See Answer
In this lab we will -
Create a dictionary from its provided description
Process the dictionary and store it in a CSV (comma separated values) file
reference Figure 5.18.2: Reading each row of a CSV file.
Introduction
Weather statistics are used for a variety of prediction metrics varying from future weather to cyclones. Although the exact amount of data required to predict a weather pattern lies in tens of gigabytes, in this lab, we will only deal with a simple dictionary.
Anita and Chen are meteorologists who want to predict the weather in Santa Barbara using past weather information.
You will need to help Anita read the CSV data from a file, extract the necessary fields, and convert the weather data to a Python dictionary, which will be later given to Chen.
Instructions
Anita's information is unfortunately stored as a table that shows the 30-year temperature averages for Santa Barbara, 1991 to 2020. It looks like this:
High °F Low °F High °C Low °C
67 47 January 19 8
67 47 February 19 9
68 49 March 20 10
71 51 April 22 11
72 54 May 22 12
73 57 June 23 14
76 60 July 25 16
78 60 August 25 16
78 60 September 25 15
76 56 October 24 13
71 50 November 22 10
66 46 December 19 8
72 53 Year 22 12
She saved it as a CSV file but she needs just the first 3 columns - temperatures in Fahrenheit and the corresponding month name.
As tempting as it was to continue working in Excel and modifying the columns there, Anita knows that she needs a reproducible workflow.
If they need to analyze 30 years' worth of data, year by year, having a script to do it automatically for them is the best solution.
She asks you for help: can you convert this information into a Python dictionary whose keys are the full names of the months (e.g.: "January") and values are list objects with 2 elements?
This request makes you happy that you read zyBooks Sections 10.6 and 10.7!
File and dictionary structure
For the two list elements, the first element is the maximum and the second is the minimum temperature in Fahrenheit - the first two columns of the file. For instance, one item in the dictionary will have the key "January" and the value [67, 47]. You need to create a dictionary that contains 12 such items, e.g.,
temperatures_dict = {
"January": [67, 47], ...
}
Steps
Create a function get_temperature_stats(filename) that accepts a string with the filename from which to load the data (includes the .csv extension). The function returns a dictionary with the month-temperature mappings: the key are the values in the 3rd column, the list values are the first two columns of the file.
At the start of the function, add the line import csv and define a new empty dictionary, which will hold the processed values.
Use the with construct to open the file with the required filename. Follow the steps in the Figure 10.7.2: Reading each row of a csv file.
After defining csv.reader, loop over all items in the file using a for loop.
Assume that the file will be properly formatted with at least 3 columns in it.
5.1. For each row: The first two elements are the value in the dictionary; the third element is the month name, which is the key. For instance, a row that contains ['67', '47', 'January', '19', '8'] needs you to extract the key as "January" and the corresponding dictionary value [67, 47].
5.2. Extract the elements from the row, remembering to correctly convert the temperatures into integers.
Return the resulting dictionary.
In your main program, i.e, in the if __name__ == "__main__" block, you should ask for a filename that stores the previous weather information provided to Anita. You should then call the function get_temperature_stats(...) to get the dictionary from the csv file.
work in python:
def get_temperature_stats(filename):
"""
"""
if __name__ == "__main__":
tfile = input()
result = get_temperature_stats(tfile)
print(result)
