- Pablo J. Boczkowski is a professor in the School of Communication at Northwestern University.
- Umbreen Bhatti is the director of the KQED Lab, the northern California public media organization’s innovation lab.
- Yvonne Leow is president of the Asian American Journalists Association.
- Jennifer Coogan is chief content officer of Newsela.
- Nikki Usher is an assistant professor at the George Washington University’s School of Media and Public Affairs.
- Hossein Derakhshan is a journalist and analyst, and coauthor of Information Disorder: Toward an interdisciplinary framework for research and policy making.
- Millie Tran is global growth editor at The New York Times. Stine Bauer Dahlberg is managing director, brand at The New York Times.
- Raju Narisetti is CEO of Gizmodo Media Group.
- Lam Thuy Vo is a data reporter at BuzzFeed News.
- Amie Ferris-Rotman is Foreign Policy’s Moscow correspondent and founder of Sahar Speaks.
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wget ftp://ftp.gisdata.mn.gov/pub/gdrs/data/pub/us_mn_state_sos/bdry_votingdistricts/shp_bdry_votingdistricts.zip && \ | |
unzip shp_bdry_votingdistricts.zip && \ | |
shp2json bdry_votingdistricts.shp | \ | |
ndjson-join --left 'd.properties.COUNTYCODE + d.properties.PCTCODE' 'd.county_id + d.precinct_num' <(ndjson-split 'd.features') <(csv2json -n precincts_2014.txt -r ";") | \ | |
ndjson-map 'Object.assign(d[0].properties, d[1]), d[0]' | \ | |
ndjson-reduce 'p.features.push(d), p' '{type: "FeatureCollection", features: []}' | \ | |
geoproject 'd3.geoIdentity().reflectY(true).fitSize([960, 960], d)' | \ | |
geo2topo precincts=- | \ | |
toposimplify -f -p 0.05 | \ | |
topoquantize 1e5 > ./mn-precincts-albers-d3.json && \ |
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[ | |
{ | |
"html_raw":"<div class=\"tease is-lead \">\n<h3><a class=\"tease-headline\" data-content-id=\"484959641\" data-linkname=\"Accused football players sue U over sexual misconduct case\" data-linktype=\"headline\" data-modulename=\"homepage left\" data-moduletype=\"zone1-well-left\" data-position=\"0-1-lead\" href=\"http://www.startribune.com/accused-football-players-sue-university-of-minnesota-over-sexual-misconduct-case/484959641/\">Accused football players sue U over sexual misconduct case</a></h3>\n<div class=\"tease-timestamp js-timestamp \" data-st-timestamp=\"2018-06-08T18:21:57.000Z\">\n\n 1:21pm\n </div>\n<div class=\"tease-summary \">The lawsuit against the University of Minnesota seeks unspecified damages for \"being falsely cast as sex offenders.\"</div>\n<div class=\"tease-related\">\n<ul class=\"tease-list\">\n<li class=\"tease-list-item related-icn-article\">\n<a class=\"tease-list-item-link\" data-linkname=\"Report: U followed rules in football suspensions, cites 'break |
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Year Suicides | |
1981-01-01 442 | |
1982-01-01 470 | |
1983-01-01 444 | |
1984-01-01 443 | |
1985-01-01 459 | |
1986-01-01 541 | |
1987-01-01 546 | |
1988-01-01 488 | |
1989-01-01 515 |
Below is a list of the key software you'll need for class, along with some resources offering tips about how to get it installed.
A good programming text editor will help you organize your code, catch typos and generally make your life a lot easier. We recommend Sublime Text 2, which you can easily download and install from their website.
Counting files in S3 buckets and folders is harder than it should be. But here's a way to get it done using s3cmd:
- Install S3cmd
- On Mac,
brew install s3cmd
- On Windows, go here
-
From the command line, run
s3cmd --configure
-
Add your credentials when prompted.
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sudo pip install virtualenvwrapper | |
export WORKON_HOME=~/Envs | |
mkdir -p $WORKON_HOME | |
source /usr/local/bin/virtualenvwrapper.sh | |
echo 'export WORKON_HOME=$HOME/Envs; source /usr/local/bin/virtualenvwrapper.sh' >> ~/.bash_profile | |
mkvirtualenv dataj | |
pip install jupyter | |
pip install agate | |
pip install WHATEVER_ELSE |
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import csv, mechanize | |
from bs4 import BeautifulSoup | |
# Get the output file ready | |
# datafile = open('output.csv', 'w') | |
# writer = csv.writer(datafile) | |
br = mechanize.Browser() | |
br.open('http://enr.sos.mo.gov/EnrNet/CountyResults.aspx') |
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''' | |
cluster.py | |
Uses the Hamming distance between perceptual hashes to surface near-duplicate | |
images. | |
To install and run: | |
1. pip install imagehash | |
2. Put some .dat files in a folder someplace (script assumes ./data/imgs/*.dat) |
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{ | |
"took": 2, | |
"timed_out": false, | |
"_shards": { | |
"total": 6, | |
"successful": 6, | |
"failed": 0 | |
}, | |
"hits": { | |
"total": 1, |
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