PyTorch is a framework that is perfect for data scientists who want to perform deep learning tasks easily. Another advantage? It comes with quality documentation and offers high performance. Scikit-learn uses the math operations of SciPy to expose a concise interface to the most common machine learning algorithms.ĭata scientists use it for handling standard machine learning and data mining tasks such as clustering, regression, model selection, dimensionality reduction, and classification. Scikits is a group of packages in the SciPy Stack that were created for specific functionalities – for example, image processing. This is an industry-standard for data science projects based in Python. It’s a great pick if you want to experiment quickly using compact systems – the minimalist approach to design really pays off! Moreover, Microsoft integrated CNTK (Microsoft Cognitive Toolkit) to serve as another backend. The library takes advantage of other packages, (Theano or TensorFlow) as its backends. It’s very straightforward to use and provides developers with a good degree of extensibility. Keras is a great library for building neural networks and modeling.
(Want to learn pandas? Check out Dataquest’s NumPy and Pandas fundamentals course, or one of our many free pandas tutorials.) It’s a must-have for data wrangling, manipulation, and visualization. Pandas allows converting data structures to DataFrame objects, handling missing data, and adding/deleting columns from DataFrame, imputing missing files, and plotting data with histogram or plot box. It’s based on two main data structures: “Series” (one-dimensional, like a list of items) and “Data Frames” (two-dimensional, like a table with multiple columns). Pandas is a library created to help developers work with “labeled” and “relational” data intuitively. The extensive documentation makes working with this library really easy. It offers efficient numerical routines such as numerical optimization, integration, and others in submodules.
SciPy works great for all kinds of scientific programming projects (science, mathematics, and engineering). Its main functionality was built upon NumPy, so its arrays make use of this library. This useful library includes modules for linear algebra, integration, optimization, and statistics. In fact, the vectorization of mathematical operations on the NumPy array type increases performance and accelerates the execution time. It helps to process arrays that store values of the same data type and makes performing math operations on arrays (and their vectorization) easier. The library offers many handy features performing operations on n-arrays and matrices in Python. NumPy (Numerical Python) is a perfect tool for scientific computing and performing basic and advanced array operations. If you want to collect data that’s available on some website but not via a proper CSV or API, BeautifulSoup can help you scrape it and arrange it into the format you need.
As a result, the tool inspires users to write universal code that can be reused for building and scaling large crawlers.īeautifulSoup is another really popular library for web crawling and data scraping. This full-fledged framework follows the Don’t Repeat Yourself principle in the design of its interface. It’s a great tool for scraping data used in, for example, Python machine learning models.ĭevelopers use it for gathering data from APIs. One of the most popular Python data science libraries, Scrapy helps to build crawling programs (spider bots) that can retrieve structured data from the web – for example, URLs or contact info. You’ve certainly heard of some of these, but is there a helpful library you might be missing? Here’s a line-up of the most important libraries for data science tasks available in the Python ecosystem covering areas such as data processing, modeling, and visualization. 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. It can be used to predict outcomes, automate tasks, streamline processes, and offer business intelligence insights.
Python is one of the most popular languages used by data scientists and software developers alike for data science tasks. Febru15 Python Libraries for Data Science You Should Know