Question

Functionality to build (4 functions)

read_csv_header

Define a function named read_csv_header with one parameter. This parameter will be a csv reader object (e.g., the value returned when calling the function csv.reader). The first row read using the parameter is the file's header row. The header row contains the keys for the data stored in the CSV file. This function must return a list containing the strings from that header row.

The function parameter will be a csv reader object and not a string. This means you do not need the with..as nor create the csv reader in your function, but this will be done in the code calling your function. For example, to execute read_csv_header using a file named "smallDataFile.csv" you would write the following code:
with open("smallDataFile.csv") as f_in:
csv_r = csv.reader(f_in)
header = read_csv_header(csv_r)

You would then want to include code to check that header had the expected value.

For example, suppose the first line of the file being read by csv_r was:

tow_date,tow_reason

then the call read_csv_header(csv_r) would need to return:

['tow_date','tow_reason']

read_data

Define a function named read_data with two parameters. The first parameter will be a csv reader object (e.g., the value returned when calling the function csv.reader) and the second parameter will be a list of strings representing the keys for this data. The list matches the order of data in the file, so the second parameter's first entry is the key to use for data in the file's first column, the second parameter's second entry is the key to use for data in the file's second column, and so on.

The function's first parameter will be a csv reader object and not a string. This means you do not need the with..as nor create the csv reader in your function, but this will be done in the code calling your function. For example, to execute read_data using a file named "smallDataFileWithoutHeaders.csv" you would write the following code:
with open("smallDataFileWithoutHeaders.csv") as f_in:
csv_r = csv.reader(f_in)
result = read_data(csv_r,['tow_date','tow_reason'])

You would then want to include code to check that header had the expected value.

The function should return a list of dictionaries. This list will contain a dictionary for each row read using the first parameter. The keys of these dictionaries will be the entries in the second parameter and the values will be the data read in from the file (you can assume there will be equal numbers list entries and columns).

For example, suppose the lines of the file being read by csv_r were:

2015-12-30,IL

2018-04-07,GA

then the call read_data(csv_r,['tow_date','tow_reason']) would need to return:

[{'tow_date': '2015-12-30', 'tow_reason': 'IL'},
{'tow_date': '2018-04-07', 'tow_reason': 'GA'}]

write_csv_header

Define a function named write_csv_header with two parameters. The first parameter will be a csv writer object (e.g., the value returned when calling the function csv.writer) and the second parameter will be a dictionary. Your function must use the first parameter to write the dictionary's keys as a row. The list of keys written out must then be returned.

The function's first parameter will be a csv writer object and not a string. This means you do not need the with..as nor create the csv writer in your function, but this will be done in the code calling your function. For example, to execute write_csv_header using a file named "smallDataFileIWrote.csv" you would write the following code:
with open("smallDataFileIWrote.csv",'w') as f_out:
csv_w = csv.writer(f_out)
result = write_csv_header(csv_w,{'td':'2020', 'tr': 'AB'})

You would then want to include code to check that result variable and file that you wrote each had the expected value.

For example, if csv_w were assigned to a csv writer object, then the call:
write_csv_header(csv_w, {'tow_date': '2020-01-20', 'tow_reason': 'AB'})
would need to write a line to the file like:
tow_date, tow_reason
and would need to return:
['tow_reason', 'tow_reason']

write_dictionaries_to_csv

Define a function named write_dictionaries_to_csv with three parameters. The first parameter will be a csv writer object (e.g., the value returned when calling the function csv.writer), the second parameter will be a list of dictionaries, and the third parameter will be a list of keys. You may assume that each key in the third parameter exists as a key in each of the dictionaries in the second parameter.

The function's first parameter will be a csv writer object and not a string. This means you do not need the with..as nor create the csv writer in your function, but this will be done in the code calling your function. For example, to execute write_dictionaries_to_csv using a file named "smallDataFileIWrote.csv" you would write the following code:
with open("smallDataFileIWrote.csv",'w') as f_out:
csv_w = csv.writer(f_out)
write_dictionaries_to_csv(csv_w,{'td':'2020', 'tr': 'AB'})

You would then want to include code to check that the file that you wrote each had the expected value.

Your function must write the values from each dictionary as a row in the CSV file. The order of the values in the row must correspond to the order of the keys in the third argument.

For example, if
data = [{'tow_reason': 'IL', 'tow_date': '2013-06-18'},
{'tow_date': '2014-09-25', 'tow_reason': 'GA'}]
and csv_w were assigned to a csv writer object, then the call:

write_dictionaries_to_csv(csv_w, data,['tow_reason','tow_date'])

would append the following two rows to the file being written to using csv_w:
IL, 2013-06-18

GA, 2014-09-25

Answer 1

import csv
#function to read header from a csv file
def read_csv_header(csv_r):
   #header is the first row in csv file
   for row in csv_r:
       return row
#function to read data from csv file into a dictionary
def read_data(csv_r, header):
   #data is a list of dictionaries
   data = []
   #for every row in csv file
   for row in csv_r:
       temp = {}
       #create dictionary
       for i in range(len(header)):
           temp[header[i]] = row[i]
       #add the dictionary to data
       data.append(temp)
   return data
#function to srite header into a csv file
def write_csv_header(csv_w, data):
   #take the keys in data
   output = list(data.keys())
   #write keys of data in csv file
   csv_w.writerow(output)
   return output
#write the dictionaries into csv file
def write_dictionaries_to_csv(csv_w, data, header):
   #write the header
   csv_w.writerow(header)
   #for every row in data
   for row in data:
       temp = []
       #create a list
       for key in header:
           temp.append(row[key])
       #write the list in csv file
       csv_w.writerow(temp)
#this code will check read_csv_header function
with open("smallDataFile.csv") as f_in:
   csv_r = csv.reader(f_in)
   header = read_csv_header(csv_r)
   print(header)
#this code will check read_data function
with open("smallDataFileWithoutHeaders.csv") as f_in:
   csv_r = csv.reader(f_in)
   result = read_data(csv_r,['tow_date','tow_reason'])
   print(result)
#this code will check write_csv_header function
with open("smallDataFileIWrote.csv",'w') as f_out:
   csv_w = csv.writer(f_out)
   result = write_csv_header(csv_w, {'tow_date': '2020-01-20', 'tow_reason': 'AB'})
   print(result)
#this code will check write-dictionaries_to_csv function
with open("smallDataFileIWrote.csv",'w') as f_out:
   csv_w = csv.writer(f_out)
   data = [{'tow_reason': 'IL', 'tow_date': '2013-06-18'},{'tow_date': '2014-09-25', 'tow_reason': 'GA'}]
   write_dictionaries_to_csv(csv_w, data,['tow_reason','tow_date'])

Folloew the images for indentation.

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