This lab works with associative arrays (dictionaries).

In the text, Zelle develops a program to count the word frequency in a file. For example, if you run the program on itself, you will get:
This program analyzes word frequency in a file
and prints a report on the n most frequent words.

File to analyze: wordfreq.py
Output analysis of how many words? 10
'                  6
text               6
words              6
counts             5
items              5
n                  5
in                 4
of                 4
word               4
1                  3
To count the number of words, he uses an associative array, or dictionary. These are similar to arrays, but only store entries where they are needed. For our counting of words above, instead of having an entry for every word in the English language, we set up a dictionary:
counts = {}
It starts out empty, but as we encounter new words, we add to the count for those words in a similar fashion as for lists or arrays:
counts[w] = counts.get(w,0) + 1
After we have processed the entire file, we turn our dictionary into a list that we can sort and print out the n items that have occurred most:
items = list(counts.items())
items.sort()
items.sort(key=byFreq, reverse=True)
for i in range(n):
word, count = items[i]
print("{0:<15}{1:>5}".format(word, count))

Let's try this on another file sandwich.txt (from the nifty program developed by John DeNero and Aditi Muralidharan). This file is a collection of tweets collected over 24 hours, that contain the word sandwich (the format follows JSON used by Twitter). When we run our program again, with a small change to the open() so it can handle the extra characters that occur in tweets:

we can print out the most common words:
This program analyzes word frequency in a file
and prints a report on the n most frequent words.

File to analyze: sandwich.txt
Output analysis of how many words? 10
sandwich         783
2011             779
08               503
09               471
co               353
http             351
t                350
a                307
3                205
4                183
Why so many numbers? Let's look at the first couple of lines of sandwich.txt
[33.979703999999998, -118.037312]	6	2011-08-28 19:35:49	Flat Iron steak sandwich, Arnold Palmer (under contract at BIOLA) (@ The 6740) http://t.co/xCE6jou
[42.917841000000003, -78.877071999999998]	6	2011-08-28 19:41:00	Pulled pork sandwich, mac-n-cheese from Fat Bob's. (@ Elmwood Avenue Festival of the Arts) http://t.co/pkedqkn
[47.864685469999998, -122.28599858]	6	2011-08-28 19:59:49	Why is lettuce so important to a sandwich/burger when it taste like nothing and has no nutritional value?
[40.70896407, -73.818375709999998]	6	2011-08-28 20:10:57	Hurricane Tuna Sandwich fix (@ Dunkin' Donuts w/ 3 others) [pic]: http://t.co/qzXaFD2
Each line has a lot of additional information, in addition to the twitter message. The first numbers are the location, a callback (which we will ignore), the date and time, and then lastly the tweet itself.

Let's break up the information and create two different dictionaries:

• counts will hold the count of words found in the tweets (the 3rd and last field of each line, if we separate by tabs), and
• hours will hold the count of how many tweets occurred each hour (the end of the second field of each line, if we separate by tabs).
Since we're treating different parts of each line differently, let's traverse the file line-by-line and create a text string variable that will hold the tweets (that can be processed as before) and a dictionary of the hours and frequency that tweets occur.

First, we'll go through the file and split each line into fields, keeping only the hour that the tweet occurred and the tweeted message:

infile = open(fname,'r',encoding='utf8')

#Split up the lines to have the number of events each hour,
#   and put the tweets in text to be processed as before.
text = ""
times = {}
fields = lines.split("\t")
text = text + fields
time = fields.split()
hour = time[0:2]
times[hour] = times.get(hour,0) + 1
print(hour, fields)	#Print for testing, remove before running final program

Try running the program with the test print above. Then comment out the print, since it's not needed once you've verified it is getting the right fields.

Next, we'll print out the hours and the number of tweets in the dictionary for each hour. We'll follow the same format as Zelle used for counting words:

#Print out when tweets occur:
items = list(times.items())
items.sort()
for item in items:
#Each item is a list of the key and the associated entry:
print("At hour", item, ", there were", item, "tweets.")

At what hour do most people tweet about sandwiches? Try your program on the other tweets collected at the nifty site. Each file there is labelled by the common topic (i.e. "soup", "obama", or "party").

The Complete Programs

wordfreq.py (from the textbook with a small modification to the open() to handle the extra characters that occur in tweets):
# wordfreq.py

def byFreq(pair):
return pair

def main():
print("This program analyzes word frequency in a file")
print("and prints a report on the n most frequent words.\n")

# get the sequence of words from the file
fname = input("File to analyze: ")
text = text.lower()
for ch in '!"#\$%&()*+,-./:;<=>?@[\\]^_`{|}~':
text = text.replace(ch, ' ')
words = text.split()

# construct a dictionary of word counts
counts = {}
for w in words:
counts[w] = counts.get(w,0) + 1

# output analysis of n most frequent words.
n = eval(input("Output analysis of how many words? "))
items = list(counts.items())
items.sort()
items.sort(key=byFreq, reverse=True)
for i in range(n):
word, count = items[i]
print("{0:<15}{1:>5}".format(word, count))

if __name__ == '__main__':  main()

tweetFreq.py
# Modified version of wordfreq.py (from Zelle's 2nd Edition)
#   to analyze tweet times

def byFreq(pair):
return pair

def main():
print("This program analyzes word frequency in a file")
print("and prints a report on the n most frequent words.\n")

# get the sequence of words from the file
fname = input("File to analyze: ")

###New code between these comments: ###

infile = open(fname,'r',encoding='utf8')

#Split up the lines to have the number of events each hour,
#   and put the tweets in text to be processed as before.
text = ""
times = {}
fields = lines.split("\t")
text = text + fields
time = fields.split()
hour = time[0:2]
times[hour] = times.get(hour,0) + 1
print(hour, fields)	#Print for testing, remove before running final program

#Print out when tweets occur:
items = list(times.items())
items.sort()
for item in items:
#Each item is a list of the key and the associated entry:
print("At hour", item, ", there were", item, "tweets.")

###New code between these comments: ###

#Process the text as before:
text = text.lower()
for ch in '!"#\$%&()*+,-./:;<=>?@[\\]^_`{|}~':
text = text.replace(ch, ' ')
words = text.split()

# construct a dictionary of word counts
counts = {}
for w in words:
counts[w] = counts.get(w,0) + 1

# output analysis of n most frequent words.
n = eval(input("Output analysis of how many words? "))
items = list(counts.items())
items.sort()
items.sort(key=byFreq, reverse=True)
for i in range(n):
word, count = items[i]
print("{0:<15}{1:>5}".format(word, count))

if __name__ == '__main__':  main()