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11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 1/41 Midterm 1: How partisan is the US Congress? Version 1.1b (Simplified sample solution for Exercise 6) This problem is about basic data processing using Python. It exercises your fundamental knowledge of Python data structures, such as lists, dictionaries, and strings. It has seven exercises, numbered 0-6. Each exercise builds on the previous one. However, they may be completed independently. That is, if you can't complete an exercise, we provide some code that can run to load precomputed results for the next exercise. That way, you can keep moving even if you get stuck. Pro-tips. If your program behavior seem strange, try resetting the kernel and rerunning everything. If you mess up this notebook or just want to start from scratch, save copies of all your partial responses and use Actions Reset Assignment to get a fresh, original copy of this notebook. (Resetting will wipe out any answers you've written so far, so be sure to stash those somewhere safe if you intend to keep or reuse them!) If you generate excessive output (e.g., from an ill-placed print statement) that causes the notebook to load slowly or not at all, use Actions Clear Notebook Output to get a clean copy. The clean copy will retain your code but remove any generated output. However , it will also rename the notebook to clean.xxx.ipynb . Since the autograder expects a notebook file with the original name, you'll need to rename the clean notebook accordingly. Good luck! Background The United States Congress is the part of the US government that makes laws for the entire country. It is dominated by two rival political parties, the Democrats and the Republicans. You would expect that these parties oppose each other on most issues but occasionally agree. Some have conjectured that, over time, the two parties agree less and less, which would reﬂect a perceived growing ideological or political divide in the US. But is that the real trend? In this problem, you'll explore this question using data collected by ProPublica (https://www.propublica.org/), a nonprofit investigative media organization. Setup and data loading Run the code cells below to load the data. This code will hold the data in two variables, one named votes and another named positions . → →

11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 2/41 In [1]: import sys print(f"* Python version: {sys.version} ") from testing_tools import load_json, save_json, load_pickle, save_pick le votes = load_json("votes.json") vote_positions = [p for p in load_json("positions.json") if p['positio ns']] print(" \n ==> Data loading complete.") ### BEGIN HIDDEN TESTS % load_ext autoreload % autoreload 2 ### END HIDDEN TESTS Part 0: Analyzing vote results The Congress votes on various things, like new laws or political nominations. The results of these votes are stored in the votes variable loaded above. Let's look at it. First, note that votes is a list: In [2]: print(type(votes)) print("Length:", len(votes)) Vote results. What is votes a list of? Each element is one vote result. Let's look at the first entry. * Python version: 3.7.5 (default, Dec 18 2019, 06:24:58) [GCC 5.5.0 20171010] * JSON module version: 2.0.9 './resource/asnlib/publicdata/votes.json': 28596 './resource/asnlib/publicdata/positions.json': 279 ==> Data loading complete. <class 'list'> Length: 28596

11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 3/41 In [3]: from testing_tools import inspect_data inspect_data(votes[0]) # View the first element of the list, `votes`

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11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 4/41 { "congress": 106, "chamber": "Senate", "session": 1, "roll_call": 374, "source": "https://www.senate.gov/legislative/LIS/roll_call_votes/ vote1061/vote_106_1_00374.xml", "url": "https://www.senate.gov/legislative/LIS/roll_call_lists/rol l_call_vote_cfm.cfm?congress=106&session=1&vote=00374", "vote_uri": "https://api.propublica.org/congress/v1/106/senate/ses sions/1/votes/374.json", "bill": { "bill_id": "h.r.3194-106", "number": "H.R..3194", "sponsor_id": null, "api_uri": null, "title": null, "latest_action": null }, "question": "On the Conference Report", "question_text": "", "description": "H.R.3194 Conference report; Consolidated Appropria tions Act, 2000", "vote_type": "1/2", "date": "1999-11-19", "time": "17:45:00", "result": "Agreed to", "democratic": { "yes": 32, "no": 12, "present": 0, "not_voting": 1, "majority_position": "Yes" }, "republican": { "yes": 42, "no": 12, "present": 0, "not_voting": 1, "majority_position": "Yes" }, "independent": { "yes": 0, "no": 0, "present": 0, "not_voting": 0 }, "total": { "yes": 74, "no": 24, "present": 0, "not_voting": 2 } }

11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 5/41 Observation 0. This first entry of the list is a dictionary, and the data structure is nested even more. For instance, the "bill" key has another dictionary as its value. Remember that what you see above is votes[0] , the first entry of the votes list. For the rest of this problem, you may assume that all other entries of votes have the same keys and nesting structure. Exercise 0 (2 points). Let's pick out a subset of the data to analyze. Complete the function below, filter_votes(votes) . The input, votes , is a list of vote results like the one above. Your function should return a copy of this list, keeping only vote results meeting the following criteria: 1. The 'vote_type' is one of the following: "1/2" , "YEA-AND-NAY" , "RECORDED VOTE" 2. Notice that the 'total' key is a dictionary. Retain only the vote results where this dictionary has all of the fields 'yes' , 'no' , 'present' , and 'not_voting' . Your copy should include vote results in the same order as the input list. Note 0: The reason for Condition 2 above is that for some vote results, the 'total' dictionary does not have those fields. For an example, see votes[18319] . You would not include that vote result in your output. Note 1: The test cell does not use real vote results, but randomly generated synthetic ones. Your solution should not depend on the presence of any specific keys other than the ones you need for filtering, namely, 'vote_type' and 'total' . As an example, suppose V is the following vote results list (only the salient keys are included): V = [ {'vote_type': "1/2", 'total': {'yes': 5, 'no': 8, 'present': 0, 'not_v oting': 2}, ...}, {'vote_type': "RECORDED VOTE", 'total': {'yes': 12, 'present': 2, 'not _voting': 1}, ...}, {'vote_type': "3/5", 'total': {'yes': 50, 'no': 14, 'present': 0, 'not _voting': 0}, ...}, {'vote_type': "YEA-AND-NAY", 'total': {'yes': 25, 'no': 3, 'present': 3, 'not_voting': 0}, ...} ] Then running filter_votes(V) would return the following new list: [ {'vote_type': "1/2", 'total': {'yes': 5, 'no': 8, 'present': 0, 'not_votin g': 2}, ...}, {'vote_type': "YEA-AND-NAY", 'total': {'yes': 25, 'no': 3, 'present': 3, 'not_voting': 0}, ...} ] In this case, V[1] is omitted because its 'total' key is missing the 'no' key; and V[2] is omitted because the 'vote_type' is not one of "1/2" , "YEA-AND-NAY" , or "RECORDED VOTE" .

11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 6/41 In [4]: def filter_votes(votes): assert isinstance(votes, list) and len(votes) >= 1 assert isinstance(votes[0], dict) ### BEGIN SOLUTION def matches(v): return (v["vote_type"] in {"1/2", "YEA-AND-NAY", "RECORDED VOT E"}) \ and (set(v["total"].keys()) == {"yes", "no", "present", "not_voting"}) return [v for v in votes if matches(v)] ### END SOLUTION In [5]: # Demo cell (feel free to use and edit for debugging) V = [ {'vote_type': "1/2", 'total': {'yes': 5, 'no': 8, 'present': 0, 'not_voting': 2}}, {'vote_type': "RECORDED VOTE", 'total': {'yes': 12, 'present': 2, 'not_voting': 1}}, {'vote_type': "3/5", 'total': {'yes': 50, 'no': 14, 'present': 0, 'not_voting': 0}}, {'vote_type': "YEA-AND-NAY", 'total': {'yes': 25, 'no': 3, 'pres ent': 3, 'not_voting': 0}} ] inspect_data(filter_votes(V)) print(len(filter_votes(votes))) [ { "vote_type": "1/2", "total": { "yes": 5, "no": 8, "present": 0, "not_voting": 2 } }, { "vote_type": "YEA-AND-NAY", "total": { "yes": 25, "no": 3, "present": 3, "not_voting": 0 } } ] 22178

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11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 7/41 In [6]: # Test cell: ex0__filter_votes (2 points) ### BEGIN HIDDEN TESTS def ex0__gen_soln(fn="votes_subset.json", overwrite= False ): from testing_tools import file_exists if file_exists(fn) and not overwrite: print(f"' {fn} ' exists; skipping ...") else : print(f"Generating ' {fn} ' ...") V = load_json("votes.json") V_out = filter_votes(V) # Assume it works save_json(V_out, fn) ex0__gen_soln() ### END HIDDEN TESTS from testing_tools import ex0__check print("Testing...") for trial in range(10): ex0__check(filter_votes) print(" \n (Passed.)") Precomputed filtered vote results. In case Exercise 0 does not pass, we've precomputed the filtered subset of votes we'll need in the remainder of the problem. Whether or not you passed, please run the following cell now to load this result, which will be stored in the variable, votes_subset . In [7]: votes_subset = load_json("votes_subset.json") print(len(votes_subset)) Observation 1-A: A passing vote. Recall the first vote result from above, which is present in votes_subset as votes_subset[0] . Here is how to interpret it. 'votes_subset.json' exists; skipping ... Testing... (Passed.) './resource/asnlib/publicdata/votes_subset.json': 22178 22178

11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 8/41 In [8]: inspect_data(votes_subset[0]) { "congress": 106, "chamber": "Senate", "session": 1, "roll_call": 374, "source": "https://www.senate.gov/legislative/LIS/roll_call_votes/ vote1061/vote_106_1_00374.xml", "url": "https://www.senate.gov/legislative/LIS/roll_call_lists/rol l_call_vote_cfm.cfm?congress=106&session=1&vote=00374", "vote_uri": "https://api.propublica.org/congress/v1/106/senate/ses sions/1/votes/374.json", "bill": { "bill_id": "h.r.3194-106", "number": "H.R..3194", "sponsor_id": null, "api_uri": null, "title": null, "latest_action": null }, "question": "On the Conference Report", "question_text": "", "description": "H.R.3194 Conference report; Consolidated Appropria tions Act, 2000", "vote_type": "1/2", "date": "1999-11-19", "time": "17:45:00", "result": "Agreed to", "democratic": { "yes": 32, "no": 12, "present": 0, "not_voting": 1, "majority_position": "Yes" }, "republican": { "yes": 42, "no": 12, "present": 0, "not_voting": 1, "majority_position": "Yes" }, "independent": { "yes": 0, "no": 0, "present": 0, "not_voting": 0 }, "total": { "yes": 74, "no": 24, "present": 0, "not_voting": 2 } }

11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 9/41 It concerns a vote on November 19, 1999 ( "date": "1999-11-19" ). There were a total of 74 "yes" votes, 24 "no" votes, and 2 non-votes (abstentions or absences). Since there was a simple majority of "yes" votes--- meaning strictly more "yes" votes than "no" votes---the result is considered to be passing . Of the "yes" votes, 32 were cast by Democrats and 42 by Republicans; of the "no" votes, 12 were by Democrats and 12 by Republicans. Observation 1-B: A failing vote. Let's take a look at a vote with a different result, votes_subset[8] :

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11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 10/41 In [9]: inspect_data(votes_subset[8]) { "congress": 106, "chamber": "House", "session": 1, "roll_call": 609, "source": "http://clerk.house.gov/evs/1999/roll609.xml", "url": "http://clerk.house.gov/evs/1999/roll609.xml", "vote_uri": "https://api.propublica.org/congress/v1/106/house/sess ions/1/votes/609.json", "bill": { "bill_id": "hr3194-106", "number": "H.R.3194", "sponsor_id": "I000047", "api_uri": "https://api.propublica.org/congress/v1/106/bills/h r3194.json", "title": "Making consolidated appropriations for the fiscal ye ar ending September 30, 2000, and for other purposes.", "latest_action": "Became Public Law No: 106-113" }, "question": "On Motion to Recommit Conference Report", "question_text": "", "description": "District of Columbia Appropriations Act, 2000", "vote_type": "YEA-AND-NAY", "date": "1999-11-18", "time": "17:25:00", "result": "Failed", "democratic": { "yes": 207, "no": 2, "present": 0, "not_voting": 3, "majority_position": "Yes" }, "republican": { "yes": 4, "no": 217, "present": 0, "not_voting": 1, "majority_position": "No" }, "independent": { "yes": 1, "no": 0, "present": 0, "not_voting": 0 }, "total": { "yes": 212, "no": 219, "present": 0, "not_voting": 4 } }

11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 11/41 This vote took place on November 18, 1999. There were a total of 207+2+0+3 = 212 votes by Democrats and 4+217+0+1 = 222 votes by Republicans. The measure did not pass: there were more "no" votes (219) than "yes" votes (212). Of the 219 "no" votes, 217 were cast by Republicans and 2 by Democrats. Exercise 1 (2 points). Suppose you are given a single voting result, v (e.g., v == votes_subset[0] or v == votes_subset[8] ). Complete the function is_passing(v) so that it returns True if the vote "passed" and False otherwise. To determine if a vote is passing or not, check whether the number of "yes" votes associated with the "total" key is strictly greater than the number of "no" votes. Note: The test cell does not use real vote results but rather randomly generated synthetic ones. Your implementation should not depend on the presence of any specific keys other than the ones mentioned in the statement of this exercise. In [10]: def is_passing(v): ### BEGIN SOLUTION return v['total']['yes'] > v['total']['no'] ### END SOLUTION In [11]: # Demo cell print(is_passing(votes_subset[0])) # Should return `True` print(is_passing(votes_subset[8])) # Should return `False` In [12]: # Another demo cell num_passing = sum([is_passing(v) for v in votes_subset]) print(f"Of {len(votes_subset)} vote results, {num_passing} passed.") True False Of 22178 vote results, 14646 passed.

11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 12/41 In [13]: # Test cell: ex1__is_passing (2 points) ### BEGIN HIDDEN TESTS def ex1__gen_soln(fn="votes_pf.json", overwrite= False ): from testing_tools import file_exists if file_exists(fn) and not overwrite: print(f"' {fn} ' exists; skipping ...") else : print(f"Generating ' {fn} ' ...") V = load_json("votes_subset.json") V_out = [] for v in V: v_pf = v.copy() v_pf["passed"] = is_passing(v_pf) # assume it works V_out.append(v_pf) save_json(V_out, fn) ex1__gen_soln() ### END HIDDEN TESTS from testing_tools import ex1__check print("Testing...") for trial in range(1000): ex1__check(is_passing) print(" \n (Passed.)") Passing and failing votes. In case your code for Exercise 1 does not pass the test cell, we've precomputed a list of vote results annotated with the result. Whether or not you passed, please run the following code cell, which produces a list of vote results named votes_pf , where votes_pf["passed"] is True if the outcome is a "pass," and False otherwise. In [14]: votes_pf = load_json('votes_pf.json') num_passed = sum([1 for v in votes_pf if v["passed"]]) print(f" {num_passed} vote results were passing, {len(votes_pf) - num_p assed} were failing.") 'votes_pf.json' exists; skipping ... Testing... (Passed.) './resource/asnlib/publicdata/votes_pf.json': 22178 14646 vote results were passing, 7532 were failing.

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11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 13/41 Definition: The partisan "vote" gap. Given a voting result, let's define a measure of how well the Democrats and Republicans agree. Suppose a bill has some outcome, either "pass" or "fail." Let be the proportion of Democrats who voted for that outcome, and let be the proportion of Republicans who voted for that outcome. Then the partisan vote gap for that bill is the absolute difference between and , or . The more Democrats and Republicans agree, the closer this value is to zero. But when they disagree strongly, this value could be . For example, recall that in the first example, votes_subset[0] , the bill passed with 74 "yes" votes, 32 from Democrats and 42 from Republicans. Since there were 45 Democrats (32 yes, 12 no, and 1 non-voting), then . And since there were 55 Republicans (42 yes, 12 no, and 1 non-voting), then . Thus, the partisan vote gap is . In the second example, votes_subset[8] , recall that the vote failed with 219 "no" votes, 217 by Republicans out of 222 total, and 2 by Democrats out of 212 total. Thus, . Comparing the two cases, the first is an example of reasonable agreement, whereas the second shows strong disagreement. Exercise 2 (2 points). Given one voting result, v , complete the function calc_partisan_vote_gap(v) so that it returns the partisan voting gap as defined above. Assume that v["passed"] is True if the vote was a passing vote (majority "yes"), or False otherwise (majority "no"). Note 0: To determine the total number of Democrats or Republicans, add together all of their "yes" , "no" , "present" , and "not_voting" values using the appropriate party's object in v . Note 1: If a vote result has no Democrats or Republicans, then use or , respectively. This scenario can happen if the sum of Democratic or Republican "yes" , "no" , "present" , and "not_voting" values is 0. Note 2: You do not need to round your results. The test cell accounts for possible rounding errors when comparing your computed result against the expected one. Note 3: The test cell does not use real vote results, but rather randomly generated synthetic ones. Your code should only depend on the presence of the relevant keys, namely, the party votes ( "democratic" and "republican" ) and "passed" . d r d r d − r 1 d = ≈ 0.711 32 45 r = ≈ 0.764 42 55 d − r = − ≈ 0.0525 ∣ ∣ 32 45 42 55 ∣ ∣ d − r = − ≈ 0.968 ∣ ∣ 2 212 217 222 ∣ ∣ d = 0 r = 0

11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 14/41 In [15]: def calc_partisan_vote_gap(v): assert isinstance(v, dict) assert "passed" in v and "democratic" in v and "republican" in v ### BEGIN SOLUTION def party_total(k): return v[k]["yes"] + v[k]["no"] + v[k]["present"] + v[k]["not_ voting"] total_d = party_total("democratic") total_r = party_total("republican") majority_key = "yes" if v["passed"] else "no" d = v["democratic"][majority_key] / total_d if total_d > 0 else 0 r = v["republican"][majority_key] / total_r if total_r > 0 else 0 return abs(d - r) ### END SOLUTION In [16]: # Demo cell to help you debug print(calc_partisan_vote_gap(votes_pf[0])) # should be about 0.0525 print(calc_partisan_vote_gap(votes_pf[8])) # ~ 0.968 In [17]: # Test cell: ex2__calc_partisan_vote_gap (2 points) ### BEGIN HIDDEN TESTS def ex2__gen_soln(fn="votes_gap.json", overwrite= False ): from testing_tools import file_exists if file_exists(fn) and not overwrite: print(f"' {fn} ' exists; skipping ...") else : print(f"Generating ' {fn} ' ...") V = load_json("votes_pf.json") for v in V: v["gap"] = calc_partisan_vote_gap(v) save_json(V, fn) ex2__gen_soln() ### END HIDDEN TESTS from testing_tools import ex2__check print("Testing...") for trial in range(2500): ex2__check(calc_partisan_vote_gap) print(" \n (Passed.)") 0.05252525252525253 0.9680435152133265 'votes_gap.json' exists; skipping ... Testing... (Passed.)

11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 15/41 Precomputed partisan vote gaps. In case your Exercise 2 did not pass the test cell, we've precomputed a list of vote results with their partisan vote gaps. Whether or not you passed, please run the following cell to load this result, which will be stored in the variable, votes_gap . Each entry v of votes_gap will have a key, v["gap"] , that holds the vote gap. In [18]: votes_gap = load_json("votes_gap.json") from statistics import mean overall_gap = mean([v["gap"] for v in votes_gap]) print(f"Average overall vote gap: {overall_gap} ") In [19]: type(votes_gap) Exercise 3 (2 points): We are now ready to calculate the voting gap over time. Complete the function, tally_gaps(votes_gap) , below, where: the input votes_gap is a list of vote results augmented with the "gap" key as defined in Exercise 2; the function returns a list of tuples holding the year-by-year average vote gaps, as follows. For example, suppose you run gaps_over_time = tally_gaps(votes_gap) is the output. Then, gaps_over_time is a list. Each element is a pair, (yyyy, g) , where yyyy is the year represented as an integer ( int ) and g is the average vote gap across all votes that took place in that year. To determine the year of a vote, recall that each vote result has a "date" field. You may assume each vote result v in votes_gap has a key, v["gap"] holding its vote gap. Note: The test cell does not use real vote results, but rather randomly generated synthetic ones. Your solution should not depend on any particular keys in a vote result other than "date" and "gap" . './resource/asnlib/publicdata/votes_gap.json': 22178 Average overall vote gap: 0.5878119584149253 Out[19]: list

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11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 16/41 In [20]: def tally_gaps(votes_gap): ### BEGIN SOLUTION from collections import defaultdict years = set() votes_per_year = defaultdict(int) gaps_per_year = defaultdict(float) for v in votes_gap: yyyy = int(v["date"][:4]) years.add(yyyy) votes_per_year[yyyy] += 1 gaps_per_year[yyyy] += v["gap"] gaps_over_time = [(yyyy, gaps_per_year[yyyy] / votes_per_year[yyy y]) for yyyy in years] return gaps_over_time ### END SOLUTION In [21]: # Demo cell you can use for debugging gaps_over_time = tally_gaps(votes_gap) for yyyy, g_avg in sorted(gaps_over_time, key= lambda x: x[0]): print(f" {yyyy} : {g_avg:.3f} ") 1991: 0.412 1992: 0.445 1993: 0.535 1994: 0.474 1995: 0.581 1996: 0.495 1997: 0.450 1998: 0.489 1999: 0.484 2000: 0.452 2001: 0.465 2002: 0.444 2003: 0.578 2004: 0.539 2005: 0.552 2006: 0.557 2007: 0.632 2008: 0.646 2009: 0.633 2010: 0.637 2011: 0.674 2012: 0.683 2013: 0.691 2014: 0.710 2015: 0.730 2016: 0.745 2017: 0.771 2018: 0.638 2019: 0.684 2020: 0.759

11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 17/41 In [22]: # Test cell: ex3__tally_gaps (2 points) from testing_tools import ex3__check print("Testing...") for trial in range(100): ex3__check(tally_gaps) print(" \n (Passed.)") Gaps over time. If your demo worked correctly, you should have seen a steady trend in which the vote gap increases over time, starting at around 0.41 in 1991 and increasing to 0.76 in 2020. That is one quantitative indicator of growing partisanship in the US Congress. Part 1: Finding "compatible" lawmakers from opposing parties Are there any pairs of lawmakers from opposing parties---that is, one Democrat and one Republican---who tend to vote similarly to one another? Perhaps such pairs can help bridge the divide between the two parties. To help find such a pair, here is one final dataset, stored in the list, vote_positions . It consists of vote results from the last two years (2019-2020) along with how each lawmaker voted (yes or no). There are 278 such vote results available, which we can confirm by printing the length of the vote_positions list: In [23]: print(len(vote_positions)) Let's inspect one of these vote results, vote_positions[0] : Testing... (Passed.) 278

11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 18/41 In [24]: inspect_data(vote_positions[0])

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11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 19/41 { "congress": 116, "session": 2, "chamber": "Senate", "roll_call": 130, "source": "https://www.senate.gov/legislative/LIS/roll_call_votes/ vote1162/vote_116_2_00130.xml", "url": "https://www.senate.gov/legislative/LIS/roll_call_lists/rol l_call_vote_cfm.cfm?congress=116&session=2&vote=00130", "bill": {}, "amendment": {}, "nomination": { "nomination_id": "PN1726-116", "number": "PN1726", "name": "Russell", "agency": "Executive Office of the President" }, "question": "On the Cloture Motion", "question_text": "On the Cloture Motion PN1726", "description": "Russell Vought, of Virginia, to be Director of the Office of Management and Budget", "vote_type": "1/2", "date": "2020-07-02", "time": "13:33:00", "result": "Cloture Motion Agreed to", "tie_breaker": "", "tie_breaker_vote": "", "document_number": "1726", "document_title": "Russell Vought, of Virginia, to be Director of the Office of Management and Budget", "democratic": { "yes": 0, "no": 42, "present": 0, "not_voting": 3, "majority_position": "No" }, "republican": { "yes": 47, "no": 0, "present": 0, "not_voting": 6, "majority_position": "Yes" }, "independent": { "yes": 0, "no": 2, "present": 0, "not_voting": 0 }, "total": { "yes": 47, "no": 44, "present": 0, "not_voting": 9 }, "positions": [

11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 20/41 { "member_id": "A000360", "name": "Lamar Alexander", "party": "R", "state": "TN", "vote_position": "Yes", "dw_nominate": 0.324 }, { "member_id": "B001230", "name": "Tammy Baldwin", "party": "D", "state": "WI", "vote_position": "No", "dw_nominate": -0.494 }, { "member_id": "B001261", "name": "John Barrasso", "party": "R", "state": "WY", "vote_position": "Yes", "dw_nominate": 0.54 }, { "member_id": "B001267", "name": "Michael Bennet", "party": "D", "state": "CO", "vote_position": "No", "dw_nominate": -0.223 }, { "member_id": "B001243", "name": "Marsha Blackburn", "party": "R", "state": "TN", "vote_position": "Yes", "dw_nominate": 0.618 }, { "member_id": "B001277", "name": "Richard Blumenthal", "party": "D", "state": "CT", "vote_position": "No", "dw_nominate": -0.431 }, { "member_id": "B000575", "name": "Roy Blunt", "party": "R", "state": "MO", "vote_position": "Yes", "dw_nominate": 0.426 }, {

11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 21/41 "member_id": "B001288", "name": "Cory Booker", "party": "D", "state": "NJ", "vote_position": "No", "dw_nominate": -0.604 }, { "member_id": "B001236", "name": "John Boozman", "party": "R", "state": "AR", "vote_position": "Yes", "dw_nominate": 0.399 }, { "member_id": "B001310", "name": "Mike Braun", "party": "R", "state": "IN", "vote_position": "Yes", "dw_nominate": null }, { "member_id": "B000944", "name": "Sherrod Brown", "party": "D", "state": "OH", "vote_position": "No", "dw_nominate": -0.43 }, { "member_id": "B001135", "name": "Richard M. Burr", "party": "R", "state": "NC", "vote_position": "Not Voting", "dw_nominate": 0.45 }, { "member_id": "C000127", "name": "Maria Cantwell", "party": "D", "state": "WA", "vote_position": "No", "dw_nominate": -0.303 }, { "member_id": "C001047", "name": "Shelley Moore Capito", "party": "R", "state": "WV", "vote_position": "Yes", "dw_nominate": 0.263 }, { "member_id": "C000141",

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11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 22/41 "name": "Benjamin L. Cardin", "party": "D", "state": "MD", "vote_position": "No", "dw_nominate": -0.321 }, { "member_id": "C000174", "name": "Thomas R. Carper", "party": "D", "state": "DE", "vote_position": "No", "dw_nominate": -0.175 }, { "member_id": "C001070", "name": "Bob Casey", "party": "D", "state": "PA", "vote_position": "No", "dw_nominate": -0.312 }, { "member_id": "C001075", "name": "Bill Cassidy", "party": "R", "state": "LA", "vote_position": "Yes", "dw_nominate": 0.457 }, { "member_id": "C001035", "name": "Susan Collins", "party": "R", "state": "ME", "vote_position": "Yes", "dw_nominate": 0.112 }, { "member_id": "C001088", "name": "Christopher A. Coons", "party": "D", "state": "DE", "vote_position": "No", "dw_nominate": -0.226 }, { "member_id": "C001056", "name": "John Cornyn", "party": "R", "state": "TX", "vote_position": "Yes", "dw_nominate": 0.494 }, { "member_id": "C001113", "name": "Catherine Cortez Masto",

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11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 23/41 "party": "D", "state": "NV", "vote_position": "No", "dw_nominate": -0.371 }, { "member_id": "C001095", "name": "Tom Cotton", "party": "R", "state": "AR", "vote_position": "Yes", "dw_nominate": 0.57 }, { "member_id": "C001096", "name": "Kevin Cramer", "party": "R", "state": "ND", "vote_position": "Yes", "dw_nominate": 0.387 }, { "member_id": "C000880", "name": "Michael D. Crapo", "party": "R", "state": "ID", "vote_position": "Yes", "dw_nominate": 0.511 }, { "member_id": "C001098", "name": "Ted Cruz", "party": "R", "state": "TX", "vote_position": "Yes", "dw_nominate": 0.818 }, { "member_id": "D000618", "name": "Steve Daines", "party": "R", "state": "MT", "vote_position": "Yes", "dw_nominate": 0.551 }, { "member_id": "D000622", "name": "Tammy Duckworth", "party": "D", "state": "IL", "vote_position": "No", "dw_nominate": -0.332 }, { "member_id": "D000563", "name": "Richard J. Durbin", "party": "D",

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11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 24/41 "state": "IL", "vote_position": "No", "dw_nominate": -0.35 }, { "member_id": "E000285", "name": "Michael B. Enzi", "party": "R", "state": "WY", "vote_position": "Not Voting", "dw_nominate": 0.545 }, { "member_id": "E000295", "name": "Joni Ernst", "party": "R", "state": "IA", "vote_position": "Yes", "dw_nominate": 0.529 }, { "member_id": "F000062", "name": "Dianne Feinstein", "party": "D", "state": "CA", "vote_position": "No", "dw_nominate": -0.267 }, { "member_id": "F000463", "name": "Deb Fischer", "party": "R", "state": "NE", "vote_position": "Yes", "dw_nominate": 0.473 }, { "member_id": "G000562", "name": "Cory Gardner", "party": "R", "state": "CO", "vote_position": "Yes", "dw_nominate": 0.443 }, { "member_id": "G000555", "name": "Kirsten E. Gillibrand", "party": "D", "state": "NY", "vote_position": "No", "dw_nominate": -0.474 }, { "member_id": "G000359", "name": "Lindsey Graham", "party": "R", "state": "SC",

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11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 25/41 "vote_position": "Yes", "dw_nominate": 0.406 }, { "member_id": "G000386", "name": "Charles E. Grassley", "party": "R", "state": "IA", "vote_position": "Yes", "dw_nominate": 0.346 }, { "member_id": "H001075", "name": "Kamala Harris", "party": "D", "state": "CA", "vote_position": "No", "dw_nominate": -0.709 }, { "member_id": "H001076", "name": "Margaret Hassan", "party": "D", "state": "NH", "vote_position": "No", "dw_nominate": -0.24 }, { "member_id": "H001089", "name": "Joshua Hawley", "party": "R", "state": "MO", "vote_position": "Yes", "dw_nominate": null }, { "member_id": "H001046", "name": "Martin Heinrich", "party": "D", "state": "NM", "vote_position": "No", "dw_nominate": -0.325 }, { "member_id": "H001042", "name": "Mazie K. Hirono", "party": "D", "state": "HI", "vote_position": "No", "dw_nominate": -0.511 }, { "member_id": "H001061", "name": "John Hoeven", "party": "R", "state": "ND", "vote_position": "Yes",

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11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 26/41 "dw_nominate": 0.342 }, { "member_id": "H001079", "name": "Cindy Hyde-Smith", "party": "R", "state": "MS", "vote_position": "Not Voting", "dw_nominate": 0.373 }, { "member_id": "I000024", "name": "James M. Inhofe", "party": "R", "state": "OK", "vote_position": "Yes", "dw_nominate": 0.556 }, { "member_id": "J000293", "name": "Ron Johnson", "party": "R", "state": "WI", "vote_position": "Yes", "dw_nominate": 0.605 }, { "member_id": "J000300", "name": "Doug Jones", "party": "D", "state": "AL", "vote_position": "No", "dw_nominate": -0.086 }, { "member_id": "K000384", "name": "Tim Kaine", "party": "D", "state": "VA", "vote_position": "No", "dw_nominate": -0.241 }, { "member_id": "K000393", "name": "John Kennedy", "party": "R", "state": "LA", "vote_position": "Yes", "dw_nominate": 0.592 }, { "member_id": "K000383", "name": "Angus King", "party": "ID", "state": "ME", "vote_position": "No", "dw_nominate": -0.154

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11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 27/41 }, { "member_id": "K000367", "name": "Amy Klobuchar", "party": "D", "state": "MN", "vote_position": "No", "dw_nominate": -0.28 }, { "member_id": "L000575", "name": "James Lankford", "party": "R", "state": "OK", "vote_position": "Yes", "dw_nominate": 0.589 }, { "member_id": "L000174", "name": "Patrick J. Leahy", "party": "D", "state": "VT", "vote_position": "No", "dw_nominate": -0.361 }, { "member_id": "L000577", "name": "Mike Lee", "party": "R", "state": "UT", "vote_position": "Yes", "dw_nominate": 0.916 }, { "member_id": "L000594", "name": "Kelly Loeffler", "party": "R", "state": "GA", "vote_position": "Yes", "dw_nominate": null }, { "member_id": "M001183", "name": "Joe Manchin III", "party": "D", "state": "WV", "vote_position": "No", "dw_nominate": -0.056 }, { "member_id": "M000133", "name": "Edward J. Markey", "party": "D", "state": "MA", "vote_position": "Not Voting", "dw_nominate": -0.511 },

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11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 28/41 { "member_id": "M000355", "name": "Mitch McConnell", "party": "R", "state": "KY", "vote_position": "Yes", "dw_nominate": 0.402 }, { "member_id": "M001197", "name": "Martha McSally", "party": "R", "state": "AZ", "vote_position": "Not Voting", "dw_nominate": 0.345 }, { "member_id": "M000639", "name": "Robert Menendez", "party": "D", "state": "NJ", "vote_position": "No", "dw_nominate": -0.366 }, { "member_id": "M001176", "name": "Jeff Merkley", "party": "D", "state": "OR", "vote_position": "No", "dw_nominate": -0.444 }, { "member_id": "M000934", "name": "Jerry Moran", "party": "R", "state": "KS", "vote_position": "Yes", "dw_nominate": 0.414 }, { "member_id": "M001153", "name": "Lisa Murkowski", "party": "R", "state": "AK", "vote_position": "Not Voting", "dw_nominate": 0.211 }, { "member_id": "M001169", "name": "Christopher S. Murphy", "party": "D", "state": "CT", "vote_position": "No", "dw_nominate": -0.272 }, {

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11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 29/41 "member_id": "M001111", "name": "Patty Murray", "party": "D", "state": "WA", "vote_position": "Not Voting", "dw_nominate": -0.351 }, { "member_id": "P000603", "name": "Rand Paul", "party": "R", "state": "KY", "vote_position": "Yes", "dw_nominate": 0.878 }, { "member_id": "P000612", "name": "David Perdue", "party": "R", "state": "GA", "vote_position": "Yes", "dw_nominate": 0.565 }, { "member_id": "P000595", "name": "Gary Peters", "party": "D", "state": "MI", "vote_position": "No", "dw_nominate": -0.245 }, { "member_id": "P000449", "name": "Rob Portman", "party": "R", "state": "OH", "vote_position": "Yes", "dw_nominate": 0.373 }, { "member_id": "R000122", "name": "Jack Reed", "party": "D", "state": "RI", "vote_position": "No", "dw_nominate": -0.369 }, { "member_id": "R000584", "name": "Jim Risch", "party": "R", "state": "ID", "vote_position": "Yes", "dw_nominate": 0.631 }, { "member_id": "R000307",

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11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 30/41 "name": "Pat Roberts", "party": "R", "state": "KS", "vote_position": "Yes", "dw_nominate": 0.413 }, { "member_id": "R000615", "name": "Mitt Romney", "party": "R", "state": "UT", "vote_position": "Yes", "dw_nominate": null }, { "member_id": "R000608", "name": "Jacky Rosen", "party": "D", "state": "NV", "vote_position": "No", "dw_nominate": -0.28 }, { "member_id": "R000605", "name": "Mike Rounds", "party": "R", "state": "SD", "vote_position": "Yes", "dw_nominate": 0.404 }, { "member_id": "R000595", "name": "Marco Rubio", "party": "R", "state": "FL", "vote_position": "Yes", "dw_nominate": 0.568 }, { "member_id": "S000033", "name": "Bernard Sanders", "party": "ID", "state": "VT", "vote_position": "No", "dw_nominate": -0.527 }, { "member_id": "S001197", "name": "Ben Sasse", "party": "R", "state": "NE", "vote_position": "Yes", "dw_nominate": 0.794 }, { "member_id": "S001194", "name": "Brian Schatz",

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11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 31/41 "party": "D", "state": "HI", "vote_position": "No", "dw_nominate": -0.448 }, { "member_id": "S000148", "name": "Charles E. Schumer", "party": "D", "state": "NY", "vote_position": "No", "dw_nominate": -0.358 }, { "member_id": "S001217", "name": "Rick Scott", "party": "R", "state": "FL", "vote_position": "Yes", "dw_nominate": null }, { "member_id": "S001184", "name": "Tim Scott", "party": "R", "state": "SC", "vote_position": "Yes", "dw_nominate": 0.637 }, { "member_id": "S001181", "name": "Jeanne Shaheen", "party": "D", "state": "NH", "vote_position": "No", "dw_nominate": -0.238 }, { "member_id": "S000320", "name": "Richard C. Shelby", "party": "R", "state": "AL", "vote_position": "Yes", "dw_nominate": 0.429 }, { "member_id": "S001191", "name": "Kyrsten Sinema", "party": "D", "state": "AZ", "vote_position": "No", "dw_nominate": -0.102 }, { "member_id": "S001203", "name": "Tina Smith", "party": "D",

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11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 32/41 "state": "MN", "vote_position": "No", "dw_nominate": -0.392 }, { "member_id": "S000770", "name": "Debbie Stabenow", "party": "D", "state": "MI", "vote_position": "No", "dw_nominate": -0.342 }, { "member_id": "S001198", "name": "Dan Sullivan", "party": "R", "state": "AK", "vote_position": "Yes", "dw_nominate": 0.483 }, { "member_id": "T000464", "name": "Jon Tester", "party": "D", "state": "MT", "vote_position": "No", "dw_nominate": -0.217 }, { "member_id": "T000250", "name": "John Thune", "party": "R", "state": "SD", "vote_position": "Yes", "dw_nominate": 0.41 }, { "member_id": "T000476", "name": "Thom Tillis", "party": "R", "state": "NC", "vote_position": "Not Voting", "dw_nominate": 0.429 }, { "member_id": "T000461", "name": "Patrick J. Toomey", "party": "R", "state": "PA", "vote_position": "Yes", "dw_nominate": 0.648 }, { "member_id": "U000039", "name": "Tom Udall", "party": "D", "state": "NM",

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11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 33/41 "vote_position": "Not Voting", "dw_nominate": -0.453 }, { "member_id": "V000128", "name": "Chris Van Hollen", "party": "D", "state": "MD", "vote_position": "No", "dw_nominate": -0.39 }, { "member_id": "W000805", "name": "Mark Warner", "party": "D", "state": "VA", "vote_position": "No", "dw_nominate": -0.201 }, { "member_id": "W000817", "name": "Elizabeth Warren", "party": "D", "state": "MA", "vote_position": "No", "dw_nominate": -0.77 }, { "member_id": "W000802", "name": "Sheldon Whitehouse", "party": "D", "state": "RI", "vote_position": "No", "dw_nominate": -0.358 }, { "member_id": "W000437", "name": "Roger Wicker", "party": "R", "state": "MS", "vote_position": "Yes", "dw_nominate": 0.376 }, { "member_id": "W000779", "name": "Ron Wyden", "party": "D", "state": "OR", "vote_position": "No", "dw_nominate": -0.329 }, { "member_id": "Y000064", "name": "Todd Young", "party": "R", "state": "IN", "vote_position": "Yes",

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11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 34/41 It is a vote result with some more keys. The most important new key is 'positions' . Its value is a list of everyone and what vote they cast. For example, for the vote result stored in vote_positions[0] , above, you can verify that "Lamar Alexander" is a Republican who voted yes ( 'name' : "Lamar Alexander" , 'party': "R" , 'vote_position': "Yes" ); and "Tammy Baldwin" is a Democrat who voted no ( 'name' : "Tammy Baldwin" , 'party': "D" , 'vote_position': "No" ). Note: The 'vote_position' key can take on a third value, which is "Not Voting" . In this example, that is the case for "Cindy Hyde-Smith" . Exercise 4 (2 points). Complete the function, get_parties(vps) , below. It should take as input a vote results list vps similar to vote_positions above. It should return a dictionary, where each key is the name of a lawmaker and the corresponding value is that person's party, taken from the 'party' key. For example, suppose you run parties = get_parties(vote_positions) . Then, parties["Lamar Alexander"] == "R" parties["Tammy Baldwin"] == "D" parties["Angus King"] == "ID" Note 0: You may assume that the party of a member does not change and that member names are unique. Note 1: You may assume there are only three possible party values in the data: "R" for Republican, "D" for Democrat, and "ID" for Independent. Note 2: Do not assume that every name appears in every vote result. Therefore, to ensure you get all the names, your solution needs to sweep all vote results. The test code may check whether you've done so. Note 3: The test cell does not use real data; rather, it uses randomly generated synthetic data. Your solution should not depend on the existence of any keys other than 'positions' and, for each element of positions, 'name' and 'party' . "dw_nominate": 0.474 } ] }

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11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 35/41 In [25]: def get_parties(vps): ### BEGIN SOLUTION parties = {} for p in vps: for v in p['positions']: name = v['name'] party = v['party'] parties[name] = party return parties ### END SOLUTION In [26]: # Demo cell you can use for debugging parties_demo = get_parties(vote_positions) print(parties_demo["Lamar Alexander"]) # "R" print(parties_demo["Tammy Baldwin"]) # "D" print(parties_demo["Angus King"]) # "ID" In [27]: # Test cell: ex4__get_parties (2 points) ### BEGIN HIDDEN TESTS def ex4__gen_soln(fn="parties.pickle", overwrite= False ): from testing_tools import file_exists if file_exists(fn) and not overwrite: print(f"' {fn} ' exists; skipping ...") else : print(f"Generating ' {fn} ' ...") P = [p for p in load_json("positions.json") if p['positions']] R = get_parties(P) save_pickle(R, fn) ex4__gen_soln() ### END HIDDEN TESTS from testing_tools import ex4__check print("Testing...") for trial in range(1000): ex4__check(get_parties) print(" \n (Passed.)") Precomputed parties. Whether your solution to the previous exercise passes or not, we have precomputed the party aﬃliations of the members for you. Run the following code cell, which defines a dictionary named parties holding these aﬃliations. You'll need parties in a subsequent exercise. R D ID 'parties.pickle' exists; skipping ... Testing... (Passed.)

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11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 36/41 In [28]: parties = load_pickle('parties.pickle') for member in ["Lamar Alexander", "Tammy Baldwin", "Angus King"]: print(member, "=>", parties[member]) Voting vectors. To help us compare voting records between lawmakers, let's construct their voting vectors . Each lawmaker will be represented by one voting vector. A voting vector is a Python list . Each element of the list is for a voting result and holds one of three values: True for a "Yes" vote, False for a "No" vote, and None for "Not Voting" . For example, since there are 278 vote results in this vote_positions list, the length of a voting vector will be 278. Now suppose Lamar Alexander's voting vector is x , Tammy Baldwin's is y , and Cindy Hyde-Smith's is z . Then for the example voting position above, we would have x[0] == True , y[0] == False , and z[0] == None . Exercise 5 (2 points). Complete the function, build_voting_vectors(vps) . It should take as input a vote results list vps like vote_positions above. It should return a dictionary where each key is the name of a lawmaker and the corresponding value is a voting vector. Recall that a voting vector is a Python list of True or False values. For example, suppose you run results = build_voting_vectors(vote_positions) . Then, from the example of vote_positions[0] , we would expect the following: results["Lamar Alexander"][0] == True results["Tammy Baldwin"][0] == False results["Cindy Hyde-Smith"][0] == None Note 0: The test cell does not use real vote results, but rather randomly generated synthetic ones. Your code should not assume the existence of keys other than the ones directly relevant to this exercise's problem statement, namely, 'positions' , and for each lawmaker's vote, 'name' and 'vote_position' . Note 1: Although the sample voting dataset has 278 vote results, each synthetic test cases might have a different number. Note 2: Your code should not assume that every lawmaker voted in every vote result. See Demo cell 0 , below, where "Anita Borg" did not vote in the first vote result, and in one instance, "Alan Turing" is marked as "Not Voting" . Lamar Alexander => R Tammy Baldwin => D Angus King => ID

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11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 37/41 In [29]: def build_voting_vectors(vps): ### BEGIN SOLUTION from collections import defaultdict vote_values = {"Yes": True , "No": False } results = defaultdict( lambda : [ None ] * len(vps)) for k, p in enumerate(vps): for v in p['positions']: name = v['name'] vote = v["vote_position"] results[name][k] = vote_values.get(vote, None ) return results ### END SOLUTION In [30]: # Demo cell 0, which you can use for debugging # Example with three vote results and three different lawmakers: vps_example = [{'positions': [{'name': 'Grace Hopper', 'vote_positio n': 'Yes'}, {'name': 'Alan Turing', 'vote_position': 'No'}]}, {'positions': [{'name': 'Anita Borg', 'vote_position': 'Yes'}, {'name': 'Alan Turing', 'vote_position': 'Not Voting'}, {'name': 'Grace Hopper', 'vote_positio n': 'No'}]}, {'positions': [{'name': 'Grace Hopper', 'vote_positio n': 'No'}, {'name': 'Anita Borg', 'vote_position': 'Yes'}, {'name': 'Alan Turing', 'vote_position': 'Yes'}]} ] build_voting_vectors(vps_example) # Should print something like # {'Grace Hopper': [True, False, False], # 'Alan Turing': [False, None, True], # 'Anita Borg': [None, True, True]} Out[30]: defaultdict(<function __main__.build_voting_vectors.<locals>.<lambda> ()>, {'Grace Hopper': [True, False, False], 'Alan Turing': [False, None, True], 'Anita Borg': [None, True, True]})

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11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 38/41 In [31]: # Demo cell 1 results = build_voting_vectors(vote_positions) # Lamar Alexander cast 236 "Yes" votes, 7 "No" votes, and did not vote 35 times. # The collections.Counter object can help verify that fact from your r esults. Lamars_votes = results["Lamar Alexander"] from collections import Counter Counter(Lamars_votes) In [32]: # Test cell: ex5__build_voting_vectors (2 points) ### BEGIN HIDDEN TESTS def ex5__gen_soln(fn="voting_vectors.pickle", overwrite= False ): from testing_tools import file_exists if file_exists(fn) and not overwrite: print(f"' {fn} ' exists; skipping ...") else : print(f"Generating ' {fn} ' ...") P = [p for p in load_json("positions.json") if p['positions']] R = build_voting_vectors(P) save_pickle(dict(R), fn) ex5__gen_soln() ### END HIDDEN TESTS from testing_tools import ex5__check for trial in range(1000): ex5__check(build_voting_vectors) print(" \n (Passed.)") Precomputed voting vectors. Whether or not your previous solution passed, we've precomputed the voting vectors for this dataset. The following code cell loads these results into the object voting_vectors , which you'll need in subsequent exercises. In [33]: voting_vectors = load_pickle('voting_vectors.pickle') from collections import Counter for example in ["Lamar Alexander", "Tammy Baldwin"]: example_vv = voting_vectors[example] print(example, "==>", Counter(example_vv)) Out[31]: Counter({True: 236, None: 35, False: 7}) 'voting_vectors.pickle' exists; skipping ... (Passed.) Lamar Alexander ==> Counter({True: 236, None: 35, False: 7}) Tammy Baldwin ==> Counter({False: 174, True: 103, None: 1})

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11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 39/41 Exercise 6 (2 points). For this last exercise, complete the function opposing_pairs(voting_vectors, parties) . This function takes two inputs: voting_vectors is the dictionary that maps each member of Congress to their voting vector parties is the dictonary that holds the party aﬃliation of each member It should then do the following: Consider every pair of opposing members. That is, let (a, b) be a pair of names, where a is some Republican and b is some Democrat. ( a must be a Republican and b a Democrat; do not ﬂip these.) Ignore any Independents. Use their voting vectors to count how many times a and b cast identical and non- None votes. That is, if they both voted "Yes" or they both voted "No" , those are identical votes. However, if either or both has None recorded for a vote, that should not be counted. Denote the count between a and b by c . Then, the function should return a list of all tuples, (a, b, c) . For example, suppose you run the following: P = {"alice": "R", "bob": "D", "carol": "D", "dale": "ID"} V = {"alice": [True, True, False, True, None], "bob": [True, False, None, True, True], "carol": [False, False, False, None, None], "dale": [False, None, None, False, False]} pairs = opposing_pairs(V, P) Then pairs should be the following list: [('alice', 'bob', 2), ('alice', 'carol', 1)] For instance, the result contains ('alice', 'bob', 2) , since 'alice' is a Republican, 'bob' is a Democrat, and they cast the same vote twice (entries 0 and 3 of their voting vectors). It also contains ('alice', 'carol', 1) , since 'alice' is a Republican and 'carol' is a Democrat, and they cast the same non- None votes just once. Note: Your function does not have to return the list with entries in the same order as above; any permutation is fine, as long as it contains all valid pairs.

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11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 40/41 In [34]: def opposing_pairs(voting_vectors, parties): ### BEGIN SOLUTION Rs = {name for name, party in parties.items() if party == "R"} Ds = {name for name, party in parties.items() if party == "D"} pairs = [] for r in Rs: v_r = voting_vectors[r] for d in Ds: v_d = voting_vectors[d] c = count_common(v_r, v_d) pairs.append((r, d, c)) return pairs def count_common(x, y): assert len(x) == len(y) return sum([(xi == yi) for xi, yi in zip(x, y) if (xi is not None ) and (yi is not None )]) ### END SOLUTION In [35]: # Demo cell P = {"alice": "R", "bob": "D", "carol": "D", "dale": "ID"} V = {"alice": [ True , True , False , True , None ], "bob": [ True , False , None , True , True ], "carol": [ False , False , False , None , None ], "dale": [ False , None , None , False , False ]} pairs = opposing_pairs(V, P) pairs In [36]: # Test cell: ex6__opposing_pairs (2 points) from testing_tools import ex6__check for trial in range(1000): ex6__check(opposing_pairs) print(" \n (Passed.)") Most and least "compatible" opposing pairs. If your opposing_pair implementation works, then the following code should print the "most compatible" opposing pairs. (If it doesn't work, that's okay -- the cell below will fail, but it shouldn't affect your final score.) Out[35]: [('alice', 'bob', 2), ('alice', 'carol', 1)] (Passed.)

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11/28/23, 8:39 PM problem file:///Users/dannie/Downloads/pmt1-sample-solutions-su21/problem23-sample-solutions.html 41/41 In [37]: congress_pairs = opposing_pairs(voting_vectors, parties) print("Top opposing pairs:") sorted(congress_pairs, key= lambda x: x[-1], reverse= True )[:11] In [38]: print("Most polar (least compatible) opposing pairs:") sorted(congress_pairs, key= lambda x: x[-1], reverse= True )[-11:] Epilogue. The analysis contained herein is just the tip of the iceberg of what you could do with this data, as well as all the other data available through ProPublica's Congress APIs (https://projects.propublica.org/api- docs/congress-api/). If you live in the US and are civic-minded, consider mining it for even more interesting information! Fin! You’ve reached the end of Midterm 1. Don’t forget to restart and run all cells again to make sure it’s all working when run in sequence; and make sure your work passes the submission process. Good luck! Top opposing pairs: Out[37]: [('Susan Collins', 'Joe Manchin III', 202), ('Susan Collins', 'Doug Jones', 190), ('Susan Collins', 'Kyrsten Sinema', 183), ('Rob Portman', 'Joe Manchin III', 176), ('Cory Gardner', 'Joe Manchin III', 175), ('Roger Wicker', 'Joe Manchin III', 175), ('Shelley Moore Capito', 'Joe Manchin III', 174), ('Roy Blunt', 'Joe Manchin III', 174), ('John Boozman', 'Joe Manchin III', 173), ('Mitch McConnell', 'Joe Manchin III', 173), ('Mitt Romney', 'Joe Manchin III', 173)] Most polar (least compatible) opposing pairs: Out[38]: [('Patrick J. Toomey', 'Elizabeth Warren', 7), ('Michael B. Enzi', 'Cory Booker', 7), ('James Lankford', 'Cory Booker', 7), ('James Lankford', 'Elizabeth Warren', 7), ('James M. Inhofe', 'Cory Booker', 7), ('James M. Inhofe', 'Elizabeth Warren', 6), ('Kelly Loeffler', 'Kamala Harris', 5), ('Kelly Loeffler', 'Cory Booker', 5), ('Kelly Loeffler', 'Kirsten E. Gillibrand', 5), ('Kelly Loeffler', 'Elizabeth Warren', 4), ('Kelly Loeffler', 'Edward J. Markey', 0)]

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