Python-fu in the GIMP
GIMP (the GNU Image Manipulation Program) is great all by itself but is even better with Python-fu. This talk demonstrates a little Python-fu to manipulate images in GIMP.
Factor analysis: simplifying high dimensional data sets for visualization and machine learning
For many machine learning problems, there are far more dimensions to our data than there need to be for efficient learning. Often a first step is dimensionality reduction to remove both redundancy and noise. In addition to more efficient automated learning, factor analysis allows us to visualize high dimensional data sets in our standard human-limited 2 or 3 dimensions. For demonstration, we will apply PCA on a set of questions asked of the audience to map everyone onto a 2D "personality" map - allowing us to visualize the underlying personality factors of those present. Beyond fun visualizations, these techniques are the basis of more efficient generalization in many machine learning problems.
Fancy genetics and simple scripts: Manipulating DNA data and becoming more proficient with Python
Our ability to read the genetic code of organisms and to use DNA sequencing to learn new biology has benefited tremendously from technological advances in the past ten years. My lab looks at how animals get colonized with specific bacteria. As we have been generating more data it has become clear that we are underutilizing the information. We are beginning to build resources to be more efficient and clever at data processing and data mining from biological samples. I'll talk a little about the science in the lab and show one of our Python projects that is functional but in its early stages. I am eager for feedback, and I think the talk will have resonance for a new motivated Python user in any field.
113 Python enthusiasts attended this meeting.
Setting Up Machine learning with anaconda
5 min What is anaconda and how do i use it
5 min What is ipython
10 min Why machine learning is fun and how to do easy classification tasks
uWSGI is a very popular software package, but most Python programmers just connect it to nginx, and leave it at that. I'll be exploring some of the more advanced features of uWSGI, and how they can make your life easier.
Why You Can't Sit With Us - Understanding Network Analysis in Python With Mean Girls
Network analysis is a handy tool used to understand group dynamics, provide product recommendations, and prevent homicides (and other things). This talk will introduce the theory behind network analysis and showcase the flexibility of Python's NetworkX library. No knowledge of network analysis (or Mean Girls) is needed, but basic knowledge of Python and the iPython Notebook, will be helpful.
I gave this talk last month in Columbus OH at PyOhio 2015.
ChiPy Mentorship Oct-Dec 2015
The wait is over! ChiPy's Mentorship program returns for the third time. We learned a lot from the previous two mentorship program and will do things a bit differently this time. This will be a quick overview how we are going to conduct the ChiPy's Python mentorship program.
114 Python enthusiasts attended this meeting.
Keep calm and conda install
Jonathan J. Helmus
Conda is a cross platform, package management system widely used in the scientific and data science Python communities. Although designed for Python packages, conda can be used to package and distribute software written in any language. This talk will cover how to use conda to install and manage scientific packages as well as how conda can be used to create isolated Python environment similar to virtualenv. Conda’s use within the Anaconda and Miniconda Python distributions will be discussed as an easy method for obtaining a full featured SciPy stack. Instructions on building packages with conda and hosting them on Anaconda.org will be covered briefly.
Data Games in Python
C. S. Schroeder
There has been recent work on the taxonomy of games which are based, one way or another, on real world data. Typically these games help people learn that data or how to cope with it. The traditional examples are simulation games (flight, driving, etc.), while other games incorporate data in such a way that it is beneficial to learn the real world data in the game play (trivia). These types of data-games commonly have a domain specific focus. We intend to explore the possibility of interactive games which help people to learn data analysis, in general, implementing some such games in python using web2py and Scipy.
Automating a fishtank with python and IoT sensors
Fish tanks are simple enough that even a child can maintain them. I don't have children yet to maintain my tank, but luckily my very patient wife has allowed me to explore over engineering a solution.
In this talk we'll explore how python scripts running on a Raspberry Pi can be used to measure and control many aspects of maintaining a fish tank or any number of IOT applications.
142 Python enthusiasts attended this meeting.
Machine Learning with Python
I will briefly describe my journey into applied machine learning using Python packages like scikit-learn and statsmodels.
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.
Brief introduction to the Quantopian api which is used for trading financial assest with python.
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.
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.
71 Python enthusiasts attended this meeting.
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.
DePy 2015 Review
A quick recap of the Chicago DePy conference that occurred this month.
PyCon 2015 Review
109 Python enthusiasts attended this meeting.