From dd37a16b0c446d29153b015a941a5963ae710696 Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Thu, 3 Oct 2019 18:31:42 -0700 Subject: [PATCH] Add files via upload --- ipynb/Electoral Votes.ipynb | 733 +++++++++------------------------ ipynb/ElectoralVotesCode.ipynb | 300 ++++++++++++++ 2 files changed, 501 insertions(+), 532 deletions(-) create mode 100644 ipynb/ElectoralVotesCode.ipynb diff --git a/ipynb/Electoral Votes.ipynb b/ipynb/Electoral Votes.ipynb index 05a240c..448e122 100644 --- a/ipynb/Electoral Votes.ipynb +++ b/ipynb/Electoral Votes.ipynb @@ -4,296 +4,55 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "
Peter Norvig
12 August 2019
\n", + "
Peter Norvig
3 October 2019
\n", "\n", "# Tracking Trump: Electoral Votes Edition\n", "\n", - "Sites such as [RealClearPolitics](https://www.realclearpolitics.com/epolls/other/president_trump_job_approval-6179.html), [538](https://projects.fivethirtyeight.com/trump-approval-ratings/), and [Mourning Consult](https://morningconsult.com/tracking-trump/) track presidential approval ratings (currently about 43% approval and 53% disapproval for a net -10%). Do approval ratings predict election results? There are three big caveats:\n", + "Sites such as [RealClearPolitics](https://www.realclearpolitics.com/epolls/other/president_trump_job_approval-6179.html), [538](https://projects.fivethirtyeight.com/trump-approval-ratings/), and [Mourning Consult](https://morningconsult.com/tracking-trump/) track Trump's approval ratings. He currently stands at 41% approval and 54% disapproval for a **net approval** of -13%. Can we use approval ratings to predict election results? There are four big caveats:\n", "\n", "1. Today is not election day 2020. \n", "\n", - "2. Approval polls are not votes. \n", + "2. We don't know for sure that Trump will run in 2020.\n", "\n", - "3. Popular votes are not electoral votes. \n", + "3. Approval polls are not votes. \n", "\n", - "We can't be conclusive about the first two points, but this notebook can use state-by-state approval polls to \n", - "compute expected electoral votes, under the assumption that Trump wins the electoral votes of states he has positive net approval (and for the purposes of computation we'll count half the electoral votes for states where approval exactly equals disapproval).\n", + "4. Popular votes are not electoral votes. \n", + "\n", + "We can't be conclusive about the first three points. But can we use state-by-state approval polls to \n", + "compute expected electoral votes, under the assumption that Trump wins the electoral votes of states where he has positive net approval and loses the states with negative net approval? *Yes we can!*\n", "\n", "\n", - "# TL;DR for policy wonks\n", + "# TL;DR \n", "\n", - "As of August 2019, Trump would expect **172 electoral votes** under these assumptions (you need **270** to win). If you list states in order of his approval, the key turning-point state is Pennsylvania; he'd need to win that and every state in which he is more popular. He currently is **7% behind in Pennsylvania**; we call that the *margin*.\n", + "As of September 1, 2019, Trump would get _**166 electoral votes**_ under these assumptions (you need **270** to win). \n", "\n", "\n", - "# The details for data science nerds\n", + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Details for data science nerds\n", "\n", - "We don't know who else will be on the ballot and what their approval levels will be, we don't know if there is systematic bias in the polling data, we don't know how many people will vote for a candidate they disapprove of or against a candidate they approve of, and we don't know who will decline to vote.\n", - "I have five ways of understanding the fluidity of the situation:\n", + "View, verify, or modify **[my code](ElectoralVotesCode.ipynb)**.\n", "\n", - "- **Undecided**: If many voters are undecided, the net approval could change a lot. So I track the number of states for which at least 5% of voters are undecided. At the inauguration in 2017, all 51 states (including DC) had at least 5% undecided; now there is only one such state (Alaska). Overall 4% of voters are undecided. Most people have made up their mind. In [one poll](https://www.pbs.org/newshour/politics/57-percent-of-voters-say-they-wont-support-trump-in-2020) 57% said they would definitely not vote for Trump in 2020; other polls have this in the 50% to 55% range.\n", + "# Details for policy wonks\n", "\n", - "- **Variance**: How much are voters changing their minds from month to month in each state? I track the standard deviation, 𝝈, of the net approval for each state over the last 12 months.\n", - "\n", - "- **Movement**: What's the most a state's net approval could be expected to move, due to random fluctuations (that is, assuming there is no big event that changes people's minds)? I define the maximum expected **movement** of a state as 1/5 of the undecided voters (i.e. assume the undecided voters broke 60/40 one way or the other) plus 2 standard deviations in the net approval over the last 12 months. \n", - "\n", - "- **Swing state**: I define a swing state as one whose maximum expected movement is greater than the absolute value of the net approval. There are 13 such states now; if Trump won them all, he would still lose the election with only 237 electoral votes. \n", - "\n", - "- **Margin**: Suppose a future event swings voters in one direction, across the board in all the key states. How much of a swing would be necessary to change the election outcome? We call that the **margin**. Today **Trump's margin is 7%:** if he got 7% more votes in 8 key states he would be over 270 electoral votes. (This could come, for example, by convincing 3% of undecided voters to break for him at a 2 to 1 ratio, and then convincing 3% of disapproving voters to switch to approving.)\n", - "\n", - "# Data and Code\n", - "\n", - "First fetch the state-by-state, month-by-month approval data from the **[Tracking Trump](https://morningconsult.com/tracking-trump/)** web page at *Morning Consult*\n", - " and cache it locally: " + "The following plot shows, for each month in office, the expected number of electoral votes with error bars indicating a 3% swing in either direction (Why 3%? That was the [average error](https://fivethirtyeight.com/features/the-polls-are-all-right/) in national presidential polls in 2016: Clinton was predicted by National polls to win the popular vote by 6% but actually only won by 3%.) Trump hasn't been above 270 since 4 months into his term, and even with the 3% swing, since 6 months in. He's been below 200 for 10 months in a row (or below 230 counting the 3% swing)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " % Total % Received % Xferd Average Speed Time Time Time Current\n", - " Dload Upload Total Spent Left Speed\n", - "100 117k 0 117k 0 0 233k 0 --:--:-- --:--:-- --:--:-- 233k\n" - ] - } - ], - "source": [ - "! curl -o evs.html https://morningconsult.com/tracking-trump-2/" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now some imports: " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import re\n", - "import ast\n", - "from collections import namedtuple\n", - "from IPython.display import display, Markdown\n", - "from statistics import stdev" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Additional data: the variable `data` contains the [electoral votes by state](https://www.britannica.com/topic/United-States-Electoral-College-Votes-by-State-1787124) and the [partisan lean by state](https://github.com/fivethirtyeight/data/tree/master/partisan-lean) (how much more Republican (plus) or Democratic (minus) leaning the state is compared to the country as a whole, across recent elections). The variable `net_usa` has the [country-wide net presidential approval](https://projects.fivethirtyeight.com/trump-approval-ratings/) by month." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "data = { # From https://github.com/fivethirtyeight/data/tree/master/partisan+lean \n", - " # a dict of {\"state name\": (electoral_votes, partisan_lean)}\n", - " \"Alabama\": (9, +27), \"Alaska\": (3, +15), \"Arizona\": (11, +9), \n", - " \"Arkansas\": (6, +24), \"California\": (55, -24), \"Colorado\": (9, -1), \n", - " \"Connecticut\": (7, -11), \"Delaware\": (3, -14), \"District of Columbia\": (3, -43),\n", - " \"Florida\": (29, +5), \"Georgia\": (16, +12), \"Hawaii\": (4, -36), \n", - " \"Idaho\": (4, +35), \"Illinois\": (20, -13), \"Indiana\": (11, +18), \n", - " \"Iowa\": (6, +6), \"Kansas\": (6, +23), \"Kentucky\": (8, +23), \n", - " \"Louisiana\": (8, +17), \"Maine\": (4, -5), \"Maryland\": (10, -23), \n", - " \"Massachusetts\": (11, -29), \"Michigan\": (16, -1), \"Minnesota\": (10, -2), \n", - " \"Mississippi\": (6, +15), \"Missouri\": (10, +19), \"Montana\": (3, +18), \n", - " \"Nebraska\": (5, +24), \"Nevada\": (6, +1), \"New Hampshire\": (4, +2), \n", - " \"New Jersey\": (14, -13), \"New Mexico\": (5, -7), \"New York\": (29, -22), \n", - " \"North Carolina\": (15, +5), \"North Dakota\": (3, +33), \"Ohio\": (18, +7), \n", - " \"Oklahoma\": (7, +34), \"Oregon\": (7, -9), \"Pennsylvania\": (20, +1), \n", - " \"Rhode Island\": (4, -26), \"South Carolina\": (9, +17), \"South Dakota\": (3, +31), \n", - " \"Tennessee\": (11, +28), \"Texas\": (38, +17), \"Utah\": (6, +31), \n", - " \"Vermont\": (3, -24), \"Virginia\": (13, 0), \"Washington\": (12, -12), \n", - " \"West Virginia\": (5, +30), \"Wisconsin\": (10, +1), \"Wyoming\": (3, +47)}\n", - "\n", - "net_usa = { # From https://projects.fivethirtyeight.com/trump-approval-ratings/\n", - " '1-Jan-17': +10, # a dict of {date: country-wide-net-approval}\n", - " '1-Feb-17': 0, '1-Mar-17': -6, '1-Apr-17': -13, '1-May-17': -11,\n", - " '1-Jun-17': -16, '1-Jul-17': -15, '1-Aug-17': -19, '1-Sep-17': -20,\n", - " '1-Oct-17': -17, '1-Nov-17': -19, '1-Dec-17': -18, '1-Jan-18': -18,\n", - " '1-Feb-18': -15, '1-Mar-18': -14, '1-Apr-18': -13, '1-May-18': -12,\n", - " '1-Jun-18': -11, '1-Jul-18': -10, '1-Aug-18': -12, '1-Sep-18': -14,\n", - " '1-Oct-18': -11, '1-Nov-18': -11, '1-Dec-18': -10, '1-Jan-19': -12,\n", - " '1-Feb-19': -16, '1-Mar-19': -11, '1-Apr-19': -11, '1-May-19': -12,\n", - " '1-Jun-19': -12, '1-Jul-19': -11}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now the code to parse and manipulate the data:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "class State(namedtuple('_', 'name, ev, lean, approvals, disapprovals')):\n", - " '''A State has a name, the number of electoral votes, the partisan lean,\n", - " and two dicts of {date: percent}: approvals and disapprovals'''\n", - "\n", - "def parse_page(filename='evs.html', data=data):\n", - " \"Read data from the file and return (list of dates, list of `State`s, last date).\"\n", - " # File format: Date headers, then [state, approval, disapproval ...]\n", - " # [[\"Demographic\",\"1-Jan-17\",\"\",\"1-Feb-17\",\"\", ... \"1-Apr-19\",\"\"],\n", - " # [\"Alabama\",\"62\",\"26\",\"65\",\"29\", ... \"61\",\"35\"], ... ] =>\n", - " # State(\"Alabama\", 9, approvals={\"1-Jan-17\": 62, ...}, disapprovals={\"1-Jan-17\": 26, ...}), ...\n", - " text = re.findall(r'\\[\\[.*?\\]\\]', open(filename).read())[0]\n", - " header, *table = ast.literal_eval(text)\n", - " dates = header[1::2] # Every other header entry is a date\n", - " states = [State(name, *data[name],\n", - " approvals=dict(zip(dates, map(int, numbers[0::2]))),\n", - " disapprovals=dict(zip(dates, map(int, numbers[1::2]))))\n", - " for (name, *numbers) in table]\n", - " return dates, states, dates[-1]\n", - "\n", - "dates, states, now = parse_page()\n", - "\n", - "assert len(states) == 51 and sum(s.ev for s in states) == 538\n", - "\n", - "def EV(states, date=now, swing=0) -> int:\n", - " \"Total electoral votes with net positive approval (plus half the votes for net zero).\"\n", - " return sum(s.ev * (1/2 if net(s, date) + swing == 0 else int(net(s, date) + swing > 0))\n", - " for s in states)\n", - "\n", - "def margin(states, date=now) -> int:\n", - " \"What's the least swing that would lead to a majority?\"\n", - " return next(swing for swing in range(-50, 50) if EV(states, date, swing) >= 270)\n", - "\n", - "def net(state, date=now) -> int: return state.approvals[date] - state.disapprovals[date]\n", - "def undecided(state, date=now) -> int: return 100 - state.approvals[date] - state.disapprovals[date]\n", - "def movement(state, date=now) -> float: return undecided(state, date) / 5 + 2 * 𝝈(state)\n", - "def 𝝈(state, recent=dates[-12:]) -> float: return stdev(net(state, d) for d in recent)\n", - "def is_swing(state) -> bool: return abs(net(state)) < movement(state)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Current expected electoral votes, with various swings" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, "outputs": [ { "data": { + "image/png": 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\n", "text/plain": [ - "172" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# How many Electoral votes would Trump expect to get today?\n", - "EV(states)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "7" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# What across-the-board increase in approval would he need to win?\n", - "margin(states)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{0: 172,\n", - " 1: 196.5,\n", - " 2: 222.5,\n", - " 3: 224,\n", - " 4: 230.5,\n", - " 5: 237,\n", - " 6: 246.0,\n", - " 7: 270.5,\n", - " 8: 286,\n", - " 9: 286,\n", - " 10: 286}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# How many votes does he get with various swings?\n", - "{s: EV(states, swing=s)\n", - " for s in range(11)}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We see that:\n", - "- Trump is currently leading in states with only **172** electoral votes; \n", - "- The margin is **7%** (if he got 7% more popular in key states, his expected total would be 270.5).\n", - "- Swings from 0 to 10% produce electoral vote totals from 172 to 286." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Electoral votes by month\n", - "\n", - "The following plot shows, for each month in office, the expected number of electoral votes with error bars indicating a 3% swing in either direction (Why 3%? That was the [average error](https://fivethirtyeight.com/features/the-polls-are-all-right/) in national presidential polls in 2016: Clinton was predicted by polls to win the popular vote by 6% but actually only won by 3%.) Trump hasn't been above 270 since 4 months into his term, and even with the 3% swing, since 6 months in." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" ] }, "metadata": {}, @@ -301,134 +60,8 @@ } ], "source": [ - "def labels(xlab, ylab): plt.xlabel(xlab); plt.ylabel(ylab); plt.grid(True); plt.legend()\n", - " \n", - "plt.rcParams[\"figure.figsize\"] = [12, 10]\n", - "plt.style.use('fivethirtyeight')\n", - " \n", - "def plot1(states, dates, swing=3):\n", - " N = len(dates)\n", - " err = [[EV(states, date) - EV(states, date, -swing) for date in dates],\n", - " [EV(states, date, swing) - EV(states, date) for date in dates]]\n", - " plt.plot(range(N), [270] * N, color='darkorange', label=\"270 EVs\")\n", - " plt.errorbar(range(N), [EV(states, date) for date in dates], fmt='D-',\n", - " yerr=err, ecolor='grey', capsize=7, label='Trump EVs ±3% swing')\n", - " labels('Months into term', 'Electoral Votes')\n", - " \n", - "plot1(states, dates)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Margin and country-wide net approval by month\n", - "\n", - "The next plot gives the swing margin needed to reach 270 for each month, along with the country-wide net approval. Trump has been in negative territory on all metrics since his fourth month in office. He's been net -10% or worse every month since his third in office. His necessary margin has been 4% or worse every month since his seventh. We see that the state-by-state margin roughly correlates with the country-wide net approval, but not exactly." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def plot2(states, dates):\n", - " N = len(dates)\n", - " plt.plot(range(N), [0] * N, label='Net zero', color='darkorange')\n", - " plt.plot(range(N), [-margin(states, date) for date in dates], 'D-', label='Margin to 270')\n", - " plt.plot(range(N), [net_usa[date] for date in dates], 'go-', label='Country-wide Net')\n", - " labels('Months into term', 'Net popularity')\n", - " \n", - "plot2(states, dates)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Month-by-month summary table\n", - "\n", - "For each month, we show the expected electoral vote total (**EVs**), the swing margin needed to get to 270 (**Margin**), the overall (popular vote) net approval across the whole country (**Country**), and then the total percentage of undecided voters and in parentheses the number of states with at least 5% undecided.\n", - "Note that the country-wide vote is not all that correlated with the state-by-state margin: recently the state-by-state margin has held at 7% while the country-wide net approval has ranged from -10% to -16%, and when the state-by-state margin jumped to 11%, the country-wide measure stayed right in the middle at 12%." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "|Month|EVs|Margin|Country|Undecided|\n", - "|-|-|-|-|-|\n", - "|Jul 2019|172|7%|-11%|4% (1)|\n", - "|Jun 2019|167|9%|-12%|4% (1)|\n", - "|May 2019|193|7%|-12%|4% (1)|\n", - "|Apr 2019|180|7%|-11%|4% (0)|\n", - "|Mar 2019|193|7%|-11%|4% (2)|\n", - "|Feb 2019|170|7%|-16%|4% (0)|\n", - "|Jan 2019|126|11%|-12%|4% (0)|\n", - "|Dec 2018|164|7%|-10%|5% (3)|\n", - "|Nov 2018|233|5%|-11%|4% (1)|\n", - "|Oct 2018|247|6%|-11%|4% (3)|\n", - "|Sep 2018|203|8%|-14%|4% (1)|\n", - "|Aug 2018|224|6%|-12%|4% (0)|\n", - "|Jul 2018|225|6%|-10%|4% (1)|\n", - "|Jun 2018|226|5%|-11%|4% (0)|\n", - "|May 2018|232|5%|-12%|4% (0)|\n", - "|Apr 2018|209|7%|-13%|4% (0)|\n", - "|Mar 2018|196|9%|-14%|4% (0)|\n", - "|Feb 2018|247|4%|-15%|4% (2)|\n", - "|Jan 2018|201|4%|-18%|5% (4)|\n", - "|Dec 2017|189|8%|-18%|5% (8)|\n", - "|Nov 2017|174|8%|-19%|5% (7)|\n", - "|Oct 2017|209|8%|-17%|5% (7)|\n", - "|Sep 2017|201|7%|-20%|5% (8)|\n", - "|Aug 2017|163|10%|-19%|7% (33)|\n", - "|Jul 2017|196|3%|-15%|5% (4)|\n", - "|Jun 2017|248|2%|-16%|5% (15)|\n", - "|May 2017|269|1%|-11%|5% (4)|\n", - "|Apr 2017|365|-7%|-13%|4% (4)|\n", - "|Mar 2017|374|-8%|-6%|5% (14)|\n", - "|Feb 2017|402|-8%|0%|6% (48)|\n", - "|Jan 2017|448|-10%|10%|11% (51)|" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def header(head) -> str: return head + '\\n' + '-'.join('|' * head.count('|'))\n", - "\n", - "def markdown(fn) -> callable: return lambda *args: display(Markdown('\\n'.join(fn(*args))))\n", - "\n", - "@markdown\n", - "def by_month(states, dates=reversed(dates)):\n", - " yield header('|Month|EVs|Margin|Country|Undecided|')\n", - " for date in dates:\n", - " month = date.replace('1-', '').replace('-', ' 20')\n", - " yield (f'|{month}|{int(EV(states, date))}|{margin(states, date)}%|{net_usa[date]}%'\n", - " f'|{sum(s.ev * undecided(s, date) for s in states) / 538:.0f}% '\n", - " f'({sum(undecided(s, date) > 5 for s in states)})|')\n", - " \n", - "by_month(states)" + "%run ElectoralVotesCode.ipynb\n", + "show_months()" ] }, { @@ -437,76 +70,101 @@ "source": [ "# State-by-state summary table\n", "\n", - "Below is each state sorted by net approval, with the state's maximum expected movement, and electoral vote allotment, followed by the cumulative running total of electoral votes and the percentages of approval, disapprovals, and undecided in the state, and finally the standard deviation of the net approval over the last 12 months. By going down the **Total** column, you can see what it takes to win. \n", + "There's a lot of uncertainty. We don't know who else will be on the ballot and what their approval levels will be, we don't know if there is systematic bias in the polling data, we don't know who will decide not to vote, we don't know who will be prevented from voting by interference foreign or domestic, we don't know if future events will change voters' perceptions.\n", + "I have five ways of understanding the fluidity of the situation:\n", "\n", - "The **CAPITALIZED bold state names** are the **swing states**, which I define as states in which the absolute value of net approval is less than two standard deviations of the net approval over time, plus a fifth of the undecided voters. The idea is that if we are just dealing with random sampling variation, you could expect future approval to be within two standard deviations 95% of the time, and if the undecideds split 60/40, then a candidate could get a net fifth of them. So it would be very unusual for the non-bold states to flip, unless some events change perception of the candidates.\n", + "- **Undecided**: Undecided voters could make up their minds late in the election cycle. But there are few undecided voters: in most states, only 3% or 4%. In [one poll](https://www.pbs.org/newshour/politics/57-percent-of-voters-say-they-wont-support-trump-in-2020) 57% said they would definitely not vote for Trump in 2020; other polls have this in the 50% to 55% range.\n", "\n", - "This analysis says that to win, Trump would need to take *all* the swing states, plus Ohio, Arizona, and Pennsylvannia, which are traditionally considered swing states, but are not under my model because Trump currently trails by a lot (6 0r 7% in each state), and movement there is low. \n" + "- **Variance**: How much are voters changing their minds from month to month in each state? I track the standard deviation, 𝝈, of the net approval for each state over the last 12 months.\n", + "\n", + "- **Movement**: What's the most a state's net approval could be expected to move, due to random fluctuations (that is, assuming there is no big future event that changes people's minds)? I define the maximum expected **movement** of a state as 1/5 of the undecided voters (i.e. assume the undecided voters broke 60/40 one way or the other) plus 2 standard deviations in the net approval over the last 12 months. \n", + "\n", + "- **Swing state**: I define a swing state as one whose maximum expected movement is greater than the absolute value of the net approval. There are 16 such states now; if Trump won them all, he would still lose the election with only 253 electoral votes. The swing states are shown below in **BOLD CAPS**.\n", + "\n", + "- **Margin**: If you list states in order of approval, the key turning-point state is Pennsylvania; Trump would need to win that and every state in which he is more popular. He currently is **7% behind in Pennsylvania**, so we say that his **margin is 7%**.\n", + "\n", + "\n", + "The following table packs in a lot of information. States are sorted by net approval. The columns are:\n", + "\n", + "- **State**: name of state. \n", + "- **Net**: President's current net approval in state.\n", + "- **Move**: Expected maximum movement: 1/5 of the undecided voters plus 2 standard deviations in the net approval over the last 12 months.\n", + "- **EV**: State's number of electoral votes.\n", + "- **ΣEV**: Cumulative running sum of electoral votes of this state and all states above.\n", + "- **+**: President's current approval in state.\n", + "- **-**: President's current disapproval in state.\n", + "- **?**: Undecideds in state.\n", + "- **𝝈**: Standard deviation in net approval over the past 12 months in state.\n", + "- **Δ**: President's change in net approval from inauguration date to today in state. \n", + "\n", + "The table shows that:\n", + "- If Trump loses all the swing states, he gets **70** electoral votes (the **ΣEV** in the South Carolina row).\n", + "- If he wins the states he is leading in, he gets **166** electoral votes (the **ΣEV** in the South Dakota row).\n", + "- If he wins all the swing states, he gets **253** electoral votes (**ΣEV** in the **OHIO** row); still not enough.\n", + "- To get to **270**, he would need Pennsylvania (where he is currently -7%), or some combination of states where he trails by double digits.\n" ] }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "scrolled": false - }, + "execution_count": 2, + "metadata": {}, "outputs": [ { "data": { "text/markdown": [ - "|State|Net|Move|EV|Total|+|-|?|𝝈|\n", - "|-|-|-|-|-|-|-|-|-|\n", - "|Alabama|+26%|7%|9|9|61%|35%|4%|2.9%|\n", - "|Mississippi|+23%|8%|6|15|59%|36%|5%|3.5%|\n", - "|Wyoming|+21%|11%|3|18|58%|37%|5%|4.9%|\n", - "|Tennessee|+20%|7%|11|29|58%|38%|4%|3.0%|\n", - "|West Virginia|+18%|10%|5|34|57%|39%|4%|4.8%|\n", - "|Idaho|+17%|5%|4|38|57%|40%|3%|2.3%|\n", - "|Louisiana|+17%|7%|8|46|56%|39%|5%|3.2%|\n", - "|Oklahoma|+16%|8%|7|53|56%|40%|4%|3.5%|\n", - "|Kentucky|+15%|3%|8|61|55%|40%|5%|1.1%|\n", - "|South Carolina|+11%|5%|9|70|54%|43%|3%|2.3%|\n", - "|**SOUTH DAKOTA**|**+10%**|**10%**|3|73|53%|43%|4%|4.7%|\n", - "|Arkansas|+9%|7%|6|79|53%|44%|3%|3.2%|\n", - "|**MISSOURI**|**+6%**|**7%**|10|89|51%|45%|4%|3.3%|\n", - "|**MONTANA**|**+6%**|**7%**|3|92|51%|45%|4%|3.3%|\n", - "|Texas|+6%|5%|38|130|51%|45%|4%|2.0%|\n", - "|**INDIANA**|**+5%**|**5%**|11|141|51%|46%|3%|2.3%|\n", - "|**KANSAS**|**+5%**|**6%**|6|147|51%|46%|3%|2.6%|\n", - "|**NORTH DAKOTA**|**+5%**|**7%**|3|150|51%|46%|3%|3.2%|\n", - "|**UTAH**|**+3%**|**8%**|6|156|50%|47%|3%|3.6%|\n", - "|**GEORGIA**|**+2%**|**6%**|16|172|49%|47%|4%|2.8%|\n", - "|**FLORIDA**|**-1%**|**5%**|29|201|48%|49%|3%|2.3%|\n", - "|**NEBRASKA**|**-1%**|**7%**|5|206|48%|49%|3%|3.3%|\n", - "|**NORTH CAROLINA**|**-1%**|**5%**|15|221|48%|49%|3%|2.3%|\n", - "|**ALASKA**|**-2%**|**11%**|3|224|46%|48%|6%|4.8%|\n", - "|**VIRGINIA**|**-4%**|**4%**|13|237|46%|50%|4%|1.8%|\n", - "|Ohio|-6%|6%|18|255|45%|51%|4%|2.4%|\n", - "|Arizona|-7%|7%|11|266|45%|52%|3%|3.1%|\n", - "|Pennsylvania|-7%|3%|20|286|45%|52%|3%|1.4%|\n", - "|Iowa|-11%|5%|6|292|43%|54%|3%|2.1%|\n", - "|Nevada|-11%|6%|6|298|43%|54%|3%|2.8%|\n", - "|Colorado|-12%|5%|9|307|43%|55%|2%|2.1%|\n", - "|Michigan|-12%|5%|16|323|42%|54%|4%|2.0%|\n", - "|Delaware|-13%|6%|3|326|42%|55%|3%|2.5%|\n", - "|Maine|-13%|7%|4|330|42%|55%|3%|3.0%|\n", - "|New Mexico|-13%|6%|5|335|42%|55%|3%|2.9%|\n", - "|Minnesota|-14%|5%|10|345|41%|55%|4%|2.2%|\n", - "|Wisconsin|-14%|5%|10|355|41%|55%|4%|2.3%|\n", - "|New Jersey|-15%|4%|14|369|41%|56%|3%|1.9%|\n", - "|New Hampshire|-17%|9%|4|373|40%|57%|3%|4.4%|\n", - "|Illinois|-18%|4%|20|393|39%|57%|4%|1.4%|\n", - "|Oregon|-19%|4%|7|400|39%|58%|3%|1.9%|\n", - "|Rhode Island|-20%|6%|4|404|38%|58%|4%|2.5%|\n", - "|Connecticut|-22%|7%|7|411|37%|59%|4%|3.0%|\n", - "|Hawaii|-22%|10%|4|415|38%|60%|2%|4.6%|\n", - "|New York|-24%|5%|29|444|36%|60%|4%|1.9%|\n", - "|Maryland|-27%|6%|10|454|35%|62%|3%|2.7%|\n", - "|Washington|-27%|5%|12|466|35%|62%|3%|2.4%|\n", - "|Vermont|-29%|8%|3|469|34%|63%|3%|3.6%|\n", - "|California|-30%|5%|55|524|33%|63%|4%|2.0%|\n", - "|Massachusetts|-30%|5%|11|535|34%|64%|2%|2.1%|\n", - "|District of Columbia|-61%|4%|3|538|18%|79%|3%|1.6%|" + "|State|Net|Move|EV|ΣEV|+|−|?|𝝈|Δ|\n", + "|-|-|-|-|-|-|-|-|-|-|\n", + "|Alabama|+22%|±6%|9|9|59%|37%|4%|±2.7%|-14%|\n", + "|Mississippi|+21%|±6%|6|15|59%|38%|3%|±2.8%|-13%|\n", + "|Idaho|+20%|±6%|4|19|58%|38%|4%|±2.7%|-9%|\n", + "|West Virginia|+19%|±8%|5|24|58%|39%|3%|±3.8%|-18%|\n", + "|Wyoming|+16%|±12%|3|27|57%|41%|2%|±5.8%|-24%|\n", + "|Kentucky|+15%|±3%|8|35|56%|41%|3%|±1.1%|-19%|\n", + "|Louisiana|+15%|±6%|8|43|56%|41%|3%|±2.7%|-16%|\n", + "|Tennessee|+13%|±7%|11|54|55%|42%|3%|±3.2%|-20%|\n", + "|Oklahoma|+11%|±7%|7|61|54%|43%|3%|±3.0%|-23%|\n", + "|South Carolina|+7%|±5%|9|70|52%|45%|3%|±2.4%|-18%|\n", + "|**MISSOURI**|**+5%**|**±7%**|10|80|51%|46%|3%|±3.3%|-14%|\n", + "|**ARKANSAS**|**+4%**|**±7%**|6|86|50%|46%|4%|±3.1%|-26%|\n", + "|**KANSAS**|**+4%**|**±6%**|6|92|50%|46%|4%|±2.4%|-20%|\n", + "|**INDIANA**|**+2%**|**±6%**|11|103|49%|47%|4%|±2.5%|-20%|\n", + "|**TEXAS**|**+2%**|**±5%**|38|141|49%|47%|4%|±2.0%|-18%|\n", + "|**ALASKA**|**+1%**|**±10%**|3|144|48%|47%|5%|±4.7%|-23%|\n", + "|**GEORGIA**|**+1%**|**±5%**|16|160|49%|48%|3%|±2.3%|-17%|\n", + "|**NORTH DAKOTA**|**+1%**|**±8%**|3|163|49%|48%|3%|±3.6%|-22%|\n", + "|**SOUTH DAKOTA**|**+1%**|**±11%**|3|166|49%|48%|3%|±5.1%|-20%|\n", + "|**FLORIDA**|**-1%**|**±4%**|29|195|48%|49%|3%|±1.9%|-23%|\n", + "|**ARIZONA**|**-3%**|**±6%**|11|206|47%|50%|3%|±2.9%|-23%|\n", + "|**MONTANA**|**-3%**|**±9%**|3|209|47%|50%|3%|±4.1%|-27%|\n", + "|**NEBRASKA**|**-3%**|**±8%**|5|214|47%|50%|3%|±3.7%|-26%|\n", + "|**NORTH CAROLINA**|**-3%**|**±5%**|15|229|47%|50%|3%|±2.2%|-21%|\n", + "|**UTAH**|**-3%**|**±7%**|6|235|47%|50%|3%|±3.2%|-30%|\n", + "|**OHIO**|**-5%**|**±5%**|18|253|46%|51%|3%|±2.4%|-19%|\n", + "|Pennsylvania|-7%|±3%|20|273|45%|52%|3%|±1.4%|-17%|\n", + "|Virginia|-7%|±5%|13|286|45%|52%|3%|±2.0%|-15%|\n", + "|Michigan|-10%|±5%|16|302|43%|53%|4%|±2.1%|-18%|\n", + "|Delaware|-11%|±5%|3|305|43%|54%|3%|±2.0%|-19%|\n", + "|Minnesota|-11%|±5%|10|315|43%|54%|3%|±2.4%|-14%|\n", + "|Nevada|-11%|±6%|6|321|43%|54%|3%|±2.6%|-21%|\n", + "|Wisconsin|-11%|±5%|10|331|43%|54%|3%|±2.1%|-17%|\n", + "|Iowa|-13%|±5%|6|337|42%|55%|3%|±2.0%|-22%|\n", + "|Maine|-13%|±6%|4|341|42%|55%|3%|±2.9%|-21%|\n", + "|Colorado|-15%|±6%|9|350|41%|56%|3%|±2.5%|-16%|\n", + "|New Mexico|-15%|±6%|5|355|41%|56%|3%|±2.9%|-32%|\n", + "|New Jersey|-17%|±4%|14|369|40%|57%|3%|±1.9%|-19%|\n", + "|Rhode Island|-20%|±4%|4|373|38%|58%|4%|±1.7%|-16%|\n", + "|Illinois|-22%|±4%|20|393|37%|59%|4%|±1.4%|-31%|\n", + "|New Hampshire|-22%|±10%|4|397|38%|60%|2%|±4.7%|-23%|\n", + "|Connecticut|-23%|±5%|7|404|37%|60%|3%|±2.3%|-28%|\n", + "|New York|-23%|±4%|29|433|37%|60%|3%|±1.5%|-31%|\n", + "|Oregon|-24%|±5%|7|440|36%|60%|4%|±1.9%|-26%|\n", + "|Maryland|-25%|±5%|10|450|36%|61%|3%|±2.3%|-12%|\n", + "|California|-28%|±4%|55|505|34%|62%|4%|±1.8%|-22%|\n", + "|Washington|-29%|±6%|12|517|34%|63%|3%|±2.7%|-30%|\n", + "|Hawaii|-30%|±9%|4|521|33%|63%|4%|±4.3%|-17%|\n", + "|Massachusetts|-31%|±5%|11|532|33%|64%|3%|±2.2%|-27%|\n", + "|Vermont|-33%|±8%|3|535|32%|65%|3%|±3.7%|-31%|\n", + "|District of Columbia|-55%|±6%|3|538|21%|76%|3%|±2.5%|-24%|" ], "text/plain": [ "" @@ -517,93 +175,112 @@ } ], "source": [ - "@markdown\n", - "def by_state(states, d=now):\n", - " total = 0\n", - " yield header('|State|Net|Move|EV|Total|+|-|?|𝝈|')\n", - " for s in sorted(states, key=net, reverse=True):\n", - " total += s.ev\n", - " b = '**' * is_swing(s)\n", - " yield (f'|{swing_name(s)}|{b}{net(s):+d}%{b}|{b}{movement(s):.0f}%{b}|{s.ev}|{total}'\n", - " f'|{s.approvals[d]}%|{s.disapprovals[d]}%|{undecided(s, now)}%|{𝝈(s):3.1f}%|')\n", - " \n", - "def swing_name(s) -> str: return ('**' + s.name.upper() + '**') if is_swing(s) else s.name\n", - "\n", - "by_state(states)" + "show_states()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Popularity Above Replacement President (PARP) table\n", + "# Margin and country-wide net approval by month\n", "\n", - "Fivethirtyeight is a combination sports/politics site, and it has a lot of statistics about sports players and how much better they are than the average replacement player. Given that, they [decided](https://fivethirtyeight.com/features/the-states-where-trump-is-more-and-less-popular-than-he-should-be/) to rate the president's approval versus each state's overall approval of his party (in recent elections), which is a way of rating the president's performance versus an average replacement candidate from the same party. I'll duplicate that work and keep it up to date.\n", - "\n", - "There are only five states where Trump is exceeding a replacement Republican (i.e., has a positive PARP): one deep-red southern state, Mississippi, and three deep-blue coastal states, Hawaii, Delaware, and Rhode Island. Again, the swing states are **BOLD CAPITALIZED**." + "The next plot gives the swing margin needed to reach 270 for each month, along with the country-wide net approval. Trump has been in negative territory on all metrics since his fourth month in office. He's been net -10% or worse country-wide every month since his third in office. His necessary margin has been 7% or worse for nine months in a row. We see that the state-by-state margin roughly correlates with the country-wide net approval, but not exactly." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "show_approval()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Popularity Above Replacement President (PARP) \n", + "\n", + "Fivethirtyeight is a combination sports/politics site, and it has a lot of statistics about sports players and how much better they are than the average replacement player. Given that, they [decided](https://fivethirtyeight.com/features/the-states-where-trump-is-more-and-less-popular-than-he-should-be/) to rate the president's approval versus each state's overall approval of his party (in recent elections), which is a way of rating the president's performance versus an average replacement candidate from the same party. I'll duplicate that work and keep it up to date.\n", + "\n", + "Trump is much less popular than the average Republican. There are only three states where Trump is exceeding a replacement Republican (i.e., has a positive PARP): one deep-red southern state, Mississippi, and two deep-blue coastal states, Hawaii and Rhode Island (where he is deeply unpopular, but not as unpopular as other Republicans). Again, the swing states are **BOLD CAPITALIZED**. \n", + "\n", + "For comparison, we show PARP and net approval today as well as in January of 2019, 2018, and 2017. Trump was more popular than an average Republican at his inauguration in January 2017, with positive PARP in 42 states. But by 2018 he had positive PARP in only 6 states, and today only 3." + ] + }, + { + "cell_type": "code", + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ - "|State|PARP|Net|Lean|EV|\n", - "|-|-|-|-|-|\n", - "|Hawaii|+14|-22|-36|4|\n", - "|Mississippi|+8|+23|+15|6|\n", - "|Rhode Island|+6|-20|-26|4|\n", - "|Delaware|+1|-13|-14|3|\n", - "|Louisiana|+0|+17|+17|8|\n", - "|Alabama|-1|+26|+27|9|\n", - "|Massachusetts|-1|-30|-29|11|\n", - "|New Jersey|-2|-15|-13|14|\n", - "|New York|-2|-24|-22|29|\n", - "|Maryland|-4|-27|-23|10|\n", - "|**VIRGINIA**|-4|-4|+0|13|\n", - "|Illinois|-5|-18|-13|20|\n", - "|Vermont|-5|-29|-24|3|\n", - "|California|-6|-30|-24|55|\n", - "|**FLORIDA**|-6|-1|+5|29|\n", - "|New Mexico|-6|-13|-7|5|\n", - "|**NORTH CAROLINA**|-6|-1|+5|15|\n", - "|South Carolina|-6|+11|+17|9|\n", - "|Kentucky|-8|+15|+23|8|\n", - "|Maine|-8|-13|-5|4|\n", - "|Pennsylvania|-8|-7|+1|20|\n", - "|Tennessee|-8|+20|+28|11|\n", - "|**GEORGIA**|-10|+2|+12|16|\n", - "|Oregon|-10|-19|-9|7|\n", - "|Colorado|-11|-12|-1|9|\n", - "|Connecticut|-11|-22|-11|7|\n", - "|Michigan|-11|-12|-1|16|\n", - "|Texas|-11|+6|+17|38|\n", - "|Minnesota|-12|-14|-2|10|\n", - "|**MONTANA**|-12|+6|+18|3|\n", - "|Nevada|-12|-11|+1|6|\n", - "|West Virginia|-12|+18|+30|5|\n", - "|**INDIANA**|-13|+5|+18|11|\n", - "|**MISSOURI**|-13|+6|+19|10|\n", - "|Ohio|-13|-6|+7|18|\n", - "|Arkansas|-15|+9|+24|6|\n", - "|Washington|-15|-27|-12|12|\n", - "|Wisconsin|-15|-14|+1|10|\n", - "|Arizona|-16|-7|+9|11|\n", - "|**ALASKA**|-17|-2|+15|3|\n", - "|Iowa|-17|-11|+6|6|\n", - "|District of Columbia|-18|-61|-43|3|\n", - "|Idaho|-18|+17|+35|4|\n", - "|**KANSAS**|-18|+5|+23|6|\n", - "|Oklahoma|-18|+16|+34|7|\n", - "|New Hampshire|-19|-17|+2|4|\n", - "|**SOUTH DAKOTA**|-21|+10|+31|3|\n", - "|**NEBRASKA**|-25|-1|+24|5|\n", - "|Wyoming|-26|+21|+47|3|\n", - "|**NORTH DAKOTA**|-28|+5|+33|3|\n", - "|**UTAH**|-28|+3|+31|6|" + "|State|Lean|EV|PARP |Net |PARP 19|Net 19|PARP 18|Net 18|PARP 17|Net 17|\n", + "|-|-|-|-|-|-|-|-|-|-|-|\n", + "|Hawaii|-36|4|+6|-30|+7|-29|+0|-36|+23|-13|\n", + "|Mississippi|+15|6|+6|+21|-2|+13|+2|+17|+19|+34|\n", + "|Rhode Island|-26|4|+6|-20|+7|-19|+4|-22|+22|-4|\n", + "|Delaware|-14|3|+3|-11|-1|-15|+0|-14|+22|+8|\n", + "|New York|-22|29|-1|-23|-2|-24|+4|-18|+30|+8|\n", + "|Louisiana|+17|8|-2|+15|-2|+15|+2|+19|+14|+31|\n", + "|Maryland|-23|10|-2|-25|-7|-30|+0|-23|+10|-13|\n", + "|Massachusetts|-29|11|-2|-31|-2|-31|-3|-32|+25|-4|\n", + "|California|-24|55|-4|-28|-6|-30|+1|-23|+18|-6|\n", + "|New Jersey|-13|14|-4|-17|-6|-19|-3|-16|+15|+2|\n", + "|Alabama|+27|9|-5|+22|-7|+20|+3|+30|+9|+36|\n", + "|**FLORIDA**|+5|29|-6|-1|-9|-4|+0|+5|+17|+22|\n", + "|Virginia|+0|13|-7|-7|-10|-10|-4|-4|+8|+8|\n", + "|Kentucky|+23|8|-8|+15|-9|+14|-8|+15|+11|+34|\n", + "|Maine|-5|4|-8|-13|-6|-11|-11|-16|+13|+8|\n", + "|New Mexico|-7|5|-8|-15|-11|-18|-13|-20|+24|+17|\n", + "|**NORTH CAROLINA**|+5|15|-8|-3|-9|-4|-6|-1|+13|+18|\n", + "|Pennsylvania|+1|20|-8|-7|-11|-10|-4|-3|+9|+10|\n", + "|Illinois|-13|20|-9|-22|-10|-23|-8|-21|+22|+9|\n", + "|Michigan|-1|16|-9|-10|-14|-15|-9|-10|+9|+8|\n", + "|Minnesota|-2|10|-9|-11|-16|-18|-12|-14|+5|+3|\n", + "|Vermont|-24|3|-9|-33|-11|-35|-12|-36|+22|-2|\n", + "|South Carolina|+17|9|-10|+7|-9|+8|-10|+7|+8|+25|\n", + "|**GEORGIA**|+12|16|-11|+1|-14|-2|-5|+7|+6|+18|\n", + "|West Virginia|+30|5|-11|+19|-6|+24|-8|+22|+7|+37|\n", + "|**ARIZONA**|+9|11|-12|-3|-17|-8|-12|-3|+11|+20|\n", + "|Connecticut|-11|7|-12|-23|-13|-24|-8|-19|+16|+5|\n", + "|District of Columbia|-43|3|-12|-55|-22|-65|-21|-64|+12|-31|\n", + "|Nevada|+1|6|-12|-11|-14|-13|-2|-1|+9|+10|\n", + "|**OHIO**|+7|18|-12|-5|-13|-6|-11|-4|+7|+14|\n", + "|Wisconsin|+1|10|-12|-11|-17|-16|-13|-12|+5|+6|\n", + "|**ALASKA**|+15|3|-14|+1|-14|+1|-14|+1|+9|+24|\n", + "|Colorado|-1|9|-14|-15|-17|-18|-13|-14|+2|+1|\n", + "|**MISSOURI**|+19|10|-14|+5|-21|-2|-17|+2|+0|+19|\n", + "|Idaho|+35|4|-15|+20|-20|+15|-24|+11|-6|+29|\n", + "|Oregon|-9|7|-15|-24|-13|-22|-11|-20|+11|+2|\n", + "|Tennessee|+28|11|-15|+13|-16|+12|-11|+17|+5|+33|\n", + "|**TEXAS**|+17|38|-15|+2|-17|+0|-10|+7|+3|+20|\n", + "|**INDIANA**|+18|11|-16|+2|-14|+4|-17|+1|+4|+22|\n", + "|Washington|-12|12|-17|-29|-14|-26|-11|-23|+13|+1|\n", + "|Iowa|+6|6|-19|-13|-20|-14|-16|-10|+3|+9|\n", + "|**KANSAS**|+23|6|-19|+4|-22|+1|-18|+5|+1|+24|\n", + "|**ARKANSAS**|+24|6|-20|+4|-14|+10|-11|+13|+6|+30|\n", + "|**MONTANA**|+18|3|-21|-3|-17|+1|-18|+0|+6|+24|\n", + "|Oklahoma|+34|7|-23|+11|-24|+10|-19|+15|+0|+34|\n", + "|New Hampshire|+2|4|-24|-22|-21|-19|-12|-10|-1|+1|\n", + "|**NEBRASKA**|+24|5|-27|-3|-24|+0|-18|+6|-1|+23|\n", + "|**SOUTH DAKOTA**|+31|3|-30|+1|-25|+6|-21|+10|-10|+21|\n", + "|Wyoming|+47|3|-31|+16|-17|+30|-22|+25|-7|+40|\n", + "|**NORTH DAKOTA**|+33|3|-32|+1|-29|+4|-22|+11|-10|+23|\n", + "|**UTAH**|+31|6|-34|-3|-37|-6|-34|-3|-4|+27|" ], "text/plain": [ "" @@ -614,15 +291,7 @@ } ], "source": [ - "def parp(state) -> int: return net(state) - state.lean \n", - "\n", - "@markdown\n", - "def by_parp(states, d=now):\n", - " yield header('|State|PARP|Net|Lean|EV|')\n", - " for s in sorted(states, key=parp, reverse=True):\n", - " yield (f'|{swing_name(s)}|{parp(s):+d}|{net(s):+d}|{s.lean:+d}|{s.ev}|')\n", - "\n", - "by_parp(states)" + "show_parp()" ] } ], diff --git a/ipynb/ElectoralVotesCode.ipynb b/ipynb/ElectoralVotesCode.ipynb new file mode 100644 index 0000000..efa2330 --- /dev/null +++ b/ipynb/ElectoralVotesCode.ipynb @@ -0,0 +1,300 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
Peter Norvig
12 August 2019
\n", + "\n", + "# Data and Code for [Tracking Trump: Electoral Votes Edition](Electoral%20Votes.ipynb)\n", + "\n", + "First fetch the state-by-state, month-by-month approval data from the **[Tracking Trump](https://morningconsult.com/tracking-trump/)** web page at *Morning Consult*\n", + " and cache it locally: " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "! curl -s -o evs.html https://morningconsult.com/tracking-trump-2/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now some imports: " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import re\n", + "import ast\n", + "from collections import namedtuple\n", + "from IPython.display import display, Markdown\n", + "from statistics import stdev" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Additional data: the variable `state_data` contains the [electoral votes by state](https://www.britannica.com/topic/United-States-Electoral-College-Votes-by-State-1787124) and the [partisan lean by state](https://github.com/fivethirtyeight/data/tree/master/partisan-lean) (how much more Republican (plus) or Democratic (minus) leaning the state is compared to the country as a whole, across recent elections). The variable `net_usa` has the [country-wide net presidential approval](https://projects.fivethirtyeight.com/trump-approval-ratings/) by month." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# From https://github.com/fivethirtyeight/data/tree/master/partisan+lean\n", + "# a dict of {\"state name\": (electoral_votes, partisan_lean)}\n", + "state_data = { \n", + " \"Alabama\": (9, +27), \"Alaska\": (3, +15), \"Arizona\": (11, +9), \n", + " \"Arkansas\": (6, +24), \"California\": (55, -24), \"Colorado\": (9, -1), \n", + " \"Connecticut\": (7, -11), \"Delaware\": (3, -14), \"District of Columbia\": (3, -43),\n", + " \"Florida\": (29, +5), \"Georgia\": (16, +12), \"Hawaii\": (4, -36), \n", + " \"Idaho\": (4, +35), \"Illinois\": (20, -13), \"Indiana\": (11, +18), \n", + " \"Iowa\": (6, +6), \"Kansas\": (6, +23), \"Kentucky\": (8, +23), \n", + " \"Louisiana\": (8, +17), \"Maine\": (4, -5), \"Maryland\": (10, -23), \n", + " \"Massachusetts\": (11, -29), \"Michigan\": (16, -1), \"Minnesota\": (10, -2), \n", + " \"Mississippi\": (6, +15), \"Missouri\": (10, +19), \"Montana\": (3, +18), \n", + " \"Nebraska\": (5, +24), \"Nevada\": (6, +1), \"New Hampshire\": (4, +2), \n", + " \"New Jersey\": (14, -13), \"New Mexico\": (5, -7), \"New York\": (29, -22), \n", + " \"North Carolina\": (15, +5), \"North Dakota\": (3, +33), \"Ohio\": (18, +7), \n", + " \"Oklahoma\": (7, +34), \"Oregon\": (7, -9), \"Pennsylvania\": (20, +1), \n", + " \"Rhode Island\": (4, -26), \"South Carolina\": (9, +17), \"South Dakota\": (3, +31), \n", + " \"Tennessee\": (11, +28), \"Texas\": (38, +17), \"Utah\": (6, +31), \n", + " \"Vermont\": (3, -24), \"Virginia\": (13, 0), \"Washington\": (12, -12), \n", + " \"West Virginia\": (5, +30), \"Wisconsin\": (10, +1), \"Wyoming\": (3, +47)}\n", + "\n", + "# From https://projects.fivethirtyeight.com/trump-approval-ratings/\n", + "# A dict of {'date': country-wide-net-approval}\n", + "net_usa = { \n", + " '1-Jan-17': +10, \n", + " '1-Feb-17': 0, '1-Mar-17': -6, '1-Apr-17': -13, '1-May-17': -11,\n", + " '1-Jun-17': -16, '1-Jul-17': -15, '1-Aug-17': -19, '1-Sep-17': -20,\n", + " '1-Oct-17': -17, '1-Nov-17': -19, '1-Dec-17': -18, '1-Jan-18': -18,\n", + " '1-Feb-18': -15, '1-Mar-18': -14, '1-Apr-18': -13, '1-May-18': -12,\n", + " '1-Jun-18': -11, '1-Jul-18': -10, '1-Aug-18': -12, '1-Sep-18': -14,\n", + " '1-Oct-18': -11, '1-Nov-18': -11, '1-Dec-18': -10, '1-Jan-19': -12,\n", + " '1-Feb-19': -16, '1-Mar-19': -11, '1-Apr-19': -11, '1-May-19': -12,\n", + " '1-Jun-19': -12, '1-Jul-19': -11, '1-Aug-19': -10, '1-Sep-19': -13,\n", + " '1-Oct-19': -13}\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now the code to parse and manipulate the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "class State(namedtuple('_', 'name, ev, lean, approvals, disapprovals')):\n", + " '''A State has a name, the number of electoral votes, the partisan lean,\n", + " and two dicts of {date: percent}: approvals and disapprovals'''\n", + "\n", + "def parse_page(filename='evs.html', data=state_data):\n", + " \"Read data from the file and return (list of dates, list of `State`s, last date).\"\n", + " # File format: Date headers, then [state, approval, disapproval ...]\n", + " # [[\"Demographic\",\"1-Jan-17\",\"\",\"1-Feb-17\",\"\", ... \"1-Apr-19\",\"\"],\n", + " # [\"Alabama\",\"62\",\"26\",\"65\",\"29\", ... \"61\",\"35\"], ... ] =>\n", + " # State(\"Alabama\", 9, +27, approvals={\"1-Jan-17\": 62, ...}, \n", + " # disapprovals={\"1-Jan-17\": 26, ...}), ...\n", + " text = re.findall(r'\\[\\[.*?\\]\\]', open(filename).read())[0]\n", + " header, *table = ast.literal_eval(text)\n", + " dates = header[1::2] # Every other header entry is a date\n", + " states = [State(name, *data[name],\n", + " approvals=dict(zip(dates, map(int, numbers[0::2]))),\n", + " disapprovals=dict(zip(dates, map(int, numbers[1::2]))))\n", + " for (name, *numbers) in table]\n", + " return states, dates, dates[-1]\n", + "\n", + "states, dates, now = parse_page()\n", + "\n", + "def EV(states, date=now, swing=0) -> int:\n", + " \"Total electoral votes with net positive approval (plus half the votes for net zero).\"\n", + " return sum(s.ev * (1/2 if net(s, date) + swing == 0 else int(net(s, date) + swing > 0))\n", + " for s in states)\n", + "\n", + "def margin(states, date=now) -> int:\n", + " \"What's the least swing that would lead to a majority?\"\n", + " return next(swing for swing in range(-50, 50) if EV(states, date, swing) >= 270)\n", + "\n", + "def net(state, date=now) -> int: return state.approvals[date] - state.disapprovals[date]\n", + "def undecided(state, date=now) -> int: return 100 - state.approvals[date] - state.disapprovals[date]\n", + "def movement(state, date=now) -> float: return undecided(state, date) / 5 + 2 * 𝝈(state)\n", + "def 𝝈(state, recent=dates[-12:]) -> float: return stdev(net(state, d) for d in recent)\n", + "def is_swing(state) -> bool: return abs(net(state)) < movement(state)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Various functions for displaying data:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def labels(xlab, ylab): plt.xlabel(xlab); plt.ylabel(ylab); plt.grid(True); plt.legend()\n", + "\n", + "def grid(): plt.minorticks_on(); plt.grid(which='minor', ls=':', alpha=0.7)\n", + " \n", + "def header(head) -> str: return head + '\\n' + '-'.join('|' * head.count('|'))\n", + "\n", + "def markdown(fn) -> callable: return lambda *args: display(Markdown('\\n'.join(fn(*args))))\n", + "\n", + "def parp(state, date=now) -> int: return net(state, date) - state.lean " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def show_months(states=states, dates=dates, swing=3):\n", + " plt.rcParams[\"figure.figsize\"] = [10, 7]\n", + " plt.style.use('fivethirtyeight')\n", + " N = len(dates)\n", + " err = [[EV(states, date) - EV(states, date, -swing) for date in dates],\n", + " [EV(states, date, swing) - EV(states, date) for date in dates]]\n", + " grid()\n", + " plt.plot(range(N), [270] * N, color='darkorange', label=\"270 EVs\", lw=2)\n", + " plt.errorbar(range(N), [EV(states, date) for date in dates], fmt='D-',\n", + " yerr=err, ecolor='grey', capsize=7, label='Trump EVs ±3% swing', lw=2)\n", + " labels('Months into term', 'Electoral Votes')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def show_approval(states=states, dates=dates):\n", + " plt.rcParams[\"figure.figsize\"] = [10, 7]\n", + " plt.style.use('fivethirtyeight')\n", + " N = len(dates)\n", + " grid()\n", + " plt.plot(range(N), [0] * N, label='Net zero', color='darkorange')\n", + " plt.plot(range(N), [-margin(states, date) for date in dates], 'D-', label='Margin to 270')\n", + " plt.plot(range(N), [net_usa[date] for date in dates], 'go-', label='Country-wide Net')\n", + " labels('Months into term', 'Net popularity')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "@markdown\n", + "def by_month(states, dates=dates[::-1]):\n", + " yield header('|Month|EVs|Margin|Country|Undecided|')\n", + " for date in dates:\n", + " month = date.replace('1-', '').replace('-', ' 20')\n", + " yield (f'|{month}|{int(EV(states, date))}|{margin(states, date)}%|{net_usa[date]:+d}%'\n", + " f'|{sum(s.ev * undecided(s, date) for s in states) / 538:.0f}% '\n", + " f'({sum(undecided(s, date) > 5 for s in states)} states)')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "@markdown\n", + "def show_states(states=states, d=now, ref='1-Jan-17'):\n", + " total = 0\n", + " yield header(f'|State|Net|Move|EV|ΣEV|+|−|?|𝝈|Δ|')\n", + " for s in sorted(states, key=net, reverse=True):\n", + " total += s.ev\n", + " b = '**' * is_swing(s)\n", + " yield (f'|{swing_name(s)}|{b}{net(s, d):+d}%{b}|{b}±{movement(s):.0f}%{b}|{s.ev}|{total}'\n", + " f'|{s.approvals[d]}%|{s.disapprovals[d]}%|{undecided(s, now)}%|±{𝝈(s):3.1f}%'\n", + " f'|{net(s, d) - net(s, ref):+d}%|')\n", + " \n", + "def swing_name(s) -> str: return ('**' + s.name.upper() + '**') if is_swing(s) else s.name" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "@markdown\n", + "def show_parp(states=states, dates=(now, '1-Jan-19', '1-Jan-18', '1-Jan-17')):\n", + " def year(date): return '' if date == now else date[-2:]\n", + " fields = [f'PARP {year(date)}|Net {year(date)}' for date in dates]\n", + " yield header(f'|State|Lean|EV|{\"|\".join(fields)}|')\n", + " for s in sorted(states, key=parp, reverse=True):\n", + " fields = [f'{parp(s, date):+d}|{net(s, date):+d}' for date in dates]\n", + " yield f'|{swing_name(s)}|{s.lean:+d}|{s.ev}|{\"|\".join(fields)}|'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I really should have some more tests." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "assert len(states) == 51, \"50 states plus DC\"\n", + "assert all(s.ev >= 3 for s in states), \"All states have two senators and at least one rep.\"\n", + "assert sum(s.ev for s in states) == 538, \"Total of 538 electoral votes.\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}