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Jan 12, 2024 · Python has a rich ecosystem of ever-growing data science libraries. It is almost impossible to cover everything in one article. The list of top libraries here is focused on only five main areas: (i) Staples of Data Science, (ii) Machine Learning, (iii) AutoML, (iv) Deep Learning, and (v) Natural Language Processing.
- 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.
- Nikita Duggal
- November 14, 2023
- TensorFlow. The first in the list of python libraries for data science is TensorFlow. TensorFlow is a library for high-performance numerical computations with around 35,000 comments and a vibrant community of around 1,500 contributors.
- SciPy. SciPy (Scientific Python) is another free and open-source Python library for data science that is extensively used for high-level computations.
- NumPy. NumPy (Numerical Python) is the fundamental package for numerical computation in Python; it contains a powerful N-dimensional array object. It has around 18,000 comments on GitHub and an active community of 700 contributors.
- Pandas. Pandas (Python data analysis) is a must in the data science life cycle. It is the most popular and widely used Python library for data science, along with NumPy in matplotlib.
Apr 10, 2024 · Many Python libraries for data science exist to explore natural language processing, such as NLTK, TextBlob, and spaCy. These libraries allow you to create applications capable of classification, sentiment analysis, tokenization, and more fairly easily.
Sep 8, 2024 · In this article, we’ve covered 25 essential Python libraries for data science in 2024. From foundational libraries like NumPy and Pandas to advanced tools like TensorFlow and Hugging Face Transformers, these libraries are crucial for building powerful data science solutions.
Aug 5, 2024 · Python has become the common language used in data science, thanks in no small part to its rich ecosystem of libraries. These libraries, pre-packaged bundles of code, offer a treasure trove of tools and functionalities that streamline the entire data science workflow, from data manipulation and analysis to visualization and machine learning.
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May 11, 2023 · Here are the top Python libraries for data science that every data scientist should know, including NumPy, Keras and Pandas.
