Ultimate Langauge Shootout
Multiple Langauge competition:
* Clojure - Cezar Jenkins
* SQL - Heather White
* Babbage's Analytical Engine programming cards - Phil Robare
* R - Parfait
* Assembly (AVR) - Nick Timkovich
* Groovy - Jerry Dumblauskas
* Swift - Matt Green
* Julia - Andrew Webster
110 Python enthusiasts attended this meeting.
Popular ORM Libraries
What's the main difference between SQLAlchemy and Django's ORM? When might a person prefer Pony ORM or peewee? -- popular Object-Relational Mapping libraries in Python are compared and contrasted.
Using Tasks in Asyncio Web Apps
In this talk, I will be talking about starting, stopping, and displaying incremental data from long-running tasks in an asyncio-based web application.
Developing with Python at Telnyx
This talk will cover the development cycle, build tools, and python frameworks commonly used by Telnyx Python engineers.
117 Python enthusiasts attended this meeting.
Expanding Our Early Intervention System for Adverse Police Interactions
Many police departments in the United States use “early intervention systems” to identify officers who may benefit from additional training, resources, or counseling. These systems attempt to determine behavioral patterns that predict a higher risk of future adverse incidents, ranging from excessive use of force and citizen complaints to on-duty accidents and personal injury. Detecting these risk factors enables departments to develop targeted interventions and make operational changes to protect officer safety and improve police/community interactions.
Last summer, DSSG worked with the Charlotte-Mecklenburg Police Department on building a better early intervention system, applying data analysis to provide insights on individual and situational risk factors for adverse interactions. This year, we will partner with additional police departments, including the Metro Nashville Police Department, to test and expand this work in new municipalities, improving both the overall model and local performance. Like last year, we will use anonymized police data and contextual data about local crime and demographics to detect the factors most indicative of future issues, so that departments can provide additional support to their officers.
Predictive Enforcement of Pollution and Hazardous Waste Violations in New York State
New York State’s Department of Environmental Conservation (NYSDEC) is the regulatory agency for environmental issues in the state. Their mission is to conserve, improve and protect New York State’s natural resources and environment and to prevent, abate and control water, land and air pollution. NYSDEC currently conducts approximately 700 inspections each year of facilities in the state that manage hazardous waste.
DSSG will work on more effectively allocating inspection resources by creating predictive models that identify facilities with high likelihood of violating environmental regulations. In 2015, we worked with the federal EPA targeting hazardous waste facilities subject to the Resource Conservation and Recovery Act (RCRA). With inspection data from NYSDEC and the public RCRA dataset, we will build a similar model to identify RCRA violators specifically in the New York region, as well as further explore the possibility of applying models to other compliance inspection programs, such as the Clean Air and Clean Water Acts.
143 Python enthusiasts attended this meeting.
Intro to Deploying Django with Ansible
Deploying Django is a breeze when using Ansible. Learn a bit about the power that Ansible provides and how easy it is to get started using it!
Getting meaningful results from unit tests
We count on unittests failure messages to give us reasonable feedback on how to proceed with a failing test. When you're working with builtin data structures and objects, unit test feedback is usually pretty good. It helps you identify and solve the problem. In contrast, the default results you get from custom objects can be practically useless.
Computer Vision in Python: How to build a basic face detection system
In this short talk Jeremy will describe the universal pipeline for performing object detection (that is, the automatic detection of objects in digital images) in Python. This will include a discussion of various classification schemes, feature extraction methods, and their fusion in the form of deep neural networks. Demo code illustrating these concepts will be shown using the IPython notebook environment.
144 Python enthusiasts attended this meeting.
JIRA + Python
JIRA is a popular issue tracking and project management software. In this talk you will learn about JIRA and how to interact with the tool using the jira-python library. Primary topics covered will include an overview of the API, creating and modifying issues, linking issues, and searching from Python.
PyCon 2016 recap
Let's give an overview of Pycon
An overview of python projects of OS X administration
In this talk Ryan will give a brief overview of several python projects used by many OS X admins to provide cached update services, imaging, software deployment, and configuration management.
Looking at the code in these tools can provide insight into Apple's preferences frameworks and if you are in a "DevOps" type role some of the projects discussed may help you immensely.
Brian will reveal the survey results that will help shed some light on the current and future projectile of the Python programming language. Has Python reached a peak? Will it popularity continues to rise? What are the users of Python at different levels saying about the state of Python Programming Language?
121 Python enthusiasts attended this meeting.