Python is the most popular and one of the widely used programming languages in the data science spectrum. It is highly productive, easy to learn and use, and executed much faster than other programming languages.
Python also has a huge collection of libraries, each with diverse use-cases, including scientific and numeric computing, machine learning, data science, and more. This post will look at ten popular Python libraries and the importance of each in detail.
TensorFlow is an open-source deep learning and machine learning library developed by Google Brain. It was released in the year 2015 when Theano and Caffe were the popular deep learning frameworks. TensorFlow has gained tremendous popularity in a short span and is a part of most of Google’s AI or ML applications.
Developers use TensorFlow for high-performance numerical computations and to build large-scale neural networks. It’s the best fit for various applications such as Speech and image recognition, Text-based applications, Time-series analysis, Video detection, and many more.
Released in 2016, PyTorch is free, open-source, and the largest machine learning library developed by Facebook’s AI Research Lab. It is based on Torch, an open-source deep-learning library implemented in C with a wrapper in Lua.
It is every data scientist’s favorite tool to achieve maximum speed and flexibility and is a strong player in the field of AI and ML as it is a research-first library. PyTorch offers high-level features, including tensor computations with powerful GPU acceleration, and allows developers to build deep neural networks on tape-based autograd systems. Some of its other features include Production Readiness, Distributed Training, a Robust Ecosystem, and excellent Cloud support.
PyTorch is used in revolutionary applications such as Computer Vision, Natural language processing (NLP), and more. It is an excellent choice for research work.
Scikit-learn is a free and open-source software for data analysis and data mining tasks. It is also used to build machine learning models and works efficiently with complex data. Scikit-learn is built atop other Python libraries, and hence it is interoperable with most of the other Python libraries (NumPy, SciPy, Pandas, etc.)
Data scientists use Scikit-learn to implement various unsupervised and supervised ML models such as Regression, Classification, Random Forests, Support Vector Machines, Naive Bayes, Decision Trees, and many more.
Pandas is a machine learning library that offers high-level data structures and a wide variety of tools for data analysis. It provides essential data structures like series, data frames, and panels, which help manipulate data sets and time series. It also offers high-level abstraction and multiple methods for convenient data filtering.
Pandas is free, open-source, and finds applications in data wrangling and cleaning, ETL (extract, transform, load) jobs for data transformation and data storage, time-series-specific functionalities (linear regression, data regeneration, etc.), and more.
Download Pandas or visit its GitHub to learn more.
NumPy is an open-source numerical Python library used for scientific computing and performing basic and advanced array operations. It supports multi-dimensional arrays and matrices along with a collection of high-level mathematical functions. It manipulates this data using complex mathematical operations like Fourier transformation, linear algebra, random number, etc. You can also use NumPy as an efficient multi-dimensional container to treat generic data.
NumPy is extensively used for data analysis, and its array interface enables developers and data scientists to reshape large datasets in multiple ways. It is also used to treat images, creating sound waves representations, and for other binary operations.
Matplotlib is a popular cross-platform library used for exploration, data visualization, and for making 2D plots from data in arrays. You can use it to design different figures in multiple formats compatible with your respective platforms. Matplotlib offers multiple charts and customizations such as histograms, bar charts, scatterplots, non-Cartesian coordinates graphs, etc. It also offers an array of colors, themes, palettes, etc., to customize and personalize your plots.
Selenium is an open-source web-based automation framework that offers Web Driver APIs for browsers to interact with user actions and return responses. It was created with a need to replace manual testing, which was mundane and inefficient. Selenium Automation Testing uses the resources that are advanced to enhance achievement without any human interference.
Selenium supports multiple browsers and multiple programming languages. It has simple commands which are easy to learn and does not require any server installation as it directly interacts with the browser.
Let’s say you want to design a market strategy by comparing your product with your competitors. Data can be manually copy-pasted from the competitor’s website, challenging because there might be hundreds of pages. Also, there might be data you cannot copy-paste. One effective way to do this is using Web scrapping, a popular method used to extract data from different websites on your computer - irrespective of the size of data. You can also extract contact information for thousands of leads, investment decisions based on different businesses, and much more. BeautifulSoup is a popular Python library for Web crawling and data scraping from XML and HTML documents.
BeautifulSoup provides simple methods and Python idioms for navigating, searching, and modifying a parse tree to extract the data you need. It automatically detects encodings and handles HTML documents with special characters. BeautifulSoup helps you save a lot of time and also keeps valuable data over the Web within your reach.
Download the latest version of BeautifulSoup or visit the GitHub repo to know more.
ScraPy is also a popular open-source Python library for large-scale web scraping by building crawling programs, also known as spiders. BeautifulSoup helps you scrape data from websites but not via CSV or API. ScraPy gathers structured data from the Web (contact info or URLs) and can be used to scrape data from APIs or Python machine learning models, data mining, information processing, and more.
ScraPy provides all the tools you need to efficiently extract data from websites, process them, and store them in preferrable structure and format.
Robot Framework is an open-source test automation framework for process automation and the testing of hardware and software systems under development.
Testing is a critical yet essential part of product development to guarantee the quality of your product. Automating your testing processes increases overall software development efficiency and allows teams to build highly robust tools. Test automation validates each phase of your development cycle and detects bugs or issues early on. It also saves time so that you can write new tests and add them to your automated cycle. Further, it helps you get your products to market faster.
Similarly, Robot Framework is useful when automating resources where programming languages cannot be easily used. Companies save a lot of time since Robot Framework is readily available, and they do not have to build a new testing framework.
Download Robot Framework or visit its GitHub repo to learn more.
We saw ten popular Python libraries; however, many other helpful libraries can be used for multiple use cases. The Python community regularly enhances and upgrades these libraries based on their popularity and growth of the Python programming language.
Knowing these popular general Python libraries will further your Python learning and make you a better Python developer.