8 Python Frameworks & Libraries to Check Out in 2019

It makes use of an existing data utility to train the chatbot during conversations.

Through this existing data utility, Chatterbot is able to build chatbots that can be trained to converse in any language.

Doing NLP through the full Natural Language Toolkit (NLTK) package is some trouble, considering its size.

With Chatterbot, you do not have to download the full NLTK package to perform Natural Language Processing.

If you are a web developer, you’d be interested in the library as it can be integrated easily with Django.

Therefore, you can build chatbots and integrate them into in your applications, without having to do a ton of machine learning work.

Installing ChatterbotChatterbot is easy to install.

As simple as using the pip install command:pip install chatterbotStart creating amazing stuff with Chatterbot right away, its documentation is quite comprehensive.

Zappa (Cloud, Web Development)GitHub: 8,537 StarsCloud computing keeps getting bigger with more services coming to limelight.

The concept of serverless applications has been gaining a lot of attention in recent times.

If you do not already know what serverless is, serverless is basically a computing service that ensures you only pay for your application’s uptime and do not have to worry about its scalability.

So you only pay when the application is invoked and tasks such as load balancing are automated.

Serverless tries to abstract the whole concept of the cloud from you the developer, so you can focus on building alone.

Thus, Zappa is a library that aims to help you focus better.

Zappa is a Python library for deploying serverless Python applications to AWS Lambda.

This amazing library ensures you don’t have to touch anything relating to AWS before you deploy.

Just leave the deployment task to it, and it would do it gracefully.

Zappa is powered by AWS Lambda (which is a compute service from Amazon to run serverless code).

Zappa should be your go-to library for deploying serverless Python web applications.

You should check out Zappa anytime you need to deploy your microservices or large applications using Flask, Pyramid or Django.

As an extra service, Zappa can also be used to schedule functions to occur at intervals which can be an alternative to the powerful Celery, especially if you do not want to get involved in the hassle that comes with setting up Celery.

Installing ZappaYou can install Zappa using the pip command, as seen below:pip install zappaAfter install, you need to have a valid AWS account and properly setup your AWS credentials.

This is because Zappa detects the application you are trying to deploy and creates the deployment configuration settings automatically.

To deploy your application, all you do is:zappa initzappa deployYour application will be deployed with the command above.

It can be as easy as that.

To learn more about Zappa, checkout its documentation.

spaCy (Data Science, Machine Learning)GitHub: 12,114 StarsIf Natural Language Processing (NLP) is something you are interested in, then spaCy is the library for you.

spaCy is an open source library for performing advanced Natural Language Processing using Python.

While NLP is quite easily done in English, spaCy has extended support for up to 34 languages including but not limited to Greek, Arabic, Spanish and German.

You can also make use of 13 statistical models available in 8 languages.

You’ll be amazed at the speed at which spaCy works.

It is about the fastest NLP library you would find.

As an NLP library, spaCy is able to help in building chatbots, but the documentation states that it is mainly for text processing.

As someone interested in machine learning, you probably would have an eye for deep learning too.

spaCy allows you bring your deep learning adventures on board.

Good news is that spaCy integrates well with frameworks for deep learning such as Tensorflow, PyTorch, Scikit-learn.

You can easily use spaCy for text processing in production as it is fast and secure.

While spaCy works fine with CPU, it also works wonders with GPU.

Installing spaCyTo make use of spaCy, you can install using the pip tool as seen below:pip install spacyAfter download, here is the documentation for spaCy to get your hands dirty right away.

Remember spaCy supports different languages for the models, you can actually download the model for the language you prefer.

This can be done with the command below:python -m spacy download language-codeThe “language-code” argument in this can be en for English, de for German, pt for Portugese, etc.

You can find out more about the models and available languages.

Graphene (Web Development)Github: 4,124 StarsIt is normal to have web applications fetch data from different sources.

While REST APIs can be great, you’d face certain roadblocks when using them — such as over-fetching and under-fetching.

GraphQL ensures those roadblocks are overcomed.

GraphQL has become a popular data query language ever since it was released by Facebook.

It has also become very important in building and querying APIs.

While GraphQL and JavaScript looks like a match made in heaven, it is also possible to use GraphQL in other languages.

Graphene is an open source Python library that allows you use GraphQL in your Python projects.

It provides you with a complete API for doing this, making the whole process a piece of cake.

This library should be of interest to every backend Python developer, you can build your own GraphQL APIs with it.

Graphene also has integrations for Django, SQLAlchemy, the Google App Engine and Mongo.

If you are a developer who is interested in GraphQL, Graphene should make your life much easier.

Installing GrapheneLike the other tools in this article, Graphene can be installed using the pip tool with the command below:pip install grapheneTo learn more about Graphene, check its documentation.

ConclusionIn this article, you’ve seen 8 Python libraries you really need to check out in 2019.

You can choose to make use of them in a side project, and they are also suitable to be used in production.

These libraries cover fields such as Cloud, Data Science, Machine Learning, Web Development which are hot fields in IT today.

Considering the size of the Python open source community, there are a lot of amazing libraries being released every now and then.

Therefore, you can’t find all of those amazing frameworks and libraries in one article.

Is there any framework or library you’d have expected to find in this article?.Share it in the comments.

Thanks for reading and Happy Pythoning!.

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