libraries. Therefore, it can perform the mathematical operations to be done quickly and in a healthy way. Its expansion is already known as Numerical (num) python (py) in python. As it can be understood from here, it is a library with strong mathematical aspect and possible to reach desired results quickly and easily. It is one of the indispensable building block libraries in Machine Learning and Deep Learning. Basically, it plays a role in the background of every transaction. What is mentioned here is the matrices in the form of arrays and the operations between them according to their states, the calculation of their outputs and the use of matrices in the basis of the work done as a project is the most necessary condition. Although we often see this frequently in Image Processing operations, people who will work in this field must have numpy knowledge in their transactions.

This library, which is used as a whole, offers you mathematical structures suitable for the models you will use. In this way, descriptive explanations of your transactions will also make more sense. As I mentioned in the upper paragraph, matrix operations are the most important event in mathematics. This spreads to the whole of the transactions you are currently doing and numpy provides you convenience in layer-based transaction processes. When we actively process images, we can see the most important layer operations visibly. Even if the OpenCV library carries the necessary load during the operations, operations that are not done through the array structure of any numpy library will not be sustainable. The numpy library is an indispensable value of these works, as there will be matrices and products of matrices behind many operations. It is a fully user-friendly library in line with the possibilities of its functional structure. It is among the top 5 most useful libraries among Python libraries, according to tests conducted by people working in this field worldwide. Usage areas are increasing in direct proportion to this.

Deep learning and Machine Learning topics do not only mean writing long lines of code contrary to popular belief. For this reason, most of the people start writing code or even making a career in this field without knowing the events that are going on in their background. Behind these events lies an extensive knowledge of mathematics and statistics, the best example of which is Image Processing. Because on the back of it is all mathematics, these operations are matrices and there are numpy in the libraries used. This is the biggest proof that this library is active almost everywhere. There is no library in python that is multifunctional in this way. Because there are two libraries that must be found in every field. These are the numpy and pandas libraries. While these provide convenience in both processing the data and performing numerical operations on the data, they show us the differences in the data perspective. This is a proof of the importance of libraries in Python, especially libraries on data processing and data analysis.

I can clearly say that the Numpy library makes a great difference in data shaping and preparation. It has functions that we would call useful in many ways such as reshape, array, exp, std, min, sum in the numpy library. This is actually the most basic level that distinguishes it from other libraries. For those who want to reach the necessary details of this, I will leave information about them in the resources section. From here, you can use the numpy library and what kind of features you can take advantage of, or what kind of convenience you can get in numerical transactions, you can find them yourself from the cheat sheet or numpy’s own website.

Thank you for reading and following my articles until this time, I wish you a good day.

References:

-https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Numpy_Python_Cheat_Sheet.pdf

-https://numpy.org/

-https://cs231n.github.io/python-numpy-tutorial/

-https://www.w3schools.com/python/numpy_intro.asp

-https://globalaihub.com/python-veri-bilimi-kutuphaneleri-1-pandas-metodoloji/

-https://globalaihub.com/python-data-science-libraries-1-pandas-methodology/