by Michael Kennedy (@mkennedy)
Talk Python to Me is a weekly podcast hosted by Michael Kennedy. The show covers a wide array of Python topics as well as many related topics (e.g. MongoDB, AngularJS, DevOps).The format is a casual 45 minute conversation with industry experts.
There has been a bunch of new Python web frameworks coming out in the past few years. Generally, these have been focused solely on Python 3 and have tried to leverage Python's new async and await features.
Voice assistants and voice interfaces are quickly becoming the new, hot way to interact with computers. Two of the notable ones are amazon echo devices and google home devices. Wouldn't it be great if we could program these with Python? Even better if we could use well-known APIs such as Flask.
It's been an amazing year for Python. We've seen its meteoric growth continue to become the most popular, major programming language. We've seen significant grants and funding come in for open source. And this just might be the year that the Python 2 or Python 3 question was finally settled.
We all know Python is becoming increasingly important in both science and machine learning. This week we journey to the very forefront of Physics.
Do you run a web application or web service? You probably do a couple of things to optimize the performance of your site. Make sure the database response quickly and more. But did you know a well of performance improvements live in your web servers themselves?
Is there some task you find yourself performing frequently, repetitively on the web? With Python and modern tooling, virtual every website has become easily scriptable.
How many Python developers do you know that learned Python quickly but then plateaued pretty quickly as well. Maybe this is someone you worked with or maybe it's even you. Python's clean and simple syntax can mean it's easy to learn but hard to master.
How do you learn libraries or parts of Python itself that you don't have actual work projects involving them? Whether that's SQLAlchemy, Slack bots, or map APIs, actually building projects (small and large) with them is really the only way to gain true competency.
Data science is one of the fastest growing segments of software development. It takes a slightly different set of skills than your average full-stack development job. This means there's a big opportunity to get into data science. But how do you get into the industry?
Have you noticed that web development is kind of hard? If you've been doing it for a long time, this is easy to forget. It probably sounds easy enough