Add files via upload
This commit is contained in:
Peter Norvig authored and 
GitHub committed
1 parent
95aec859f7
commit
81ef969e05
3 files changed
+36
-34
No files matched your search
+15
-15
@@ -8,7 +8,7 @@
|
|||||||
"\n",
|
"\n",
|
||||||
"# Predicting Presidential Electoral Votes from Approval Polls\n",
|
"# Predicting Presidential Electoral Votes from Approval Polls\n",
|
||||||
"\n",
|
"\n",
|
||||||
"Various sites aggregate opinion polls on presidential job performance. The polls are broken out state-by-state, month-by-month at [Mourning Consult](https://morningconsult.com/tracking-trump/). Trump's national **net approval** (percent approving minus percent disapproving) is currently -10% on [RealClearPolitics](https://www.realclearpolitics.com/epolls/other/president_trump_job_approval-6179.html), -13% on [538](https://projects.fivethirtyeight.com/trump-approval-ratings/), and -16% on [Gallup](https://news.gallup.com/poll/203198/presidential-approval-ratings-donald-trump.aspx). There are four big caveats in jumping from these numbers to conclusions about the election:\n",
|
"Various sites aggregate opinion polls on presidential job performance. The polls are broken out state-by-state, month-by-month at [Mourning Consult](https://morningconsult.com/tracking-trump/). Trump's national **net approval** (percent approving minus percent disapproving) is currently -7% on [RealClearPolitics](https://www.realclearpolitics.com/epolls/other/president_trump_job_approval-6179.html), -8% on [538](https://projects.fivethirtyeight.com/trump-approval-ratings/), and -9% on [Gallup](https://news.gallup.com/poll/203198/presidential-approval-ratings-donald-trump.aspx). There are four big caveats in jumping from these numbers to conclusions about the election:\n",
|
||||||
"\n",
|
"\n",
|
||||||
"1. Today is not election day 2020. \n",
|
"1. Today is not election day 2020. \n",
|
||||||
"\n",
|
"\n",
|
||||||
@@ -25,11 +25,11 @@
|
|||||||
"# TL;DR: In this model, Trump gets 104 to 235 Electoral Votes\n",
|
"# TL;DR: In this model, Trump gets 104 to 235 Electoral Votes\n",
|
||||||
"\n",
|
"\n",
|
||||||
"There is a lot of uncertainty, so I won't try to give an exact single number. Rather, I will say that, as of December 2019, under the assumption that Trump wins the electoral votes of states where he currently has positive net approval and loses the states where he has negative net approval:\n",
|
"There is a lot of uncertainty, so I won't try to give an exact single number. Rather, I will say that, as of December 2019, under the assumption that Trump wins the electoral votes of states where he currently has positive net approval and loses the states where he has negative net approval:\n",
|
||||||
"- His mean expected number of electoral votes is 149.5.\n",
|
"- His mean expected number of electoral votes is **149.5**.\n",
|
||||||
"- He has net approval of zero in two big states, Texas and North Carolina, so depending on how they go, the total would be 123 to 176.\n",
|
"- He is tied (net approval zero) in Texas and North Carolina, so depending on how they go, the total would be **123 to 176**.\n",
|
||||||
"- If you allow for a swing of up to 3.5% across the board, then the totals range anywere from 104 to 235.\n",
|
"- If you allow for a swing of up to 3.5% across the board, then the totals range anywere from **104 to 235**.\n",
|
||||||
"- Recall that you need 270 to win.\n",
|
"- Recall that you need **270** to win.\n",
|
||||||
"- To reach 270, he'd need to take all the states where he is ahead or tied, plus six states where he is behind, the toughest being Pennsylvania, where he is 5% behind.\n"
|
"- To reach 270, he'd need to take all the states where he is ahead or tied, plus six states where he is behind by up to **5%**.\n"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
@@ -60,16 +60,16 @@
|
|||||||
"source": [
|
"source": [
|
||||||
"%run ElectoralVotesCode.ipynb\n",
|
"%run ElectoralVotesCode.ipynb\n",
|
||||||
"\n",
|
"\n",
|
||||||
"EV(states)"
|
"EV(states) # Number of electoral vote Trump can expect according to this model"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "markdown",
|
"cell_type": "markdown",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"source": [
|
"source": [
|
||||||
"The `.5` indicates a tie (net zero approval) in one or more states. \n",
|
"The `.5` indicates a tie in one or more states. \n",
|
||||||
"\n",
|
"\n",
|
||||||
"To get a better feel for the situation, imagine a swing in voter approval of just 0.5% in either direction, for every state across the board. That would break ties, and we'd get a range of electoral votes. If we allowed larger swings in approval, we'd get a larger range. Here's how it goes:"
|
"Imagine a swing in voter approval of just 0.5% in either direction, for every state across the board. That would break ties, and we'd get a range of electoral votes: fewer in one direction, more in the other. If we allowed larger swings in approval, we'd get an even larger range. Here's how it goes:"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
@@ -89,7 +89,9 @@
|
|||||||
"±4.5% 104 to 253\n",
|
"±4.5% 104 to 253\n",
|
||||||
"±5.5% 89 to 286\n",
|
"±5.5% 89 to 286\n",
|
||||||
"±6.5% 79 to 290\n",
|
"±6.5% 79 to 290\n",
|
||||||
"±7.5% 67 to 290\n"
|
"±7.5% 67 to 290\n",
|
||||||
|
"±8.5% 60 to 290\n",
|
||||||
|
"±9.5% 52 to 306\n"
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
@@ -110,9 +112,7 @@
|
|||||||
"source": [
|
"source": [
|
||||||
"# Details for policy wonks\n",
|
"# Details for policy wonks\n",
|
||||||
"\n",
|
"\n",
|
||||||
"The following plot shows, for each month in office, the expected number of electoral votes, with error bars indicating a 3% approval swing in either direction (that is: the blue diamond is the total number of electoral votes for states where he has net positive approval; the top and bottom of the grey bars give the total if he were to gain or lose 3% in every state. 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 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. He's been below 200 for a year.\n",
|
"The following plot shows, for each month in office, the expected number of electoral votes, with error bars indicating a 3% approval swing in either direction (that is: the blue diamond is the total number of electoral votes for states where he has net positive approval; the top and bottom of the grey bars give the total if he were to gain or lose 3% in every state. 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 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. He's been below 200 for a year."
|
||||||
"\n",
|
|
||||||
"The most recent month (month 35) is December 2019."
|
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
@@ -122,7 +122,7 @@
|
|||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"image/png": "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 truncated
|
"image/png": "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 truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<Figure size 720x504 with 1 Axes>"
|
"<Figure size 720x504 with 1 Axes>"
|
||||||
]
|
]
|
||||||
@@ -265,7 +265,7 @@
|
|||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"image/png": "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 truncated
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAArcAAAHzCAYAAAA6vDWQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3Xd8VFX++P/XJKRBylACCZAQOggoCEgHEUVFAVlFVoqIq6xRsazlI7sK+nV/sqLu2rGtqIu4NlTQVRYB6V0UFgSyQmgppM2ElJnJlN8f2ZnkTsnMZJLcO+H9fDx4PLzl3Ps+99xr3rk59xydwWBwIIQQQgghRDMQoXYAQgghhBBCNBRJboUQQgghRLMhya0QQgghhGg2JLkVQgghhBDNhiS3QgghhBCi2ZDkVgghhBBCNBuS3AohhBBCiGZDktsmlJWVdUGX10IMUge5Bg1RXgsxSB3kGjREeS3EIHWQa9DQJLkVQgghhBDNhiS3QgghhBCi2ZDkVgghhBBCNBuS3AL5+flYrVYsFgsFBQUAGI1GysrKAMjLy8Nms2E2myksLATAYDBQXl4OQG5uLna7HZPJRFFREQAlJSVUVFQAkJOT4zpXSUkJAEVFRZhMJux2O7m5uQCUl5djMBgAKCwsxGw2Y7PZyMvLc5U3Go0AFBQUYLFYsFqt5OfnA1BaWkppaanPOgEh1QnwqFNFRUVQdQIUdSorKwuqTkBI7QT4bSd/dXIe11c7+auTs16+2slfnZzX1Vc7+auT8xoEc++51wkI6t5zrxMQ8vMU7L0XbDtp/XlyXsNwf54au520/jw526C+dQIa5OeT1ttJ688TEFKdgAuinRqyTnXRGQwGR517iAaTlZVFz549L9jyWohB6iDXoCHKayEGqYNcg/qWLy8vx2q1upZLS0tJTEysdwxql9dCDGqX10IMDVEHd61ataJFixZBlwu+hBBCCCHCktlsBiApKcm1LiYmhtjY2HofU+3yWohB7fJaiKEh6lCbw+HAYDCQkJAQdIIr3RKEEEKIC4TJZKJly5ZqhyGEXzqdDr1er+i6EShJboUQQogLiE6nUzsEIQJS33tVklshhBBCCNFsSHIrhBBCCCGaDUluhRBCCCFEsyHJrRBCCCGEaDbCKrndtm0bv/3tb+nbty96vZ4PP/xQsd3hcLBkyRL69OlDSkoK1113Hb/88otK0SptzjUzeU8sm3PNaocihBBChI3MzEz0ej1Lly5VrN+yZQt6vV4xkUMgx5oxY0ZDhyg0JqyS2/Lyci666CL+8pe/EBcX57H9pZde4rXXXuPZZ59lw4YNJCcnM23aNM6fP69CtDX+386VTP10IHnnLmbqpwP5fztXqhqPEEIIEU5iY2N55ZVXFDPLhROLxaJ2CBeUsJrEYeLEiUycOBGAu+++W7HN4XCwbNkyHnjgAaZOnQrAsmXL6NmzJ5999hnz5s1r8nihOrH9684/AKbqOO25/1uGRcNnqhKTEEII4dRhRUpI5ZP876JgvMPgfyc3Y8aM4ezZsyxdutTjDW5tR44cYdGiRWzfvp3Y2FjGjRvHM888Q4cOHViyZAkfffQRAHq9HoA1a9YwZswYxTFOnjzJJZdc4nHstLQ0Dh486PU8o0eP5tlnn6VDhw5A9Rvi4uJiRowYwVtvvYXFYuG///0vBoOBxx57jG+//Raz2cywYcP4y1/+QteuXYO+JsK3sHpzW5eTJ0+Sn5/PFVdc4VoXFxfHyJEj2bVrlyoxbc4187fdT+NMbGuY+Nvup6WLghBCCBGAiIgInnzySZYvX86JEye87pOXl8ekSZPo27cv69ev58svv6SsrIyZM2dit9tZsGAB06ZN4/LLL+fo0aMcPXqUYcOGeRync+fOru1Hjx5l3759pKWlMXr0aJ/nKS8vd53Hadu2bRw6dIjPPvuMr776CqhOevft28fKlStZv349cXFx3HTTTVRWVjbCVbtw6QwGg0PtIOqjU6dOLF26lFmzZgGwa9curr76ag4ePEhaWpprv3vuuYfc3FxWrVrl9ThZWVmNFuPkPbHknRvgY6uO9skH+OYy98RXCCGEaByxsbEkJycr1oX65jZY+bPzgtr/vvvuo7i4mBUrVvCb3/yG5ORk3nzzTbZt28aNN97IoUOHaNu2Lc8++yx79uzhs88+c5U1GAz06dOHf/3rX1x66aWKYwXCbrdz6623UlJSwueff05sbGzA5/n+++/Zv38/MTExABw/fpyRI0fyxRdfMGLECABKS0sZPHgwTz75pCufEUoFBQWYTJ65Us+ePX2WCatuCY2hrosTqrfizUz5pD04znlu1KVg1UVSmJjOiA4xAR0vKysrpHjVLq+FGKQOcg0aorwWYpA6yDWoT3mj0UhsbGy9z9cQ3M9vMpnqjCkyMpLIyEhiY2N5+umnueqqq3jwwQeJjo5WHO/QoUPs3LmT7t27exwjJyeHkSNHKo4VSAxPPPEER44cYcOGDa6uDN7O43A4PM5z0UUXkZRU02kjOzubiIgIRo8eTVRUlCv2fv36cezYsZDbxd911Hp5XxITExUvLQPRbJJbZz+XgoICxUUoKCigffv2qsQ0NjWGB4Y+wYu7F7htiYXYByg2O7j+20IWD05kQf94mRJRCCFEk8ufnafJpMabwYMHM2XKFBYtWsQjjzyi2Ga325k4cSJ//vOfPcq5v60OxMqVK1m+fDnffvutIo/wdh6z2UxMTIziPK1atQr6nKJhNJs+t126dKFDhw5s3LjRtc5kMrFjxw6vfWqaypMj55AY3Ua5MuZuiL4eAJsDFu0tZeb6Ygxmu5cjCCGEEMJp0aJF7Nixg/Xr1yvWX3LJJRw5coS0tDS6deum+JeQkABAdHQ0NpvN7zl27drFQw89xJtvvsmAAcruhd7O07VrV8V5vOnduzd2u53du3e71pWWlnL48GF69+4dzCUQfoRVcltWVsaBAwc4cOAAdrudM2fOcODAAU6fPo1OpyMzM5OXXnqJ1atXc/jwYe6++25atWrFTTfdpGrcgzooH4wp3ft77PPtaRNjV59jf6EMFyKEEEL40q1bN2677TbeeOMNxfo77riD0tJS5s2bx969e8nOzuaHH37g/vvvdw0Jmp6ezi+//EJWVhZFRUVUVVV5HD8/P5/Zs2fzu9/9jiFDhpCfn09+fr5rGDJv59m8ebPiPN50796dSZMm8eCDD7J9+3YOHTrE/PnzSUhIYNq0aQ14hURYJbf79+9n7NixjB07lsrKSpYsWcLYsWN55plnALj//vvJzMzkkUceYfz48eTl5bFq1ao6f5NqCl2SuiiWRyaf459XtkEfreyGcKrMxtXfFPDOL2Wu/jtCCCGEUHr00Udp0ULZszI1NZW1a9cSERHBjTfeyPDhw3n44YeJjo52fdQ1d+5cevXqxfjx4+nevTs7d+70OPaxY8coKCjg1VdfpXfv3q5/48eP93mehQsXKs7jy+uvv86ll17KLbfcwoQJE6isrOSzzz7zOna/qL+w6nM7ZswYDAbf4+PpdDoWLlzIwoULmzAq/zISMxTL2cZs7hoUx6YpUcz7oZgfC2t+c7TY4eGdRnbkW3hxlJ6EqLD6/UMIIYRoUMuWLfNYl5yczJkzZ1zLzq/pu3fvzgcffODzWO3ateOLL76o83z+cg1v53Hvd+wtZqgeX9f9jXPt+EXDkMypCWQkZSiWT5aeBKBLQgu+m5TM/L6enc4/P1HJFWsKOFTs+ScTIYQQQgjhnSS3TcC9W8JJ40nXf0dH6lg6XM/yy1uTEKXsppBltHLl1wV8mFXO5lwzk/fE1nvih1DLCyGEEEKEA0lum4D7m9tsY7ZHn9ppXVuycXIy/Vore4pU2hzcs9XAtLWF5JkjmLGuKOgEdXOumRnriupdXgghhBAiXIRVn9tw1Sa2DQnRCZy3VH9FWWGtoLCykOSWynH3eiRF8f317Xlkp4EVWRWKbbb/5cKVNgc3fFdIn9YtSIr2/7uJ0WLnSIkV5yBjlTYHM9YV8fFVbRmbGtjkEUIIIYQQ4UKS2yag0+lIT0znUOEh17psY7ZHcgsQ10LHq6NbM6JDNA9uN2DxMvStHThcYq13PJLgCiGEEKK5km4JVI9pZ7VasVgsFBQUANVTFJaVlQGQl5eHzWbDbDa7xrkzGAyUl5cDkJubi91ux2QyUVRUBEBJSQkVFdVvX3Nycjy6Jvzn7H8wmUzY7XZyc3MBKC8vd32heXXrSlpHN96MZZU2B3dvKQ6qToCiTs7lkpISAIqKiuqsk/M8NpuNvLzqucXLysowGo1A9WxyFosFq9VKfn4+UD3AdWlpKYBrXSjtBNTZToHUyXlcZ53MZnNQdXLWy1mnYO8953X11U7+6uS8BnW1k786AXW2k786ASE9T4G0U7B18nfvudfJeQxf7dTYz5PzGob789TY7aT158nZBvWtExD0vWe1Wl0TGVgsFtcxrFara7vdbsfhcHjdXlVV5druHCc2kO3Oc1ZVVeFwOLDb7a7ttWPyt91isbi2O8/pPEd96wSEVCcgpDrVLl875ubWTvWpk9Vq9fo81UVnMBhkQNUm8KfNf+K1H19zLT8+8nEevuzhOstU95UtpNL/ZCpBi4qAzye2C+rNrczjro0Y1C6vhRjULq+FGKQOcg3qU95oNJKUlKRYF+r0uWqX10IMapfXQgyNNQ2zt3vWH+mW0ES6JCpHTMg2ZvstMzY1ho+vaseMdUVU2mp+B4mJgCeHJHJJ22i/x/i5yMKTe0txn9lXB6S1igwkdCGEEEKIsCHJbRPxNmJCIKoT3LauBDcuUhdUX9mRKTH0axPNzesKMdV6A2yxw4JtJay+ph0Rusbr/iCEEEII0ZSkz20T8ZjIodZYt/44E9yUGHu9PgIbmxrDJ1e1I6mFsgfK1jwLy4+WB3UsIYQQ4kK1ZcsW9Hq9ou+80B5JbptIemK6Yvls2VkLine truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<Figure size 720x504 with 1 Axes>"
|
"<Figure size 720x504 with 1 Axes>"
|
||||||
]
|
]
|
||||||
|
|||||||
@@ -57,7 +57,9 @@
|
|||||||
"cell_type": "markdown",
|
"cell_type": "markdown",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"source": [
|
"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."
|
"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). \n",
|
||||||
|
"\n",
|
||||||
|
"The variable `net_usa` has the [country-wide net presidential approval](https://projects.fivethirtyeight.com/trump-approval-ratings/) by month."
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
@@ -67,7 +69,7 @@
|
|||||||
"outputs": [],
|
"outputs": [],
|
||||||
"source": [
|
"source": [
|
||||||
"# From https://github.com/fivethirtyeight/data/tree/master/partisan+lean\n",
|
"# From https://github.com/fivethirtyeight/data/tree/master/partisan+lean\n",
|
||||||
"# a dict of {\"state name\": (electoral_votes, partisan_lean)}\n",
|
"# A dict of {\"state name\": (electoral_votes, partisan_lean)}\n",
|
||||||
"state_data = { \n",
|
"state_data = { \n",
|
||||||
" \"Alabama\": (9, +27), \"Alaska\": (3, +15), \"Arizona\": (11, +9), \n",
|
" \"Alabama\": (9, +27), \"Alaska\": (3, +15), \"Arizona\": (11, +9), \n",
|
||||||
" \"Arkansas\": (6, +24), \"California\": (55, -24), \"Colorado\": (9, -1), \n",
|
" \"Arkansas\": (6, +24), \"California\": (55, -24), \"Colorado\": (9, -1), \n",
|
||||||
@@ -88,10 +90,10 @@
|
|||||||
" \"West Virginia\": (5, +30), \"Wisconsin\": (10, +1), \"Wyoming\": (3, +47)}\n",
|
" \"West Virginia\": (5, +30), \"Wisconsin\": (10, +1), \"Wyoming\": (3, +47)}\n",
|
||||||
"\n",
|
"\n",
|
||||||
"# From https://projects.fivethirtyeight.com/trump-approval-ratings/\n",
|
"# From https://projects.fivethirtyeight.com/trump-approval-ratings/\n",
|
||||||
"# A dict of {'date': country-wide-net-approval}\n",
|
"# A dict of {'date': country-wide-net-approval}, taken from 1st of month.\n",
|
||||||
"net_usa = {\n",
|
"net_usa = {\n",
|
||||||
" 'Jan-17': 10, 'Jan-18': -18, 'Jan-19': -12, \n",
|
" 'Jan-17': 10, 'Jan-18': -18, 'Jan-19': -12, 'Jan-20': -11,\n",
|
||||||
" 'Feb-17': 0, 'Feb-18': -15, 'Feb-19': -16, \n",
|
" 'Feb-17': 0, 'Feb-18': -15, 'Feb-19': -16, 'Feb-20': -10,\n",
|
||||||
" 'Mar-17': -6, 'Mar-18': -14, 'Mar-19': -11, \n",
|
" 'Mar-17': -6, 'Mar-18': -14, 'Mar-19': -11, \n",
|
||||||
" 'Apr-17': -13, 'Apr-18': -13, 'Apr-19': -11, \n",
|
" 'Apr-17': -13, 'Apr-18': -13, 'Apr-19': -11, \n",
|
||||||
" 'May-17': -11, 'May-18': -12, 'May-19': -12, \n",
|
" 'May-17': -11, 'May-18': -12, 'May-19': -12, \n",
|
||||||
@@ -168,15 +170,16 @@
|
|||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [],
|
"outputs": [],
|
||||||
"source": [
|
"source": [
|
||||||
"def labels(xlab, ylab): plt.xlabel(xlab); plt.ylabel(ylab); 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",
|
"def header(head) -> str: return head + '\\n' + '-'.join('|' * head.count('|'))\n",
|
||||||
"\n",
|
"\n",
|
||||||
"def markdown(fn) -> callable: return lambda *args: display(Markdown('\\n'.join(fn(*args))))\n",
|
"def markdown(fn) -> callable: return lambda *args: display(Markdown('\\n'.join(fn(*args))))\n",
|
||||||
"\n",
|
"\n",
|
||||||
"def parp(state, date=now) -> int: return net(state, date) - state.lean "
|
"def parp(state, date=now) -> int: return net(state, date) - state.lean\n",
|
||||||
|
"\n",
|
||||||
|
"def grid(dates, xlab, ylab): \n",
|
||||||
|
" plt.minorticks_on(); plt.grid(which='minor', axis='y', ls=':', alpha=0.7)\n",
|
||||||
|
" plt.xticks(range(len(dates)), dates, rotation=90)\n",
|
||||||
|
" plt.xlabel(xlab); plt.ylabel(ylab); plt.legend()"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
@@ -192,11 +195,11 @@
|
|||||||
" N = len(dates)\n",
|
" N = len(dates)\n",
|
||||||
" err = [[EV(states, date) - EV(states, date, -swing) for date in 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",
|
" [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.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",
|
" 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",
|
" yerr=err, ecolor='grey', capsize=7, label='Trump EVs ±3% swing', lw=2)\n",
|
||||||
" labels('Months into term', 'Electoral Votes')"
|
" grid(dates, 'Date', 'Electoral Vptes')\n",
|
||||||
|
" #labels('Date', 'Electoral Votes')"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
@@ -210,11 +213,10 @@
|
|||||||
" plt.rcParams[\"figure.figsize\"] = [10, 7]\n",
|
" plt.rcParams[\"figure.figsize\"] = [10, 7]\n",
|
||||||
" plt.style.use('fivethirtyeight')\n",
|
" plt.style.use('fivethirtyeight')\n",
|
||||||
" N = len(dates)\n",
|
" N = len(dates)\n",
|
||||||
" grid()\n",
|
|
||||||
" plt.plot(range(N), [0] * N, label='Net zero', color='darkorange')\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), [-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",
|
" plt.plot(range(N), [net_usa[date] for date in dates], 'go-', label='Country-wide Net')\n",
|
||||||
" labels('Months into term', 'Net popularity')"
|
" grid(dates, 'Date', 'Net popularity')"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
@@ -223,7 +225,7 @@
|
|||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [],
|
"outputs": [],
|
||||||
"source": [
|
"source": [
|
||||||
"def show_swings(swings=range(8)):\n",
|
"def show_swings(swings=range(10)):\n",
|
||||||
" print('Swing EV Range')\n",
|
" print('Swing EV Range')\n",
|
||||||
" for swing in swings:\n",
|
" for swing in swings:\n",
|
||||||
" s = swing + 0.5\n",
|
" s = swing + 0.5\n",
|
||||||
@@ -273,7 +275,7 @@
|
|||||||
"cell_type": "markdown",
|
"cell_type": "markdown",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"source": [
|
"source": [
|
||||||
"**Tests** (I really should have more)."
|
"**Tests** (I really should have more):"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
|
|||||||
+3
-3
@@ -1250,7 +1250,7 @@ img.emoji {
|
|||||||
height="0" width="0" style="display:none;visibility:hidden"></iframe></noscript>
|
height="0" width="0" style="display:none;visibility:hidden"></iframe></noscript>
|
||||||
<!-- End Google Tag Manager (noscript) -->
|
<!-- End Google Tag Manager (noscript) -->
|
||||||
|
|
||||||
<a rel="nofollow" style="display:none;" href="https://morningconsult.com/?blackhole=46439e9721" title="Blackhole for Bad Bots">Do NOT follow this link or you will be banned from the site!</a>
|
<a rel="nofollow" style="display:none;" href="https://morningconsult.com/?blackhole=0422d4fb89" title="Blackhole for Bad Bots">Do NOT follow this link or you will be banned from the site!</a>
|
||||||
<script type='text/javascript'>
|
<script type='text/javascript'>
|
||||||
/* <![CDATA[ */
|
/* <![CDATA[ */
|
||||||
var mc_state_trend = [["Demographic","Jan-17","","Feb-17","","Mar-17","","Apr-17","","May-17","","Jun-17","","Jul-17","","Aug-17","","Sep-17","","Oct-17","","Nov-17","","Dec-17","","Jan-18","","Feb-18","","Mar-18","","Apr-18","","May-18","","Jun-18","","Jul-18","","Aug-18","","Sep-18","","Oct-18","","Nov-18","","Dec-18","","Jan-19","","Feb-19","","Mar-19","","Apr-19","","May-19","","Jun-19","","Jul-19","","Aug-19","","Sep-19","","Oct-19","","Nov-19","","Dec-19",""],["Alabama","62","26","65","29","62","32","63","33","56","38","54","41","58","38","56","38","59","36","60","35","60","35","60","35","63","33","61","35","63","33","62","34","63","33","63","33","63","33","63","33","62","34","62","34","62","34","58","37","58","38","61","35","61","35","61","35","61","34","60","36","61","35","60","36","59","37","59","38","59","37","59","37"],["Alaska","55","31","60","35","65","29","64","32","60","35","54","38","54","41","48","46","52","43","54","42","49","46","51","43","48","47","55","42","52","43","52","44","51","44","53","43","55","40","52","43","48","44","54","41","53","41","49","45","48","47","49","46","47","47","48","47","47","47","48","45","46","48","48","47","48","47","47","48","53","43","47","49"],["Arizona","55","35","54","40","53","43","54","41","51","45","48","47","47","48","46","49","44","51","47","49","44","50","47","48","46","49","50","47","48","49","48","48","49","47","48","48","48","48","47","49","47","50","49","47","47","49","44","51","44","52","45","51","45","51","44","52","45","51","45","52","45","52","46","50","47","50","46","50","46","50","47","50"],["Arkansas","59","29","59","34","60","35","56","39","54","41","53","41","55","39","54","40","54","40","54","41","51","43","53","42","53","40","52","40","55","40","54","42","54","41","55","41","55","41","56","40","54","42","55","40","55","41","53","42","53","43","51","44","52","44","53","43","52","44","51","45","53","44","53","44","50","46","51","45","52","44","53","43"],["California","42","48","43","51","45","50","44","52","41","54","40","54","38","55","33","56","36","58","34","61","35","60","35","60","36","59","34","61","34","62","35","61","37","58","37","59","36","59","35","61","35","61","36","60","35","61","33","62","33","63","33","63","34","62","34","63","34","62","33","62","33","63","33","63","34","62","32","64","34","62","34","62"],["Colorado","45","44","46","47","52","43","47","49","45","50","42","52","41","53","40","55","41","54","40","55","40","56","42","54","41","55","40","56","41","56","40","56","40","56","42","55","43","53","42","54","42","55","42","54","43","53","40","56","39","57","42","54","42","55","42","55","41","55","42","55","43","55","39","57","41","56","40","58","41","56","39","57"],["Connecticut","47","42","43","52","46","48","46","50","40","55","38","58","38","57","34","62","36","59","38","58","39","58","37","58","39","58","40","58","38","59","38","58","40","56","42","54","40","56","40","56","39","56","40","56","38","58","38","58","36","60","37","59","37","60","37","60","36","60","37","60","37","59","37","61","37","60","36","61","37","59","38","59"],["Delaware","49","41","48","46","53","42","49","47","45","51","41","54","45","51","40","54","38","57","40","56","38","57","40","56","41","55","43","54","39","57","41","55","40","57","42","54","41","56","39","57","39","57","41","55","42","55","40","56","41","56","42","55","42","55","41","56","41","56","44","53","42","55","41","56","43","54","37","60","39","57","41","56"],["District of Columbia","30","61","32","63","32","64","30","67","27","70","21","74","23","73","16","78","17","78","16","81","16","80","19","77","16","80","16","82","17","80","19","78","19","77","22","75","20","77","17","79","17","79","17","77","18","78","17","79","16","81","18","79","17","80","18","78","17","79","19","78","18","79","17","80","21","76","17","79","17","79","19","77"],["Florida","56","34","56","38","56","39","56","40","51","44","48","47","47","49","47","48","48","47","49","47","47","48","49","47","50","45","49","47","48","48","48","48","50","45","51","45","50","46","49","47","49","47","49","47","49","47","46","49","46","50","47","49","47","49","47","49","48","48","47","50","48","49","48","48","48","49","47","49","49","48","48","49"],["Georgia","53","35","54","39","55","40","51","45","50","45","48","47","49","47","46","48","47","47","50","46","48","47","47","47","51","44","50","46","50","45","49","46","51","44","50","45","50","46","50","45","50","45","49","46","49","46","46","49","47","49","47","48","50","45","49","46","48","47","48","48","49","47","48","48","49","48","46","49","46","49","47","49"],["Hawaii","38","51","40","54","43","52","39","57","34","61","32","63","31","65","30","63","31","64","31","65","32","64","33","63","30","66","32","65","33","64","33","64","35","61","37","60","37","60","37","60","32","64","33","63","38","58","34","62","34","63","36","61","34","63","31","65","32","65","37","60","38","60","35","62","33","63","32","64","31","66","34","62"],["Idaho","59","30","61","33","60","35","64",Line truncated
|
var mc_state_trend = [["Demographic","Jan-17","","Feb-17","","Mar-17","","Apr-17","","May-17","","Jun-17","","Jul-17","","Aug-17","","Sep-17","","Oct-17","","Nov-17","","Dec-17","","Jan-18","","Feb-18","","Mar-18","","Apr-18","","May-18","","Jun-18","","Jul-18","","Aug-18","","Sep-18","","Oct-18","","Nov-18","","Dec-18","","Jan-19","","Feb-19","","Mar-19","","Apr-19","","May-19","","Jun-19","","Jul-19","","Aug-19","","Sep-19","","Oct-19","","Nov-19","","Dec-19",""],["Alabama","62","26","65","29","62","32","63","33","56","38","54","41","58","38","56","38","59","36","60","35","60","35","60","35","63","33","61","35","63","33","62","34","63","33","63","33","63","33","63","33","62","34","62","34","62","34","58","37","58","38","61","35","61","35","61","35","61","34","60","36","61","35","60","36","59","37","59","38","59","37","59","37"],["Alaska","55","31","60","35","65","29","64","32","60","35","54","38","54","41","48","46","52","43","54","42","49","46","51","43","48","47","55","42","52","43","52","44","51","44","53","43","55","40","52","43","48","44","54","41","53","41","49","45","48","47","49","46","47","47","48","47","47","47","48","45","46","48","48","47","48","47","47","48","53","43","47","49"],["Arizona","55","35","54","40","53","43","54","41","51","45","48","47","47","48","46","49","44","51","47","49","44","50","47","48","46","49","50","47","48","49","48","48","49","47","48","48","48","48","47","49","47","50","49","47","47","49","44","51","44","52","45","51","45","51","44","52","45","51","45","52","45","52","46","50","47","50","46","50","46","50","47","50"],["Arkansas","59","29","59","34","60","35","56","39","54","41","53","41","55","39","54","40","54","40","54","41","51","43","53","42","53","40","52","40","55","40","54","42","54","41","55","41","55","41","56","40","54","42","55","40","55","41","53","42","53","43","51","44","52","44","53","43","52","44","51","45","53","44","53","44","50","46","51","45","52","44","53","43"],["California","42","48","43","51","45","50","44","52","41","54","40","54","38","55","33","56","36","58","34","61","35","60","35","60","36","59","34","61","34","62","35","61","37","58","37","59","36","59","35","61","35","61","36","60","35","61","33","62","33","63","33","63","34","62","34","63","34","62","33","62","33","63","33","63","34","62","32","64","34","62","34","62"],["Colorado","45","44","46","47","52","43","47","49","45","50","42","52","41","53","40","55","41","54","40","55","40","56","42","54","41","55","40","56","41","56","40","56","40","56","42","55","43","53","42","54","42","55","42","54","43","53","40","56","39","57","42","54","42","55","42","55","41","55","42","55","43","55","39","57","41","56","40","58","41","56","39","57"],["Connecticut","47","42","43","52","46","48","46","50","40","55","38","58","38","57","34","62","36","59","38","58","39","58","37","58","39","58","40","58","38","59","38","58","40","56","42","54","40","56","40","56","39","56","40","56","38","58","38","58","36","60","37","59","37","60","37","60","36","60","37","60","37","59","37","61","37","60","36","61","37","59","38","59"],["Delaware","49","41","48","46","53","42","49","47","45","51","41","54","45","51","40","54","38","57","40","56","38","57","40","56","41","55","43","54","39","57","41","55","40","57","42","54","41","56","39","57","39","57","41","55","42","55","40","56","41","56","42","55","42","55","41","56","41","56","44","53","42","55","41","56","43","54","37","60","39","57","41","56"],["District of Columbia","30","61","32","63","32","64","30","67","27","70","21","74","23","73","16","78","17","78","16","81","16","80","19","77","16","80","16","82","17","80","19","78","19","77","22","75","20","77","17","79","17","79","17","77","18","78","17","79","16","81","18","79","17","80","18","78","17","79","19","78","18","79","17","80","21","76","17","79","17","79","19","77"],["Florida","56","34","56","38","56","39","56","40","51","44","48","47","47","49","47","48","48","47","49","47","47","48","49","47","50","45","49","47","48","48","48","48","50","45","51","45","50","46","49","47","49","47","49","47","49","47","46","49","46","50","47","49","47","49","47","49","48","48","47","50","48","49","48","48","48","49","47","49","49","48","48","49"],["Georgia","53","35","54","39","55","40","51","45","50","45","48","47","49","47","46","48","47","47","50","46","48","47","47","47","51","44","50","46","50","45","49","46","51","44","50","45","50","46","50","45","50","45","49","46","49","46","46","49","47","49","47","48","50","45","49","46","48","47","48","48","49","47","48","48","49","48","46","49","46","49","47","49"],["Hawaii","38","51","40","54","43","52","39","57","34","61","32","63","31","65","30","63","31","64","31","65","32","64","33","63","30","66","32","65","33","64","33","64","35","61","37","60","37","60","37","60","32","64","33","63","38","58","34","62","34","63","36","61","34","63","31","65","32","65","37","60","38","60","35","62","33","63","32","64","31","66","34","62"],["Idaho","59","30","61","33","60","35","64",Line truncated
|
||||||
@@ -1274,11 +1274,11 @@ var gf_global = {"gf_currency_config":{"name":"U.S. Dollar","symbol_left":"$","s
|
|||||||
</script>
|
</script>
|
||||||
<script type='text/javascript' src='https://morningconsult.com/wp-content/plugins/gravityforms/js/gravityforms.min.js?ver=2.4.9'></script>
|
<script type='text/javascript' src='https://morningconsult.com/wp-content/plugins/gravityforms/js/gravityforms.min.js?ver=2.4.9'></script>
|
||||||
<script type='text/javascript' src='https://morningconsult.com/wp-content/plugins/gravityforms/js/placeholders.jquery.min.js?ver=2.4.9'></script>
|
<script type='text/javascript' src='https://morningconsult.com/wp-content/plugins/gravityforms/js/placeholders.jquery.min.js?ver=2.4.9'></script>
|
||||||
<script type="text/javascript">var tcb_post_lists=JSON.parse('[]');</script><script type="text/javascript">/*<![CDATA[*/if ( !window.TL_Const ) {var TL_Const={"security":"c0c9d963fa","ajax_url":"https:\/\/morningconsult.com\/wp-admin\/admin-ajax.php","forms":[],"action_conversion":"tve_leads_ajax_conversion","action_impression":"tve_leads_ajax_impression","ajax_load":1,"custom_post_data":[],"current_screen":{"screen_type":3,"screen_id":104806},"ignored_fields":["email","_captcha_size","_captcha_theme","_captcha_type","_submit_option","_use_captcha","g-recaptcha-response","__tcb_lg_fc","__tcb_lg_msg","_state","_form_type","_error_message_option","_back_url","_submit_option","url","_asset_group","_asset_option","mailchimp_optin"]};} else {ThriveGlobal.$j.extend(true, TL_Const, {"security":"c0c9d963fa","ajax_url":"https:\/\/morningconsult.com\/wp-admin\/admin-ajax.php","forms":[],"action_conversion":"tve_leads_ajax_conversion","action_impression":"tve_leads_ajax_impression","ajax_load":1,"custom_post_data":[],"current_screen":{"screen_type":3,"screen_id":104806},"ignored_fields":["email","_captcha_size","_captcha_theme","_captcha_type","_submit_option","_use_captcha","g-recaptcha-response","__tcb_lg_fc","__tcb_lg_msg","_state","_form_type","_error_message_option","_back_url","_submit_option","url","_asset_group","_asset_option","mailchimp_optin"]})} /*]]> */</script><script type='text/javascript'> jQuery(document).bind('gform_post_render', function(event, formId, currentPage){if(formId == 99) {if(typeof Placeholders != 'undefined'){
|
<script type="text/javascript">var tcb_post_lists=JSON.parse('[]');</script><script type="text/javascript">/*<![CDATA[*/if ( !window.TL_Const ) {var TL_Const={"security":"ade65de242","ajax_url":"https:\/\/morningconsult.com\/wp-admin\/admin-ajax.php","forms":[],"action_conversion":"tve_leads_ajax_conversion","action_impression":"tve_leads_ajax_impression","ajax_load":1,"custom_post_data":[],"current_screen":{"screen_type":3,"screen_id":104806},"ignored_fields":["email","_captcha_size","_captcha_theme","_captcha_type","_submit_option","_use_captcha","g-recaptcha-response","__tcb_lg_fc","__tcb_lg_msg","_state","_form_type","_error_message_option","_back_url","_submit_option","url","_asset_group","_asset_option","mailchimp_optin"]};} else {ThriveGlobal.$j.extend(true, TL_Const, {"security":"ade65de242","ajax_url":"https:\/\/morningconsult.com\/wp-admin\/admin-ajax.php","forms":[],"action_conversion":"tve_leads_ajax_conversion","action_impression":"tve_leads_ajax_impression","ajax_load":1,"custom_post_data":[],"current_screen":{"screen_type":3,"screen_id":104806},"ignored_fields":["email","_captcha_size","_captcha_theme","_captcha_type","_submit_option","_use_captcha","g-recaptcha-response","__tcb_lg_fc","__tcb_lg_msg","_state","_form_type","_error_message_option","_back_url","_submit_option","url","_asset_group","_asset_option","mailchimp_optin"]})} /*]]> */</script><script type='text/javascript'> jQuery(document).bind('gform_post_render', function(event, formId, currentPage){if(formId == 99) {if(typeof Placeholders != 'undefined'){
|
||||||
Placeholders.enable();
|
Placeholders.enable();
|
||||||
}} } );jQuery(document).bind('gform_post_conditional_logic', function(event, formId, fields, isInit){} );</script><script type='text/javascript'> jQuery(document).ready(function(){jQuery(document).trigger('gform_post_render', [99, 1]) } ); </script><script type="text/javascript">
|
}} } );jQuery(document).bind('gform_post_conditional_logic', function(event, formId, fields, isInit){} );</script><script type='text/javascript'> jQuery(document).ready(function(){jQuery(document).trigger('gform_post_render', [99, 1]) } ); </script><script type="text/javascript">
|
||||||
_etmc.push(["setOrgId", "7245018"]);
|
_etmc.push(["setOrgId", "7245018"]);
|
||||||
_etmc.push(["trackPageView"]);
|
_etmc.push(["trackPageView"]);
|
||||||
</script>
|
</script>
|
||||||
<script type="text/javascript">window.NREUM||(NREUM={});NREUM.info={"beacon":"bam.nr-data.net","licenseKey":"86459759b9","applicationID":"85124163","transactionName":"bwdSNktRDxUCAUZZXVZNcQFNWQ4ITBZAUVFTC14FFEQTEw4S","queueTime":0,"applicationTime":471,"atts":"Q0BFQANLHEpBAxAKSUUf","errorBeacon":"bam.nr-data.net","agent":""}</script></body>
|
<script type="text/javascript">window.NREUM||(NREUM={});NREUM.info={"beacon":"bam.nr-data.net","licenseKey":"86459759b9","applicationID":"85124163","transactionName":"bwdSNktRDxUCAUZZXVZNcQFNWQ4ITBZAUVFTC14FFEQTEw4S","queueTime":0,"applicationTime":530,"atts":"Q0BFQANLHEpBAxAKSUUf","errorBeacon":"bam.nr-data.net","agent":""}</script></body>
|
||||||
</html>
|
</html>
|
||||||
Reference in new issue
Block a user