8 Python Libraries Data Scientists Should Know

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Blog Objectives 

  • Python has become among the first few things recruiters search for in data scientists’ skillset
  • Data scientists should know the called libraries Python
  • NumPy, Matplotlib, and Pandas are the top three Python libraries for data science.

Python’s design philosophy highlights code readability with the use of whitespace. This high-level and general-purpose programming language has rapidly become the go-to language in data science. Its popularity is getting higher at an ever-increasing rate. So, Data Science with Python has become among the first few things recruiters search for in data scientist’s skill set. Developed to support multiple programming paradigms, including structured, object-oriented, and functional programming, it is ideal for small, medium, and large-scale projects. NumPy, Matplotlib, and Pandas are the top three Python libraries for data science.

Pandas

As we have mentioned earlier, NumPy, Matplotlib, and Pandas are the widely used Python library for data science. Pandas, designed to work with structured data effectively, is heavily used for data analysis and cleaning. Instrumental in providing fast and flexible data structures, it is also used in statistics, finance, and neuroscience.

SciKit-Learn

SciKit-Learn is a machine learning library for the Python programming language. Previously

known as scikits.learn and sklearn, it was first released in 2007. Mainly written in Python, this library was featured with classification, clustering, model selection, regression, and dimensionality reduction. It is used with NumPy extensively for high-performance linear algebra and array operations.

PyTorch

PyTorch is another machine learning library that is used in natural language processing and computer vision. Written in Python, CUDA, and C++, this library is known for its capacity of tensor computation with strong GPU acceleration support. It is also used on neural networks on a tape-based autograd system. Data Science with Python Training will help you in learning the dynanics of this library.

Scrapy

Scrapy, written in Python, is an open-source web-crawling framework that is used in data mining, information processing, or historical archival. It comes with built-in applications to handle user-agent spoofing, robots.txt, and HTTP features such as authentication and compression.

BeautifulSoup 

BeautifulSoup is another python library for data science that is used in data scraping and data crawling. It is known for its powerful functions such as get(), get_text(), find(), strip(), and split(). Developed by Leonard Richardson, this Python package is mainly used for web scraping.

TensorFlow

TensorFlow is an end-to-end open-source platform for machine learning which is blessed with an all-in-one eco-system for libraries, tools, and community resources. It is designed to use in a wide range of tasks focused on training and inference of deep neural networks. This symbolic math library is based on dataflow and differentiable programming.

NumPy

Numerical Python or NumPy contains a powerful N-dimensional array object. This general-purpose array-processing package provides high-performance multidimensional items. It was developed as the fundamental package for numerical computation in Python and used to address the slowness problem.

Keras

Keras is another easy-to-use Python Library that is featured with neural-network building blocks such as layers, objectives, activation functions, optimizers, and a host of tools. The developers claimed over 0.375 million worldwide users for this Python Library, which acts as an interface for the TensorFlow library. Besides, it provides support for convolutional and recurrent neural networks.

 

About the Author: Rajib Kar, who finds reading and writing therapeutic, is a technical content writer in Multisoft. An avid blogger, he has worked in various IT companies that provide employee training to develop employability-skill and workplace-based attitude. This content marketer usually writes about the recent developments of corporate and industrial training.

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