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Define a Python function named cheapest_rent_per_state that has one parameter. The parameter is a string representing the name of a CSV file. The CSV file will be portion of a dataset published by the US overnment showing the median (middle) rent in every county in the US. Each row in the CSV file has the same format Area Name, Efficiency, 1-Bedroom, 2-Bedroom, 3-Bedroom, 4-Bedroom, State Code he data in the Area Name and State Code columns are text. The data in all of the other columns are decimal numbers. our function will need to use the accumulator pattern to return a dictionary. The keys of that dictionary will be the state codes read in from the file (state codes are at index 6). For each key in the dictionary, it's alue should be the smallest median rent for an efficiency in that state (median rents for an efficiency are at index 1). mportant Hints: You will really, really want to use the built-in csv library because it makes processing a CSy file much easier and Area Name values may include commas.

Define a Python function named cheapest_rent_per_state that has one parameter. The parameter is a string representing the name of a CSV file. The CSV file will be portion of a dataset published by the US overnment showing the median (middle) rent in every county in the US. Each row in the CSV file has the same format Area Name, Efficiency, 1-Bedroom, 2-Bedroom, 3-Bedroom, 4-Bedroom, State Code he data in the Area Name and State Code columns are text. The data in all of the other columns are decimal numbers. our function will need to use the accumulator pattern to return a dictionary. The keys of that dictionary will be the state codes read in from the file (state codes are at index 6). For each key in the dictionary, it's alue should be the smallest median rent for an efficiency in that state (median rents for an efficiency are at index 1). mportant Hints: You will really, really want to use the built-in csv library because it makes processing a CSy file much easier and Area Name values may include commas.

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Part B - reading CSV files You will need a Python repl to solve part B. Define a Python function named cheapest_rent_per_state that has one parameter. The parameter is a string representing the name of a CSV file. The CSV file will be portion of a dataset published by the US government showing the median (middle) rent in every county in the US. Each row in the CSV file has the same format Area Name, Efficiency, 1-Bedroom, 2-Bedroom, 3-Bedroom, 4-Bedroom, State Code The data in the Area Name and State Code columns are text. The data in all of the other columns are decimal numbers. Your function will need to use the accumulator pattern to return a dictionary. The keys of that dictionary will be the state codes read in from the file (state codes are at index 6). For each key in the dictionary, it's value should be the smallest median rent for an efficiency in that state (median rents for an efficiency are at index 1). Important Hints: * You will really, really want to use the built-in csv library because it makes processing a CSV file much easier and Area Name values may include commas. * You should get the value the accumulator associates with a key, since the state code may be new. A good default value would be 1000000 since that will always be greater than an area's median rent for an efficiency.) Sample test cases: cheapest_rent_per_state("empty.csv") would evaluate to { } (finds bug if accumulator variable not initialized correctly) cheapest_rent_per_state("simple.csv") would evaluate to { 'CA' : 2103.0, 'NY' : 1665.0} (finds bug if accumulator variable not updated correctly)
Transcribed Image Text:Part B - reading CSV files You will need a Python repl to solve part B. Define a Python function named cheapest_rent_per_state that has one parameter. The parameter is a string representing the name of a CSV file. The CSV file will be portion of a dataset published by the US government showing the median (middle) rent in every county in the US. Each row in the CSV file has the same format Area Name, Efficiency, 1-Bedroom, 2-Bedroom, 3-Bedroom, 4-Bedroom, State Code The data in the Area Name and State Code columns are text. The data in all of the other columns are decimal numbers. Your function will need to use the accumulator pattern to return a dictionary. The keys of that dictionary will be the state codes read in from the file (state codes are at index 6). For each key in the dictionary, it's value should be the smallest median rent for an efficiency in that state (median rents for an efficiency are at index 1). Important Hints: * You will really, really want to use the built-in csv library because it makes processing a CSV file much easier and Area Name values may include commas. * You should get the value the accumulator associates with a key, since the state code may be new. A good default value would be 1000000 since that will always be greater than an area's median rent for an efficiency.) Sample test cases: cheapest_rent_per_state("empty.csv") would evaluate to { } (finds bug if accumulator variable not initialized correctly) cheapest_rent_per_state("simple.csv") would evaluate to { 'CA' : 2103.0, 'NY' : 1665.0} (finds bug if accumulator variable not updated correctly)
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