Later in the course, we will use two basic but important data structures: dictionaries and priority queues. In this exercise, you will be asked to use both to parse and process text. To test your code, you'll need some text dump. Project Gutenberg (https://www.gutenberg.org/) has thousands of books in various formats, including plain text. For example, here you can find the full text of Alice in Wonderland: https://www.gutenberg.org/files/11/11-0.txt. You don't need to parse the entire book – a chapter or two are sufficient. 1. The first function you need to write takes a filename as input (fname) and returns a list con- taining the words that appear in the text. Only the words, no numbers, no punctuation. The words must be stored lowercase. Use the function str.lower() to make a string lowercase (see https://docs.python.org/2/library/stdtypes.html#str.lower). Use the function str.translate() (see https://docs.python.org/2/library/stdtypes.html#str.translat to remove punctuation. To split a string into words, use str.split() (see https://docs. python.org/2/library/stdtypes.html#str.split). Treat the apostrophe as punctuation, so "I'm" would become "I m". Also, consider words like "rabbit-hole" in the original text as two separated words: “rabbit" and “hole". def file_to_wordlist(fname): wordlist = [] # your code here return wordlist
Later in the course, we will use two basic but important data structures: dictionaries and priority queues. In this exercise, you will be asked to use both to parse and process text. To test your code, you'll need some text dump. Project Gutenberg (https://www.gutenberg.org/) has thousands of books in various formats, including plain text. For example, here you can find the full text of Alice in Wonderland: https://www.gutenberg.org/files/11/11-0.txt. You don't need to parse the entire book – a chapter or two are sufficient. 1. The first function you need to write takes a filename as input (fname) and returns a list con- taining the words that appear in the text. Only the words, no numbers, no punctuation. The words must be stored lowercase. Use the function str.lower() to make a string lowercase (see https://docs.python.org/2/library/stdtypes.html#str.lower). Use the function str.translate() (see https://docs.python.org/2/library/stdtypes.html#str.translat to remove punctuation. To split a string into words, use str.split() (see https://docs. python.org/2/library/stdtypes.html#str.split). Treat the apostrophe as punctuation, so "I'm" would become "I m". Also, consider words like "rabbit-hole" in the original text as two separated words: “rabbit" and “hole". def file_to_wordlist(fname): wordlist = [] # your code here return wordlist
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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Question
Language is Python
![Later in the course, we will use two basic but important data structures: dictionaries and priority
queues. In this exercise, you will be asked to use both to parse and process text.
To test your code, you'll need some text dump. Project Gutenberg (https://www. gutenberg.org/)
has thousands of books in various formats, including plain text. For example, here you can find
the full text of Alice in Wonderland: https://www.gutenberg.org/files/11/11-0.txt. You don't
need to parse the entire book – a chapter or two are sufficient.
1. The first function you need to write takes a filename as input (fname) and returns a list con-
taining the words that appear in the text. Only the words, no numbers, no punctuation. The
words must be stored lowercase. Use the function str.lower() to make a string lowercase
(see https://docs.python.org/2/library/stdtypes.html#str.lower). Use the function
str.translate() (see https://docs.python.org/2/library/stdtypes.html#str.translate)
to remove punctuation. To split a string into words, use str.split() (see https://docs.
python.org/2/library/stdtypes.html#str.split). Treat the apostrophe as punctuation,
so "I'm" would become "I m". Also, consider words like "rabbit-hole" in the original text as two
separated words: "rabbit" and “hole".
def file_to_wordlist(fname):
wordlist =
[]
# your code here
return wordlist](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fd8694f02-79fe-4b76-9079-b1456b2e1bd2%2F1de6312c-220e-4113-aa75-a8d645086e9a%2Fq9yaajb_processed.png&w=3840&q=75)
Transcribed Image Text:Later in the course, we will use two basic but important data structures: dictionaries and priority
queues. In this exercise, you will be asked to use both to parse and process text.
To test your code, you'll need some text dump. Project Gutenberg (https://www. gutenberg.org/)
has thousands of books in various formats, including plain text. For example, here you can find
the full text of Alice in Wonderland: https://www.gutenberg.org/files/11/11-0.txt. You don't
need to parse the entire book – a chapter or two are sufficient.
1. The first function you need to write takes a filename as input (fname) and returns a list con-
taining the words that appear in the text. Only the words, no numbers, no punctuation. The
words must be stored lowercase. Use the function str.lower() to make a string lowercase
(see https://docs.python.org/2/library/stdtypes.html#str.lower). Use the function
str.translate() (see https://docs.python.org/2/library/stdtypes.html#str.translate)
to remove punctuation. To split a string into words, use str.split() (see https://docs.
python.org/2/library/stdtypes.html#str.split). Treat the apostrophe as punctuation,
so "I'm" would become "I m". Also, consider words like "rabbit-hole" in the original text as two
separated words: "rabbit" and “hole".
def file_to_wordlist(fname):
wordlist =
[]
# your code here
return wordlist
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