mpacer/agree_plot.ipynb

## agree_plot.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Stacked Bar Graph\n",
    "\n",
    "\n",
    "This is an example of creating a stacked bar plot without error bars\n",
    "using `~matplotlib.pyplot.bar`.  Note: the parameter *left* is used to stack the bars to the right.\n",
    "\n",
    "Original example came from https://matplotlib.org/gallery/lines_bars_and_markers/bar_stacked.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [\n",
    "    (\"Strongly disagree\",             0.00),\n",
    "    (\"Moderately disagree\",           0.06),\n",
    "    (\"Neither agree nor disagree\",    0.15),\n",
    "    (\"Moderately agree\",              0.65),\n",
    "    (\"Strongly agree\",                0.15)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dm2FtW52p7DN1AIw9pIabOoA2IrDd1CEw1iHw1TGMMcYYMwlOQhhjjDFmEpyEMMYYY8wkOAlhjDF214go5Mknn+zZ+Lq+vh6Ojo7Bw4cP97mTejw8PG5akO5uLV++3LmwsPC2S91GRUXJ1qxZ49gWbbK7w5foMsbYQ2IfxoW0ZX3DsP3w7cpYW1vrcnNzrauqqsjW1lZs3brV3s3Nrb4t42iOVqtFS7eB/+KLL1x69+5dI5PJ7nscrVVfX4+m69gw7glhjDF2jyIiIiq+/vrrzgCwceNGp6ioqOuLvZ0/f94sIiLCWy6XK4ODgxUHDx60BoBz586ZPf74474+Pj4BEydO7GF8u4jExESnXr16+SsUCuWkSZN6aLVaAIBUKu0zbdo0Tz8/P+XevXtt582b5x4YGOjv6+sb8Oyzz/bQ6XRYs2aNo0qlksbFxT2mUCiUVVVV9PPPP0v79+/vFxAQ4D948GDf06dP35ANbN++3S4iIsK78fXWrVvtR4wY4Y0mmmsPANLT06VyuVypUCiUM2bM8PT19Q0A9D0y4eHhPgMHDpQPGjTIDwDefvttt8DAQH+5XK6cM2dOt9sd88OOkxDGGGP35Pnnn7+8efNmx+rqasrOzpaGhYVdbdz2xhtvdAsODq7Oy8tTv/fee8Xx8fE9AWDBggXdwsLCqgoKCrKefvrpK6WlpZYAkJGRYZWSkuJ06NChnJycHLVEIhErVqxwBoCamhpJaGjo1dzcXPXIkSOr5s+ff0GlUmXn5+dn1dTUSDZt2uQwZcqU8sDAwOr169efzMnJUVtYWGD27Nle27ZtO5GVlZUdHx9/ad68eR7G8Y8ZM6byxIkTViUlJeYAsHr1aucpU6bcdP+V5toDgKlTp/ZMTEw8nZOTozYzM7vh/llZWVnSbdu2nfjjjz9yU1NT7QsKCqyOHTuWnZ2drT5y5Ih0165dtrc65ocdD8cwxhi7J6GhoTVnz57ttHLlSqeIiIgK422///673ZYtWwoAYNy4cZXTp083v3z5suS3336zS01NLQCAmJiYihkzZjQAwO7du+1UKpU0ODjYHwBqa2slXbp00QKAmZkZJk+eXN5Y965du+w++uijrrW1tZIrV66YK5XKGgA3tH/s2LFO+fn51uHh4XIA0Ol0cHV1vWGYRiKRYMKECWUrV650evnll8syMjJsU1NTTzU9zubau3TpUtXVq1clERERVwEgPj7+8g8//HB98bshQ4Zo3NzcGo/Nfv/+/fZKpVIJANXV1ZKcnByrzMxMaumYH3achDDGGLtnkZGRV955553ue/bsyb1w4cJdf7cIISg6Orrss88+K266zdLSUtc4D6S6uppef/31HgcPHlT7+PjUz507t1ttbe1NvftCCPLx8ak5cuRIzq3aTUhIKBs9erSPlZWVGDt2bHnT+Rutba8pqVSqM4oFr732Wun8+fNv6GX5+9//3qWlY37Y8XAMY4yxe5aQkHBp3rx5JQMGDKgxfj80NLRyzZo1zgCwc+dOO0dHR62Tk5Nu4MCBlWvXrnUGgK+++speo9GYAUBkZKRm586djsXFxeaAfk5JXl6eZdP2qqurJQDQtWtXbUVFhWTHjh3Xr3KxtbVtqKioMAOAoKCg2suXL5unpaXZAEBdXR0dOnTIqml9Mpms3s3NrX7ZsmXu06dPv2kopqX2XFxcGmxsbHQ//vijDQAkJyc7tXSORo0apUlOTnapqKiQAMCpU6csiouLzVt7zA8j7glhjDF2z7y9vesXLlx4oen7H374YUlsbKxMLpcrra2tdWvXrj0FAEuWLCmJiop6zMfHJ6Bfv35V7u7u1wAgJCSkduHChcVPPPGEvHGxuuXLl5+Ry+XXjOt1cXFpiI2Nvejv7x/g6uqqDQ4Ovj4PJS4u7tIrr7zSY/78+bpDhw5lb9q06cTs2bO9KisrzRoaGighIeF8v379apvGGhMTU/bZZ5+Z9+3b96Ztt2ovKSmpcObMmT0kEgnCwsIq7ezsml08b/z48ZqsrCyr/v37KwB9L8mXX355qrXH/DC6twXs9o1r2wXshrVpbSazz9QBMPaQeojWjuEF7DqguLg4rz59+lTPmTPnjs5pRUWFxMHBQQcAb775ZtfS0lKLNWvWFN2fKB9M92cBO8YYY+whEBAQ4G9tba1LSkq64+Thq6++cli2bJl7Q0MDeXh41G3YsKHwPoT4UOIkhDHG2CMvKysr+273nTZtWvm0adPKb1+SNcUTUxljjDFmEpyEMMYYY8wkOAlhjDHGmElwEsIYY4wxk+AkhDHG2F0jopBp06Z5Nr7+61//6jZ37txut9rnyy+/dHjzzTe7AkBycnLnw4cPX7952IABA/z2798vvX8RPzyMz9XQoUN9Ll26ZGbqmO4UXx3DGGMPjXEhbVvf9sO3K2FpaSm+++47x9LS0nPu7u6tWu8kNja2AoY1Xr755pvOWq22IiQk5KYbhN2p+vp6NL3dekeq7362n56eXtAR4rhT3BPCGGPsrpmZmYm4uLiL77//vlvTbSUlJeYjR470DgwM9A8MDPTfs2ePDaBf4j4uLs7rhx9+sElLS+u8cOFCT4VCoczKyuoEABs3bnTs1auXv0wmC9y9e7ctAGi1WsyYMcMzMDDQXy6XK5cuXeoC6G8FHxIS4hceHu7j6+sb2DSG2NhYr8DAQH8fH5+AOXPmXO+h2bx5s0PPnj0DAgIC/CdPntx9+PDhPgAwd+7cbk899VTPvn37KsaPH9+zpXYB4O2333ZrfN+4bmNSqbTPK6+84uHn56cMDg5WFBUVmQNAbm6u5cCBA+VyuVwZFhYmz8/PtwSAqKgo2aRJk7yCgoIUCQkJnsZ1VVVV0ZgxYx577LHHAkaMGOFdW1t7/aZ3Hh4evUpLS801Go1k2LBhPn5+fkpfX9+AlStXOgLAvHnz3AMDA/19fX0Dnn322R46nX5Jm/T0dKlcLlcqFArljBkzPH19fQMaP6Pw8HCfgQMHygcNGuR3q+NNTEx06tWrl79CoVBOmjSph1bb+rX3OAlhjDF2T+bPn38hNTXVqays7IbhgBkzZnSfO3fueZVKlb1169YTM2fOlBlvHzFixNWIiIgrixcvPpuTk6MOCAioAwCtVkvHjx/P/vDDD4sWLVrUDQA+/vhjFwcHhwaVSpV99OjR7HXr1rnm5ORYAoBarZYmJiaeKSwsVDWN7aOPPipWqVTZOTk5WQcOHLA7ePCgdXV1Nb366qs9du3alZ+VlZVdVlZ2w6hAfn6+1f79+3N37NhxqqV2U1NT7QsKCqyOHTuWnZ2drT5y5Ih0165dtk3br6mpkYSFhVXl5uaqw8LCqj755BNXAEhISPCKjY0ty8vLU0+cOLEsISGhe+M+paWllhkZGTmrVq06a1zX//3f/3WxtrbWnTx5Mmvx4sUlarXapml7qamp9l27dq3Pzc1V5+fnZ40fP17T+BmpVKrs/Pz8rJqaGsmmTZscAGDq1Kk9ExMTT+fk5KjNzMxuuAt6VlaWdNu2bSf++OOP3JaONyMjwyolJcXp0KFDOTk5OWqJRCJWrFjh3MyfSbN4OIYxxtg9cXJy0kVHR5ctWbKki7W19fVVYw8cOGCfn59v3fi6qqrKrHHxtluJjo4uB4BBgwZdnT9/viUApKWl2efk5Ei3b9/uCACVlZVmarXaytLSUgQFBV1VKBTNrrOybt06p7Vr17potVq6ePGixdGjR60aGhrQvXv3usZ9YmJiLq9atcq1cZ/IyMgrtra24lbt7t69237//v32SqVSCegXuMvJybEaNWpUlXH7FhYWIiYmpgIAQkJCrqalpdkDQGZmps2uXbtOAEBCQsLld99993qvx/jx48sbVws29ssvv9jOnj37AgCEhobWyOXy6qZl+vbtW/PWW291T0hI8HjyyScrIiMjqwBg165ddh999FHX2tpayZUrV8yVSmXNpUuXqq5evSqJiIi4CgDx8fGXf/jhh86NdQ0ZMkTj5ubWAAAtHW9mZiapVCppcHCwPwDU1tZKunTp0uquEE5CGGOM3bO//OUv5/v27auMiYm5vu6KEAIZGRnZUqn0jtYZs7KyEgBgbm6OhoYGMtRFy5YtOxMVFaUxLrtz5047qVSqa66enJwcy08//dTt8OHD2a6urg1RUVGy2tra2yZBNjY21+trqd1du3bZv/baa6Xz58+/5Toz5ubmQiKRND6HVqu97bpBtra2zR5PawQFBdVlZGSot2zZ4vD22297pKWlaRYtWnTu9ddf73Hw4EG1j49P/dy5c7u15jwYn1chBJo73r///e9doqOjyz777LPiu4mXh2MYY4zdMzc3t4axY8eWb9iw4fqcicGDB2s++OCDLo2vf/31V+um+9na2jZoNJrbfheNGDGi4l//+pdrXV0dAcCxY8c63W6/8vJyM2tra52Tk1NDUVGR+b59+xwAICgoqLaoqKhTbm6uJQBs3rzZ6U7bHTVqlCY5OdmlsWfn1KlTFsXFxa3+Yd+nT5+rq1atcgSApKQkp379+lXdbp/BgwdXffnll04A8Mcff1jl5eXddBVRYWGhhZ2dne6ll166PHfu3HNHjhyRVldXSwCga9eu2oqKCsmOHTscAf3KwDY2Nroff/zRBgCSk5NbPA8tHW9kZKRm586djo3Hfv78ebO8vDzL1p4H7glhjDHWJt56661z69atuz6s8fnnnxdNnTrVSy6XKxsaGig0NLRy0KBBZ4z3iY2NvZyQkCBbsWKFW0pKyomW6p4zZ86lwsLCTr169fIXQpCTk1P9d99912J5AAgLC6sJDAys9vb2DnR3d78WEhJSBQC2trbio48+Oh0ZGekrlUp1wcHBV++03fHjx2uysrKs+vfvrwD0vQZffvnlKQ8Pj1YNRaxYseJMXFyc7J///GdXZ2dn7fr16wtvt8+8efMuxMTE9HzssccCfHx8apVK5U1xHz582Povf/mLp0Qigbm5uUhMTDzt4uLSEBsbe9Hf3z/A1dVVa3y8SUlJhTNnzuwhkUgQFhZWaWdn19Bc2y0db0hISO3ChQuLn3jiCblOp4OFhYVYvnz5Gblc3uzwWFMkxB31kt1o37h72LkZw9q0NpPZZ+oAGHtIDTd1AG1EYPttu+Rb4+jRo4XBwcF3tOw806uoqJA4ODjodDod4uLivHx9fWvfeeedC6aOq701ngcAePPNN7uWlpZarFmz5o5XEr6do0ePugQHB8uavs89IYwxxh45H3/8scvGjRtd6uvrKSAgoHru3LmPZDL31VdfOSxbtsy9oaGBPDw86jZs2FDYnu1zT8h9sM/UATD2kOKekBtxTwh7ULTUE8ITUxljjDFmEpyEMMYYY8wkOAlhjDHGmElwEsIYY4wxk7inq2No+I4sAPe88uE9cgFg6olZHSEGoGPEwTFwDBxD867HQaDdQohIE8fTJogoZNy4cZe3bdt2CtCvuNqlS5fg3r17X/3pp59avbKrh4dHr0OHDmW3diXeW1m+fLnzuHHjNDKZrP5W5aKiomRjxoypmDJlSvmdtrFz5067ZcuWuf30008FX375pUNWVpb1+++/f+7uo3403eslurVCiH5tEsldIqJDHEPHiYNj4Bg4BhPGsW9cSJvWN2z74dsVsba21uXm5lpXVVWRra2t2Lp1q72bm9stv/zbglarRXPrqwDAF1984dK7d++a2yUhbSU2NrYCQMX9qv9Wx/qg4+EYxhhj9yQiIqLi66+/7gwAGzdudIqKirrcuO38+fNmERER3nK5XBkcHKw4ePCgNQCcO3fO7PHHH/f18fEJmDhxYg/j20W0tDS8VCrtM23aNE8/Pz/l3r17bZtbnn7NmjWOKpVKGhcX95hCoVBWVVXRzz//LO3fv79fQECA/+DBg31Pnz5tYRz/9u3b7SIiIrwbX2/dutV+xIgR3mgiJSXFvmfPngFKpdI/JSXl+kJvy5cvd46Li/MCgNWrVzv6+voG+Pn5Kfv16+cHALm5uZYhISF+SqXSX6lU+v/www82ANDQ0IDnnnvOq2fPngGDBg3yHTp0qM+aNWscAX3PUEJCgodSqfRfvXq1Y1ZWVqchQ4b4BgQE+IeEhPhlZmZaAUBJSYn5yJEjvQMDA/0DAwM6jrYIAAASlklEQVT99+zZc9PKuh0ZJyGMMcbuyfPPP3958+bNjtXV1ZSdnS0NCwu7flvwN954o1twcHB1Xl6e+r333iuOj4/vCQALFizoFhYWVlVQUJD19NNPXyktLbUEgFstDV9TUyMJDQ29mpubqx45cmRVc8vTT5kypTwwMLB6/fr1J3NyctQWFhaYPXu217Zt205kZWVlx8fHX5o3b56HcfxjxoypPHHihFVJSYk5AKxevdp5ypQpNwzhVVdX06xZs2Tbt28vUKlU2RcuXLghkWm0ZMkS9z179uTl5uaqd+/eXQAA3bp10/788895arU6e/PmzSfnzJnjBQDr1693LCoqsiwoKMjatGnTqczMTFvjupydnbVqtTp7+vTp5VOnTu2RmJh4JisrK3vp0qVnExISvABgxowZ3efOnXtepVJlb9269cTMmTNl9/RhtrN77d/5vE2iuDccw391hDg4Bj2OQY9j+K+OEkebCw0NrTl79mynlStXOkVERNwwLPH777/bbdmypQAAxo0bVzl9+nTzy5cvS3777Te71NTUAgCIiYmpmDFjRuOS8XYtLQ1vZmaGyZMnX5+/0dzy9GgyLHLs2LFO+fn51uHh4XIA0Ol0cHV1vWGYRiKRYMKECWUrV650evnll8syMjJsU1NTTxmXOXLkiJWnp2ddr1696gAgNja2bNWqVa5ool+/flWxsbGyqKio8tjY2HIAuHbtGr344os91Gq1tUQiwenTpzsBwM8//2w7fvz4cjMzM3h5eWkHDhxYaVxXXFxcOaC/tXpmZqZtdHT09d6Za9euEQAcOHDAPj8///rCgFVVVWbGt2Lv6O4pCRFCmPwfFcfwXx0hDo6BY+AYmtdR4rhfIiMjr7zzzjvd9+zZk3vhwoW7/m4RQlBLS8NbWlrqGudGVFdXU2uWpxdCkI+PT82RI0dybtVuQkJC2ejRo32srKzE2LFjyy0smu3ouK0NGzac+fHHH222b9/uEBISojx8+LD6f//3f926dOlSv2XLllM6nQ7W1tatmrtjZ2enA/TDNnZ2dtqcnBx1M8eHjIyMbKlU2rZ3MG8nPBzDGGPsniUkJFyaN29eyYABA2qM3w8NDa1cs2aNM6C/osTR0VHr5OSkGzhwYOXatWudAeCrr76y12g0ZgDQ2qXhW1qeHgBsbW0bKioqzAAgKCio9vLly+ZpaWk2AFBXV0eHDh2yalqfTCard3Nzq1+2bJn79OnTb7qaqnfv3rXFxcWWWVlZnQBg06ZNzS57n5WV1Sk8PPzqxx9/XOLo6Kg9efKkZUVFhZm7u3u9mZkZEhMTnRsa9AvVDh48uOqbb75xbGhoQFFRkfnBgwftmqvTyclJ5+npeW316tWOgL435z//+Y+1oQ7NBx980KWx7K+//mrdXB0dVauSECKKJKJcIiogogXNbO9ERJsN2w8SkaytA21FDP+PiDKISEtEz7R1+62MYS4RqYnoGBHtJaIeJohhJhEdJ6IjRPQLESnbOwajclFEJIjovlwR0IpzMZmILhrOxREimtreMRjKTDD8XWQR0Yb2joGI/mF0DvKI6IoJYvAiop+IKNPw7+NPJoihh+Hf5TEi2kdEnvchhtVEdIGIVC1sJyJabojxGBH1besYTMXb27t+4cKFN61C++GHH5ZkZmZK5XK58q233vJYu3btKQBYsmRJyYEDB2x9fHwCUlNTHd3d3a8BgPHS8HK5XBkeHi4vKiq6qVvCeHn64cOHy42Xp4+Li7v0yiuv9FAoFEqtVotNmzadWLBggaefn58yICBAmZ6ebtu0PgCIiYkpc3d3v9a3b9+bbj0hlUrFJ598cnrMmDE+SqXS38XFpdlLiefMmeMpl8uVvr6+Af37968aOHBgzWuvvXZh48aNzn5+fsqcnBwra2trHQDEx8eXu7u7XzNMzu0ZEBBQ3blz54bm6t24cePJNWvWuPj5+Sl9fX0DtmzZ0hkAPv/886KMjAwbuVyu9Pb2Dvj0009vGiLqyG67gB0RmQHIAzACwFkAfwB4VgihNirzEoAgIcRMIooB8LQQYmKbBdm6GGQA7AHMA7BdCJHSVu3fQQzDARwUQlQTUQKAYSY4D/ZCCI3h+TgAL7Xl/QhaE4OhnB2AbwFYApglhDjUVjG0Ng4imgygnxBiVlu2fYcx+AL4CkC4EKKciLoIIdpsufDWfh5G5V8B0EcI8UJ7xkBEnwPIFEL8y5AYfyeEkLVzDF8D2CmEWEdE4QCmCCGeb6sYDG38PwBVANYLIQKb2f4nAK8A+BOAUAD/FEKE3m17vIBd24qLi/Pq06dP9Zw5c9rtnDbO3zh37pxZ//79/Q8cOJDj5eV1z/dK6WjuZQG7AQAKhBAnhRDXAGwC8GSTMk8CWGd4ngLgCSJqk1UiWxuDEKJQCHEMwP2ajNOaGH4SQlQbXv4GoK1/abUmBo3RSxsAbT1O2Jq/BwB4D8CHuH83s2ttHPdTa2KYBuAzIUQ5ALRlAnIHMRh7FsBGE8QgoP+RAAAOAEpMEIMSwI+G5z81s/2eCSH2A7h8iyJPQp+gCCHEbwA6E5F7W8fB7lxAQIC/Wq22njlzZll7tjtixAhfhUKhfPzxxxXz588vfRgTkFtpzeQhDwBFRq/PQp/BN1tGCKElogoAzmi7uxS2Job77U5jeBHALlPEQEQvA5gLfS9EeHvHYOhi7i6E+JaI5rdx+62OwyDK8Os0D8AcIURRM2XuZwxyACCiAwDMAPxNCLG7nWOAIYYeAHriv1/E7RnD3wDsMfTE2ACIMEEMRwGMB/BPAE8DsCMiZyFEe37pNBenB4DSdoyBNSMrKyvbFO3+/vvvuaZot6Pgian3ARE9B6AfgKWmaF8I8ZkQwhvAnwEsbM+2iUgC4CMAr7dnuy3YAUAmhAgC8AP+21vXnswB+AIYBn0vxEoi6nzLPe6fGAApQohmx5zvs2cBrBVCeEI/FJFs+FtpT/MADCWiTABDARQDMMW5YIwZtOY/gWIA3Y1eexrea7YMEZlD393alr8uWhPD/daqGIgoAsBbAMYJIepMEYORTQCeaucY7AAEAthHRIUABgLYfh8mp972XAghyow+g1UA2vaW1q37PM5CP0epXghxCvoeGd92jqFRDNp+KKa1MbwI/dwYCCH+A8AK+rVU2i0GIUSJEGK8EKIP9P9GIYRo80m6t9ER/i9jrMNoTRLyBwBfIupJRJbQ/0e2vUmZ7QDiDc+fAfCjuN2M1zvTmhjut9vGQER9ACRBn4C09dh/a2Mw/oIbDSC/PWMQQlQIIVyEEDLDxMPfoD8fbTox9XZxAECTsfZxANq6u7U1f5ffQN8LAiJygX545mQ7xwAiUgBwBPCfNmz7TmI4A+AJQyz+0CchF9szBiJyMep9+QuA1W3YfmttBxBnuEpmIIAKIQQPxbBH1m3nhBjmeMwC8D30Y9qrhRBZRLQIwCEhxHYA/4a+e7UA+klZMW0ZZGtiIKL+ALZC/x/tWCJ6VwgR0J4xQD/8Ygvga8O83DNCiHHtHMMsQ29MPYBy/Dc5bM8Y7rtWxjHbcIWQFvq/y8kmiOF7AP9DRGrou/7nt+UchDv4PGIAbGrjHwd3EsPr0A9FzYF+kurktoyllTEMA/ABEQkA+wG83FbtNyKijYZ2XIjoLIB3AFgYYlwB4Dvoh6MKAFQDmNLWMTD2ILntJbqMMcY6po5wie6f//znrlu2bHGWSCRCIpEgMTHxdHh4+NVFixZ1mTNnzqXGu37eT1KptE91dXXm3ewbFRUlGzNmTMWUKVPKJ06c2OONN944HxIScr+u6ntktXSJ7sO5NjBjjD2KaFzbznsS2w/fanNaWprN999/3/n48eNqa2trUVpaal5XV0cAkJSU5DZt2rTLzSUhHXVp+s2bN5++X3XX19fjbm8F/zDjq2MYY4zdleLiYgsnJyettbW1AAB3d3etTCarX7x4cZcLFy5YDB06VB4aGioH9L0V06ZN8/Tz81Pu3bvXdtu2bXb+/v5KuVyujI6OltXU1BCgX8J+zpw53ZRKpb9cLlcaL1k/aNAgX8PdRXt069atV2lp6Q2ZzNNPPy1LTk6+fvXZuHHjen7xxRc3XI2m0+kQFxfnJZPJAgcNGiS/dOnS9ToGDBjgt3//fqlWq0VUVJTM19c3QC6XK999990uALBs2TKXwMBAfz8/P+XIkSO9KysrJYD+Vu3BwcEKuVyunD17djepVNoH0N+mPiQkxC88PNzH19c3EAASExOdevXq5a9QKJSTJk3qodXqbwuSmppq37t3b4VSqfQfNWrUYxUVFY/E9/MjcZCMMcba3lNPPaUpKSmxlMlkgc8995zXt99+awsACxcuvNClS5f69PT0vIMHD+YBQE1NjSQ0NPRqbm6uesiQIVdnzJjRc/PmzSfy8vLUWq0WS5cuvX67cRcXF61arc5+4YUXLi5ZssQNABYsWNBt6NChlQUFBVnR0dHlpaWlN60nM3Xq1Evr1q1zBoCysjKzw4cP206cOPGGK6CSk5M7FxQUdCooKFBt2LDhVEZGxk23cP/Pf/4jLS0ttcjPz8/Ky8tTv/zyy2UAEBsbW65SqbJzc3PVfn5+NcuXL3cBgFmzZnV/6aWXLuTl5ak9PT1vWKFXrVZLExMTzxQWFqoyMjKsUlJSnA4dOpSTk5OjlkgkYsWKFc6lpaXm77//vvv+/fvz1Gp1dt++favfe+89t3v9fB4EnIQwxhi7Kw4ODjqVSqX+9NNPT7u6umrj4+O9ly9f7txcWTMzM0yePLkcAI4ePWrl6elZFxQUVAcAkydPLvvll1+uL942adKkcgAYMGBAdVFRUScA+P33323j4+MvA8Azzzyjsbe3v+keL6NHj64qLCy0KikpMf/3v//tNHr06JtWw01PT7ebMGHCZXNzc8hksvqwsLDKpvUoFIq6oqKiTvHx8d1TUlLsHR0dGwDg8OHD1iEhIX5yuVy5ZcsW56ysLCsAyMzMtH3hhRcuA8DUqVNvmHgeFBR0VaFQXAOA3bt326lUKmlwcLC/QqFQ/vLLL/YnT57stG/fPpsTJ05YDRgwQKFQKJSbNm1yPnPmzE1J1sOo4w3KMcYYe2CYm5tjzJgxlWPGjKkMCgqqSU5Odp49e/ZNV4BZWlrqWjsPxMrKShjqFlqt9o6WAJk4cWLZypUrnbZs2eK0Zs2awjvZt5Grq2uDSqVSb9261X7FihWumzdvdvr6668Lp0+f3jMlJaUgLCysZvny5c7p6enNrnprTCqVXp8TI4Sg6Ojoss8+++yGe8Ns2LDBYfDgwZodO3acupt4H2TcE8IYY+yuHD16tNPx48c7Nb7OzMy09vT0vAYANjY2DS3NawgODq4tLi62VKlUnQBg/fr1zkOGDLmpR8JY//79q5KTk50A/fwJjUZj1ly5mTNnXkpKSnID9CvyNt0+dOjQypSUFCetVovTp09b/PbbbzclEqWlpeYNDQ2YPHnylQ8++KD4+PHjUgCorq6WeHl51dfV1dGmTZucGsv37t27au3atY4AsHr1aqem9TWKjIzU7Ny507G4uNgcAM6fP2+Wl5dnOWzYsKuHDh2ybTwfGo1GcuzYsU4t1fMw4Z4Qxhhjd0Wj0ZjNnj3bS6PRmJmZmQmZTFa3bt260wAQHx9/KTIyUu7m5natcV5II6lUKlasWFEYHR3t3dDQgODg4Op58+bd8uZ1S5YsKXnmmWce8/X1dQ4JCalycXGpb27Z++7du2u9vb1rx44d2+zdcJ9//vkre/futffx8Qns1q1bXZ8+faqaliksLLR48cUXZTqdjgBg0aJFZwFgwYIFJQMGDPB3cnLS9u3bt6qqqsoMAD755JOi2NjYnkuXLnUPDw/X2NraNrscQEhISO3ChQuLn3jiCblOp4OFhYVYvnz5mSeeeOJqUlJSYUxMzGPXrl0jAHjnnXeKG4erHmZ8nxDGGHtAdYT7hLSXmpoaMjc3FxYWFkhLS7OZNWtWj5ycHHXTcpWVlRKlUqk8cuRItrOzc7usDVRZWSmxsbHRSSQSfP75546bN2922rt374n2aPtBwfcJYYwx9sAqKCiwnDBhgndjD0JSUlJh0zLffPON3UsvvSSbOXPm+fZKQADgwIED0ldffdVLCAF7e/uGtWvX3hQbax73hDDG2APqUeoJYQ+2lnpCeGIqY4wxxkyCkxDGGHtw6RonTzLWURn+RptdQ4iTEMYYe3CpLl686MCJCOuodDodXbx40QGAqrntPDGVMcYeUFqtduq5c+dWnTt3LhD8o5J1TDoAKq1WO7W5jTwxlTHGGGMmwZkzY4wxxkyCkxDGGGOMmQQnIYwxxhgzCU5CGGOMMWYSnIQwxhhjzCT+P+E851QFUfuMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "fig, ax_lst = plt.subplots(1, 1)  # a figure with a 2x2 grid of Axes\n",
    "ind = [.2]\n",
    "width = 0.2       # the width of the bars: can also be len(x) sequence\n",
    "means = [x[1] for x in data]\n",
    "bottoms = [sum(means[:i]) for i,x in enumerate(means)]\n",
    "blue = .3\n",
    "colors = [(1,0,blue,1.0), (1,.75,blue,1), (1,1,blue,1), (.75,1,blue,1), (0,1,blue,1)]\n",
    "# colors = [(1-.2*i, .2*i, 0, 1.0) for i,x in enumerate(means)]\n",
    "ps = [plt.barh(0, [x[1]], width, left=bottoms[i], color=colors[i]) for i,x in enumerate(data)]\n",
    "# p2 = plt.bar(ind, womenMeans, width,\n",
    "#              bottom=menMeans, yerr=womenStd)\n",
    "# plt.ylabel('Scores')\n",
    "plt.title('Proportions of responses')\n",
    "\n",
    "ax_lst.axes.get_yaxis().set_visible(False)\n",
    "ax_lst.spines['right'].set_visible(False)\n",
    "ax_lst.spines['top'].set_visible(False)\n",
    "ax_lst.spines['left'].set_visible(False)\n",
    "ax_lst.set_aspect(aspect=.5)\n",
    "ax_lst.margins(0)\n",
    "\n",
    "plt.yticks(np.linspace(0., .2, 11))\n",
    "plt.xticks(np.linspace(0.0, 1.0, 11))\n",
    "plt.legend(ps[::-1], [x[0] for x in data[::-1]], bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.1)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def agree_plot(data):\n",
    "    fig, ax_lst = plt.subplots(1, 1)  # a figure with a 2x2 grid of Axes\n",
    "    ind = [.2]\n",
    "    width = 0.2       # the width of the bars: can also be len(x) sequence\n",
    "    means = [x[1] for x in data]\n",
    "    bottoms = [sum(means[:i]) for i,x in enumerate(means)]\n",
    "    blue = .3\n",
    "    colors = [(1,0,blue,1.0), (1,.75,blue,1), (1,1,blue,1), (.75,1,blue,1), (0,1,blue,1)]\n",
    "    # colors = [(1-.2*i, .2*i, 0, 1.0) for i,x in enumerate(means)]\n",
    "    ps = [plt.barh(0, [x[1]], width, left=bottoms[i], color=colors[i]) for i,x in enumerate(data)]\n",
    "    # p2 = plt.bar(ind, womenMeans, width,\n",
    "    #              bottom=menMeans, yerr=womenStd)\n",
    "    # plt.ylabel('Scores')\n",
    "    plt.title('Proportions of responses')\n",
    "\n",
    "    ax_lst.axes.get_yaxis().set_visible(False)\n",
    "    ax_lst.spines['right'].set_visible(False)\n",
    "    ax_lst.spines['top'].set_visible(False)\n",
    "    ax_lst.spines['left'].set_visible(False)\n",
    "    ax_lst.set_aspect(aspect=.5)\n",
    "    ax_lst.margins(0)\n",
    "    plt.yticks(np.linspace(0., .2, 11))\n",
    "    plt.xticks(np.linspace(0.0, 1.0, 11))\n",
    "    plt.legend(ps[::-1], [x[0] for x in data[::-1]], bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.1)\n",
    "    return plt;\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [\n",
    "    (\"Strongly disagree\",             0.00),\n",
    "    (\"Moderately disagree\",           0.06),\n",
    "    (\"Neither agree nor disagree\",    0.15),\n",
    "    (\"Moderately agree\",              0.65),\n",
    "    (\"Strongly agree\",                0.15)\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/Users/mpacer/miniconda3/envs/dev/lib/python3.6/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "agree_plot(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}