7 Best python libraries of 2017

Let’s highlight 7 best Python libraries of 2017. this list highlights new & innovative Python libraries. they had an explosion in their popularity as compared to traditional python libraries.

1 – TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow also includes TensorBoard, a data visualization toolkit.

Google built the underlying TensorFlow software with the C++ programming language. But in developing applications for this AI engine, coders can use either C++ or Python, the most popular language among deep learning researchers. The hope, however, is that outsiders will expand the tool to other languages, including Google Go, Java, and perhaps even Javascript, so that coders have more ways of building apps.

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2 – Scrapy

Scrapy is a fast high-level web crawling and web scraping framework, used to crawl websites and extract structured data from their pages. It can be used for a wide range of purposes, from data mining to monitoring and automated testing.

Scrapy is one of the most popular and powerful Python scraping libraries; it takes a “batteries included” approach to scraping, meaning that it handles a lot of the common functionality that all scrapers need so developers don’t have to reinvent the wheel each time. It makes scraping a quick and fun process!Scrapy, like most Python packages, is on PyPI (also known as pip). PyPI, the Python Package Index, is a community-owned repository of all published Python software.

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3 – Scikit-learn

Scikit-learn provides a range of supervised and unsupervised machine learning or learning algorithms via a consistent interface in Python. it is licensed under a permissive simplified BSD license and is distributed under many Linux distributions, encouraging academic and commercial use.

The library is built upon the SciPy (Scientific Python) that must be installed before you can use scikit-learn.  scikit-learn also uses CBLAS, the C interface to the Basic Linear Algebra Subprograms library. scikit-learn comes with a reference implementation, but the system CBLAS will be detected by the build system and used if present .

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4 – OpenAI universe

Universe is a software platform for measuring and training an AI’s general intelligence across the world’s supply of games, websites and other applications .

In Universe, AI agents interact with the virtual world by sending simulated mouse and keyboard strokes via what’s called Virtual Network Computing, or VNC. In this way, Universe facilitates reinforcement learning, an AI technique where agents learn tasks by trial and error, carefully keeping tabs on what works and what doesn’t, what brings the highest score or wins a game or grabs some other reward.

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5 – zappa

Zappa works with any WSGI-compatible that works with Python 2.7. That means – pretty much all of them! Django, Flask, Pyramid, Bottle, and many others all work with Zappa.



Zappa is the serverless framework for Python, although .  it only has support for AWS Lambda and AWS API Gateway. Each request then calls your application from a memory cache in AWS Lambda and returns the response via Python’s WSGI interface. After your app returns, the “server” dies.

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6  – Arrow

Arrow is a Python library that offers a sensible, human-friendly approach to creating, manipulating, formatting and converting dates, times, and timestamps. It implements and updates the datetime type, plugging gaps in functionality, and provides an intelligent module API that supports many common creation scenarios.

arrow python librarayArrow is a replacement for the datetime type that supports Python 2 or 3, and provides a much nicer interface as well as filling the gaps with new functionality

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7- Theano

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.


Theano provides tight integration with NumPy , transparent use of a GPU  efficient symbolic differentiation , speed and stability optimizations , dynamic C code generation , extensive unit-testing and self-verification .

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About the author

Deven Rathore

I'm Deven Rathore, a multidisciplinary & self-taught designer with 3 years of experience. I'm passionate about technology, music, coffee, traveling and everything visually stimulating. Constantly learning and experiencing new things.

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