problem14

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Georgia Institute Of Technology *

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CS6040

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Dec 6, 2023

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11/28/23, 8:13 PM problem14 file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem14-sample-solutions.html 1/15 Problem 14: Scraping data from "FiveThirtyEight" There are a ton of fun interactive visualizations at the website, FiveThirtyEight (http://fivethirtyeight.com). For example, consider the one that tracks the US President's approval ratings: https://projects.fivethirtyeight.com/trump-approval-ratings/ (https://projects.fivethirtyeight.com/trump-approval- ratings/) Here is a screenshot of the interactive graph it contains: In it, you can select each day ("movable cursor") and get information about the approval ratings for that day.

11/28/23, 8:13 PM problem14 file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem14-sample-solutions.html 2/15 As it turns out, this visualization is implemented in JavaScript and all of the individual data items are embedded within the web page itself. For example, here is a 132-page PDF file, which is the source code for the web page taken on September 6, 2018: PDF file (https://cse6040.gatech.edu/datasets/538-djt-pop/2018-09-06.pdf). The raw data being rendered in the visualization starts on page 50. Of course, that means you can use your Python-fu to try to extract this data for your own purposes! Indeed, that is your task for this problem. Although the data in this problem comes from an HTML file with embedded JavaScript, you do not need to know anything about HTML or JavaScript to solve this problem. It is purely an exercise of rudimentary Python and computational problem solving. Reading the raw HTML file Let's read the raw contents of the FiveThirtyEight approval ratings page (i.e., the same contents as the PDF) into a variable named raw_html . Like the groceries problem in Notebook 2, this cell contains a bunch of code for getting the data file you need, which you can ignore.

11/28/23, 8:13 PM problem14 file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem14-sample-solutions.html 3/15 In [1]: def download(url, local_file, overwrite= False ): import os , requests if not os.path.exists(local_file) or overwrite: print("Downloading: {} ...".format(url)) r = requests.get(url) with open(local_file, 'wb') as f: f.write(r.content) return True return False # File existed already def get_checksum(local_file): import io , hashlib with io.open(local_file, 'rb') as f: body = f.read() body_checksum = hashlib.md5(body).hexdigest() return body_checksum def download_or_load_locally(file, local_dir="", url_base= None , checks um= None ): if url_base is None : url_base = "https://cse6040.gatech.edu/datase ts/" local_file = " {}{} ".format(local_dir, file) remote_url = " {}{} ".format(url_base, file) download(remote_url, local_file) if checksum is not None : body_checksum = get_checksum(local_file) assert body_checksum == checksum, \ "Downloaded file ' {} ' has incorrect checksum: ' {} ' instead of ' {} '".format(local_file, body_checksum, checksum) print("' {} ' is ready!".format(file)) def on_vocareum(): import os return os.path.exists('.voc') if on_vocareum(): URL_BASE = None DATA_PATH = "./resource/asnlib/publicdata/538-djt-pop/" else : URL_BASE = "https://cse6040.gatech.edu/datasets/538-djt-pop/" DATA_PATH = "" datasets = {'2018-09-06.html': '291a7c1cbf15575a48b0be8d77b7a1d6'} for filename, checksum in datasets.items(): download_or_load_locally(filename, url_base=URL_BASE, local_dir=DA TA_PATH, checksum=checksum) with open(' {}{} '.format(DATA_PATH, '2018-09-06.html')) as fp: raw_html = fp.read() print(" \n (All data appears to be ready.)")

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11/28/23, 8:13 PM problem14 file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem14-sample-solutions.html 4/15 File snippets. Run the following code cell. It takes the raw_html string and prints the substring just around the start of the raw data you'll need, i.e., starting at page 50 of the PDF: In [2]: sample_offset, sample_len = 69950, 1500 print(raw_html[sample_offset:sample_offset+sample_len]) Run the following code cell to see the end of the raw data region. '2018-09-06.html' is ready! (All data appears to be ready.) thPrefix="/trump-approval-ratings/"; var subgroup="All polls"; var showMoreCutoff=5; var approval=[{"date":"2017-01-23","future":false,"subgroup":"All poll s","approve_estimate":"45.46693","approve_hi":"50.88971","approve_l o":"40.04416","disapprove_estimate":"41.26452","disapprove_hi":"46.687 29","disapprove_lo":"35.84175"},{"date":"2017-01-24","future":false,"s ubgroup":"All polls","approve_estimate":"45.44264","approve_hi":"50.82 922","approve_lo":"40.05606","disapprove_estimate":"41.87849","disappr ove_hi":"47.26508","disapprove_lo":"36.49191"},{"date":"2017-01-25","f uture":false,"subgroup":"All polls","approve_estimate":"47.76497","app rove_hi":"52.66397","approve_lo":"42.86596","disapprove_estimate":"42. 52911","disapprove_hi":"47.42811","disapprove_lo":"37.63011"},{"dat e":"2017-01-26","future":false,"subgroup":"All polls","approve_estimat e":"44.37598","approve_hi":"48.93261","approve_lo":"39.81936","disappr ove_estimate":"41.06081","disapprove_hi":"45.61743","disapprove_lo":"3 6.50418"},{"date":"2017-01-27","future":false,"subgroup":"All poll s","approve_estimate":"44.13586","approve_hi":"48.70494","approve_l o":"39.56679","disapprove_estimate":"41.67268","disapprove_hi":"46.241 75","disapprove_lo":"37.1036"},{"date":"2017-01-28","future":false,"su bgroup":"All polls","approve_estimate":"43.87527","approve_hi":"48.468 21","approve_lo":"39.28233","disapprove_estimate":"41.91362","disappro ve_hi":"46.50656","disapprove_lo":"37.32067"},{"date":"2017-01-29","fu ture":false,"subgroup":"All

11/28/23, 8:13 PM problem14 file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem14-sample-solutions.html 5/15 In [3]: sample_end = 257500 print(raw_html[sample_end:sample_end+sample_len]) Please make the following observations about the file snippets shown above: The raw data of approval ratings begins with the text, 'var approval=[' and ends with a closing square bracket, ']' . No other square brackets appear between these two. Each "data point" or "data record" is encoded in JavaScript Object Notation (JSON), which is essentially the same as a Python dictionary. That is, it is enclosed in curly brackets, {...} and contains a number of key-value pairs. These include the date ( "date":"yyyy-mm-dd" ), approval and disapproval rating estimates ( "approve_estimate":"45.46693" and "disapprove_estimate":"41.26452" ), as well as upper and lower error bounds ( "..._hi" and "..._lo" ). The estimates correspond to the green (approval) and orange (disapproval) lines, and the error bounds form the shaded regions around those lines. Each data record includes a key named "future" . That's because FiveThirtyEight has projected the ratings into the future, so some records correspond to observed values ( "future":false ) while others correspond to extrapolated values ( "future":true ). In addition, for the exercises below, you may assume the data records are encoded in the same way, e.g., the fields appear in the same order and there are no variations in punctuation or whitespace from what you see in the above snippets. ","approve_lo":"29.24131","disapprove_estimate":"51.94407","disapprove _hi":"63.94288","disapprove_lo":"39.94526"},{"date":"2019-05-10","futu re":true,"subgroup":"All polls","approve_estimate":"41.47093","approve _hi":"53.72246","approve_lo":"29.2194","disapprove_estimate":"51.9422 5","disapprove_hi":"63.96438","disapprove_lo":"39.92012"},{"date":"201 9-05-11","future":true,"subgroup":"All polls","approve_estimate":"41.4 719","approve_hi":"53.74633","approve_lo":"29.19748","disapprove_estim ate":"51.94044","disapprove_hi":"63.98589","disapprove_lo":"39.895"}, {"date":"2019-05-12","future":true,"subgroup":"All polls","approve_est imate":"41.47285","approve_hi":"53.77016","approve_lo":"29.17555","dis approve_estimate":"51.93866","disapprove_hi":"64.0074","disapprove_l o":"39.86993"},{"date":"2019-05-13","future":true,"subgroup":"All poll s","approve_estimate":"41.47378","approve_hi":"53.79396","approve_l o":"29.15361","disapprove_estimate":"51.9369","disapprove_hi":"64.0289 2","disapprove_lo":"39.84487"},{"date":"2019-05-14","future":true,"sub group":"All polls","approve_estimate":"41.47469","approve_hi":"53.8177 3","approve_lo":"29.13165","disapprove_estimate":"51.93515","disapprov e_hi":"64.05045","disapprove_lo":"39.81984"}]; </script> <div class="container"> <div id="footer"> <div class="notes"> <p> When the dates of tracking polls from the same pollster overlap, only the most recent version is shown. </p> </div> <div class="additional-credits"> <p>

11/28/23, 8:13 PM problem14 file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem14-sample-solutions.html 6/15 Your task: Extracting the approval ratings Exercise 0 (1 point). Recall that the data begins with 'var approval=[...' and ends with a closing square bracket, ']' . Complete the function, extract_approval_raw(html) , below. The input variable, html , is a string corresponding to the raw HTML file. Your function should return the substring beginning immediately after the opening square bracket and up to, but excluding , the last square bracket. It should return exactly that substring from the file, and should not otherwise modify it. While you don't have to use regular expressions for this problem, if you wish to, observe that the cell below imports the re module. In [4]: import re def extract_approval_raw(html): assert isinstance(html, str), "`html` is not a string." ### BEGIN SOLUTION match = re.search(r'var\s+approval\s*=\s*\[([^\]]*)\];', html) if match: return match.groups(0)[0] return '' ### END SOLUTION raw_data = extract_approval_raw(raw_html) print("type(raw_data) == {} (should be a string!) \n ".format(type(raw _data))) print("=== First and last 300 characters === \n{}\n ... \n{} ".forma t(raw_data[:300], raw_data[-300:])) type(raw_data) == <class 'str'> (should be a string!) === First and last 300 characters === {"date":"2017-01-23","future":false,"subgroup":"All polls","approve_es timate":"45.46693","approve_hi":"50.88971","approve_lo":"40.04416","di sapprove_estimate":"41.26452","disapprove_hi":"46.68729","disapprove_l o":"35.84175"},{"date":"2017-01-24","future":false,"subgroup":"All pol ls","approve_estimat ... e_estimate":"51.9369","disapprove_hi":"64.02892","disapprove_lo":"39.8 4487"},{"date":"2019-05-14","future":true,"subgroup":"All polls","appr ove_estimate":"41.47469","approve_hi":"53.81773","approve_lo":"29.1316 5","disapprove_estimate":"51.93515","disapprove_hi":"64.05045","disapp rove_lo":"39.81984"}

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11/28/23, 8:13 PM problem14 file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem14-sample-solutions.html 7/15 In [5]: # Test cell: `test__extract_approval_raw` (1 point) raw_data = extract_approval_raw(raw_html) assert isinstance(raw_data, str), "Your function did not return a stri ng!" assert len(raw_data) == 188678, "Did your function return all of the s ubstring? It should have {} characters, but your function has {} inste ad, making it a bit too {} .".format(188678, len(raw_data), "short" if len(raw_data) < 188678 else "long") print(" \n (Passed!)") Exercise 1 (3 points). Complete the function, extract_approval_estimates(data_substr) , below. It takes as input a string, data_substr , which would be a data substring like the one returned by extract_approval_raw() . It should return a dictionary where each key is a date, stored as a string in the form '2017-01-23' ( without the quotes ); each corresponding value is the approval rating, stored as a float . For example, executing approvals = extract_approval_estimates(data_substr) print (type(approvals['2017-01-23']), approvals['2017-01-23']) should display <class 'float'> 45.46693 . (Refer to the first entry of ratings from the PDF, which begins on page 50.) Also, your function should keep only records for which "future":false , that is, it should not include the extrapolated values . You may make the following assumptions: 1. Dates are not duplicated. 2. All encountered dates have valid approval estimates. 3. All data records are encoded using the same pattern as shown in the file snippets above. (Passed!)

11/28/23, 8:13 PM problem14 file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem14-sample-solutions.html 8/15 In [6]: def extract_approval_estimates(data_substr): assert isinstance(data_substr, str), "`data_substr` is not a strin g." assert data_substr[0] == '{' and data_substr[-1] == '}', "Input ma y be malformed." ### BEGIN SOLUTION estimates = {} pattern = r" \" date \" : \" (\d {4} -\d {2} -\d {2} ) \" , \" future \" :(false|tru e),.* \" approve_estimate \" : \" ([^ \" ]*) \" " for record in data_substr.split('}'): matches = re.search(pattern, record) if matches: date, future, rating = matches.groups() if future == "true": continue assert date not in estimates, "Duplicate date detected!" estimates[date] = float(rating) return estimates ### END SOLUTION approvals = extract_approval_estimates(raw_data) print("Found {} data records.".format(len(approvals))) In [7]: # Test cell 0: test__extract_approval_estimates__0 (2 points) num_approvals_expected, min_date_expected, max_date_expected = 592, '2 017-01-23', '2018-09-06' approvals = extract_approval_estimates(raw_data) assert isinstance(approvals, dict), "Your function returned an object of type {} instead of a dictionary (type `dict`).".format(type(approva ls)) assert len(approvals) == num_approvals_expected, \ "Your function should have found {} records but has {} instead, which is too {} .".format(num_approvals_expected, len(approvals), "few" if len(approvals) < num_approvals_expected else "many") for date_expected, date_occurred in zip([min_date_expected, max_date_e xpected], [min(approvals.keys()), max(ap provals.keys())]): assert date_occurred == date_expected, "A record for {} is missin g!".format(date_expected) print(" \n (Passed!)") Found 592 data records. (Passed!)

11/28/23, 8:13 PM problem14 file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem14-sample-solutions.html 9/15 In [8]: # Test cell 1: test__extract_approval_estimates__1__HIDDEN (1 points) def sample_ratings(ratings, k): from random import sample ratings_sample = [] for date in sample(ratings.keys(), k): ratings_sample.append((date, ratings[date])) return ratings_sample if False : print(sample_ratings(approvals, 20)) import math sample_approvals = [('2017-06-04', 38.89021), ('2017-10-30', 37.2644 2), ('2017-03-07', 44.3455), ('2017-02-19', 44.10547), ('2017-05-18', 39.45036), ('2017-09-04', 37.50347), ('2017-03-26', 42.08798), ('2018- 06-29', 41.75614), ('2017-10-24', 37.1561), ('2017-11-23', 38.42519), ('2017-11-06', 37.69767), ('2017-06-17', 38.7223), ('2018-02-17', 41.3 879), ('2017-05-16', 39.90671), ('2017-03-18', 42.70947), ('2018-07-1 7', 42.0509), ('2017-10-31', 37.24255), ('2017-02-28', 42.93886), ('20 18-08-17', 41.97367), ('2018-08-28', 41.37738)] for date, value in sample_approvals: assert date in approvals, "Approvals is missing a record for the d ate ' {} '!".format(date) assert math.isclose(approvals[date], value, abs_tol=1e-5), \ "Approval rating for {} should be {} , not {} .".format(date, value, approvals[date]) print(" \n (Passed!)") Exercise 2 (2 points). Complete the function, extract_disapproval_estimates(data_substr) , below. It should behave just like extract_approval_estimates() except that it should extract disapproval estimates rather than approval estimates. For instance, executing disapprovals = extract_disapproval_estimates(raw_data) print (type(disapprovals['2017-01-23']), disapprovals['2017-01-23']) should display, <class 'float'> 41.26452 . (Passed!)

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11/28/23, 8:13 PM problem14 file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem14-sample-solutions.html 10/15 In [9]: def extract_disapproval_estimates(data_substr): assert isinstance(data_substr, str), "`data_substr` is not a strin g." assert data_substr[0] == '{' and data_substr[-1] == '}', "Input ma y be malformed." ### BEGIN SOLUTION estimates = {} pattern = r" \" date \" : \" (\d {4} -\d {2} -\d {2} ) \" , \" future \" :(false|tru e),.* \" disapprove_estimate \" : \" ([^ \" ]*) \" " for record in data_substr.split('}'): matches = re.search(pattern, record) if matches: date, future, rating = matches.groups() if future == "true": continue assert date not in estimates, "Duplicate date detected!" estimates[date] = float(rating) return estimates ### END SOLUTION disapprovals = extract_disapproval_estimates(raw_data) print("Found {} data records.".format(len(disapprovals))) In [10]: # Test cell 0: test__extract_disapproval_estimates__0 (1 point) disapprovals = extract_disapproval_estimates(raw_data) assert isinstance(disapprovals, dict), "Your function returned an obje ct of type {} instead of a dictionary (type `dict`).".format(type(disa pprovals)) for date in approvals.keys(): assert date in disapprovals, "The date ' {} ' is missing.".format(da te) print(" \n (Passed!)") Found 592 data records. (Passed!)

11/28/23, 8:13 PM problem14 file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem14-sample-solutions.html 11/15 In [11]: # Test cell 1: test__extract_disapproval_estimates__1__HIDDEN (1 poin t) if False : print(sample_ratings(disapprovals, 20)) import math sample_disapprovals = [('2018-05-27', 52.33014), ('2017-07-18', 55.656 4), ('2017-04-22', 52.33886), ('2017-05-14', 53.41823), ('2018-07-18', 52.83702), ('2018-03-31', 53.42228), ('2018-05-21', 52.68593), ('2017- 05-18', 54.28648), ('2017-07-04', 54.46843), ('2018-08-31', 54.46505), ('2018-07-20', 52.74246), ('2018-02-13', 53.64082), ('2017-07-07', 55. 12906), ('2017-12-22', 57.08867), ('2018-06-20', 51.4082), ('2017-12-2 0', 56.98474), ('2017-09-27', 54.94124), ('2018-06-13', 52.53515), ('2 018-02-20', 53.43631), ('2017-06-25', 55.20745)] for date, value in sample_disapprovals: assert date in disapprovals, "Disapprovals is missing a record for the date ' {} '!".format(date) assert math.isclose(disapprovals[date], value, abs_tol=1e-5), \ "Disapproval rating for {} should be {} , not {} .".format(da te, value, disapprovals[date]) print(" \n (Passed!)") Exercise 3 (1 point). Complete the function, filter_dates_by_month(month_str, dates) , below, so that it filters a list of dates by a particular target month and year. Its inputs are as follows. The month_str argument is a string of the form yyyy-mm . This string encodes a target month and year. The dates argument is a Python set of dates of the form yyyy-mm-dd . Think of these as the keys in the approval and disapproval ratings dictionaries, for instance. The function should return a Python set containing only those elements of dates that match the given month_str . For example, the call filter_dates_by_month('2018-07', {'2018-05-02', '2018-07-05', '2018-07-01', '2017-07-02', '2016-07-31'}) would return {'2018-07-05', '2018-07-01'} (Passed!)

11/28/23, 8:13 PM problem14 file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem14-sample-solutions.html 12/15 In [12]: def filter_dates_by_month(month_str, dates): assert isinstance(month_str, str), "`month_str` is of type {} , rat her than `str`.".format(type(month_str)) assert re.match(r'\d {4} -\d {2} $', month_str), "`month_str == ' {} '` is invalid.".format(month_str) ### BEGIN SOLUTION return {date for date in dates if re.match(month_str + r'-\d {2} ', date)} # Alternative: `return {date for date in dates if date[:7] == mont h_str}` ### END SOLUTION

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11/28/23, 8:13 PM problem14 file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem14-sample-solutions.html 13/15 In [ ]: # Test cell: `test__filter_dates_by_month` (1 point) def test__filter_dates_by_month(): from random import sample padded_digits = lambda a, b: sample(range(a, b), 1)[0] rand_year = lambda : padded_digits(0, 10000) rand_month = lambda : padded_digits(1, 13) rand_day = lambda m: padded_digits(1, 28 if m == 2 else 31 if m in [1, 3, 5, 7, 8, 10, 12] else 30) coin_flip = lambda : sample([ False , True ], 1)[0] yyyy_0, mm_0 = rand_year(), rand_month() month_str = ' {:04d} - {:02d} '.format(yyyy_0, mm_0) all_dates = set() answer = set() for _ in range(10): match = coin_flip() if match: yyyy, mm = yyyy_0, mm_0 else : yyyy, mm = rand_year(), rand_month() date = ' {:04d} - {:02d} - {:02d} '.format(yyyy, mm, rand_day(mm)) all_dates |= {date} if match: answer |= {date} filtered_dates = filter_dates_by_month(month_str, all_dates) print("All dates: {} ".format(all_dates)) print("Target month: {} ".format(month_str)) print("Your matches: {} ".format(filtered_dates)) if filtered_dates != answer: print("*** Your solution is incorrect! ***") excesses = filtered_dates - answer omissions = answer - filtered_dates if excesses: print("It erroneously includes {} .".format(excesses)) if omissions: print("It erroneously omits {} .".format(omissions)) assert False else : print("==> Looks good! \n ") for k in range(5): print("=== Test case {} ===".format(k)) test__filter_dates_by_month()

11/28/23, 8:13 PM problem14 file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem14-sample-solutions.html 14/15 Exercise 4 (3 points). Let the discrepancy rating of a given day be that day's approval rating minus its disapproval rating. Complete the function, avg_discrepancy_in_month(month_str, approvals, disapprovals) , below, so that it returns the average daily discrepancy rating for a given month. The month_str argument is a string of the form yyyy-mm . This string encodes a target month and year. The approvals argument is a dictionary, whose keys are dates and whose values are approval ratings. The disapprovals argument is a dictionary, whose keys are dates and whose values are disapproval ratings. You may assume that if a date is in approvals then it is also in disapprovals , and vice-versa. You can ignore missing dates; that is, compute the averages based on whatever dates exist in the input dictionaries. In [ ]: def avg_discrepancy_in_month(month_str, approvals, disapprovals): assert isinstance(month_str, str), "`month_str` is of type {} , rat her than `str`.".format(type(month_str)) assert re.match(r'\d {4} -\d {2} $', month_str), "`month_str == ' {} '` is invalid.".format(month_str) assert all([date in disapprovals for date in approvals]), "`disapp rovals` is missing dates that appear in `approvals`." assert all([date in approvals for date in disapprovals]), "`approv als` is missing dates that appear in `disapprovals`." ### BEGIN SOLUTION days = filter_dates_by_month(month_str, set(approvals.keys())) discrepancies = {day: approvals[day] - disapprovals[day] for day i n days} return avg_rating(discrepancies) def avg_rating(ratings, days= None ): if days is None : days = ratings.keys() assert all([day in ratings for day in days]), "`ratings` is missin g days!" ratings_subset = [ratings[day] for day in days] if len(ratings_subset) > 0: return sum(ratings_subset) / len(ratings_subset) return 0.0 ### END SOLUTION for month in ['2017-01', '2018-09']: disc = avg_discrepancy_in_month(month, approvals, disapprovals) print("Average daily discrepancy for {} : {} ".format(month, disc))

11/28/23, 8:13 PM problem14 file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem14-sample-solutions.html 15/15 In [ ]: # Test cell: test__avg_discrepancy_in_month__0 (1 point) import math for month, disc_true in [('2017-01', 2.799275555555555), ('2018-09', - 14.008344999999997)]: disc = avg_discrepancy_in_month(month, approvals, disapprovals) assert math.isclose(disc, disc_true, abs_tol=1e-2), \ "Your average daily discrepancy is {} instead of {} .".forma t(disc, disc_true) print(" \n (Passed!)") In [ ]: # Test cell: test__avg_discrepancy_in_month__1__HIDDEN (2 points) all_complete_months = ['2017-02', '2017-03', '2017-04', '2017-05', '20 17-06', '2017-07', '2017-08', '2017-09', '2017-10', '2017-11', '2017-12', '2018-01', '20 18-02', '2018-03', '2018-04', '2018-05', '2017-06', '2018-07', '2018-08'] if False : # Generate solutions solutions = [(month, avg_discrepancy_in_month(month, approvals, di sapprovals)) for month in all_complete_months] else : # Assume solutions solutions = [('2017-02', -5.236897857142858), ('2017-03', -7.59084 3548387097), ('2017-04', -11.404569), ('2017-05', -13.22706935483871 1), ('2017-06', -16.525405666666668), ('2017-07', -16.39675064516129), ('2017-08', -19.508681612903224), ('2017-09', -16.797917), ('2017-10', -18.355616129032256), ('2017-11', -17.916010999999994), ('2017-12', -1 9.313219354838708), ('2018-01', -16.359479999999998), ('2018-02', -13. 414909642857141), ('2018-03', -13.186816129032259), ('2018-04', -13.38 2937666666669), ('2018-05', -10.298456774193546), ('2017-06', -16.5254 05666666668), ('2018-07', -11.030132580645164), ('2018-08', -11.177329 354838706)] print(solutions) import math for month, disc_true in solutions: disc = avg_discrepancy_in_month(month, approvals, disapprovals) assert math.isclose(disc, disc_true, abs_tol=1e-2), \ "Your average daily discrepancy is {} instead of {} .".forma t(disc, disc_true) print(" \n (Passed!)") Fin! Remember to test your solutions by running them as the autograder will: restart the kernel and run all cells from "top-to-bottom." Also remember to submit to the autograder; otherwise, you will not get credit for your hard work!

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