bhavika

tmux cheatsheet

As configured in my dotfiles.

start new:

tmux

start new with session name:

bhavika

#!/usr/bin/env bash
#Code adapted from https://gist.github.com/yangj1e/3641843c758201ebbc6c (Modified to Python3.5)

cd ~
#wget https://3230d63b5fc54e62148e-c95ac804525aac4b6dba79b00b39d1d3.ssl.cf1.rackcdn.com/Anaconda2-2.4.0-Linux-x86_64.sh
wget https://3230d63b5fc54e62148e-c95ac804525aac4b6dba79b00b39d1d3.ssl.cf1.rackcdn.com/Anaconda3-2.4.1-Linux-x86_64.sh
bash Anaconda3-2.4.1-Linux-x86_64.sh -b
echo 'PATH="/home/ubuntu/anaconda3/bin:$PATH"' >> .bashrc
. .bashrc

bhavika

​Can you solve this puzzle? While not a requirement, we give priority consideration to candidates supplying a solution.

The 2010 Census puts populations of 26 largest US metro areas at 18897109, 12828837, 9461105, 6371773, 5965343, 5946800, 5582170, 5564635, 5268860, 4552402, 4335391, 4296250, 4224851, 4192887, 3439809, 3279833, 3095313, 2812896, 2783243, 2710489, 2543482, 2356285, 2226009, 2149127, 2142508, and 2134411.

Can you find a subset of these areas where a total of exactly 100,000,000 people live, assuming the census estimates are exactly right? Provide the answer and code or reasoning used.

bhavika

1. Using Dask for Parallel Computing in Python (http://pydata.org/dc2016/schedule/presentation/59/)
Github: https://github.com/jseabold/dask-pydata-dc-2016

2. Building Your First Data Pipelines (http://pydata.org/dc2016/schedule/presentation/10/)
Github: https://github.com/hunterowens/data-pipelines

3. Doing frequentist statistics in Python (http://pydata.org/dc2016/schedule/presentation/9/)
Github: https://github.com/gapatino/Doing-frequentist-statistics-with-Scipy

4. Machine Learning with Text in scikit-learn (http://pydata.org/dc2016/schedule/presentation/12/)

bhavika

http://dask.pydata.org/en/latest/use-cases.html
Progress bars: http://distributed.readthedocs.io/en/latest/web.html

bhavika

from urllib.request import urlopen
from bs4 import BeautifulSoup as bs
import csv as csv

url = 'http://www.finder.com/in/netflix-india-vs-netflix-us-titles-list'
html = urlopen(url).read()

soup = bs(html, "lxml")
table = soup.find('table', id='tablepress-9')

bhavika

set source= source path
set destination= dest path
for /f "tokens=2 delims==" %%G in ('wmic os get localdatetime /value') do set datetime=%%G
set year=%datetime:~0,4%
set month=%datetime:~4,2%
set day=%datetime:~6,2%
set folder = "Books"
xcopy /s /i %source% %destination%_%year%-%month%-%day% /E /y

pause

bhavika

using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.IO;
using System.Linq;
using System.Text;
using System.Web.Helpers;
using System.Web.Mvc;
using System.Web.UI;