Statistics for Software: Instrumenting Code for Reliability and Performance
https://www.paypal-engineering.com/2016/04/11/statistics-for-software/
/r/pystats
https://redd.it/4ecj6q
https://www.paypal-engineering.com/2016/04/11/statistics-for-software/
/r/pystats
https://redd.it/4ecj6q
Solving a nonlinear ordinary differential equation
http://nbviewer.jupyter.org/github/Jabberwockyll/NumericalODE/blob/master/numerical_ode.ipynb
/r/JupyterNotebooks
https://redd.it/4awf63
http://nbviewer.jupyter.org/github/Jabberwockyll/NumericalODE/blob/master/numerical_ode.ipynb
/r/JupyterNotebooks
https://redd.it/4awf63
nbviewer.jupyter.org
Notebook on nbviewer
Check out this Jupyter notebook!
Jupyter Events Calendar
This calendar contains the dates and locations of Jupyter Developer talks and workshops.
[Jupyter Events](https://calendar.google.com/calendar/embed?src=p51j0ac1iccmj44tae12hq4dk0%40group.calendar.google.com&ctz=America/Los_Angeles)
/r/IPython
https://redd.it/4t2g9y
This calendar contains the dates and locations of Jupyter Developer talks and workshops.
[Jupyter Events](https://calendar.google.com/calendar/embed?src=p51j0ac1iccmj44tae12hq4dk0%40group.calendar.google.com&ctz=America/Los_Angeles)
/r/IPython
https://redd.it/4t2g9y
Causal Discovery Software Available for Big Data Analysis
The Center for Causal Discovery (CCD) (www.ccd.pitt.edu) has released the Fast Greedy Search (FGS) algorithm (an optimized version of Chickering's Greedy Equivalence Search algorithm) for use by biomedical investigators who are searching for causal associations in large sets of continuous data. A technical paper describing the optimization is available at http://arxiv.org/abs/1507.07749. It is available as free and open source software. This release is just the first step toward providing a suite of algorithms that will assist biomedical researchers in analyzing their data to obtain causal insights.
Using simulated data, FGS was able to learn a causal network on data containing 50,000 variables and 1,000 samples in about 15 minutes on a laptop computer. While FGS does not model hidden variables that cause two or more measured variables, an upcoming release of another algorithm will do so.
FGS is available as a command line implementation (Causal-cmd) that calls a local Java library or as a Java web application (Causal-web) that runs the analysis at the Pittsburgh Supercomputing Center; the API’s can also be run through R (R-causal) or Python (Py-causal). Additional details and instructions for downloading both these versions of the software are available at
http://www.ccd.pitt.edu/wiki/index.php?title=Tools_and_Software.
Our goal is to help the biomedical community use causal modeling to gain novel insights and drive innovative research, so we hope to make these tools as usable and useful as possible. We welcome any and all feedback that you might have, which will help us improve this and future releases.
/r/pystats
https://redd.it/4cqctx
The Center for Causal Discovery (CCD) (www.ccd.pitt.edu) has released the Fast Greedy Search (FGS) algorithm (an optimized version of Chickering's Greedy Equivalence Search algorithm) for use by biomedical investigators who are searching for causal associations in large sets of continuous data. A technical paper describing the optimization is available at http://arxiv.org/abs/1507.07749. It is available as free and open source software. This release is just the first step toward providing a suite of algorithms that will assist biomedical researchers in analyzing their data to obtain causal insights.
Using simulated data, FGS was able to learn a causal network on data containing 50,000 variables and 1,000 samples in about 15 minutes on a laptop computer. While FGS does not model hidden variables that cause two or more measured variables, an upcoming release of another algorithm will do so.
FGS is available as a command line implementation (Causal-cmd) that calls a local Java library or as a Java web application (Causal-web) that runs the analysis at the Pittsburgh Supercomputing Center; the API’s can also be run through R (R-causal) or Python (Py-causal). Additional details and instructions for downloading both these versions of the software are available at
http://www.ccd.pitt.edu/wiki/index.php?title=Tools_and_Software.
Our goal is to help the biomedical community use causal modeling to gain novel insights and drive innovative research, so we hope to make these tools as usable and useful as possible. We welcome any and all feedback that you might have, which will help us improve this and future releases.
/r/pystats
https://redd.it/4cqctx
Practical Deep Learning For Coders—18 hours of lessons for free
http://course.fast.ai/
/r/Python
https://redd.it/5jgv98
http://course.fast.ai/
/r/Python
https://redd.it/5jgv98
Practical Deep Learning for Coders
Practical Deep Learning for Coders - Practical Deep Learning
A free course designed for people with some coding experience, who want to learn how to apply deep learning and machine learning to practical problems.
Why class-based views are not all that great
https://medium.com/@patrys/why-class-based-views-are-not-all-that-great-4b3202f38309
/r/django
https://redd.it/5jgcf6
https://medium.com/@patrys/why-class-based-views-are-not-all-that-great-4b3202f38309
/r/django
https://redd.it/5jgcf6
Medium
Why class-based views are not all that great
Disclaimer: the views and opinions presented here are mine and do not reflect those of my employer, Mirumee Software.
Using R with Jupyter Notebooks running a Python Kernel
http://www.rittmanmead.com/2016/07/using-r-jupyter-notebooks-big-data-discovery/
/r/IPython
https://redd.it/4stbeg
http://www.rittmanmead.com/2016/07/using-r-jupyter-notebooks-big-data-discovery/
/r/IPython
https://redd.it/4stbeg
Rittman Mead
Using R with Jupyter Notebooks and Oracle Big Data Discovery
Oracle's Big Data Discovery encompasses a good amount of exploration, transformation, and visualisation capabilities for datasets residing in your organisation’s data reservoir. Even with this though, there may come a time when your data scientists want to…
Recycle Python Program
I am still in the mist of learning python programming and I wanted to create a little program. This program basically is an alternative to the "rm" command we love and hate on our linux/unix based systems. The program instead sends things to the recycle/trash bin. I am still trying to expand on the functionality of the program but I do hope you all can check it out and maybe test it out a bit and let me know what you think. Something like this may exist already but I guess I just wanted to create my first "why isn't there..." based program.
I created it on my my mac, and I think the program will work on linux too. I had issues getting the install script to run on systems that use "yum" package manager. I tried to automate it all, but honestly all you need to run the program is python3 and send2trash module using pip3. Would love to have suggestions on improving the code to make it as elegant as possible.
https://github.com/tarrell13/Recycle
/r/Python
https://redd.it/5jib2h
I am still in the mist of learning python programming and I wanted to create a little program. This program basically is an alternative to the "rm" command we love and hate on our linux/unix based systems. The program instead sends things to the recycle/trash bin. I am still trying to expand on the functionality of the program but I do hope you all can check it out and maybe test it out a bit and let me know what you think. Something like this may exist already but I guess I just wanted to create my first "why isn't there..." based program.
I created it on my my mac, and I think the program will work on linux too. I had issues getting the install script to run on systems that use "yum" package manager. I tried to automate it all, but honestly all you need to run the program is python3 and send2trash module using pip3. Would love to have suggestions on improving the code to make it as elegant as possible.
https://github.com/tarrell13/Recycle
/r/Python
https://redd.it/5jib2h
GitHub
tarrell13/Recycle
Recycle - Command line program used to send documents to the Trash Bin instead of deleting.
Image Processing 101
http://nbviewer.jupyter.org/github/piratefsh/image-processing-101/blob/master/Image%20Processing%20101.ipynb
/r/JupyterNotebooks
https://redd.it/4aeiyr
http://nbviewer.jupyter.org/github/piratefsh/image-processing-101/blob/master/Image%20Processing%20101.ipynb
/r/JupyterNotebooks
https://redd.it/4aeiyr
New contest: make real life IoT projects using Python and microcontrollers
http://www.zerynth.com/blog/make-real-life-iot-projects-using-python-and-microcontrollers/
/r/Python
https://redd.it/5jjfro
http://www.zerynth.com/blog/make-real-life-iot-projects-using-python-and-microcontrollers/
/r/Python
https://redd.it/5jjfro
Zerynth - Python for Microcontrollers, IoT and Embedded Solutions
Make Real Life IoT projects using Python and Microcontrollers
IoT is potentially one of the most important trends in the history of the industry. But it’s time to move beyond "potential.” It’s time to make IoT real!
nbextension: HTML WYSIWYG editor for Markdown/HTML cells
https://github.com/genepattern/jupyter-wysiwyg
/r/IPython
https://redd.it/4sp3rv
https://github.com/genepattern/jupyter-wysiwyg
/r/IPython
https://redd.it/4sp3rv
GitHub
genepattern/jupyter-wysiwyg
A WYSIWYG markdown/HTML editor for Jupyter Notebook - genepattern/jupyter-wysiwyg
Internal Server Error!
Hi all!
Back again here with a flask question. I am a beginner and learn some awesome things from reddit (how to legit code).
Here is my code that I have grabbed certain information from an API that I am now trying to host locally through flask.
from flask import Flask, render_template
import httplib
import json
app = Flask(__name__)
@app.route('/')
def index():
connection = httplib.HTTPConnection('api.football-data.org')
headers = {'X-Auth-Token': 'this is my api token here', 'X-Response-Control': 'minified'}
connection.request('GET', '/v1/competitions/426/leagueTable', None, headers)
response = json.loads(connection.getresponse().read().decode())
return response
if __name__ == '__main__':
app.run()
When I run 127.0.0.1:5000 I get:
Internal Server Error
The server encountered an internal error and was unable to complete your request. Either the server is overloaded or there is an error in the application.
Here is what my server is telling me!
MacBooks-MBP:Football macbookpro13$ python Footy_Web.py
* Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)
[2016-12-20 13:58:17,493] ERROR in app: Exception on / [GET]
Traceback (most recent call last):
File "/Library/Python/2.7/site-packages/flask/app.py", line 1988, in wsgi_app
response = self.full_dispatch_request()
File "/Library/Python/2.7/site-packages/flask/app.py", line 1642, in full_dispatch_request
response = self.make_response(rv)
File "/Library/Python/2.7/site-packages/flask/app.py", line 1746, in make_response
rv = self.response_class.force_type(rv, request.environ)
File "/Library/Python/2.7/site-packages/werkzeug/wrappers.py", line 847, in force_type
response = BaseResponse(*_run_wsgi_app(response, environ))
File "/Library/Python/2.7/site-packages/werkzeug/wrappers.py", line 57, in _run_wsgi_app
return _run_wsgi_app(*args)
File "/Library/Python/2.7/site-packages/werkzeug/test.py", line 871, in run_wsgi_app
app_rv = app(environ, start_response)
TypeError: 'dict' object is not callable
127.0.0.1 - - [20/Dec/2016 13:58:17] "GET / HTTP/1.1" 500 -
I should mention this code works outside of the flask framework!
EDIT:
I HAVE SOLVED THIS THANKS TO STACKOVERFLOW:
I NEEDED TO print(jsonify(response))
/r/flask
https://redd.it/5jfdnx
Hi all!
Back again here with a flask question. I am a beginner and learn some awesome things from reddit (how to legit code).
Here is my code that I have grabbed certain information from an API that I am now trying to host locally through flask.
from flask import Flask, render_template
import httplib
import json
app = Flask(__name__)
@app.route('/')
def index():
connection = httplib.HTTPConnection('api.football-data.org')
headers = {'X-Auth-Token': 'this is my api token here', 'X-Response-Control': 'minified'}
connection.request('GET', '/v1/competitions/426/leagueTable', None, headers)
response = json.loads(connection.getresponse().read().decode())
return response
if __name__ == '__main__':
app.run()
When I run 127.0.0.1:5000 I get:
Internal Server Error
The server encountered an internal error and was unable to complete your request. Either the server is overloaded or there is an error in the application.
Here is what my server is telling me!
MacBooks-MBP:Football macbookpro13$ python Footy_Web.py
* Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)
[2016-12-20 13:58:17,493] ERROR in app: Exception on / [GET]
Traceback (most recent call last):
File "/Library/Python/2.7/site-packages/flask/app.py", line 1988, in wsgi_app
response = self.full_dispatch_request()
File "/Library/Python/2.7/site-packages/flask/app.py", line 1642, in full_dispatch_request
response = self.make_response(rv)
File "/Library/Python/2.7/site-packages/flask/app.py", line 1746, in make_response
rv = self.response_class.force_type(rv, request.environ)
File "/Library/Python/2.7/site-packages/werkzeug/wrappers.py", line 847, in force_type
response = BaseResponse(*_run_wsgi_app(response, environ))
File "/Library/Python/2.7/site-packages/werkzeug/wrappers.py", line 57, in _run_wsgi_app
return _run_wsgi_app(*args)
File "/Library/Python/2.7/site-packages/werkzeug/test.py", line 871, in run_wsgi_app
app_rv = app(environ, start_response)
TypeError: 'dict' object is not callable
127.0.0.1 - - [20/Dec/2016 13:58:17] "GET / HTTP/1.1" 500 -
I should mention this code works outside of the flask framework!
EDIT:
I HAVE SOLVED THIS THANKS TO STACKOVERFLOW:
I NEEDED TO print(jsonify(response))
/r/flask
https://redd.it/5jfdnx
reddit
Internal Server Error! • /r/flask
Hi all! Back again here with a flask question. I am a beginner and learn some awesome things from reddit (how to legit code). Here is my code...
Visualize missing data with the missingno package
https://github.com/ResidentMario/missingno
/r/pystats
https://redd.it/4c8vnb
https://github.com/ResidentMario/missingno
/r/pystats
https://redd.it/4c8vnb
GitHub
GitHub - ResidentMario/missingno: Missing data visualization module for Python.
Missing data visualization module for Python. Contribute to ResidentMario/missingno development by creating an account on GitHub.
Django Development on OSX - Best Practices?
I have very little experience with OSX, but work is getting me a macbook pro.
I have 20 years experience with windows and maybe effectively 2-3 years with linux.
I use Windows for work. I've learned three things about python/django development on windows:
1. It's possible
2. Don't. It's a trap!
3. Windows is great for running VMs which allow you to use Vagrant or a remote interpreter.
Just learned that I'm getting a macbook pro for work, so I figured I'd put a little thought into my work environment before I get it. Might be a few weeks with the holidays.
For the most part, I use Pycharm Professional for django development.
Are there pitfalls or best practices developing django on OSX?
/r/django
https://redd.it/5jk12r
I have very little experience with OSX, but work is getting me a macbook pro.
I have 20 years experience with windows and maybe effectively 2-3 years with linux.
I use Windows for work. I've learned three things about python/django development on windows:
1. It's possible
2. Don't. It's a trap!
3. Windows is great for running VMs which allow you to use Vagrant or a remote interpreter.
Just learned that I'm getting a macbook pro for work, so I figured I'd put a little thought into my work environment before I get it. Might be a few weeks with the holidays.
For the most part, I use Pycharm Professional for django development.
Are there pitfalls or best practices developing django on OSX?
/r/django
https://redd.it/5jk12r
reddit
Django Development on OSX - Best Practices? • /r/django
I have very little experience with OSX, but work is getting me a macbook pro. I have 20 years experience with windows and maybe effectively 2-3...
Cool New Features & Syntax Changes in Python 3.6 (+video overview)
https://dbader.org/blog/cool-new-features-in-python-3-6#
/r/Python
https://redd.it/5jkv4w
https://dbader.org/blog/cool-new-features-in-python-3-6#
/r/Python
https://redd.it/5jkv4w
dbader.org
Cool new features in Python 3.6 – dbader.org
Python 3.6 adds a couple of new features and improvements that’ll affect the day to day work of Python coders. In this article I’ll give you an overview of the new features I found the most interesting.
Which NYC borough has the most noise complaints?
https://github.com/jvns/pandas-cookbook/blob/master/cookbook/Chapter%203%20-%20Which%20borough%20has%20the%20most%20noise%20complaints%20%28or,%20more%20selecting%20data%29.ipynb
/r/JupyterNotebooks
https://redd.it/49lbze
https://github.com/jvns/pandas-cookbook/blob/master/cookbook/Chapter%203%20-%20Which%20borough%20has%20the%20most%20noise%20complaints%20%28or,%20more%20selecting%20data%29.ipynb
/r/JupyterNotebooks
https://redd.it/49lbze
GitHub
jvns/pandas-cookbook
pandas-cookbook - Recipes for using Python's pandas library
Three ways to do a two-way ANOVA with Python
http://www.marsja.se/three-ways-to-carry-out-2-way-anova-with-python/
/r/pystats
https://redd.it/4bwnr8
http://www.marsja.se/three-ways-to-carry-out-2-way-anova-with-python/
/r/pystats
https://redd.it/4bwnr8
Erik Marsja
Three ways to do a two-way ANOVA with Python - Erik Marsja
This is a tutorial on how to carry out two-way ANOVA for independent measures using Python. It covers calculation itself, using Statsmodels, and Pyvttbl.