As Python has gained a lot of traction in the recent years in Data Science industry, I wanted to outline some of its most useful libraries for data scientists and engineers, based on recent experience. And, since all of the libraries are open sourced, we have added commits, contributors count and other metrics from Github, which could be served as a proxy metrics for library popularity. When starting to deal with the scientific task in Python, one inevitably comes for help to Python's SciPy Stack, which is a collection of software specifically designed for scientific computing in Python (do not confuse with SciPy library, which is part of this stack, and the community around this stack). This way we want to start with a look at it. However, the stack is pretty vast, there is more than a dozen of libraries in it, and we want to put a focal point on the core packages (particularly the most essential ones).
Python is one of the most popular languages used by data scientists and software developers. In this article, you'll see a line-up of the most important Python libraries for data science tasks, covering areas such as data processing, modeling, and visualization. Python is one of the most popular languages used by data scientists and software developers alike for data science tasks. It can be used to predict outcomes, automate tasks, streamline processes, and offer business intelligence insights. It's possible to work with data in vanilla Python, but there are quite a few open-source libraries that make Python data tasks much, much easier.
Python continues to take leading positions in solving data science tasks and challenges. Last year we made a blog post overviewing the Python's libraries that proved to be the most helpful at that moment. This year, we expanded our list with new libraries and gave a fresh look to the ones we already talked about, focusing on the updates that have been made during the year. Our selection actually contains more than 20 libraries, as some of them are alternatives to each other and solve the same problem. Therefore we have grouped them as it's difficult to distinguish one particular leader at the moment.
Data science is all about exploring massive data sets to extract valuable information from them and convert it into actionable solutions. To be a successful data professional, one must be able to analyze data using Python. Well, it is not only one of the most popular programming languages among coders and developers, but today, it has gained an enormous following in the data science community. The greatest feature of python has to be its simplicity. The precise and efficient syntax of this language makes it particularly beneficial to accomplish specific tasks faster than other languages.
Data science and machine learning are the most in-demand technologies of the era, and this demand has pushed everyone to learn the different libraries and packages to implement them. This blog post will focus on the Python libraries for Data Science and Machine Learning. These are the libraries you should know to master the two most hyped skills in the market. Here's a list of topics that will be covered in this blog: When I started my research on data science and machine learning, there was always this question that bothered me the most. What led to the buzz around these two topics?