Hi folks, today I’m going to Explain Something about python, Yeh I know, First time this is happing on TechJunkGigs, So let’s talk about python
Python is an object-oriented, high-level programming language with integrated dynamic semantics primarily for web and app development. It is extremely attractive in the field of Rapid Application Development because it offers dynamic typing and dynamic binding options.
We can do Python to solve many problems : –
- Python can be used on a server to create web applications.
- Python can be used alongside software to create workflows.
- Python can connect to database systems. It can also read and modify files.
- Python can be used to handle big data and perform complex mathematics.
- Python can be used for rapid prototyping, or for production-ready software development.
Fact: – NASA uses Python when they are programming their equipment and space machinery.
Python continues to take leading positions in solving data science tasks and challenges.
So, the python libraries for data science means, some modules are already written and we have to just use as per our condition. Here are some essential python libraries which are used for data science.
NumPy is one of the principal packages in this area. It is intended for processing large multidimensional arrays and matrices, and an extensive collection of high-level mathematical functions and implemented methods makes it possible to perform various operations with these objects.
NumPy, it integrates flawlessly with other programming languages like C/C++ and Fortran. The versatility of the NumPy library allows it to easily and swiftly coalesce with an extensive range of databases and tools
Pandas is another great library that can enhance your Python skills for data science. Pandas is a high-level data manipulation tool developed by Wes McKinney. It is built on the Numpy package and its key data structure is called the DataFrame. DataFrames allow you to store and manipulate tabular data in rows of observations and columns of variables.
SciPy is open-source software for mathematics, science, and engineering. It includes modules for statistics, optimization, integration, linear algebra, Fourier transforms, signal and image processing, ODE solvers, and more.
SciPy depends on NumPy, which provides convenient and fast N-dimensional array manipulation. SciPy is built to work with NumPy arrays and provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization.
Matplotlib is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack. It was introduced by John Hunter in the year 2002.
One of the greatest benefits of visualization is that it allows us visual access to huge amounts of data in easily digestible visuals. Matplotlib consists of several plots like line, bar, scatter, histogram etc.
In summary, use the documentation to learn the mechanics of pandas operations and use real datasets, to learn how to use python libraries to do data analysis. Finally, test your knowledge with Stack Overflow.
I hope this post helped you to know Top 4 Python Libraries for Data Science in 2018. To get the latest news and updates follow us on twitter & facebook, subscribe to our YouTube channel. And If you have any query then please let us know by using the comment form.