Formula One Data Visualization and Interpretation: adventures in mentorship
We participated in the Chipy mentorship program.
Our plan for the mentorship was to use Python to visualize and interpret Formula One racing data. Join us to hear about the triumphs and obstacles we encountered along the way.
Building a Temperature Control Program for Monitoring Aquaculture Tanks Using Raspberry Pi and Python
Growth of the Mentee as a Pythonista
I have turned from totally no experience with Python to gaining a good amount of knowledge in this language. I have learned from the very basic syntaxes to writing functions, then writing functions for different types of data (list, string, integer, decimal, float, epoch, threshold…) to serve various purposes; I know how to install redis, bokeh and flask for data acquisition, storage and performance; I also learned how to send an email alert from the Raspberry Pi with Python, thanks to the hackathon midterm meetup and my mentor. And because our project covers a wide range of activities, I have learned a lot of the fundamental elements of Python as well as programming in general.
Above all, the best thing I have learned about Python through this Mentorship program is being confident and feeling more comfortable with it. Before this project, I wasn’t really sure about Python. Is it what I want or I might be better off with other languages? But after finished the project, I can say it was fun, and it serves well what I want to do. So I decided to move forward with it. And even though this is my very first programming language, but the dynamic from its strong supportive community, rich wonderful open sources and inspiring opportunities like this Mentorship program, all makes me feel that Python is a good choice.
The Mentor's role
When I asked my mentor for his advices on learning programming, he told me that to him, the best way to learn is doing projects, just like what we are doing. And that is so true. Sometimes I feel like the best way of learning how to swim is just jumping into the water, like doing a project; it can be scary, uncertain, and possibly failed, but it can also be very exciting and thrilling. Of course, one should only jump with a life preserver if she never knows how to swim before. And our mentors are life preservers. For a novice, it could be very confused at first of where to go, what direction to take, or how to get there; and easy be overwhelmed by too much information. The life saver may not be able to tell you what direction to take either, but at least, it will help you have some time to think and to practice before you decide your next moves. Obviously, a mentor is much better than a life saver, because no life saver can talk nor answer questions; and the best part is, they have a lot of experiences in their hands and are willing to share them with you.
Brief introduction to the Quantopian api which is used for trading financial assest with python.
Why learning process matters to student dev's
I took up learning Python and Web Development early this year. I started attending Django lessons held by folks in the community. After the lessons students had trouble finding help learning together. To help everyone organize I founded the Django Study Group. I've been learning for the last six months but I am still a student.
I joined the Chipy mentorship program to learn from a local professional Python developer. While enrolled in that I took the opportunity to join a student team led by Brian Ray for more experience learning to code. It was working alongside Brian that I learned the importance of how you build software.
Machine Learning with Python
I will briefly describe my journey into applied machine learning using Python packages like scikit-learn and statsmodels.
71 Python enthusiasts attended this meeting.
PyCon 2015 Review
DePy 2015 Review
A quick recap of the Chicago DePy conference that occurred this month.
Introduction to PySpark
Big Shoulders Data Camp presents an “Introduction to PySpark”. One of our top instructors and data scientists, Adam McElhinney, will be giving a talk on working with PySpark, and presenting a use case. Audience is encouraged to come prepared to take notes, ask questions, and get a high-level understanding on one of Python's many analytical libraries.
109 Python enthusiasts attended this meeting.
QML vs. Python
Patrick K. O'Brien
If you think Python is Pythonic, wait until you see QML from the point of view of an experienced Python developer. QML is the Qt Meta Language or Qt Modeling Language. http://en.wikipedia.org/wiki/QML
Is True true? : A mini-venture into Python & Ruby truth testing
Review of truth testing in Python and Ruby. If "Explicit is better than Implicit" then why does Python decide that values like empty sequences are "falsey"? How is it that Ruby only defines false and nil as false values, isn't this more explicit?
Highlight how languages embed their own philosophies of what is correct and true with surprising overlaps and at times odd contradictions.
ULS Erlang entry
Go: Concurrency is Built In
Discussing the pros and cons of Golang from a Python user's perspective, particularly focusing on its built-in support for concurrency and the advantages over asyncio.
as former C# developer the lessons I learned to become pythonic
language comparison in 5 minutes
Conway's Game of Life: Programming in a non-language
The Game of Life is Turing Complete. That means it can (theoretically) calculate anything that any computer can calculate. What does this mean in practice and how can you program a calculation when the total syntax is just flipping cells in a 2D bit field?
R and Python for regression
Let's compare our favorite language to an 'upstart' highly focused statistical language.
Postscript. Yes, it's a programming language
I'll describe Postscript - a interpreted, stack-based "page description language" used to produce vector graphics and documents.
106 Python enthusiasts attended this meeting.
From Code to Coffee Table with Blender
I've been developing a Python library for turning 3D models into CNC-machinable parts. I will demonstrate the basics of the library and how I used it to build a wood coffee table.
A Talk on Giving a Pythonic Talk
Xan Vongsathorn and Catherine Vongsathorn will be giving a talk about talks. It turns out that many of python's core principles apply very well to presentations -- or for that matter, communication more generally -- which may be why we like python so much. Xan and Catherine want to get people excited not only about giving talks but also about using them to *actually communicate*. You don’t have to be an expert, nor do you need natural talent, to give a good talk; this metatalk will discuss guiding principles that set effective presentations apart and can be applied to any technical talk.
99 Python enthusiasts attended this meeting.
Django+Elasticsearch+Haystack to Search PDFs and Such
Have you ever wanted to search the contents of uploaded PDFs, Docs, and other document formats on your website? Django Haystack (with the Elasticsearch search backend) is a great way to add search to your site, but it does not support full document indexing out of the box. I'd like to share a solution that I cobbled together to allow this combination of tools the ability to search document contents
REST on Django
A quick overview through the components that make up Django REST Framework with a dive into a sample project.
Video Link: <<https://www.youtube.com/watch?v=UVC62eGQTOQ>>
83 Python enthusiasts attended this meeting.