6 A simple word count Loop through the passage of text below and store each word in a dictionary that has a key value pair of word of occurrences ( for example there are 1 0 occurrences of 'the' in the passage, one dictionary entry would be 'the' 1 0 ) text list ' Python ' , 'was', 'conceived', ' in ' , 'the', 'late', ' 1 9 8 0 s ' , 'and', 'its', 'implementation', 'began', ' in ' , 'December', ' 1 9 8 9 ' , ' by ' , 'Guido', 'van', 'Rossum', ' at ' , 'Centrum', 'Wiskunde', ' ' , 'Informatica', ' in ' , 'the', 'Netherlands', ' as ' , ' a ' , 'successor', ' to ' , 'the', 'ABC', 'language', 'itself', 'inspired', ' by ' , 'SETL', 'capable', ' of ' , 'exception', 'handling', 'and', 'interfacing', 'with', 'the', 'Amoeba', 'operating', 'system', ' ' , 'Van', 'Rossum', 'remains', Python's , 'principal', 'author', ' ' , 'His', 'continuing', 'central', 'role', ' in ' , Python's , 'development', ' is ' , 'reflected', ' in ' , 'the', 'title', 'given', ' to ' , 'him', ' by ' , 'the', 'Python', 'community', 'Benevolent', 'Dictator', 'For', 'Life', ' ' , ' On ' , 'the', 'origins', ' of ' , 'Python', 'Van', 'Rossum', 'wrote', ' in ' , ' 1 9 9 6 ' , ' In ' , 'December', ' 1 9 8 9 ' , ' I ' , 'was', 'looking', 'for', ' a ' , 'hobby', 'programming', 'project', 'that', 'would', 'keep', ' me ' , 'occupied', 'during', 'the', 'week', 'around', 'Christmas', ' ' , ' My ' , 'office', 'would', ' be ' , 'closed', 'but', ' I ' , 'had', ' a ' , 'home', 'computer', 'and', 'not', 'much', 'else', ' on ' , ' my ' , 'hands', ' ' , ' I ' , 'decided', ' to ' , 'write', ' an ' , 'interpreter', 'for', 'the', 'new', 'scripting', 'language', ' I ' , 'had', 'been', 'thinking', 'about', 'lately', ' a ' , 'descendant', ' of ' , 'ABC', 'that', 'would', 'appeal', ' to ' , 'Unix C ' , 'hackers', ' ' , ' I ' , 'chose', 'Python', ' as ' , ' a ' , 'working', 'title', 'for', 'the', 'project', 'being', ' in ' , ' a ' , 'slightly', 'irreverent', 'mood', 'and', ' a ' , 'big', 'fan', ' of ' , 'Monty', Python's , 'Flying', 'Circus', ' ' 7 Loop through the dictionary you made above of word of occurrences , sum up the total number of words and divide each entry in the dictionary by that total sum and store it in a new dictionary ( this will take 2 separate loops ) ( In data science this is called Term Frequency or TF ) Extra challenge Sort the output by the TF in descending order

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Question: 6 . A simple word count! Loop through the passage of text below and store each word in a dictionary that has a key:value pair

6 .

A simple word count! Loop through the passage of text below and store each word in a dictionary that has a key:value pair of word: # of occurrences

(

for example: there are

10

occurrences of 'the' in the passage, one dictionary entry would be 'the':

10)

text

_

list

= ['

Python

',

'was', 'conceived',

'

',

'the', 'late',

' 1980

',

'and', 'its', 'implementation', 'began',

'

',

'December',

' 1989','

',

'Guido', 'van', 'Rossum',

'

',

'Centrum', 'Wiskunde',

'

',

'Informatica',

'

',

'the', 'Netherlands',

'

','

',

'successor',

'

',

'the', 'ABC', 'language', 'itself', 'inspired',

'

',

'SETL', 'capable',

'

',

'exception', 'handling', 'and', 'interfacing', 'with', 'the', 'Amoeba', 'operating', 'system',

' .',

'Van', 'Rossum', 'remains', "Python's", 'principal', 'author',

' .',

'His', 'continuing', 'central', 'role',

'

',

"Python's", 'development',

'

',

'reflected',

'

',

'the', 'title', 'given',

'

',

'him',

'

',

'the', 'Python', 'community', 'Benevolent', 'Dictator', 'For', 'Life',

' .','

',

'the', 'origins',

'

',

'Python', 'Van', 'Rossum', 'wrote',

'

',' 1996','

',

'December',

' 1989','

',

'was', 'looking', 'for',

'

',

'hobby', 'programming', 'project', 'that', 'would', 'keep',

'

',

'occupied', 'during', 'the', 'week', 'around', 'Christmas',

' .','

',

'office', 'would',

'

',

'closed', 'but',

'

',

'had',

'

',

'home', 'computer', 'and', 'not', 'much', 'else',

'

','

',

'hands',

' .','

',

'decided',

'

',

'write',

'

',

'interpreter', 'for', 'the', 'new', 'scripting', 'language',

'

',

'had', 'been', 'thinking', 'about', 'lately',

'

',

'descendant',

'

',

'ABC', 'that', 'would', 'appeal',

'

',

'Unix

/

',

'hackers',

' .','

',

'chose', 'Python',

'

','

',

'working', 'title', 'for', 'the', 'project', 'being',

'

','

',

'slightly', 'irreverent', 'mood', 'and',

'

',

'big', 'fan',

'

',

'Monty', "Python's", 'Flying', 'Circus',

' .'] 7 .

Loop through the dictionary you made above of

{

word: # of occurrences

},

sum up the total number of words and divide each entry in the dictionary by that total sum and store it in a new dictionary

(

this will take

2

separate loops!

) (

In data science this is called Term

-

Frequency or TF

)

Extra challenge: Sort the output by the TF in descending order.

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