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Python Framework for Data Science


There are several python frameworks for data science. In this article, we will examine the popular ones.

  1. PyTorch: PyTorch is a framework that is perfect for data scientists who want to perform deep learning tasks easily. The tool allows us to perform tensor computations with GPU acceleration. It’s also used for other tasks for example, for creating dynamic computational graphs and calculating gradients automatically.
  2. Scrapy:  Scrapy helps to build crawling programs that can retrieve structured data from the web for example, URLs or contact info. It’s a great tool for scraping data used in machine learning models. Developers use it for gathering data from APIs. This full-fledged framework follows the Don’t Repeat Yourself principle in the design of its interface. As a result, the tool inspires users to write universal code that can be reused for building and scaling large crawlers.
  3. BeautifulSoup: BeautifulSoup is python library for web crawling and data scraping. If you want to collect data that’s available on a website but not via a proper CSV or API, BeautifulSoup can help you scrape it and arrange it into the format you need.
  4. Numpy: NumPy is a perfect tool for scientific computing and performing basic and advanced array operations. The library offers many handy features performing operations on n-arrays and matrices in Python. It helps to process arrays that store values of the same data type and makes performing math operations on arrays easier.
  5. Scipy: Scipy includes modules for linear algebra, integration, optimization, and statistics. Its main functionality was built upon NumPy, so its arrays make use of this library. SciPy works great for all kinds of scientific programming projects (science, mathematics, and engineering). It offers efficient numerical routines such as numerical optimization, integration, and others in submodules. The extensive documentation makes working with this library really easy.
  6. Pandas: Pandas is a library created to help developers work with “labeled” and “relational” data intuitively. 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 allows converting data structures to data frame objects, handling missing data, and adding/deleting columns from data frame, imputing missing files, and plotting data with histogram or plot box. It’s a must-have for data wrangling, manipulation, and visualization.
  7. TensorFlow: TensorFlow is a popular Python framework for machine learning and deep learning, which was developed by Google. It’s the best tool for tasks like object identification, speech recognition, and many others. It helps in working with artificial neural networks that need to handle multiple data sets. The library includes various layer-helpers (tflearn, tf-slim, skflow), which make it even more functional. TensorFlow is constantly expanded with its new releases including fixes in potential security vulnerabilities or improvements in the integration of TensorFlow and GPU.
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