From 133a4af69abf92d34d5fc5744230100ee5b97252 Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Tue, 7 May 2019 22:22:37 -0700 Subject: [PATCH] Add files via upload --- ipynb/Electoral Votes.ipynb | 265 +++++++++++++++++++++--------------- 1 file changed, 155 insertions(+), 110 deletions(-) diff --git a/ipynb/Electoral Votes.ipynb b/ipynb/Electoral Votes.ipynb index 9949d8e..61c6c86 100644 --- a/ipynb/Electoral Votes.ipynb +++ b/ipynb/Electoral Votes.ipynb @@ -16,26 +16,30 @@ "\n", "3. These are popular votes, not electoral votes. \n", "\n", - "We can't be conclusive about the first two points, but this notebook can take the state-by-state, month-by-month approval data from \n", - "[Morning Consult](https://morningconsult.com/tracking-trump/) and compute electoral votes, under the assumption that Trump wins the electoral votes of states he has positive net approval, and wins half the votes for states with zero net approval (i.e. approval exactly equals disapproval).\n", + "We can't be conclusive about the first two points, but this notebook can take the state-by-state, month-by-month approval data from Morning Consult's\n", + "[Tracking Trump](https://morningconsult.com/tracking-trump/) web page and compute electoral votes, under the assumption that Trump wins the electoral votes of states he has positive net approval (and wins half the electoral votes for states where approval exactly equals disapproval).\n", "\n", "\n", "# TL;DR for policy wonks\n", "\n", "As of 1 April 2019, Trump would expect **180 electoral votes** under these assumptions (recall that you need **270** to win). He's been below 270 every month for the last two years.\n", - "I have three ways of understanding the fluidity of the situation:\n", + "I have five ways of understanding the fluidity of the situation:\n", "\n", - "- **Undecided**: if many voters are undecided, the numbers could change. 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 are no such states. **Most people have made up their mind.**\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 are no such states. Overall 4% of voters are undecided. Most people have made up their mind.\n", "\n", - "- **Variance**: how much are voters changing their minds from month to month in each state? I track what would happen in each state if the undecided voters broke 60/40 for Trump, and the other voters swung in his favor by an amount equal to two standard deviations of their month-by-month change. The answer is that he would take **259** electoral votes (and if the states all swung the other way, he would take 79 electoral votes).\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", - "- **Margin**: Suppose a future event swings voters in one direction or another uniformly, across the board in all states. How much of a swing would be necessary to change the results? We call that the **margin**. Today **Trump's margin is a 7%:** he would need 7% more votes in all states to win. (This could come, for example, by convincing undecided voters to break for him at a 2% to 1% ratio, and then convincing 3% of disapproving voters to switch to approving.)\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. If all the states had maximum expected movement towards Trump he would take **259** electoral votes, and if the states all swung the other way, he would take **79** electoral votes.\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 15 such states now.\n", + "\n", + "- **Margin**: Suppose a future event swings voters in one direction or another uniformly, across the board in all 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 all states he would be over 270 electoral votes. (This could come, for example, by convincing 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", "\n", "\n", "# The details for data science nerds\n", "\n", - "First fetch the web page and cache it locally, then define the code:" + "First fetch the Tracking Trump web page and cache it locally, then define the code:" ] }, { @@ -49,7 +53,7 @@ "text": [ " % Total % Received % Xferd Average Speed Time Time Time Current\n", " Dload Upload Total Spent Left Speed\n", - "100 113k 0 113k 0 0 243k 0 --:--:-- --:--:-- --:--:-- 243k\n" + "100 113k 0 113k 0 0 230k 0 --:--:-- --:--:-- --:--:-- 231k\n" ] } ], @@ -90,8 +94,8 @@ " 'Virginia': 13, 'Vermont': 3, 'Washington': 12,\n", " 'Wisconsin': 10, 'West Virginia': 5, 'Wyoming': 3}\n", "\n", - "# net_usa: From https://projects.fivethirtyeight.com/trump-approval-ratings/\n", - "# a dict of {date: country-wide net approval}\n", + "# From https://projects.fivethirtyeight.com/trump-approval-ratings/\n", + "# a dict of {date: country-wide net approval}\n", "net_usa = {'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", @@ -99,17 +103,18 @@ " '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}\n", + " '1-Feb-19': -16, '1-Mar-19': -11, '1-Apr-19': -11, '1-May-19': -12}\n", "\n", "State = namedtuple('State', 'name, ev, apps, diss')\n", "State.__doc__ = '''A State has a name, the number of electoral votes (.ev),\n", "and two dicts of {date: percent}, .apps (approvals) and .diss (disapprovals)'''\n", "\n", "def parse_page(filename='evs.html'):\n", - " \"Read data from the file and return (list of dates, list of `State`s).\"\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", + " # [\"Alabama\",\"62\",\"26\",\"65\",\"29\", ... \"61\",\"35\"], ... ] =>\n", + " # State(\"Alabama\", 9, apps={\"1-Jan-17\": 62, ...}, diss={\"1-Jan-17\": 26, ...}), ...\n", " text = re.findall(r'\\[\\[.*?\\]\\]', open(filename).read())[0]\n", " table = ast.literal_eval(text)\n", " dates = table[0][1::2]\n", @@ -117,10 +122,9 @@ " dict(zip(dates, map(int, numbers[0::2]))),\n", " dict(zip(dates, map(int, numbers[1::2]))))\n", " for (name, *numbers) in table[1:]]\n", - " return dates, states\n", + " return dates, states, dates[-1]\n", "\n", - "dates, states = parse_page()\n", - "now = dates[-1]\n", + "dates, states, now = parse_page()\n", "\n", "assert len(states) == 51 and sum(s.ev for s in states) == 538\n", "\n", @@ -133,8 +137,12 @@ " \"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): return state.apps[date] - state.diss[date]\n", - "def undecided(state, date=now): return 100 - state.apps[date] - state.diss[date]\n", + "def net(state, date=now): return state.apps[date] - state.diss[date]\n", + "def undecided(state, date=now): return 100 - state.apps[date] - state.diss[date]\n", + "def movement(state, date=now): return undecided(state, date) / 5 + 2 * 𝝈(state)\n", + "def 𝝈(state, recent=dates[-12:]): return stdev(net(state, d) for d in recent)\n", + "def is_swing(state): return abs(net(state)) < movement(state)\n", + "\n", "def md(lines): display(Markdown('\\n'.join(lines)))" ] }, @@ -149,6 +157,46 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "180" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "EV(states)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "margin(states)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, "outputs": [ { "data": { @@ -165,7 +213,7 @@ " 9: 298}" ] }, - "execution_count": 3, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -179,7 +227,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The first number says that Trump is currently leading in states with only 180 electoral votes, and we see that the margin is 7%, because that leads to 271 electoral votes, and any smaller swing is below 270." + "We see that:\n", + "- Trump is currently leading in states with only 180 electoral votes; \n", + "- The margin is 7%; \n", + "- Swings from 0 to 9% produce electoral vote totals from 180 ro 298." ] }, { @@ -193,12 +244,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -210,15 +261,15 @@ } ], "source": [ - "def plot(states, dates):\n", + "def labels(xlab, ylab): plt.xlabel(xlab); plt.ylabel(ylab); plt.grid(True); plt.legend()\n", + " \n", + "def plot(states, dates, swing=4):\n", " N = len(dates)\n", - " err = [EV(states, date, swing=4) - EV(states, date) for date in dates]\n", - " plt.errorbar(range(N), [EV(states, date) for date in dates], \n", - " yerr=err, ecolor='grey', capsize=5)\n", + " err = [EV(states, date, swing) - EV(states, date) for date in dates]\n", + " plt.errorbar(range(N), [EV(states, date) for date in dates],\n", + " yerr=err, ecolor='grey', capsize=5, label='EVs')\n", " plt.plot(range(N), [270] * N, color='darkorange')\n", - " plt.xlabel('Months into term')\n", - " plt.ylabel('Electoral Votes with Net Positive Approval')\n", - " plt.grid(True)\n", + " labels('Months into term', 'Electoral Votes with Net Positive Approval')\n", " \n", "plot(states, dates)" ] @@ -227,19 +278,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Margin and popular net approval by month\n", + "# 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." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -255,11 +306,8 @@ " N = len(dates)\n", " plt.plot(range(N), [-margin(states, date) for date in dates], label='Margin')\n", " plt.plot(range(N), [0] * N, label='Net zero')\n", - " plt.plot(range(N), [net_usa[date] for date in dates], label='Popular')\n", - " plt.xlabel('Months into term')\n", - " plt.ylabel('Net popularity')\n", - " plt.legend()\n", - " plt.grid(True)\n", + " plt.plot(range(N), [net_usa[date] for date in dates], label='Country-wide Net')\n", + " labels('Months into term', 'Net popularity')\n", " \n", "plot2(states, dates)" ] @@ -270,20 +318,20 @@ "source": [ "# Month-by-month summary\n", "\n", - "For each month, we show the expected electoral vote total (**EV**), the swing margin needed to get to 270 (**Margin**), the overall (popular vote) net approval across the whole country (**Pop**), and then the total percentage of undecided voters and in parentheses the number of states with at least 5% undecided.\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": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ - "| Month| EV|Margin|Pop|Undecided|\n", - "|--------|---|------|---|---------|\n", + "| Month|EVs|Margin|Country|Undecided|\n", + "|--------|---|------|-------|---------|\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", @@ -323,13 +371,13 @@ ], "source": [ "def monthly(states, dates=reversed(dates)):\n", - " yield '| Month| EV|Margin|Pop|Undecided|'\n", - " yield '|--------|---|------|---|---------|'\n", + " yield '| Month|EVs|Margin|Country|Undecided|'\n", + " yield '|--------|---|------|-------|---------|'\n", " for date in dates:\n", - " us_un = sum(s.ev * undecided(s, date) for s in states) / 538\n", - " undec = sum(undecided(s, date) > 5 for s in states)\n", " month = date.replace('1-', '').replace('-', ' 20')\n", - " yield f'|{month}|{int(EV(states, date))}|{margin(states, date)}%|{net_usa[date]}%|{us_un:.0f}% ({undec})|'\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", "md(monthly(states))" ] @@ -338,9 +386,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# State-by-state net approval\n", + "# State-by-state summary\n", "\n", - "Below is each state sorted by net approval, with the state's electoral vote allotment, and the cumulative running total of electoral votes, followed by the percentages of approval, dissaproval, and undecided, and then the standard deviation of the net approval over the last 12 months (bolded if it is over **5%**). By going down the **Total** column, you can see what it takes to win. \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, dissaproval, and undecided, 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", "\n", "The **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", "\n", @@ -349,65 +397,65 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ - "|State|Net|EV|Total|+|-|?|𝝈|\n", - "|-----|---|--|-----|-|-|-|-|\n", - "|Wyoming|+28|3|3|62%|34%|4%|3.5\n", - "|Alabama|+26|9|12|61%|35%|4%|3.4\n", - "|Louisiana|+20|8|20|58%|38%|4%|3.7\n", - "|Mississippi|+20|6|26|58%|38%|4%|3.8\n", - "|West Virginia|+20|5|31|58%|38%|4%|3.6\n", - "|Tennessee|+18|11|42|57%|39%|4%|3.1\n", - "|Idaho|+17|4|46|57%|40%|3%|1.8\n", - "|Kentucky|+16|8|54|56%|40%|4%|1.1\n", - "|Oklahoma|+11|7|61|54%|43%|3%|3.4\n", - "|Arkansas|+10|6|67|53%|43%|4%|2.8\n", - "|South Carolina|+10|9|76|53%|43%|4%|2.2\n", - "|South Dakota|+10|3|79|53%|43%|4%|4.3\n", - "|**North Dakota**|+6|3|82|51%|45%|4%|2.8\n", - "|**Utah**|+5|6|88|51%|46%|3%|3.6\n", - "|**Indiana**|+4|11|99|50%|46%|4%|2.0\n", - "|**Missouri**|+4|10|109|50%|46%|4%|3.0\n", - "|**Nebraska**|+4|5|114|50%|46%|4%|2.7\n", - "|**Texas**|+4|38|152|50%|46%|4%|2.6\n", - "|**Georgia**|+3|16|168|49%|46%|5%|3.2\n", - "|**Montana**|+3|3|171|50%|47%|3%|3.4\n", - "|**Kansas**|+2|6|177|49%|47%|4%|2.9\n", - "|**Alaska**|+1|3|180|48%|47%|5%|5.1\n", - "|**Florida**|-2|29|209|47%|49%|4%|3.3\n", - "|**North Carolina**|-2|15|224|47%|49%|4%|2.2\n", - "|**Ohio**|-4|18|242|46%|50%|4%|2.4\n", - "|**Nevada**|-6|6|248|45%|51%|4%|3.1\n", - "|Virginia|-6|13|261|45%|51%|4%|1.8\n", - "|Pennsylvania|-7|20|281|45%|52%|3%|1.6\n", - "|**Arizona**|-8|11|292|44%|52%|4%|3.7\n", - "|Iowa|-8|6|298|44%|52%|4%|2.6\n", - "|Michigan|-10|16|314|43%|53%|4%|2.2\n", - "|New Mexico|-12|5|319|42%|54%|4%|2.9\n", - "|Colorado|-13|9|328|42%|55%|3%|2.4\n", - "|Minnesota|-13|10|338|42%|55%|3%|2.2\n", - "|Wisconsin|-13|10|348|42%|55%|3%|2.4\n", - "|Delaware|-15|3|351|41%|56%|3%|2.0\n", - "|Maine|-15|4|355|41%|56%|3%|4.0\n", - "|New Jersey|-17|14|369|40%|57%|3%|2.4\n", - "|New Hampshire|-19|4|373|39%|58%|3%|3.6\n", - "|Illinois|-22|20|393|37%|59%|4%|1.2\n", - "|Oregon|-22|7|400|37%|59%|4%|2.0\n", - "|Rhode Island|-22|4|404|37%|59%|4%|2.8\n", - "|Connecticut|-23|7|411|37%|60%|3%|3.8\n", - "|New York|-24|29|440|36%|60%|4%|1.8\n", - "|Washington|-26|12|452|35%|61%|4%|2.1\n", - "|Massachusetts|-28|11|463|34%|62%|4%|2.2\n", - "|California|-29|55|518|34%|63%|3%|3.1\n", - "|Maryland|-30|10|528|33%|63%|4%|3.5\n", - "|Hawaii|-34|4|532|31%|65%|4%|4.2\n", - "|Vermont|-37|3|535|30%|67%|3%|4.8\n", - "|District of Columbia|-60|3|538|18%|78%|4%|3.1" + "|State|Net|Move|EV|Total|+|-|?|𝝈|\n", + "|-----|---|----|--|-----|-|-|-|-|\n", + "|Wyoming|+28%|8%|3|3|62%|34%|4%|3.5%|\n", + "|Alabama|+26%|8%|9|12|61%|35%|4%|3.4%|\n", + "|Louisiana|+20%|8%|8|20|58%|38%|4%|3.7%|\n", + "|Mississippi|+20%|8%|6|26|58%|38%|4%|3.8%|\n", + "|West Virginia|+20%|8%|5|31|58%|38%|4%|3.6%|\n", + "|Tennessee|+18%|7%|11|42|57%|39%|4%|3.1%|\n", + "|Idaho|+17%|4%|4|46|57%|40%|3%|1.8%|\n", + "|Kentucky|+16%|3%|8|54|56%|40%|4%|1.1%|\n", + "|Oklahoma|+11%|7%|7|61|54%|43%|3%|3.4%|\n", + "|Arkansas|+10%|6%|6|67|53%|43%|4%|2.8%|\n", + "|South Carolina|+10%|5%|9|76|53%|43%|4%|2.2%|\n", + "|South Dakota|+10%|9%|3|79|53%|43%|4%|4.3%|\n", + "|**North Dakota**|**+6%**|**6%**|3|82|51%|45%|4%|2.8%|\n", + "|**Utah**|**+5%**|**8%**|6|88|51%|46%|3%|3.6%|\n", + "|**Indiana**|**+4%**|**5%**|11|99|50%|46%|4%|2.0%|\n", + "|**Missouri**|**+4%**|**7%**|10|109|50%|46%|4%|3.0%|\n", + "|**Nebraska**|**+4%**|**6%**|5|114|50%|46%|4%|2.7%|\n", + "|**Texas**|**+4%**|**6%**|38|152|50%|46%|4%|2.6%|\n", + "|**Georgia**|**+3%**|**7%**|16|168|49%|46%|5%|3.2%|\n", + "|**Montana**|**+3%**|**7%**|3|171|50%|47%|3%|3.4%|\n", + "|**Kansas**|**+2%**|**7%**|6|177|49%|47%|4%|2.9%|\n", + "|**Alaska**|**+1%**|**11%**|3|180|48%|47%|5%|5.1%|\n", + "|**Florida**|**-2%**|**7%**|29|209|47%|49%|4%|3.3%|\n", + "|**North Carolina**|**-2%**|**5%**|15|224|47%|49%|4%|2.2%|\n", + "|**Ohio**|**-4%**|**6%**|18|242|46%|50%|4%|2.4%|\n", + "|**Nevada**|**-6%**|**7%**|6|248|45%|51%|4%|3.1%|\n", + "|Virginia|-6%|4%|13|261|45%|51%|4%|1.8%|\n", + "|Pennsylvania|-7%|4%|20|281|45%|52%|3%|1.6%|\n", + "|**Arizona**|**-8%**|**8%**|11|292|44%|52%|4%|3.7%|\n", + "|Iowa|-8%|6%|6|298|44%|52%|4%|2.6%|\n", + "|Michigan|-10%|5%|16|314|43%|53%|4%|2.2%|\n", + "|New Mexico|-12%|7%|5|319|42%|54%|4%|2.9%|\n", + "|Colorado|-13%|5%|9|328|42%|55%|3%|2.4%|\n", + "|Minnesota|-13%|5%|10|338|42%|55%|3%|2.2%|\n", + "|Wisconsin|-13%|5%|10|348|42%|55%|3%|2.4%|\n", + "|Delaware|-15%|5%|3|351|41%|56%|3%|2.0%|\n", + "|Maine|-15%|9%|4|355|41%|56%|3%|4.0%|\n", + "|New Jersey|-17%|5%|14|369|40%|57%|3%|2.4%|\n", + "|New Hampshire|-19%|8%|4|373|39%|58%|3%|3.6%|\n", + "|Illinois|-22%|3%|20|393|37%|59%|4%|1.2%|\n", + "|Oregon|-22%|5%|7|400|37%|59%|4%|2.0%|\n", + "|Rhode Island|-22%|6%|4|404|37%|59%|4%|2.8%|\n", + "|Connecticut|-23%|8%|7|411|37%|60%|3%|3.8%|\n", + "|New York|-24%|4%|29|440|36%|60%|4%|1.8%|\n", + "|Washington|-26%|5%|12|452|35%|61%|4%|2.1%|\n", + "|Massachusetts|-28%|5%|11|463|34%|62%|4%|2.2%|\n", + "|California|-29%|7%|55|518|34%|63%|3%|3.1%|\n", + "|Maryland|-30%|8%|10|528|33%|63%|4%|3.5%|\n", + "|Hawaii|-34%|9%|4|532|31%|65%|4%|4.2%|\n", + "|Vermont|-37%|10%|3|535|30%|67%|3%|4.8%|\n", + "|District of Columbia|-60%|7%|3|538|18%|78%|4%|3.1%|" ], "text/plain": [ "" @@ -420,16 +468,13 @@ "source": [ "def by_state(states, d=now):\n", " total = 0\n", - " yield '|State|Net|EV|Total|+|-|?|𝝈|'\n", - " yield '|-----|---|--|-----|-|-|-|-|'\n", + " yield '|State|Net|Move|EV|Total|+|-|?|𝝈|'\n", + " yield '|-----|---|----|--|-----|-|-|-|-|'\n", " for s in sorted(states, key=net, reverse=True):\n", " total += s.ev\n", - " std = stdev(net(s, d) for d in dates[-12:])\n", - " und = f'{undecided(s, now)}%'\n", - " b = '**' if swing(s, std) else ''\n", - " yield f'|{b}{s.name}{b}|{net(s):+d}|{s.ev}|{total}|{s.apps[d]}%|{s.diss[d]}%|{und}|{std:3.1f}'\n", - " \n", - "def swing(s, std): return abs(net(s)) < 2 * std + undecided(s, now) / 5\n", + " b = '**' if is_swing(s) else ''\n", + " yield (f'|{b}{s.name}{b}|{b}{net(s):+d}%{b}|{b}{movement(s):.0f}%{b}|{s.ev}|{total}'\n", + " f'|{s.apps[d]}%|{s.diss[d]}%|{undecided(s, now)}%|{𝝈(s):3.1f}%|')\n", "\n", "md(by_state(states))" ]