Guest lecture at Coding Culture, Utrecht

Why should I learn Python?


Explaining a basic Python script

Your turn

Questions?

Python in the Social Sciences
A brief introduction by means of real-life
examples
Damian Trilling
d.c.trilling@uva.nl
@damian0604
www.damiantrilling.net
Afdeling Communicatiewetenschap
Universiteit van Amsterdam

Coding Culture, Utrecht, 5 March 2014

Damian Trilling




Your turn

Questions?

What I won’t do today
I won’t give you a structured introduction about
• variables
• commands
• data types
• ...

and all the other technical stuﬀ.


Damian Trilling




Your turn

Questions?

• variables
• commands
• data types
• ...

You’ll do that yourself the next weeks.


Damian Trilling




Your turn

Questions?

• variables
• commands
• data types
• ...

You’ll do that yourself the next weeks.
I’ll give you some examples of what you can do with the knowledge
you’re going to acquire.


Damian Trilling




Your turn

Questions?

Some exmples


Some examples


Damian Trilling




Your turn

Questions?

Some exmples

A recent bachelor thesis

Tone in tweets


Damian Trilling




Your turn

Questions?

Some exmples


Tone in tweets
Imagine you want to know something about someone’s behavior on
twitter. Or how a speciﬁc topic is discussed on Twitter.


Damian Trilling




Your turn

Questions?

Some exmples


Tone in tweets
Imagine you want to know something about someone’s behavior on
twitter. Or how a speciﬁc topic is discussed on Twitter.
Do you really want to go through thousands of tweets by hand?


Damian Trilling




Your turn

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Some exmples

So you’d better think about automating your coding
Finding out how negative or positive politicians are towards
their opponents

Schut, L. (2013). Verenigde Staten vs. Verenigd Koninkrijk: Een automatische inhoudsanalyse naar verklarende
factoren voor het gebruik van positive campaigning en negative campaigning door vooraanstaande politici en
politieke partijen op Twitter. Bachelor Thesis, Universiteit van Amsterdam.


Damian Trilling




Your turn

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Some exmples

their opponents
The student took lists with positive and negative words and made
additional ones with a politician’s opponents.



Damian Trilling




Your turn

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Some exmples

their opponents
She used a Python-script to check which type of words was used to
refer to opponents.



Damian Trilling




Your turn

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Some exmples

their opponents
She used a Python-script to check which type of words was used to
refer to opponents.
For further analysis, the results where imported in SPSS.


Damian Trilling




Your turn

Questions?

Some exmples


Damian Trilling




Your turn

Questions?

Some exmples

Frame adoption on Twitter

Which phrases used by Merkel and Steinbrück on TV make it
to the #tvduell discussion on Twitter?
As part of the project, I wrote a Python-script to identify word
co-occurrences on Twitter. The script produced not only lists with
word counts, but also a GDF-ﬁle that could be used for
visualization.


Damian Trilling

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#!/Library/Frameworks/Python.framework/Versions/2.7/bin/python2.7
# -*- coding: utf-8 -*from __future__ import division
from itertools import combinations
from collections import defaultdict
from collections import Counter
from unicsv import CsvUnicodeReader
import codecs, cStringIO, sys, re, unicodedata, os

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gdfbestand="resultaten/netwerk.gdf"
wordsplitbestand="resultaten/wordsplit.csv"
tempbestand="allewoorden.tmp"

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minedgeweight=20
cooc=defaultdict(int)
tweets=[]

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print "nReading tweet nr. "
reader=CsvUnicodeReader(open(wordsplitbestand,"r"))
i=0
for row in reader:
i=i+1
# skip first row, as it contains column headers
if i>1:
print "r",str(i)," ",
sys.stdout.flush()
tweets.append(row[9])

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f = codecs.open(tempbestand, ’wb’, encoding="utf-8")
i=0
print "Making tempfile to count word frequencies"
allestems=[]
for tweet in tweets:
for stems in tweet.split():
allestems.append(stems)
for k in range(0,len(allestems)):
f.write(allestems[k]+"n")
print "Couting..."
c=Counter()
with codecs.open(tempbestand,"rb", encoding="utf-8") as r:
for l in r:
c[l.rstrip(’n’)] += 1
os.remove(tempbestand)
f = codecs.open(gdfbestand, ’wb’, encoding="utf-8")
for tweet in tweets:
words=tweet.split()
for a,b in combinations(words,2):
if a!=b:
cooc[(a,b)]+=1

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f.write("nodedef>name VARCHAR, width DOUBLEn")
algenoemd=[]
verwijderen=[]
for k in cooc:
if cooc[k]<minedgeweight:
verwijderen.append(k)
else:
if k[0] not in algenoemd:
f.write(k[0]+","+str(c[k[0]])+"n")
algenoemd.append(k[0])
if k[1] not in algenoemd:
f.write(k[1]+","+str(c[k[1]])+"n")
algenoemd.append(k[1])
for k in verwijderen:
del cooc[k]
f.write("edgedef>node1 VARCHAR,node2 VARCHAR, weight DOUBLEn")
for k, v in cooc.iteritems():
regel= ",".join(k)+","+str(v)
f.write(regel+"n")




Your turn

Questions?

Some exmples

Frame adoption on Twitter


Damian Trilling




Your turn

Questions?

Summing up what you can use it for




Damian Trilling




Your turn

Questions?


One tool to rule them all?


Damian Trilling




Your turn

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Of course there are ready-made tool for some of the questions we
want to answer. But for many, there isn’t. Python oﬀers us the
possibility to build exactly the tool we need.


Damian Trilling




Your turn

Questions?



Of course there are ready-made tool for some of the questions we
want to answer. But for many, there isn’t. Python oﬀers us the
possibility to build exactly the tool we need.

fun!


And it’s

Damian Trilling




Your turn

Questions?


1st group of tasks

Highly repetitive tasks
Simple tasks (counting things, comparing texts, . . . ) that can be
described in a formalized way. Saves time even with few cases, but
there is virtually no size limit.
Example: Retweets start with RT, optionally followed by a space,
and some letters. So it is very easy to identify them automatically


Damian Trilling




Your turn

Questions?


2nd group of tasks

Task for which speciﬁc Python modules exist
There are thousands of modules suitable for text analysis. You
basically only have to write code for data input and output.
Example: Sentiment analysis


Damian Trilling




Your turn

Questions?


3rd group of tasks

API’s, RSS, webscraping . . .
You can use Python if you want to collect and store information.
Example: Collecting bio’s of Twitter users, scraping the web (data
journalism!), downloading Facebook data


Damian Trilling




Your turn

Questions?


Why we should use Python in the social sciences
It is a programming language
• It is ﬂexible. You can use it for (in principle) any kind of data
• There are virtually no limits regarding the amount of data to

process
• You can run it on every platform


Damian Trilling




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process
• And yet it is easy to learn!


Damian Trilling




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process
• And yet it is easy to learn!

It is widely used for content analysis
• Many online ressources and toolkits
• Books about NLP and Web Scraping with Python


Damian Trilling




Your turn

Questions?


Think of the following task

RQ: What are the diﬀerences in terms of actors mentioned
between Israeli and Palestinian news coverage?


Damian Trilling




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The data structure: You have a folder with articles


Damian Trilling




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1


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The desired output: You want a table with the ﬁle names and
a column per actor, counting how often they are mentioned


Damian Trilling




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The desired output: You want a table with the ﬁle names and
a column per actor, counting how often they are mentioned

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A typical task for a short Python script!


Damian Trilling




Your turn

Questions?


You need someting like this:
for every file in folder:
read the file
count actors
add new row to table with filename and actor counts
save table
(such a notation is called pseudo-code)


Damian Trilling




Your turn

Questions?


and in Python, it’s not that diﬀerent!


Damian Trilling

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mypath ="C:UsersRicardaDocumentsArtikelen"
regex54 = re.compile(r’Israel.*[minister|politician.*|[Aa]uthorit’)
filename_list=[]
matchcount54=0
matchcount54_list=[]
onlyfiles = [ f for f in listdir(mypath) if isfile(join(mypath,f)) ]
for f in onlyfiles:
matchcount54=0
artikel=open(join(mypath,f),"r")
for line in artikel:
matches54 = regex54.findall(line)
for word in matches54:
matchcount54=matchcount54+1
filename_list.append(f)
matchcount54_list.append(matchcount54)
artikel.close()
output=zip(filename_list,matchcount54_list)
writer = csv.writer(open("overzichtstabel.csv", ’wb’))
writer.writerows(output)




Your turn

Questions?

Pseudo-code

Explaining a basic Python script:
Pseudo code


Damian Trilling




Your turn

Questions?

Pseudo-code

We collected tweets on the UNFCC-conference with
yourTwapperkeeper.

Our task: Identify all tweets that include a reference to Poland
Let’s start with some pseudo-code!
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open csv-table
for each line:
append column 1 to a list of tweets
append column 3 to a list of corresponding users
look for searchstring in column 1
append search result to a list of results
save lists to a new csv-file


Damian Trilling




Your turn

Questions?

Python code

Python code


Damian Trilling

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#!/usr/bin/python
from unicsv import CsvUnicodeWriter
import re
inputfilename="mytweets.csv"
outputfilename="myoutput.csv"
user_list=[]
tweet_list=[]
search_list=[]
searchstring1 = re.compile(r’[Pp]olen|[Pp]ool|[Ww]arschau|[Ww]arszawa’)
print "Opening "+inputfilename
reader=CsvUnicodeReader(open(inputfilename,"r"))
for row in reader:
tweet_list.append(row[0])
user_list.append(row[2])
matches1 = searchstring1.findall(row[0])
matchcount1=0
search_list.append(matchcount1)
print "Constructing data matrix"
outputdata=zip(tweet_list,user_list,search_list)
headers=zip(["tweet"],["user"],["how often is Poland mentioned?"])
print "Write data matrix to ",outputfilename
writer=CsvUnicodeWriter(open(outputfilename,"wb"))
writer.writerows(headers)
writer.writerows(outputdata)




Your turn

Questions?

Python code

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#!/usr/bin/python
# We start with importing some modules:
from unicsv import CsvUnicodeWriter
import re

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# Let us define two variables that contain
# the names of the files we want to use
inputfilename="mytweets.csv"
outputfilename="myoutput.csv"


Damian Trilling




Your turn

Questions?

Python code

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# We create some empty lists that we will use later on.
# A list can contain several variables
# and is denoted by square brackets.
user_list=[]
tweet_list=[]
search_list=[]


Damian Trilling




Your turn

Questions?

Python code

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# What do we want to look for?
searchstring1 = re.compile(r’[Pp]olen|[Pp]ool|[Ww]arschau|[Ww]arszawa’)

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# Enough preparation, let the program begin!
# We tell the user what is going on...
print "Opening "+inputfilename

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# ... and call the module that reads the input file.
reader=CsvUnicodeReader(open(inputfilename,"r"))


Damian Trilling




Your turn

Questions?

Python code

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# Now we read the file line by line.
# The indented block is repeated for each row
# (thus, each tweet)
for row in reader:
# append data from the current row to our lists.
# Note that we start counting with 0.
tweet_list.append(row[0])
user_list.append(row[2])

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# Let us count how often our searchstring is used in
# in this tweet
matches1 = searchstring1.findall(row[0])
matchcount1=0
search_list.append(matchcount1)


Damian Trilling




Your turn

Questions?

Python code

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# Time to put all the data in one container
# and save it:

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print "Constructing data matrix"
outputdata=zip(tweet_list,user_list,search_list)
headers=zip(["tweet"],["user"],["how often is Poland mentioned?"])
print "Write data matrix to ",outputfilename
writer=CsvUnicodeWriter(open(outputfilename,"wb"))
writer.writerows(headers)
writer.writerows(outputdata)


Damian Trilling




Your turn

Questions?

The output

The output (myoutput.csv)


Damian Trilling




Your turn

Questions?

The output

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tweet,user,how often is Poland mentioned?
:-) #Lectrr #wereldleiders #uitspraken #Wikileaks #klimaattop http://t.
co/Udjpk48EIB,henklbr,0
Wat zijn de resulaten vd #klimaattop in #Warschau waard? @EP_Environment
ontmoet voorzitter klimaattop @MarcinKorolec http://t.co/4
Lmiaopf60,Europarl_NL,1
RT @greenami1: De winnaars en verliezers van de lachwekkende #klimaattop
in #Warschau (interview): http://t.co/DEYqnqXHdy #Misserfolg #Kli
...,LarsMoratis,1
De winnaars en verliezers van de lachwekkende #klimaattop in #Warschau (
interview): http://t.co/DEYqnqXHdy #Misserfolg #Klimaschutz #FAZ,
greenami1,1


Damian Trilling




Your turn

Questions?

The output


Damian Trilling




Your turn

Questions?

Try it yourself!


Damian Trilling




Your turn

Questions?

Doe je mee?


Damian Trilling




Your turn

Questions?

Vragen of opmerkingen?

Damian Trilling
d.c.trilling@uva.nl
@damian0604
www.damiantrilling.net

Damian Trilling

Guest lecture at Coding Culture, Utrecht

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