| """Fabric demo. | |
| Setup Instructions: | |
| pip install Fabric3 | |
| How to use: | |
| fab --list | |
| fab hello |
| """Reactive expressions for Dash! | |
| Dash[1] is an interesting project build reactive web applications in Python. | |
| While the ideas are exciting, the syntax for specifying custom code is way | |
| too complicated that it should be. | |
| Here is a sample code to display sum of two input values. | |
| @app.callback( | |
| [Input(component_id='x', component_property='value')] |
| """Script to find most common features found from | |
| multiple runs of an algorithm with different parameters. | |
| """ | |
| from collections import Counter | |
| runs = [ | |
| ["a", "b", "c", "d", "e"], | |
| ["a", "b", "c", "d", "f"], | |
| ["a", "b", "c", "e", "g"], | |
| ["a", "b", "d", "f", "h"] |
We are looking for an experienced python engineer to join our team at Rorodata, a data analytics startup based in Hyderabad. Interesting work in the intersection of data science, infrastructure and microservices.
This is a full-time position based out of Hyderabad. We can explore remote working only in exceptional cases.
Please ping us on [email protected] if this sounds interesting to you.
About Rorodata
| <div id="boxoffice-widget" style="padding-top: 0px;"><script src="https://checkout.razorpay.com/v1/checkout.js"></script> <div class="payment-progress-wrapper payment-stages-bg" id="payment-stages"><ul class="progress-indicator"><li class="indicator active "><span class="bubble"></span>Select Tickets<br></li><li class="indicator "><span class="bubble"></span>Payment<br></li><li class="indicator "><span class="bubble"></span>Confirm<br></li></ul></div> <div class="payment-stages-wrapper"><div id="boxoffice-selectItems" class="boxoffice-section ticket-selection-content clearfix"><div id="workshop"><div><h1 class="category-heading">Workshop</h1></div> <div><div class="ticket-booking clearfix " id="python-generators-inside-out"><div class="ticket-details"><p class="ticket-title">Python Generators Inside Out</p> <div class="ticket-description"><p><i class="fa fa-calendar"></i>18 September 2016</p><p><i class="fa fa-map-marker ticket-venue"></i>TERI, Domlur, Bangalore</p><p><i class="fa fa-info" aria-hidden="tru |
| root@docker-blr:~# mtr -w --report apt.dockerproject.org | |
| Start: Mon Aug 1 02:22:19 2016 | |
| HOST: docker-blr Loss% Snt Last Avg Best Wrst StDev | |
| 1.|-- 139.59.16.253 0.0% 10 2.1 2.3 1.8 2.8 0.0 | |
| 2.|-- 138.197.249.14 0.0% 10 0.3 0.3 0.2 0.3 0.0 | |
| 3.|-- 124.124.67.154 0.0% 10 2.0 7.1 1.6 53.7 16.4 | |
| 4.|-- ??? 100.0 10 0.0 0.0 0.0 0.0 0.0 | |
| 5.|-- ??? 100.0 10 0.0 0.0 0.0 0.0 0.0 | |
| 6.|-- 220.227.64.209 0.0% 10 12.1 13.0 12.0 21.2 2.8 | |
| 7.|-- ??? 100.0 10 0.0 0.0 0.0 0.0 0.0 |
| I've noticed that on digital ocean droplets, the second download is way faster than the first one. Does it mean digital ocean diverting all the traffic in the droplets via a caching proxy? | |
| ****** | |
| anand@docker0:/tmp$ wget https://apt.dockerproject.org/repo/pool/main/d/docker-engine/docker-engine_1.10.0-0~jessie_amd64.deb | |
| --2016-07-28 14:35:51-- https://apt.dockerproject.org/repo/pool/main/d/docker-engine/docker-engine_1.10.0-0~jessie_amd64.deb | |
| Resolving apt.dockerproject.org (apt.dockerproject.org)... 54.192.151.51 | |
| Connecting to apt.dockerproject.org (apt.dockerproject.org)|54.192.151.51|:443... connected. | |
| HTTP request sent, awaiting response... 200 OK | |
| Length: 8552538 (8.2M) [application/vnd.debian.binary-package] |
| def search(array, sum, expected, head, tail) | |
| if head > tail | |
| return false | |
| elsif sum == expected | |
| return [head, tail] | |
| else | |
| return (search(array, sum - array[head], expected, head+1, tail) or search(array, sum - array[tail], expected, head, tail-1)) | |
| end | |
| end |
| """Program to reproduce a bug that appears when used with Python 3. | |
| How to reproduce: | |
| 1. Run the dev server | |
| $ python unibug.py | |
| http://0.0.0.0:8080/ | |
| 2. Open the following URL in your browser. |
| def plot_decision_boundaries(X, y, model_class, **model_params): | |
| """Function to plot the decision boundaries of a classification model. | |
| This uses just the first two columns of the data for fitting | |
| the model as we need to find the predicted value for every point in | |
| scatter plot. | |
| One possible improvement could be to use all columns fot fitting | |
| and using the first 2 columns and median of all other columns | |
| for predicting. |