7 Emerging Python Libraries You Should Explore in 2017
Discover seven lesser‑known Python libraries—from Arrow’s datetime handling and TensorFlow’s machine‑learning power to Zappa’s serverless deployment, Peewee’s lightweight ORM, Sanic’s high‑performance web framework, Bokeh’s interactive visualizations, and Blaze’s data‑analysis capabilities—each poised to gain traction in 2017.
#1 Arrow
Arrow addresses the shortcomings of Python's standard datetime library, offering simplified creation, formatting, manipulation, and conversion of dates, times, and timestamps across time zones, making it suitable for internationalized applications.
The library supports both Python 2 and 3, enabling developers to easily convert between time zones and providing a one‑stop solution for globalized apps.
#2 TensorFlow
Released by Google in November 2015, TensorFlow is an open‑source library for numerical computation that quickly gained popularity among Python developers.
It allows developers to run data‑flow graphs on CPUs, GPUs, desktops, servers, or mobile devices via a single API, and was originally built for machine learning and deep neural network research, proving suitable for production use.
#3 Zappa
Zappa improves Python web‑app deployment on AWS Lambda, enabling serverless architectures where no permanent infrastructure is required.
It simplifies deploying any Python WSGI application to AWS Lambda + API Gateway, offering fast, scalable, and low‑cost cloud deployment without server management hassles.
#4 Peewee
Peewee is a simple, expressive ORM that supports SQLite, MySQL, and PostgreSQL, allowing developers to interact with databases using Python classes instead of raw SQL.
It provides an easier alternative to SQLAlchemy for many use cases and works well with the Flask web framework.
Learn how to create a database with Peewee in the official documentation.
#5 Sanic + uvloop
Sanic is a Flask‑like web framework built on uvloop, enabling high‑performance asynchronous Python applications using async/await syntax.
Benchmarks show uvloop handling over 33 k requests per second, surpassing Node.js performance, and the project continues to evolve with community contributions.
#6 Bokeh
Bokeh is a modern interactive visualization library for the web, allowing developers to create D3‑style graphics and dashboards that can handle large or streaming datasets.
It integrates well with Jupyter Notebooks and can complement other libraries such as Matplotlib, Seaborn, and ggplot.
#7 Blaze
Blaze provides a unified interface for querying and migrating data across various storage systems (PostgreSQL, MongoDB, Hadoop, Spark, etc.) when datasets exceed memory limits.
It abstracts the complexities of different back‑ends, making it easier to perform analytical computations on large data.
Other notable but less‑known Python libraries include Gym + Universe, Boto3, Hug, Scrapy, and Beautiful Soup.
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