Python Data Science Libraries 2 – Numpy Methodology

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/

Python Data Science Libraries 1 – Pandas Methodology

I am putting the topics I have been working on into a series that I will tell you one by one. For this reason, I will explain the methodology and usage aspects of almost all libraries that I am actively working on. I’ll start with pandas, which allows functional operations such as data preprocessing without reading data first. With this library, we can easily perform data pre-processing steps, which are vital steps for data science, such as observing missing data and extracting that data from the data set. In addition, you can bypass data types and the front part of numerical or categorical operations that you will do on them. This provides us with significant convenience before proceeding. Each library in Python has its own specialties, but speaking for pandas, it is responsible for all of the pre-part modifications to the data to form the basis of the data science steps. Data classification processes in Pandas can be designed and activated quickly with a few functional codes. This is the most critical point in the data preprocessing stage, in the previous steps of data modeling.

 

 

We can store the data as “dataframe” or “series” and perform operations on it. The fact that Pandas library performs every operation on data in a functional, easy and fast way reduces the workload in data science processes on behalf of data scientists. In this way, it can handle steps such as the beginning and most difficult part of the process, such as data preprocessing, and focus on the last steps of the job. By reading data such as .csv, .xlsx, .json, .txt prepared in different types, it takes the data that has been entered or collected through data mining into python to process. Pandas library, which has the dataframe method, is more logical and even sustainable than other libraries in terms of making the data more functional and scalable. Those who will work in this field should work on the methodology of pandas library, which has the basic and robust structure of the python programming language, not to write code directly. Because new assignments on the data, column names, grouping variables, removing empty observations from the data or filling empty observations in a specific way (mean, 0 or median assignment) can be performed.

 

 

Data cannot be processed or analyzed before the Pandas library is known. To be clear, the pandas library can be called the heart of data science. Specially designed functions such as apply (), drop (), iloc (), dtypes () and sort_values ​​() are the most important features that make this library exclusive. It is an indispensable library for these operations, even if it is not based here on the basis of its original starting point. In the steps to be taken, it has a structure with tremendous features and a more basic case in terms of syntax. It is possible to host the results from the loops in clusters and convert them into dataframe or series. The acceleration of the processes provides a great advantage in functional terms if the project that will emerge has a progressing process depending on time, which is generally the case. Looking at its other possibilities, it is one of the most efficient libraries among the python libraries. The fact that it is suitable for use in many areas can be considered as a great additional feature. Pandas is among the top 3 libraries in the voting among data processing libraries made by software developers using the python programming language. You can reach this situation, which I quoted with datarequest in the sources section.

 

 

The concept of “data science”, which has been developing since 2015, has brought the pandas library to the forefront and this library, which has been developing in silence for years, has come to light. After Pandas, I will explain numpy and talk about numerical and matrix operations. In general, Pandas is a library that has high-level features in basic data analysis and data processing. In addition, if you specify the topics you will talk about and the things you want me to mention, I will draw a more solid way in terms of efficiency. I hope these articles that I will publish in series will help people who will work in this field. In the future, I will add the cheatsheet style contents that I will prepare on github to the bibliography section. If you want to take advantage of such notes, I will put my github account in the resource section, and you can easily access there.

 

 

References:

https://www.geeksforgeeks.org/python-pandas-dataframe/

https://medium.com/deep-learning-turkiye/adan-z-ye-pandas-tutoriali-ba%C5%9Flang%C4%B1%C3%A7-ve-orta-seviye-4edf0094e0d5#:~:text=Pandas%2C%20Python%20programlama%20dili%20i%C3%A7in,sonuca%20kolayca%20ula%C5%9Fmak%20i%C3%A7in%20kullan%C4%B1lmaktad%C4%B1r.

https://www.dataquest.io/blog/15-python-libraries-for-data-science/

https://github.com/tanersekmen/

https://www.edureka.co/blog/python-pandas-tutorial/

https://globalaihub.com/importance-of-data-quality-and-data-processing/

https://globalaihub.com/hareketli-ortalama-algoritmasiyla-al-sat-tavsiyeleri/

https://www.dataquest.io/course/pandas-fundamentals/Python Data Science Libraries 1 – Pandas Methodology