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Python for Everyone: The PreTeXt Interactive Edition

Section 8.16 Group Work: Using a CSV reader/writer

It is best to use a POGIL approach with the following. In POGIL students work in groups on activities and each member has an assigned role. For more information see https://cspogil.org/Home 1 .

Note 8.16.1.

If you work in a group, have only one member of the group fill in the answers on this page. You will be able to share your answers with the group at the bottom of the page.
Learning Objectives
Students will know and be able to do the following.
Content Objectives:
  • Use csv.reader to read a file with columns that may contain commas
  • Use csv.writer to write a csv file
Process Objectives:
  • Fix code that uses a csv.reader
  • Fix code that uses a csv.writer

Subsection 8.16.1 CSV Reader

Many people need to read and process CSV files. There is a module that makes this process easier.
Look at the data in the file below.
Hint.
3-Dec-01,9848.93,10220.78,9651.87,10021.57
1-Nov-01,9087.45,10054.58,8987.61,9851.56
1-Oct-01,8845.97,9626.54,8659.9,9075.14
4-Sep-01,9946.98,10238.5,7926.93,8847.56
1-Aug-01,10527.38,10663.07,9829.35,9949.75
2-Jul-01,10504.95,10758.14,10049.38,10522.81
1-Jun-01,10913.57,11236.68,10313.4,10502.4
1-May-01,10734.05,11436.42,10638.48,10911.94
2-Apr-01,9877.16,10973.15,9303.48,10734.97
1-Mar-01,10493.25,10940.45,9047.56,9878.78
1-Feb-01,10884.82,11140.09,10225.14,10495.28
2-Jan-01,10790.92,11224.41,10325.71,10887.36
1-Dec-00,10416.76,11044.7,10158.16,10787.99
1-Nov-00,10966.21,11152.02,10204.8,10414.49
2-Oct-00,10659.06,11108.79,9571.4,10971.14
1-Sep-00,11219.54,11518.83,10439.31,10650.92
1-Aug-00,10523.81,11415.99,10428.58,11215.1
3-Jul-00,10450.36,10980.34,10303.28,10521.98
1-Jun-00,10532.27,11013.05,10161.51,10447.89
1-May-00,10749.42,11086.72,10163.2,10522.33
3-Apr-00,10863.28,11600.43,10128.62,10733.91
1-Mar-00,10128.11,11311.28,9611.75,10921.92
1-Feb-00,10937.74,11228.44,9760.36,10128.31
3-Jan-00,11501.85,11908.5,10610.43,10940.53
1-Dec-99,10876.47,11658.68,10798.07,11497.12
1-Nov-99,10730.78,11195.34,10449.42,10877.81
1-Oct-99,10335.69,10883.1,9884.2,10729.86
1-Sep-99,10828.44,11218.39,10055.17,10336.95
2-Aug-99,10654.83,11428.94,10487.34,10829.28
1-Jul-99,10972.39,11321.61,10594.99,10655.15
1-Jun-99,10549.08,11120.24,10334.42,10970.8
3-May-99,10788.75,11244.36,10372.96,10559.74
1-Apr-99,9825.29,11072.25,9707.91,10789.04
1-Mar-99,9315.27,10158.57,9163.41,9786.16
1-Feb-99,9405.43,9662.77,9025.41,9306.58
4-Jan-99,9212.84,9759.44,8994.26,9358.83
1-Dec-98,9039.57,9390.75,8610.63,9181.43
2-Nov-98,8645.65,9457.95,8573.56,9116.55
1-Oct-98,7749.42,8718.25,7399.78,8592.1
1-Sep-98,7583.09,8253.79,7379.7,7842.62
3-Aug-98,8868.1,8948.17,7517.7,7539.07
1-Jul-98,9011.56,9412.64,8786.48,8883.29
1-Jun-98,8907.93,9155.04,8524.55,8952.02
1-May-98,9106.47,9311.98,8760.95,8899.95
1-Apr-98,8818.5,9287.32,8715.61,9063.37
2-Mar-98,8528.78,8997.11,8377.32,8799.81
2-Feb-98,7987.46,8616.72,7987.46,8545.72
2-Jan-98,7908.25,8072.91,7391.59,7906.5
1-Dec-97,7823.62,8209.56,7563.23,7908.25
3-Nov-97,7443.07,7934.53,7334.77,7823.13
1-Oct-97,7945.26,8218.34,6936.45,7442.08
2-Sep-97,7650.99,8078.36,7556.23,7945.26
1-Aug-97,8222.61,8340.14,7580.85,7622.42
1-Jul-97,7672.79,8328.99,7613.53,8222.61
2-Jun-97,7331.04,7868.44,7214.29,7672.79
1-May-97,7008.99,7430.2,6891.39,7331.04
1-Apr-97,6583.48,7081.23,6315.84,7008.99
3-Mar-97,6877.74,7158.28,6532.49,6583.48
3-Feb-97,6813.09,7112.87,6683.4,6877.74
2-Jan-97,6448.27,6953.55,6318.96,6813.09
2-Dec-96,6521.7,6623.96,6206.83,6448.27
1-Nov-96,6029.38,6606.3,5975.34,6521.7
1-Oct-96,5882.17,6162.8,5833.72,6029.38
3-Sep-96,5616.21,5952.08,5550.37,5882.17
1-Aug-96,5528.91,5761.95,5507.83,5616.21
1-Jul-96,5654.63,5769.88,5170.11,5528.91
3-Jun-96,5643.18,5770.61,5559.69,5654.63
1-May-96,5569.08,5833.04,5327.74,5643.18
1-Apr-96,5587.14,5737.07,5382.66,5569.08
1-Mar-96,5485.62,5755.86,5395.3,5587.14
1-Feb-96,5395.3,5693.36,5319.43,5485.62
2-Jan-96,5117.12,5433.24,5000.07,5395.3
1-Dec-95,5074.49,5266.69,5016.68,5117.12
1-Nov-95,4755.48,5143.13,4719.72,5074.49
2-Oct-95,4789.08,4845.08,4638.43,4755.48
1-Sep-95,4610.56,4839.48,4594.71,4789.08
1-Aug-95,4708.47,4772.56,4552.8,4610.56
3-Jul-95,4556.1,4767.99,4530.26,4708.47
1-Jun-95,4465.14,4614.2,4394.59,4556.1
1-May-95,4321.27,4480.7,4278.73,4465.14
3-Apr-95,4157.69,4348.94,4129.68,4321.27
1-Mar-95,4011.05,4213.71,3935.31,4157.69
1-Feb-95,3843.86,4034.62,3809.21,4011.05
3-Jan-95,3834.44,3955.56,3794.4,3843.86
1-Dec-94,3739.23,3882.21,3638.97,3834.44
1-Nov-94,3908.12,3919.9,3612.05,3739.23
3-Oct-94,3843.19,3958.25,3736.2,3908.12
1-Sep-94,3913.42,3972.72,3804.5,3843.19
1-Aug-94,3764.5,3954.54,3722.41,3913.42
1-Jul-94,3624.96,3782.63,3611.04,3764.5
1-Jun-94,3758.37,3839.88,3603.92,3624.96
2-May-94,3681.69,3788.76,3609.71,3758.37
4-Apr-94,3633.08,3733.15,3520.8,3681.69
1-Mar-94,3832.02,3911.78,3544.12,3635.96
1-Feb-94,3978.36,3998.06,3811.76,3832.02
3-Jan-94,3754.09,4002.84,3715.24,3978.36
1-Dec-93,3683.95,3818.92,3673.33,3754.09
1-Nov-93,3680.59,3749.9,3585.86,3683.95
1-Oct-93,3555.12,3713.57,3541.71,3680.59
1-Sep-93,3651.25,3665.5,3501.47,3555.12
2-Aug-93,3539.47,3681.71,3523.54,3651.25
1-Jul-93,3516.08,3604.86,3443.28,3539.47
1-Jun-93,3527.43,3577.25,3445.77,3516.08
3-May-93,3427.55,3582.23,3402.42,3527.43
1-Apr-93,3435.11,3499.41,3338.39,3427.55
1-Mar-93,3370.81,3497.25,3334.07,3435.11
1-Feb-93,3310.03,3472.94,3262.48,3370.81
4-Jan-93,3301.11,3338.12,3219.25,3310.03
1-Dec-92,3305.16,3364.87,3229.79,3301.11
2-Nov-92,3226.28,3326.51,3176.84,3305.16
1-Oct-92,3271.66,3291.39,3087.41,3226.28
1-Sep-92,3257.35,3391.35,3226.55,3271.66
3-Aug-92,3393.78,3413.23,3200.86,3257.35
1-Jul-92,3318.52,3414.85,3255.43,3393.78
1-Jun-92,3396.88,3435.27,3242.32,3318.52
1-May-92,3359.12,3433.98,3316.64,3396.88
1-Apr-92,3235.47,3387.97,3141.77,3359.12
2-Mar-92,3267.67,3318.42,3176.21,3235.47
3-Feb-92,3223.39,3307.47,3193.42,3267.67
2-Jan-92,3168.83,3313.51,3119.86,3223.39
2-Dec-91,2894.68,3204.61,2832.29,3168.83
1-Nov-91,3069.1,3091.91,2861.14,2894.68
1-Oct-91,3016.77,3091.01,2925.54,3069.1
3-Sep-91,3043.6,3066.64,2963.1,3016.77
1-Aug-91,3024.82,3068.65,2836.31,3043.6
1-Jul-91,2911.67,3039.58,2897.36,3024.82
3-Jun-91,3027.5,3057.47,2879.25,2906.75
1-May-91,2887.87,3044.5,2834.53,3027.5
1-Apr-91,2913.86,3030.45,2848.51,2887.87
1-Mar-91,2882.18,3017.82,2829.21,2913.86
1-Feb-91,2736.39,2955.2,2694.31,2882.18
2-Jan-91,2633.66,2747.28,2447.03,2736.39
3-Dec-90,2559.65,2662.62,2534.65,2633.66
1-Nov-90,2442.33,2581.19,2415.59,2559.65
1-Oct-90,2452.48,2565.35,2344.31,2442.33
4-Sep-90,2614.36,2665.35,2367.82,2452.48
1-Aug-90,2905.2,2931.19,2459.41,2614.36
2-Jul-90,2880.69,3024.26,2833.17,2905.2
1-Jun-90,2876.66,2956.93,2821.53,2880.69
1-May-90,2656.76,2908.21,2651.35,2876.66
2-Apr-90,2707.21,2793.47,2627.7,2656.76
1-Mar-90,2627.25,2775,2607.88,2707.21
1-Feb-90,2590.54,2674.32,2540.99,2627.25
2-Jan-90,2753.2,2834.04,2513.06,2590.54
1-Dec-89,2706.27,2784.77,2658.7,2753.2
1-Nov-89,2645.08,2718.22,2563.11,2706.27
2-Oct-89,2692.82,2809.08,2496.93,2645.08
1-Sep-89,2737.27,2768.24,2636.78,2692.82
1-Aug-89,2660.66,2758.73,2619.71,2737.27
3-Jul-89,2440.06,2668.25,2431.53,2660.66
1-Jun-89,2480.15,2544.95,2412.94,2440.06
1-May-89,2418.8,2521.63,2356.3,2480.15
3-Apr-89,2293.62,2433.1,2282.07,2418.8
1-Mar-89,2258.39,2351.07,2234.46,2293.62
1-Feb-89,2342.32,2369.29,2232.14,2258.39
3-Jan-89,2168.39,2350.18,2127.14,2342.32
We can write Python code to read the data using a csv reader and find the date with the highest value at the close.

Checkpoint 8.16.2.

Run the code below to find the date with the highest value at the close.
import csv

# get the lines from the file
inFile = open("stocks.txt")
csv_file = csv.reader(inFile)

# init max_close and max_date
max_close = 0
max_date = ""

# for each row in the file
for row in csv_file:

    # get the values
    date = row[0]
    close = float(row[4])

    # if the current close is greater then save it and the date
    if close > max_close:
        max_close = close
        max_date = date

inFile.close()
print(f"Max close {max_close} on {max_date}")

Checkpoint 8.16.3.

    Q-2: What type of thing is row in the code above?
  • string
  • It is not a string when you use a csv reader
  • list/iterator
  • The csv reader returns an iterator but you can process it like a list of strings
  • dictionary
  • It is not a dictionary
  • integer
  • It is not an integer

Checkpoint 8.16.4.

    Q-3: Which of these must you do to use a csv reader to read a csv file?
  • import csv
  • To use a csv reader you must import csv
  • convert the file handle object to a csv reader object
  • To use a csv reader you must convert the file handle object to a csv reader object
  • split the data at commas
  • Incorrect, the csv reader will split at commas (or the specified delimeter) automatically
  • close the file handle object
  • You should close the file handle object whenever you open it but it won't give you an error if you don't

Subsection 8.16.2 Reading Comma-Separated Values (CSV) Files with a Header Row

Here is another sample example CSV file. It contains the number of passengers (in thousands) for transatlantic air travel for each month for the years 1958 to 1960. The first row is a header that explains the data. The data is from https://people.sc.fsu.edu/~jburkardt/data/csv/csv.html 2 .
Hint.
"Month", "1958", "1959", "1960"
"JAN",  340,  360,  417
"FEB",  318,  342,  391
"MAR",  362,  406,  419
"APR",  348,  396,  461
"MAY",  363,  420,  472
"JUN",  435,  472,  535
"JUL",  491,  548,  622
"AUG",  505,  559,  606
"SEP",  404,  463,  508
"OCT",  359,  407,  461
"NOV",  310,  362,  390
"DEC",  337,  405,  432
We can read the data from the file and store it in a nested dictionary. In this case the outer dictionary will use the month as the key and the inner dictionary will use the years as the keys. It will use the data from the header row for the year keys.

Checkpoint 8.16.5.

Run the code below. It is supposed to print the nested dictionary and then the total number of passengers (in thousands) for 1958, but there are errors. Fix the errors so that all tests pass.
import csv

def get_dict(file):

    d = {}

    # get the file handler
    inFile = open(file)
    csvFile = csv.Reader(inFile)

    # read the header row
    headers = next(csvFile)

    # read the rest of the lines from the file handler
    for row in inFile:

        if len(row) == 4:
            month = row[0]
            data_1 = int(row[1])
            data_2 = int(row[2])
            data_3 = int(row[3])

            year_d = {}
            year_d[headers[1]] = data_1
            year_d[headers[2]] = data_2
            year_d[headers[3]] = data_3
            d[month] = year_d

    inFile.close()
    return d

def get_total_for_year(travel_d, year):
    total = 0
    for key in travel_d:
        data_d = travel_d[key]
        total += data_d[year]
    return total

travel_d = get_dict("airtravel.csv")
print(travel_d)
total = get_total_for_year(travel_d, "1958")
print(total)

=====

import unittest

class myTests(unittest.TestCase):

   def testOne(self):
      travel_d = get_dict("airtravel.csv")
      self.assertEqual(get_total_for_year(travel_d, "1958"), 4572, 'get_total_for_year(travel_d, "1958")')
      self.assertEqual(get_total_for_year(travel_d, "1959"), 5140, 'get_total_for_year(travel_d, "1959")')
      self.assertEqual(get_total_for_year(travel_d, "1960"), 5714, 'get_total_for_year(travel_d, "1960")')

unittest.main()

Checkpoint 8.16.6.

Q-5: What function can you use to read just one row from a csv reader?

Checkpoint 8.16.7.

Fix the code below to work correctly. It should print the month with the highest number of passengers in 1958.
def get_dict(file):

    d = {}

    # get the file handler
    inFile = open(file)
    csvFile = csv.reader()

    # skip the header
    headers = next(inFile)

    # read the rest of the lines from the file handler
    for row in csvFile:

        if len(values) == 4:
            month = values[0]
            data_1 = int(row[1])
            data_2 = int(row[2])
            data_3 = int(row[3])

            year_d = {}
            year_d[headers[0]] = data_1
            year_d[header[2]] = data_2
            year_d[header[3]] = data_3
            d[month] = year_d

    inFile.close()
    return d

def get_max_month(travel_d, year):
    d = {}
    for month in travel_d:
        month_d = travel_d[month]
        d[month] = month_d[year]
    tup_list = sorted(d.items(), key = lambda t: t[0], reverse = True)
    return tup_list[0]

travel_d = get_dict("airtravel.csv")
print(travel_d)
month, amount = get_max_month(travel_d, "1958")
print(month, amount)

=====

import unittest

class myTests(unittest.TestCase):

   def testOne(self):
      travel_d = get_dict("airtravel.csv")
      self.assertEqual(get_max_month(travel_d, "1958")[0], "AUG", 'get_max_month(travel_d, "1958")[0]')
      self.assertEqual(get_max_month(travel_d, "1959")[0], "AUG", 'get_max_month(travel_d, "1959")[0]')
      self.assertEqual(get_max_month(travel_d, "1960")[0], "JUL", 'get_max_month(travel_d, "1960")[0]')

unittest.main()

Subsection 8.16.3 Writing a Comma-Separated Values (CSV) File with CSV Writer

Here is another sample example CSV file. It contains the Oscar winners for Best Actress from 1928 to 2016. It has a header row to explain the data in each column.
Hint.
"Index", "Year", "Age", "Name", "Movie"
 1, 1928, 22, "Janet Gaynor", "Seventh Heaven, Street Angel and Sunrise: A Song of Two Humans"
 2, 1929, 37, "Mary Pickford", "Coquette"
 3, 1930, 28, "Norma Shearer", "The Divorcee"
 4, 1931, 63, "Marie Dressler", "Min and Bill"
 5, 1932, 32, "Helen Hayes", "The Sin of Madelon Claudet"
 6, 1933, 26, "Katharine Hepburn", "Morning Glory"
 7, 1934, 31, "Claudette Colbert", "It Happened One Night"
 8, 1935, 27, "Bette Davis", "Dangerous"
 9, 1936, 27, "Luise Rainer", "The Great Ziegfeld"
10, 1937, 28, "Luise Rainer", "The Good Earth"
11, 1938, 30, "Bette Davis", "Jezebel"
12, 1939, 26, "Vivien Leigh", "Gone with the Wind"
13, 1940, 29, "Ginger Rogers", "Kitty Foyle"
14, 1941, 24, "Joan Fontaine", "Suspicion"
15, 1942, 38, "Greer Garson", "Mrs. Miniver"
16, 1943, 25, "Jennifer Jones", "The Song of Bernadette"
17, 1944, 29, "Ingrid Bergman", "Gaslight"
18, 1945, 40, "Joan Crawford", "Mildred Pierce"
19, 1946, 30, "Olivia de Havilland", "To Each His Own"
20, 1947, 35, "Loretta Young", "The Farmer's Daughter"
21, 1948, 32, "Jane Wyman", "Johnny Belinda"
22, 1949, 33, "Olivia de Havilland", "The Heiress"
23, 1950, 29, "Judy Holliday", "Born Yesterday"
24, 1951, 38, "Vivien Leigh", "A Streetcar Named Desire"
25, 1952, 54, "Shirley Booth", "Come Back, Little Sheba"
26, 1953, 24, "Audrey Hepburn", "Roman Holiday"
27, 1954, 25, "Grace Kelly", "The Country Girl"
28, 1955, 48, "Anna Magnani", "The Rose Tattoo"
29, 1956, 41, "Ingrid Bergman", "Anastasia"
30, 1957, 28, "Joanne Woodward", "The Three Faces of Eve"
31, 1958, 41, "Susan Hayward", "I Want to Live!"
32, 1959, 39, "Simone Signoret", "Room at the Top"
33, 1960, 29, "Elizabeth Taylor", "BUtterfield 8"
34, 1961, 27, "Sophia Loren", "Two Women"
35, 1962, 31, "Anne Bancroft", "The Miracle Worker"
36, 1963, 31, "Patricia Neal", "Hud"
37, 1964, 29, "Julie Andrews", "Mary Poppins"
38, 1965, 25, "Julie Christie", "Darling"
39, 1966, 35, "Elizabeth Taylor", "Who's Afraid of Virginia Woolf?"
40, 1967, 60, "Katharine Hepburn", "Guess Who's Coming to Dinner"
41, 1968, 61, "Katharine Hepburn", "The Lion in Winter"
42, 1969, 26, "Barbra Streisand", "Funny Girl"
43, 1970, 35, "Maggie Smith", "The Prime of Miss Jean Brodie"
44, 1971, 34, "Glenda Jackson", "Women in Love"
45, 1972, 34, "Jane Fonda", "Klute"
46, 1973, 27, "Liza Minnelli", "Cabaret"
47, 1974, 37, "Glenda Jackson", "A Touch of Class"
48, 1975, 42, "Ellen Burstyn", "Alice Doesn't Live Here Anymore"
49, 1976, 41, "Louise Fletcher", "One Flew Over the Cuckoo's Nest"
50, 1977, 36, "Faye Dunaway", "Network"
51, 1978, 32, "Diane Keaton", "Annie Hall"
52, 1979, 41, "Jane Fonda", "Coming Home"
53, 1980, 33, "Sally Field", "Norma Rae"
54, 1981, 31, "Sissy Spacek", "Coal Miner's Daughter"
55, 1982, 74, "Katharine Hepburn", "On Golden Pond"
56, 1983, 33, "Meryl Streep", "Sophie's Choice"
57, 1984, 49, "Shirley MacLaine", "Terms of Endearment"
58, 1985, 38, "Sally Field", "Places in the Heart"
59, 1986, 61, "Geraldine Page", "The Trip to Bountiful"
60, 1987, 21, "Marlee Matlin", "Children of a Lesser God"
61, 1988, 41, "Cher", "Moonstruck"
62, 1989, 26, "Jodie Foster", "The Accused"
63, 1990, 80, "Jessica Tandy", "Driving Miss Daisy"
64, 1991, 42, "Kathy Bates", "Misery"
65, 1992, 29, "Jodie Foster", "The Silence of the Lambs"
66, 1993, 33, "Emma Thompson", "Howards End"
67, 1994, 36, "Holly Hunter", "The Piano"
68, 1995, 45, "Jessica Lange", "Blue Sky"
69, 1996, 49, "Susan Sarandon", "Dead Man Walking"
70, 1997, 39, "Frances McDormand", "Fargo"
71, 1998, 34, "Helen Hunt", "As Good as It Gets"
72, 1999, 26, "Gwyneth Paltrow", "Shakespeare in Love"
73, 2000, 25, "Hilary Swank", "Boys Don't Cry"
74, 2001, 33, "Julia Roberts", "Erin Brockovich"
75, 2002, 35, "Halle Berry", "Monster's Ball"
76, 2003, 35, "Nicole Kidman", "The Hours"
77, 2004, 28, "Charlize Theron", "Monster"
78, 2005, 30, "Hilary Swank", "Million Dollar Baby"
79, 2006, 29, "Reese Witherspoon", "Walk the Line"
80, 2007, 61, "Helen Mirren", "The Queen"
81, 2008, 32, "Marion Cotillard", "La Vie en rose"
82, 2009, 33, "Kate Winslet", "The Reader"
83, 2010, 45, "Sandra Bullock", "The Blind Side"
84, 2011, 29, "Natalie Portman", "Black Swan"
85, 2012, 62, "Meryl Streep", "The Iron Lady"
86, 2013, 22, "Jennifer Lawrence", "Silver Linings Playbook"
87, 2014, 44, "Cate Blanchett", "Blue Jasmine"
88, 2015, 54, "Julianne Moore", "Still Alice"
89, 2016, 26, "Brie Larson", "Room"
We can read the data from the file and store it in a list of dictionaires where the keys in the dictionary are ‘year', ‘age', ‘name', and ‘movie'.

Checkpoint 8.16.8.

Fix the code below. It should read all the data into a list of dictionaries. Then it should create a new dictionary where the key is the age and the value is the number of actresses who won at that age. It should sort the items in the dictionary by the number of winners descending and return the top five tuples. Then it will write the data to a csv file using a csv writer.
import csv

def get_list(file):

    l = []

    # get the file handler
    inFile = open(file)
    csv_file = csv.reader(inFile,quotechar='"', delimiter=',',
                          quoting=csv.QUOTE_MINIMAL, skipinitialspace=True)

    # read the header
    headers = next(csfFile)

    # read the rest of the lines from the file handler
    for row in csvFile:

        d = {}

        if len(values) == 5:
            year = row[1].strip()
            d["year"] = year
            age = row[2].strip()
            d["age"] = age
            name = row[3]
            d["name"] = name.strip('" ')
            movie = row[4]
            d["movie"] = movie.strip('" ')
            l.append(d)

    inFile.close()
    return l

def get_top_five_by_age(l):
    age_d = {}
    for d in l:
        age = d["age"]
        age_d[age] = age_d.get(age,0) + 1
    out = sorted(age_d.items(), key = lambda t: t[1], reverse = True)
    return out[0:5]

def write_top_five(file_name,tuple_list):
    outFile = open("file_name", "w")
    csvOut = csv.writer(outFile)
    for tuple in tuple_list:
        csvOut.write(tuple)

dict_list = get_list('oscar_age_actress.csv')
print(dict_list[0])
age_list = get_top_five_by_age(dict_list)
print(age_list)
write_top_five("top_five.csv", age_list)

=====

import unittest

class myTests(unittest.TestCase):

   def testOne(self):
      l = get_list('oscar_age_actress.csv')
      self.assertEqual(get_top_five_by_age(l)[0][0], '29', 'get_top_five_by_age(l)[0][0]')
      self.assertEqual(get_top_five_by_age(l)[0][1], 8, 'get_top_five_by_age(l)[0][1]')
      self.assertEqual(get_top_five_by_age(l)[1][0], '26', 'get_top_five_by_age(l)[1][0]')
      self.assertEqual(get_top_five_by_age(l)[1][1], 6, 'get_top_five_by_age(l)[1][1]')
      self.assertEqual(get_top_five_by_age(l)[2][0], '33', 'get_top_five_by_age(l)[2][0]')
      self.assertEqual(get_top_five_by_age(l)[2][1], 6, 'get_top_five_by_age(l)[2][1]')
      self.assertEqual(get_top_five_by_age(l)[3][0], '35', 'get_top_five_by_age(l)[3][0]')
      self.assertEqual(get_top_five_by_age(l)[3][1], 5, 'get_top_five_by_age(l)[3][1]')
      self.assertEqual(get_top_five_by_age(l)[4][0], '41', 'get_top_five_by_age(l)[4][0]')
      self.assertEqual(get_top_five_by_age(l)[4][1], 5, 'get_top_five_by_age(l)[4][1]')


unittest.main()

Checkpoint 8.16.9.

    Q-8: What type of things can be specified when you create the csv reader?
  • delimeter
  • You can specify the delimter
  • quote character
  • You can specify the quote character
  • if initial space should be skipped
  • You can specify if initial space should be skipped
  • the number of columns
  • The number of columns is determined by the size of the list passed to writerow

Checkpoint 8.16.10.

    Q-9: How is a csv writer different from a file writer?
  • You can write all the rows at once
  • No, you still write out each line one at a time
  • It automatically adds a new line
  • Yes, it automatically adds a new line
  • You write out a list of values
  • Yes, you write out a list of values rather than a string
  • It automatically converts numbers to strings to write each line
  • Yes, it automatically converts values to strings
You can read more about the CSV module at https://docs.python.org/3/library/csv.html 3 .
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