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• Scrapy. 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.
• BeautifulSoup. BeautifulSoup is another really popular library for web crawling and data scraping. 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.
• NumPy. NumPy (Numerical Python) 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.
• SciPy. This useful library includes modules for linear algebra, integration, optimization, and statistics. Its main functionality was built upon NumPy, so its arrays make use of this library.
• 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).
• Keras. Keras is a great library for building neural networks and modeling. It’s very straightforward to use and provides developers with a good degree of extensibility.
• SciKit-Learn. This is an industry-standard for data science projects based in Python. Scikits is a group of packages in the SciPy Stack that were created for specific functionalities – for example, image processing.
• PyTorch. PyTorch is a framework that is perfect for data scientists who want to perform deep learning tasks easily. The tool allows performing tensor computations with GPU acceleration.
• TensorFlow. TensorFlow is a popular Python framework for machine learning and deep learning, which was developed at Google Brain. It’s the best tool for tasks like object identification, speech recognition, and many others.
• XGBoost. Use this library to implement machine learning algorithms under the Gradient Boosting framework. XGBoost is portable, flexible, and efficient.

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