Python Weekly (Issue 382 January 31 2019)

Python Weekly - Issue 382

January 31, 2019

Python Weekly

Welcome to issue 382 of Python Weekly. Let's get straight to the links this week.

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Articles, Tutorials and Talks

Learn to do a complete data analysis project using only basic Python to find out what genre of apps an app developer should focus on.

Build your own deep learning dataset and detection model using public Instagram photos of #streetart, the TensorFlow and Keras frameworks, and FloydHub.

Ever wondered how to programmatically define technical patterns in price data? At the fundamental level, technical patterns come from local minimum and maximum points in price. From there, the technical patterns may be defined by relative comparisons in these min/max points. Let’s see if we could have played this by algorithmically identifying any inverse head & shoulders patterns!

Make django completely serverless using Lambda & Aurora-Serverless.

It’s easy to talk about pretty code. Let’s talk about nasty code and see what we can learn from it.

In this post I’ll take you from start to finish in integrating SendGrid to your django project via django-anymail, for using outgoing email and inbound parse to send, receive, and/or forward your custom domain email messages. For free

In this tutorial, you will learn how to train a Convolutional Neural Network (CNN) for regression prediction with Keras. You’ll then train a CNN to predict house prices from a set of images.

This tutorial explains matplotlib’s way of making plots in simplified parts so you gain the knowledge and a clear understanding of how to build and modify full featured matplotlib plots

This article introduce the basic concept of Django and the Django REST Framework and let you build a basic CRUD API on Fedora.

A quick and dirty tutorial on how to get things done.

This is an updated article that shows how to use pandas to create a diff tool that compares two similar Excel spreadsheets.

Books

Mine the rich data tucked away in popular social websites such as Twitter, Facebook, LinkedIn, and Instagram. With the third edition of this popular guide, data scientists, analysts, and programmers will learn how to glean insights from social media—including who’s connecting with whom, what they’re talking about, and where they’re located—using Python code examples, Jupyter notebooks, or Docker containers.

Python Jobs of the Week

We are an innovative company and have made it our mission to promote the integration of renewable and energy-efficient technologies. You will be involved in conceptualizing design and development of an innovative system for visualization, decentralized control and real-time optimization of energy systems. You will also be doing architecture design, planning and structuring of the technical implementation.

Interesting Projects, Tools and Libraries

Push Notifications that work with just about every platform!

Convert any Python file into a single line of code.

Serve click scripts over the web with minimal effort.

A Telegram bot to monitor and control deep learning experiments.

A pre-trained biomedical language representation model for biomedical text mining.

A Simple and Versatile Framework for Object Detection and Instance Recognition.

Simple and powerful NLP framework, build your own state-of-art model in 5 minutes.

A Linear-Operator Library for Python.

New Releases

This is a major release from 0.23.4 and includes a number of API changes, new features, enhancements, and performance improvements along with a large number of bug fixes.

Upcoming Events and Webinars

The SciPy Japan Conference aims to bring together the growing Scientific Python community in Japan to showcase the latest projects, and learn from skilled users and developers. The Call for Proposals is open through Feb. 15.

There will be following talks

  • Generating Bibliographies in Python

  • PySpark for Data Engineering

  • Processing Hobby Smarthome Data with Open Source Python and Displaying it on the Web

Our Other Newsletters

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