Data Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Visualization - Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the required libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.60670869 0.6798678  0.41683952 0.88919799 0.96987478 0.33041737\n",
      " 0.10840018 0.96670519 0.54566654 0.24966939]\n"
     ]
    }
   ],
   "source": [
    "randomNumber = np.random.rand(10)\n",
    "print(randomNumber)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEaCAYAAAD+E0veAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO3dd3TUZfb48ffU9AITSEzooeMChhpQCBJFbKBSFlRE1PVLFVRQWbEhigUwKCwWQIFVsawgiMgGUBRYf0Fg6ZDQqyEJkDpJpvz+mJ1JAimTMH3u65ycQ5KZyZMPk7lzn3Kvwmw2mxFCCCEApbsHIIQQwnNIUBBCCGEjQUEIIYSNBAUhhBA2EhSEEELYSFAQQghhI0FB+LUXX3yRtm3bunsYNgaDAYVCwZdffunuoQg/JUFB+LTRo0ejUCiu+bC+6D7//PP89ttv1/1zFAoFK1asqPF2Dz30kG0MarWapk2bMm7cOHJycur8s3/++WcUCgVnzpyp82MIYaV29wCEcLZbbrmFr776qsLXIiMjAQgNDSU0NLTK+5pMJsxmMyqVymHj6devH59//jkGg4G0tDSeeOIJzp49y+rVqx32M4SoK8kUhM/TarXExMRU+AgMDASunT6yfv7FF1/Qpk0btFot6enp7N27l9tuu43IyEhCQkJo164dn3/+OQCNGjUC4OGHH7ZlAPaMp1GjRtx3331MnDiRdevWUVxcXOntz549y7Bhw4iMjCQoKIh+/fqxa9cuADIyMujXrx8AjRs3RqFQkJycfH0XTPg1yRSEuMrp06f56KOPWL58OREREcTFxdG9e3e6dOnC9u3bCQgI4NChQ7bb79q1i4YNG/LBBx/wwAMPoFAoavXzgoKCMBqNGI3GazISs9nMoEGDMJlMrFu3jtDQUF577TWSk5NJT0+nefPmfPvttzzwwAPs3LmTG264gYCAAIdcB+GfJCgIn/fzzz9XmCKKjo7m6NGjVd5er9ezYsUK4uLibF87efIk06dPp127dgC0aNHC9r0GDRoAEBERQUxMTK3Gtm/fPv7xj3/Qq1cvgoODMRgMFb6/YcMG/vjjDw4dOkSbNm0AWL58Oc2aNWPRokVMnz6d+vXr28ZR258vxNUkKAif16NHDz777DPb5zVN78TGxlYICABTp07l0UcfZfHixSQlJTFo0CA6d+5cp/GkpqYSGhqK0WikuLiY2267jUWLFlV62/379xMdHW0LCGDJLLp168b+/fvr9POFqI6sKQifFxQURMuWLW0fzZo1q/b2ISEh13ztlVde4dChQwwZMoQ9e/bQvXt3XnnllTqNp1evXuzevZuDBw+i1+v56aefaN68eZW3r2w6ymw213qaSgh7SFAQwk7x8fGMHz+eb7/9lpdeeol//OMftu9pNBqMRqNdjxMcHGwLTlqtttrbdujQgQsXLnD48GHb14qKitixYwcdOnQAsD2GvT9fiOpIUBCiBleuXGHixIls3ryZEydOsHPnTn766Sfat29vu03z5s3ZtGkT586dIzs722E/+/bbb6dLly6MGDGCbdu2sXfvXh5++GEMBgNPPvkkAE2bNkWhUPDDDz+QmZlJbm6uw36+8D8SFISogUajISsri0cffZQ2bdowcOBAGjVqVOGw2ty5c/n9999p1qwZ0dHRDvvZCoWC1atX07JlSwYOHEj37t3Jzs7m3//+t22BOS4ujlmzZjFr1ixuuOEG7r//fof9fOF/FNJ5TQghhJVkCkIIIWwkKAghhLCRoCCEEMJGgoIQQggbl5xoXrhwITt37iQiIoI5c+Zc832z2czSpUvZtWsXAQEBjBs3rkIZASGEEK7hkqCQlJTEHXfcwYIFCyr9/q5du7hw4QLz588nPT2dTz75hDfeeMOuxz537lydxhQVFUVWVlad7uuL5HpUJNejjFyLinzhesTGxlb5PZdMH7Vv377amvU7duygT58+KBQKWrduTUFBAZcuXXLF0IQQQpTjEQXxcnJyiIqKsn2u0+nIycmhXr1619w2NTWV1NRUAGbPnl3hfrWhVqvrfF9fJNejIrkeZeRaVOTr18MjgkJl5+eqKvaVnJxcoYlIXdM4X0gBHUmuR0VyPcrItajIF65HddNHHhEUdDpdhYucnZ1daZZgD7PZjF6vx2QyVVtF8s8//6yy05U/MJvNKJVKAgMDpdqmEMLGI4JC165dWb9+Pb179yY9PZ3g4OA6BwW9Xo9Go6mxZr5arXZo311vZDAY0Ov1BAUFuXsoQggP4ZKg8N5773HgwAHy8vL4v//7P4YNG2brMHX77bdz0003sXPnTiZNmoRWq2XcuHF1/lkmk6nGgCAs1Gq1X2dLQohrueTVc/LkydV+X6FQ8PjjjzvkZ8lUSO3I9RJClCcnmoX4H7MZ1q8PZPz4SA4ckGAp/JMEBSdo1aoVABcuXOCJJ55w82iEPXbs0HDffToee6w+q1YFM3u2/Gl4kocfrs/99+v436yzcCKZfHeimJgYPv74Y3cPQ1Tj6FEVs2eHs26dZbE9LMxEXp6S7dslKHiKrCwlmzYFArBtm5Y+fUrcPCLfJs98Jzp9+jS33norACtXruTxxx/nwQcfpHfv3rz++uu22/3yyy/cc889DBgwgL/97W8UFBRc81j79u3j7rvvJjk5mccee4zLly8DMGTIEGbNmsVdd93FzTffzO+//w5Y+vXOnDmTO++8k+TkZJYvX+6C39h7ZGUp+fvfI7j11oasWxdEYKCJp57K4//9vz8JDzdx6pSCc+fkz8MTHDpU9t519WrZKedsPp0pxMVVfUDjepw9W7d6S/v37+enn35Cq9XSp08fHn30UYKCgkhJSWHlypUEBwezYMECPvroI6ZMmVLhvpMnT2bmzJkkJibyzjvvMHfuXF577TXAsrX0hx9+YOPGjcydO5eVK1fyxRdfEBYWxrp16yguLmbw4MH07duXJk2aXPfv780KCxV89FEICxeGUlCgRKk0M3JkAc88k0dMjAmALl1K2Lw5kLQ0LYMG6d08YnH4sMb273Xrgpg16wqBgW4ckI/z6aDgaW6++WbCw8MBaN26NWfPnuXKlSscOXKEQYMGAVBaWkqXLl0q3C83N5crV66QmJgIwNChQ21N2wHuvPNOADp27MiZM2cAS/Zx8OBBfvjhBwDy8vI4fvy43wYFgwG++iqYd98N488/LedTkpP1TJ+eS5s2FSequ3a1BIUdOyQoeILDh8tepnJzlWzeHMjAgfL/4iw+HRSqe0evVqttZyVcRavV2v6tVCoxGAyYzWb69OnDwoULr/txVSpVhd/p9ddfJykpqc6P6wvMZti4MYA33gi3vePs1KmEF1/MpVevyuemu3WzfD0tTVvp94VrHTpk+X+79VY9mzYFsmpVkAQFJ5JJUzfr0qULaWlpHD9+HICioiKOHj1a4Tbh4eFERETY1gu+/fZbevbsWe3j9u3bl2XLllFaWgrA0aNHKSwsdMJv4Ln++18NQ4fqeOQRHYcPa2jSxMDChTmsXZtVZUAAuOmmUlQqMwcOaCgokK2p7mQ2l2UK06blAZCaGkhenvy/OItPZwreQKfTMW/ePMaPH09JieWFatq0acTHx1e43Xvvvcfzzz+PXq+nSZMmzJ07t9rHHTlyJKdPn+aOO+7AbDZTv359lixZ4rTfw5OcPKnirbfCWL06GIDISBOTJ+cxalQBAQE13z842Eznzmb++EPJzp0abrlFdru4y7lzKvLzlURFGfnLX0rp0aOY338P4KefAhkypMjdw/NJCnNlJUq9yNVNdgoLCwkODq7xfu6YPvJE1uvlC5Ufc3IUpKSE8dlnIZSWKggIMPP44/mMH59PRETtnuZvvhnNBx+oePbZXKZMyXfSiL2DO58bGzcGMGqUjt69i/nqq2yWLQvmhRci6ddPz4oVOW4Zky/8rbi9yY4QzlRUBAsWhNK7dzSffBKKwQBDhxby66+ZTJ+eV+uAANCrl2UnkqwruJd1HahtW8s06N1361GrzWzZEkB2trx8OYNcVeG1jEb4+usg+vRpyBtvhJObqyQpSc9PP13kvfcuExdnrPNjJyZaAskff2gx1v1hxHWynlGw7hCrX99Enz7FGI0K1qyRfanO4HNBwctnw1zOW6/XL78EcMcdDZg8uR7nzqlp376UL77I5p//zKFDh+ufFoyNhcaNDeTnKyscnhKuZV1kbtOm1Pa1++6zrCWsWiUH2ZzB54KCdaunqJnBYECp9K6nwL59akaMqM/IkToOHNAQG2sgJeUSP/10kT59HFsGXLamupfRCOnplumj8mdJBgzQExhoIi0tgDNn/LsnijP43FugwMBA9Ho9xcXF1ZaFDggI8OteAuU7r3mDs2ctO4r+9a8gzGYF4eEmJk3K49FHC5x2urVr1xL+9a9gduzQMnq0f23n9QQnTqgoLlYQF2cgLKwsow0JMXP77cV8/30Qq1cHMX68f28EcDSfCwoKhcKuTmK+sIPAH1y5ouCDD0JZvDiU4mIFGo2Z0aPzmTQpj/r1nTv1JZmCe1kXma8+cQ5w332FfP99EN99J0HB0XwuKAjfUFwMn30WQkpKGJcvW6a4Bg8u5Lnn8mjSxDUrv23aGAgLM3HmjJrz55XccIPJJT9XWJStJ1wbFPr2LSYiwsTBgxoOH1ZXehtRN941oSx8nslkWUDs27chr74aweXLShITi1m37iILFlx2WUAAUKksxfFAsgV3sJa3KL/IbBUQAHfdJQvOziBBQXiM7du13H13FOPH1+P0aTVt2pSybFk2X3+dTadO174wuELXrpagsGOHBAVXs2YKbdtWngUMGmQJCqtXB+Glm+g8kkwfCY+wb5+a4cN1GI0KYmKMPPtsHsOGFaJy8+YSWVdwj+JiOHZMjUJhpmXLyt8QJCaWEB1t5ORJNbt2aUhIcM8bB18jmYLwCKtXB2E0KrjrriJ+/TWTESPcHxAAEhIsxfH275fieK507Jgao1FBs2ZGqto3olLBvffKFJKjSVAQbmc2w48/Wv6oR48uIDjYc+YCgoPNdOhQitGoYNcuTc13EA5xdXmLqgwebAkK338fJCfPHUSCgnC7jAw1x4+riYw00b2751UklSkk17u6vEVVOnUqpVkzAxcvqti6Vf5/HEGCgnC7n36ynD677TY9ag9c5ZLFZterrLxFZRSKsmxh1aqaqyOLmklQEG63fr0lKNxxh2d207JmClIcz3XKpo9qPn9gDQo//hiI3jOfQl5FgoJwqwsXlOzapSUw0ETfvp5ZduSGG0w0amQgL09ZoV+wcI7CQgUnT6rRaMw0b15zUGjVysCNN5bY+jeL6yNBQbjVhg2WP+K+fYsJCvKcBearybqC6xw5Ygm8LVsa0Ni5tl82hSS7kK6XBAXhVtb1hAEDPDvvl3UF17F3PaE869ZU6d98/SQoCLfJzVWwdWsASqWZ227zzKkjK8kUXKesvIX99Yzi4kz06FGMXq+wrVGJupGgINxm8+YASksVdO9eQv36nl1srm1bS3G806fVXLggfzbOVFbeonYnlK1TSKtXyxTS9ZBnt3Cb9estf7yePnUEltOzCQmSLbhCdSWzqyP9mx1Drpxwi+Ji2LQpAPCOoAAyheQKly4puHBBRVCQicaNa7f/V/o3O4YEBeEW27cHkJ+vpF27Upo29Y7N/7LY7HxHjpRlCXXpFCv9m6+fBAXhFp5+YK0y1uJ4+/ZpKCyUHS7OYG95i6pI/+brJ0FBuJzJVHY+4Y47itw8GvuFhJhp316K4zlT2XpC3cpgW/s3gyw415UEBeFyu3dr+PNPFXFxBjp08K42irKu4Fw1Ndaxx333FQLw3XcSFOpCgoJwufIH1hReNgsj6wrOYzZX34LTXlf3bxa147Irtnv3bpYuXYrJZKJ///4MHjy4wvezsrJYsGABBQUFmEwmRo4cSUJCgquGJ1zIup7gLbuOyitfHM9kok6LoaJymZlKLl9WEhlpIjq67udWrP2bP/88hFWrgnjuuTwHjtL3ueQpbTKZWLx4MdOnT2fevHls3bqVM2fOVLjNt99+S2JiIm+//TaTJ09m8eLFrhiacLGMDBUZGRoiI0306OF5vRNqEhtrIi7OQG6uFMdztPLrCdebQZY/yCb9m2vHJUEhIyODmJgYoqOjUavV9OrVi7S0tAq3USgUFBZa5gILCwupV6+eK4YmXGzDBss8b//+eruLnXkaWVdwDuvOo9atr3+dqWfPiv2bhf1c8lYnJycHnU5n+1yn05Genl7hNkOHDuX1119n/fr1FBcXM2PGjEofKzU1ldTUVABmz55NVFRUncakVqvrfF9f5KrrsXGj5Sk3bJjGo69/ddcjKUnJqlWwd28YUVG+39jFVc+NkyctW0i7dAlwyM8bPhzmz4f16+tz++2OOwvj668dLgkK5kryN8VV+eHWrVtJSkrinnvu4ciRI7z//vvMmTMH5VWTtsnJySQnJ9s+z8rKqtOYoqKi6nxfX+SK6/Hnn0p+/z2awEAzCQlZZGV5bl5f3fVo104NNOS338x+8Rxy1d/Kf/8bBaho1OgyWVnXP7U4YICG+fMb8NVXMG1alsO6+vnCa0dsbGyV33PJ9JFOpyM7O9v2eXZ29jXTQ5s2bSIxMRGA1q1bU1paSl6eLBD5kn//OxCzWcEttxQTHOy5AaEm7doZCA01ceqUmj//lJVmRzCZypfMdsw25fL9m7dtk6k+e7nkGR0fH8/58+fJzMzEYDCwbds2unbtWuE2UVFR7Nu3D4AzZ85QWlpKeHi4K4YnXMS6FdWbDqxVRorjOd7ZsyoKC5U0bGh0WMVchaJ82Qvfn+ZzFJcEBZVKxZgxY5g1axZTpkwhMTGRxo0bs3LlSnbs2AHAqFGj2LhxI1OnTiUlJYVx48ZdM8UkvFdenoLffgtAoTCTnOzZvRPsIYvNjnW95S2qIv2ba89le+oSEhKuOXcwfPhw278bNWrEzJkzXTUc4WKbNwdQUqKge/dioqI8u3eCPeQQm2Ndb3mLqrRsaenfvG+fls2bAxk4UCJDTWRCVLiEtdaRNx5Yq0xCQilKpZm9e6U4niM4orxFVazZgpS9sI8EBeF0JSWwcaNvBYXQUCmO50iOKG9RFWv/5o0bpX+zPSQoCKf7z38CyM1V0rZtKc2be0fvBHvIuoJjGAyQkeG4g2tXi4sz0bOn9G+2lwQF4XTeXOuoOtagIOsK1+fECTUlJQoaNzYQGuqcrcqDBkn/ZntJUBBOZTKV34rqW0HButhsLY4n6sZZO4/KK9+/OStLXvaqI1dHONWePRouXFBxww1G/vIXx88Xu1NcnInYWCmOd72sO4/atnXe86N8/+a1a2UKqToSFIRTlZ868sVjJ7KucP1ckSmA9G+2lwQF4VRlDXW8+xRzVSQoXL+y8hbOzSSlf7N9JCgIpzl2TMWRIxrCw00kJnpf7wR7yGLz9dHr4fhxNSqVmfh452YK0r/ZPhIUhNNYD6wlJ3tv74SatG1rICREiuPVVUaGGpNJQfPmBgJdMNUv/ZtrJs9i4TS+uhW1PLXacroZZAqpLsrKWzg3S7BKSiomMlL6N1dHgoJwiosXlezYoSUgwExSkvcXwKuOrCvUXVl5C9fsTNNq4c47ZcG5OhIUhFNYeyfcfHOx0w4keQpZV6i7svIWrskUoKwW0qpV0r+5MhIUhFNYp4587cBaZRISSlAqzezbp6GoyAf33TqRq3YelWft33zqlJqdO310ses6SFAQDpefX9Y74bbbfD8ohIaaadfOgMEgxfFqIy9PwZkzagICzDRr5rqaWCpVWZE82YV0LQkKwuF+/jmA4mIFXbqU0qCBf9R/kHWF2jtyxJIltGxpcFj/ZHtZD7J9/30QBtfNXHkFCQrC4Xyl7WZtyLpC7TmrsY49OnaU/s1VkaAgHKq01Pd6J9jDGhSkOJ79XFXeojLSv7lqEhSEQ/3nP1quXFHSunUpLVr4Tu+EmsTFGbnhBiNXriht0yKieu7MFKBsF9K6ddK/uTwJCsKhymod+d9fmawr1I4zW3Daw9q/OS9PyebNUjnVSoKCG+XlKcjO9p3/ArMZ1q+37Obwh62oV5OgYL+cHCUXL6oICTERF+e+jNI6hSRlL8r4ziuSF8nOVjJrVhgJCdH07t2QU6d8o2Lj3r0azp9XERNjpGNH3+qdYA9ZbLafNUto3dqA0o2vQvfcI/2bryZBwYWyspTMnBlOjx4NWbgwjMJCJXl5St58M9zdQ3OI8rWO3PmH7i7t2pUSHGzi5Ek1mZl+eAFqwdXlLaoi/ZuvJc9cF7h4Uclrr4XTs2dDFi0KpahISf/+epYsySEw0Mz33wexY4f3H3ry5/UEkOJ4teGO8hZVsfZvllpIFhIUnCgzU8krr1iCwYcfWoLBbbfpWbfuIsuW5TBggJ4nnsgH4NVXI7y6DsuJEyoOHdIQFmYiMdG3C+BVR9YV7OOO8hZVsfZv/vVX6d8MEhSc4s8/lbz0UjiJidF8/HEoer2SAQOKWL/+Ip9+mkOnTmV/CBMm5BMVZWTnTi1r1nhv+mrNEvr316P149dDWVeomdlcvi+z+zOF+vVN9O0r/ZutJCg40PnzSmbMsASDxYtD0esVDBxYxE8/ZbJkyaVKG9eHhpqZOjUPgDfeCPfa/dL+PnVkZS2Ot3evFMeryoULSq5cUVKvntFjyqCUr5zq7yQoOMC5c0r+/vcIeveOZsmSUIqLFdx5ZxEbNmTyySeXuPHG6t8N/fWvhbRtW8rp02qWLg1x0agdJytLSVqaFq3WTL9+/jt1BBAWZqZtW0txvN27vX+dyBnKZwkKD4mb5fs3nz7tG7sB60qCwnU4e1bJ9OmWYPDppyEUFyu4++4iUlMz+fjjS3ToYF9qrFbDjBm5AKSkhHnd2YXU1ABMJkvvhLAwL14YcRBZV6ieO8tbVCUkxGzLcv29cqp3vfp4iLNnVTz/vCUYfPZZCKWllv3OGzdm8uGHl2jXrvZP9qSkYpKS9OTlKZk7N8wJo3Ye64E1f586sureXYJCddxd3qIqMoVkIUGhFs6cUTFtWgS9ezdk+fIQDAYYNKiQTZsusmjRpeteNJsxIxel0szy5cFkZHhH/ZzCQgW//mrpnXD77RIUQIrj1cTd5S2qUr5/szWb8UcSFOxw6pSKqVMtweCf/wzBaIT77itk8+aLLFx4mdatHfPkbtvWwIgRhRiNCmbO9I4Dbb/8EoBer+Cmm0pp2FBeAaFicbz0dP99camMyVT+NLNnZQrSv9lCgkI1Tp5U8cwzEdxyS0M+/zwEkwnuv7+QzZsz+eCDy7Rq5fh3OlOn5hESYiI1NZBff/X86Qd/artZG7KuULlTp1To9UpiYoxERnre+pN1Cmn1av/t32xXUDCZTOzbtw+Dn7QoOnFCxdNPR3LLLQ358ktLMBgypJCff87k/fcv07Kl8wp4NWhgYsIEy4G2116LwOjB1acNBkhNtW5F9Z+GOvaQoFC5sp1HnpUlWPXsWUJMjH/3b7YrKCiVSt5++23Uru6Z52LHjqmYPDmSPn0asnKlpfHGsGGFbNmSSUrKZeLjXfMK/cQT+cTGGjhwQMM333huGvv771ouX1bSsmWpUwOlN5JDbJXzxJ1H5alUZUXy/HUXkt3TR+3atePIkSPOHIvbHD2qYtKkSPr2bcjXX1uCwfDhlmAwb95lmjd37QteUBC88ILlQNtbb4VTUOAhm7mvUtZ2U6aOrmYtjnfihJqLF2WW1sqTyltUxd/7N9v91r9Bgwa8+eabdO3aFZ1Oh6LcqZPhw4c7ZXDOlpGhJiUllFWrgjCZFKjVZoYPL2DixHyaNnXvO9/Bg4tYvDiE3bu1LFoUyjPP5Ll1PFez9E6QU8xVsRbH++23ANLStNx5p1wj8KzyFlWx9m8+cULNtm0B9OnjXwcy7X4LU1JSQrdu3VAoFOTk5JCdnW37sMfu3bt56qmnmDhxIqtWrar0Ntu2bWPKlCk8/fTTpKSk2Du0WktPVzNqlIqkpAb861/BKJXw4IMF/PZbJu++e8XtAQFAqYSXX7YcaFu4MITz5z3r3eb+/WrOnlUTHW2kc2fPfdfnTrKuUFFJieWNmEJhdsomDUep2L/Z/6aQ7M4Uxo0bV+cfYjKZWLx4MS+++CI6nY4XXniBrl270qhRI9ttzp8/z6pVq5g5cyahoaFcuXKlzj+vOmYzPP54PTIyVGg0ZZlBo0buDwRX6969hDvvLGLduiDefjucefMuu3tINtYDa7ff7p+9E+whQaGi48fVGAwKmjUzEBzs2Vt7Bg8uYt68MNatC+SNNyDQj+rk1erP+cyZM3zzzTcsXrwYgHPnznHy5Mka75eRkUFMTAzR0dGo1Wp69epFWlpahdts3LiRAQMGEBoaCkBERERthmY3hQKmTMnnb38zsnVrJm+9dcUjA4LV3/+ei0Zj5uuvg9i3z3MW+qUAXs2sxfH27dNQJJuzyi0ye35m6c/9m+1+ldm+fTuffPIJPXr0YOvWrTz22GMUFRXx+eefM2PGjGrvm5OTg06ns32u0+lIT0+vcJtz584BMGPGDEwmE0OHDqVz587XPFZqaiqpqakAzJ49m6ioKHt/BZvHHwe1WoHBUK/W93W1qCgYO9bE/Pkq3nwzivXrnVNETK1W230tjx+HAwc0hIWZuffeMAICvKsshz1qcz2qEhUFN95oZs8eJSdONOCWWzz73XFVHHEtAFuhuc6dtQ55PGd78EElL7wA69ZF8vDDZdNdjroensruoPDVV18xY8YMmjVrxvbt2wFo2rQpJ06cqPG+5kpOgSiuemUzmUycP3+el19+mZycHF566SXmzJlDSEjFqqHJyckkJyfbPs/KyrL3V6ggKiqqzvd1tSefVLBsWTQ//6zkiy/yuP12xy981eZ6fPllCKClXz89eXmXyPOsNXCHcNTz46abItizJ4TU1CLatct3wMhcz1HXYteuekAQTZrkkpXl+alT//5KIIYfflBw/Hi2rdijN712VCU2NrbK79k9fXTlyhWaNm1a4WsKheKaF/fK6HS6CgvS2dnZ1KtX8V16/fr16datG2q1moYNGxIbG8v58+ftHZ5Pi4w0M2WK5ZV35swISiagc6YAACAASURBVN2cfZdtRfX8P2x3k3WFMmUtOD1/+gjK+jcXF/tX/2a7g0KLFi3YsmVLha9t3bqVli1b1njf+Ph4zp8/T2ZmJgaDgW3bttG1a9cKt+nevTv79u0DIDc3l/PnzxMdHW3v8HzeqFEFNG9u4NgxNStWBLttHDk5Sn7/XYtGI70T7CHF8SyKiixlY9RqM/Hxnrvz6Gr+WDnV7qDw6KOP8uWXX/Lyyy9TXFzMrFmzWLlyJY888kiN91WpVIwZM4ZZs2YxZcoUEhMTady4MStXrmTHjh0AdOrUibCwMKZMmcKrr77KQw89RFiY781V15VWCy++aNmiOmdOGFeuuOdA27//bemd0Lt3MeHh3jlH7kpxcUZiYoxcvqz0msq3zpCRocFsVtCihcGr2rXedVeR3/VvtvtZGhcXx3vvvccff/xBly5d0Ol0dOnShUA792olJCSQkJBQ4WvlD70pFAoeeeQRu4KMvxowQE9iYjHbtwcwf36YrTGPK8muo9pRKCzZwpo1QaSlaR1WUdfbeHp5i6rUr2+mb99iNm4MZO3aQEaPLnT3kJyuVqEvICCAtm3b0r59e9q1a2d3QBCOoVDASy9ZAsGSJSGcPOnatoFFRQp++SUAQHon1IKsK3huYx17WA+yffed+6ZtXcnuTCErK4v58+eTnp5OSEgIBQUFtGzZkkmTJtGgQQNnjlGU07FjKUOGFPLNN8G88UY4H354yWU/e8uWAPR6JTfdVEJMjB9PkNeSBAXPbaxjj9tvt/Rv3rFDy+nTKnx4NypQi0xhwYIFtGjRgqVLl/LJJ5+wdOlS4uPjWbBggTPHJyrx3HO5BAaaWLs2iLQ015X3lVpHddO+vRTH86aDa1fzt/7Ndj9Djx07xkMPPWSbMgoMDOShhx7i2LFjThucqFxsrIknnywA4NVXI1zSDMRgsCwyg1RFrS21Gm66yfJi6I+ltHNzFZw7pyYw0OwRdcXqwp92IdkdFFq1akVGRkaFrx09epTWrVs7fFCiZuPH59OwoZFdu7R8/73z13bS0rRcuqSiRQsDLVt63xSAu/nzFJJ16qhVq1JUrl0Gc5jy/Zv37/fMUvaOUu2awsqVK23/jo6O5s033yQhIcF2GG3Xrl3cfPPNTh+kuFZIiJmpU/OYOjWSN94IZ8AAvVOLdpW13SxySpkNX+ffQcG6yOy9bya0Wsv21H/+M4SVK5VMmuTuETlPtZlC+fLYpaWl9OjRA41GQ25uLhqNhu7du1NSUuKqsYqrDB9eSLt2pZw5o2bx4lCn/RyzWbaiXq+EhBIUCjN79/pfcbyyRWbvW08ob9Agy3/c8uVKin343Ga1mcL1lMsWzqdSWbaojhihY/78UIYPLyQqyvG7gg4cUHP6tJoGDYwkJHj3H7a7hIebadvWwMGDGvbs0dKjh/+8mSorb+G9mQJAYmIJ7dqVcvCghi++CPbZMwu12gpRXFzMyZMnOXz4cIUP4T59+hRz66168vOVzJnjnBPg1ixBeidcH3+dQvKGFpz2UCqx1SD74IMwn80W7D6n8Msvv7BkyRLUajXaq86p/+Mf/3D4wIT9ZszI5ZdfAlixIphHHy1w+KlZmTpyjG7dSli2LMSvgkJWlpLsbBVhYSZiY73/bMvAgXo6dDCxf7+KlSuDGTXK97IFu4PCihUreOaZZ+jYsaMzxyPqoHVrAw8+WMiyZSHMnBnO8uU5DnvsM2dU7NunJSTERO/ePvrWyEWsmcKOHZbieP6QdZUvb+ELGxSUSnjxRSMjRih5/33LlG1AgLtH5Vh2Py3VajXt27d35ljEdXjmmTxCQ01s2hTIli2Oe5Zas4R+/Yr9qiWhMzRqVFYc7+hR/yiO583lLaoyeLCZtm1LOXdOzVdf+V7pC7uDwvDhw1m2bBm5ua4vwiZqFhVlYuJESxOX114Lx+igM0JlW1Fl6uh6KRTQtat/rSt4c3mLqiiVMHmyZW3h/fdD8bUNmHYHhdjYWHbs2METTzzB8OHDK3wIz/D44/k0amTZ4eKIdzA5OQp+/12LWm3m1lslKDiCvy02e1tjHXvddZeeNm1KOXvW97IFu3PY999/nz59+tCrV69rFpqFZwgMhBdeyGP8+Hq8/XYY995bREhI3WtgbNwYiNGooE8fPRER0jvBEfwpKJjNvpkpQFm2MHZsfebPD2XYsEKv6hNRHbszhfz8fIYPH06TJk2IiYmp8CE8x6BBRdx0UwmZmSoWLry+A22y68jx2rcvJSjIxPHjap9v2nLunIr8fCVRUUZ0Ou/feXS1u+/W07q1JVv4+mvfyRbsflYmJSVd045TeB6FAl5++QoAixaFcO5c3V54iorg55+ld4KjaTT+UxzPWxvr2Kv82sL8+b6ztmD39FFGRgbr16/nX//6F5GRkRW+9+qrrzp8YKLuunUr5e67i1i7Noi33gonJeVyrR/j118DKCpS0qlTiU/sL/ck3bqVsG1bAGlpWp9ewPfFnUdXu/tuPfPmlZKeruGbb4IZOdL7zy3YHRT69+9P//79nTkW4UDTp+eyYUMg33wTzGOPFdCxY+3+MGXqyHn8ZV3B1zMFsJSamTw5n/Hj6zF/fihDhxaicV2LE6ewOygkJSU5cRjC0Zo2NTJmTAGLFoXy2mvhfP11tt2Hh4xG2LBBtqI6S5cuZcXx9Hp89vyHr5S3qMk99xQxb14oGRmWbGHECO/OFuwOCps2barye7feeqtDBiMca9KkPFauDGL79gA2bAi0+13/jh1acnJUNGtm8NtG8850dXG87t19ZDK6HKMRMjJ8oxBeTazZwoQJ9UhJCWXIEO/OFuxehfz1118rfKxZs4aPP/6YX3/91ZnjE9chIsLM009bDrTNnBlu90JY+QNrvlCawBP5+iG2kydV6PUKYmMNhIf7/nbme+8tIj6+lNOn1Xz7rXd3Z7M7U3j55Zev+dqmTZs4e/asQwckHOvhhwtYujSEY8fULF8ewmOPFVR7+/K9E2TqyHm6dSth+XLfLY5nXWT2tfMJVbFmCxMn1mP+/DAeeKDIa7OF69oonZSUVO20knA/jQZmzLBsUZ07N4zLl6t/63/okJqTJ9VERRlJSPC9aQ1PUVYcT+OSHtuu5g+LzFcbNKiIFi0MnDyp5l//8t5swe6gYDKZKnzo9XpSU1MJCQlx5viEA9x2WzG9ehVz+bKSlJTqey6U753grf10vUHjxkaio41cuqTyyeJ4/rAd9WqWbMF6biEMg5fGQ7ufjSNGjLjma/Xr1+fJJ5906ICE41kPtN1xRwOWLg1h1KgCmjevvGJe+aAgnMdaHO+HH4JIS9PSsqWXvoJUwVfLW9Rk0KAi3nsvjGPHLGsLw4d7X+9Vu4PCBx98UOHzgIAAwsPDHT4g4Rw33mhg6NAivvoqmDfeCOfjjy9dc5uzZ5Xs2aMlONjEzTdL7wRn69atLCh4+zbG8oqL4dgxNQqFmZYt/SdTAFCr4amn8njqqbK1BbWXJYI1Drem08oKhYKXXnrJYQMSzjNtWi5r1gSybl0Qv/9ecE2fYOvZhKSkYoK8d0rUa/jqIbZjx9QYDAqaNzf45fNo8GBLtnD8uGVtYdgw78oWagwKt9xyS6Vfz8nJ4ccff6TYVxuV+qAbbjAxdmwBc+eG8dpr4axZk1Wh+9f69Za/YNl15BodOliK4x07piY7W+kzRePKdh75V5ZgZc0WJk+uR0pKGPff713ZQo0LzbfeemuFj27dunHmzBnWrFlD9+7dSUlJccU4hYOMHZtPdLSR3bu1rF5d9jbu0iXYvl2LSmWmf38JCq6g0UDnzr5XHM8fdx5d7b77imjWzMCJE2q++8670iW7dx8VFhby5ZdfMmnSJK5cucJbb73Fk08+iU6nc+b4hIMFB5uZNs3SPe+NN8Io+l9m++OPSoxGBYmJJURG+uAeSQ/li1NI/lLeojrWbAEgJcW7diLVGBRKSkr47rvvmDhxImfPnuW1115j4sSJ0kfBiw0dWkT79pYes598Yum58P33lqfCHXd41/ynt/PNoOBfB9eqcv/9lmzh+HE1q1Z5T7ZQY1AYP348a9eu5d5772XAgAFcuXKFffv2VfgQ3kWlgpdeshxoe//9UM6cUbFhg+VQm2xFdS1rcbw9eyzF8bxdYaGCkyfVaDRmmjf376CgVlvqj4F3ZQs1Ln9YW29u2LCh0u8rFIprtqsKz3fLLSUkJ+tJTQ1k1Kj6FBQo+MtfSoiL843FTm8REWGmTRsDhw5p2LtXa8scvNWRI5aXlJYtDV5b5sGRHnigiJQUy7mF1auDeOABz8/EawwKCxYscMU4hBvMmJHL5s0BtnRfeie4R9euJRw6pCEtzfuDgqwnVGRdW3j6aUsF1cGDizy+UoBvN4kV1WrZ0sDDD5cdmpKtqO5Rtq7g/W+tDx3yj3LZtXH//UU0aWLg6FEN33/v+WsLEhT83NNP5xETY6RbN5PfLwy6S1lxPK3XF8crK28hmYKVRlO2E2nevFCMlVeY8RguCwq7d+/mqaeeYuLEiaxatarK2/3nP/9h2LBhHD161FVD82s6nYnffstk82aD9E5wkyZNjDRsaCQnR8XRox4+t1CDskJ48gajvAceKMsW1qzx7GzBJUHBZDKxePFipk+fzrx589i6dStnzpy55nZFRUX8+OOPtGrVyhXDEv8TFGSWRUE3shbHA+8+xHbpkoILF1QEBZlo3NjD3w67mEYDkyZZGl55erbgkqCQkZFBTEwM0dHRqNVqevXqRVpa2jW3W7lyJffeey8aeYUSfsYXziscOVKWJShlYvoaQ4YU0rixgYwMDWvXem5jbpdU5MjJyalw8lmn05Genl7hNsePHycrK4suXbqwZs2aKh8rNTWV1NRUAGbPnk1UVFSdxqRWq+t8X18k16MiV1+P225T8OqrsHNnMFFRnvWmyN5rceaMJRJ06qTy6efS9Tw3XngBxo2D+fPrMXp0qUfuRHJJUDBXsnqmKDeBbTKZ+Oyzzxg3blyNj5WcnExycrLt86ysrDqNKSoqqs739UVyPSpy9fVo1AgCA2M4ckTJkSM51K/vOedF7L0Wf/wRAahp0iSfrKzq2756s+t5bgwcCI0aNeTQITWffprPoEHu2fEXGxtb5fdckuTpdDqys7Ntn2dnZ1OvXj3b53q9ntOnT/Pqq68yfvx40tPTefvtt2WxWfgNjQZuuslaHM+zMgV7+WtjndrQamHiRMvawnvvhWHynNhv45KgEB8fz/nz58nMzMRgMLBt2za6du1q+35wcDCLFy9mwYIFLFiwgFatWjFt2jTi4+NdMTwhPIJ1sdkb1xXMZjm4Zq9hwwqJizNw5Ihnri24JCioVCrGjBnDrFmzmDJlComJiTRu3JiVK1eyY8cOVwxBCI/nzYvNFy8quXRJRUSEiZgYD3z760E8PVtwWeuHhIQEEhISKnxt+PDhld72lVdeccGIhPAsXbpYgsKePVqKiyEgwM0DqoWyHgqlct7FDsOHFzJ/fiiHD2v44YdA7rnHc6oJyMYxITxEZKSZNm1KKS5WsGePd60ryKG12imfLcyb51nZggQFITyItx5ik/IWtTd8eCGxsQYOH9awbp3nrC1IUBDCg3jruoIUwqu9gACYMMHzsgUJCkJ4EG8sjmcylfVRkKBQO3/9ayE33GDk0CENP/7oGdmCBAUhPEjTpkYaNDCSna3i2DEPPO5aibNnVRQUKGnY0OhRh+68gSVbsFZQ9YxsQYKCEB5EoaiYLXiDsp1HkiXUxYgRhcTEGDl4UMP69e7PFiQoCOFhvO0QW9nOI1lkrouAAJg40XOyBQkKQngYb1tslvIW1++vf7VkCwcOaPjpJ/dmCxIUhPAwN95YSmCgmYwMDTk5nn8SrGznkWQKdRUYWHFtwZ2bDCQoCOFhtFro3NmSLfz6q2cfazYYICPDkim0bi2ZwvWwri3s3+/ebEGCghAeaOBAS9mD118PJy/Pc7OFEyfUlJQoaNzYQGiol+yh9VCBgTB+vOXcwty57ssWJCgI4YFGjy6gU6cSzp1T8/rr4e4eTpVk55FjjRxZQHS0JVvYsME92YIEBSE8kFoNc+deRqs1s2JFCFu2eOais3XnkZS3cIyK2UKoW7IFCQpCeKi2bQ1MmWJZfJw6NZL8fM+bRpJMwfFGjiygYUMj+/Zp+fe/Xb+mJEFBCA82blw+HTuWcOaMmlmzPG8aSRrrOF5QkHvXFiQoCOHBrNNIGo2ZZctC+O03z5lG0uvh+HE1KpWZ+HjJFBzpwQct2cLeva7PFiQoCOHh2rUz8NRTlmmkZ5+NpKDAM6aRMjLUmEwKmjc3EOj+6gw+JSgIxo4tq6DqymxBgoIQXmDChHw6dCjl9Gk1b7zhGdNI0ljHuR5+uJAGDYzs2aNl40bXZQsSFITwAhoNzJt3CbXazKefhrBtm/unkaSxjnMFBZlt2YIr1xYkKAjhJTp0KJtGeuaZSAoL3TuNZC1vISeZnWfUqEKiooz8979aNm1yTbYgQUEILzJhQj7t25dy6pSaN98Mc+tYrI11pBCe87gjW5CgIIQX0WrLppGWLAll+3b3TCPl5ys4fVqNVmumWTMJCs5kzRZ279ayebPzswUJCkJ4mRtvNDBxouXd47PPumcayZoltGxpQK12+Y/3K8HBrs0WJCgI4YUmTcqjXbtSTpxQM3u266eRpLyFa40aVYhOZ2TXLi0//+zcbEGCghBeyDKNdBmVysySJSH8/rtrp5GkvIVrlc8W5sxxbrYgQUEIL/WXv5QyYUI+ZrOCp5+OpKjIddNI0oLT9R55pJD69S3Zwi+/OC9bkKAghBd76qk82ra1TCO99ZbrppGkBafrWbKFAsC52YIEBSG8WECApTaSSmXmk09CXNLXOSdHSWamipAQE3FxRqf/PFHmkUcKqF/fyM6dWrZscU62IEFBCC/XqVMp48ZZppGmTImkqMi5P8+aJbRubUApryAuFRJi5v/+z5ItzJ3rnMxQ/kuF8AFTpuTRunUpx4+reecd59ZGkvIW7jV6dAH33VfIzJlXnPL4EhSE8AEBAZbdSEqlmY8+CiEtTeO0n2UtbyE7j9wjJMTMBx9cpmNH5wRlCQpC+IjOncumkZ55xnnTSNJYx7dJUBDCh0yZkkerVqUcPaphzhzHTyOZzeUPrkmm4IskKAjhQwIDLbuRlEozH34Yws6djp1GunBByZUrSurVM9Kggcmhjy08gwQFIXxMQkIpTz5ZgMlkOdSm1zvusctnCQrPaAAnHEyCghA+6JlncomPLyU9XcO8eY7buijlLXyfBAUhfFBQkGUaSaEws3BhKLt3O2YaScpb+D6XFb3dvXs3S5cuxWQy0b9/fwYPHlzh+2vXrmXjxo2oVCrCw8MZO3YsDRo0cNXwhPA5XbuW8re/FfDhh6FMmRLJ+vUXCbjOQ7BS3sL3uSRTMJlMLF68mOnTpzNv3jy2bt3KmTNnKtymWbNmzJ49m3fffZeePXuyYsUKVwxNCJ82dWouLVoYOHLk+qeRTKbyp5klU/BVLgkKGRkZxMTEEB0djVqtplevXqSlpVW4zY033kjA/97GtGrVipycHFcMTQifdvU00n//W/dppFOnVOj1SmJijERGuqiLvHA5l0wf5eTkoNPpbJ/rdDrS09OrvP2mTZvo3Llzpd9LTU0lNTUVgNmzZxMVFVWnManV6jrf1xfJ9ajIl67HwIEwcaKJ+fNVTJ2qY/t2Q62mkazXYts2y3ajv/xF4TPXpi586blRGZcEBXMlNV4VVexn27JlC8eOHeOVV16p9PvJyckkJyfbPs/KyqrTmKKioup8X18k16MiX7sekyYp+P77Buzfr2bGjGKmTcuz+77Wa5GWFgpoaNGikKysXOcN1sP5wnMjNja2yu+5ZPpIp9ORnZ1t+zw7O5t69epdc7s9e/bw3XffMW3aNDQa59VuEcLfBAWZbdNIH3wQyt69tf/7kvIW/sElQSE+Pp7z58+TmZmJwWBg27ZtdO3atcJtjh8/zscff8y0adOIiIhwxbCE8Cs9epQwZkwBRqOlxHZJSe3uf+SIFMLzBy6ZPlKpVIwZM4ZZs2ZhMpno168fjRs3ZuXKlcTHx9O1a1dWrFiBXq9n7ty5gCVFe+6551wxPCH8xvPP57FxYyAHD2qYPz+MZ5+1bxqptBQyMsr6KAjfpTBXNuHvRc6dO1en+/nCvKAjyfWoyJevx3/+o+WBB6JQq8388MNFbryx+hd5yyLzZfr1a0jTpga2bct00Ug9ky88N9y+piCE8Bw9e5YwZkw+BoOCKVPq2TWNVFbeQtYTfJ0EBSH80Asv5NG0qYEDBzR88EFojbcvK28hU0e+ToKCEH4oONjMu+9eBiAlJYz9+6tfXpTyFv5DgoIQfqpXrxIeeaTANo1UWs3MUFkLTpk+8nUSFITwY3//ey6NGxvYv7/qaaSiIjhxQoVabSY+XjIFXydBQQg/FhJScRrpwIFrp5EOHVJgNito0cKAVuvqEQpXk6AghJ+7+eYSHn64gNJSS6e2q6eR9u+3lKSRRWb/IEFBCMGLL+YSF2dg714tCxdWnEYqCwqynuAPJCgIIQgNLZtGmjcvzHYuAcqCguw88g8SFIQQAPTpU8KDD5ZNIxn+FwMOHJBMwZ9IUBBC2MyYkUtsrIH//lfLokWh5OYqOH1aQWCgmaZNje4ennABCQpCCJuwMDPvvnsFgDlzwli7NgiAVq1KUancOTLhKhIUhBAV9O1bzMiRBZSUKPj73y1l7GXnkf+QoCCEuMaMGbnccIORkhLrIrOsJ/gLCQpCiGuEh5t5553Lts8lU/AfLmmyI4TwPv36FTNlSh7/7/+F0KNHLdu0Ca8lQUEIUaVnn80jKiqArCyv7sUlakGmj4QQQthIUBBCCGEjQUEIIYSNBAUhhBA2EhSEEELYSFAQQghhI0FBCCGEjQQFIYQQNgqz2SynUoQQQgB+nCk8//zz7h6CR5HrUZFcjzJyLSry9evht0FBCCHEtSQoCCGEsPHboJCcnOzuIXgUuR4VyfUoI9eiIl+/HrLQLIQQwsZvMwUhhBDXkqAghBDCxi+b7OzevZulS5diMpno378/gwcPdveQ3CIrK4sFCxZw+fJlFAoFycnJ3Hnnne4eltuZTCaef/556tev7/PbD2tSUFDAokWLOH36NAqFgrFjx9K6dWt3D8st1q5dy6ZNm1AoFDRu3Jhx48ah1WrdPSyH87ugYDKZWLx4MS+++CI6nY4XXniBrl270qhRI3cPzeVUKhUPP/wwLVq0oKioiOeff56OHTv65bUob926dcTFxVFUVOTuobjd0qVL6dy5M8888wwGg4Hi4mJ3D8ktcnJy+PHHH5k3bx5arZa5c+eybds2kpKS3D00h/O76aOMjAxiYmKIjo5GrVbTq1cv0tLS3D0st6hXrx4tWrQAICgoiLi4OHJyctw8KvfKzs5m586d9O/f391DcbvCwkIOHjzIrbfeCoBarSYkJMTNo3Ifk8lESUkJRqORkpIS6tWr5+4hOYXfZQo5OTnodDrb5zqdjvT0dDeOyDNkZmZy/PhxWrZs6e6huNWnn37KQw89JFkCludEeHg4Cxcu5OTJk7Ro0YLRo0cTGBjo7qG5XP369bnnnnsYO3YsWq2WTp060alTJ3cPyyn8LlOobAeuQqFww0g8h16vZ86cOYwePZrg4GB3D8dt/vjjDyIiImzZk78zGo0cP36c22+/nbfffpuAgABWrVrl7mG5RX5+PmlpaSxYsIAPP/wQvV7Pli1b3D0sp/C7oKDT6cjOzrZ9np2d7bNpoD0MBgNz5szhlltuoUePHu4ejlsdPnyYHTt2MH78eN577z327dvH/Pnz3T0st9HpdOh0Olq1agVAz549OX78uJtH5R579+6lYcOGhIeHo1ar6dGjB0eOHHH3sJzC76aP4uPjOX/+PJmZmdSvX59t27YxadIkdw/LLcxmM4sWLSIuLo67777b3cNxu5EjRzJy5EgA9u/fz5o1a/z2uQEQGRmJTqfj3LlzxMbGsnfvXr/dhBAVFUV6ejrFxcVotVr27t1LfHy8u4flFH4XFFQqFWPGjGHWrFmYTCb69etH48aN3T0stzh8+DBbtmyhSZMmTJ06FYARI0aQkJDg5pEJTzFmzBjmz5+PwWCgYcOGjBs3zt1DcotWrVrRs2dPnnvuOVQqFc2aNfPZchdS5kIIIYSN360pCCGEqJoEBSGEEDYSFIQQQthIUBBCCGEjQUEIIYSNBAUhhBA2fndOQYjqjB8/nsuXL6NUKgkMDKRz58489thjflnvR/gnyRSEuMpzzz3H8uXLeeeddzhx4gTfffedu4ckhMtIpiBEFSIjI+nUqRMnTpwAYOfOnXz55Zf8+eefBAcH069fP4YNGwZYKopOmDCBcePGsXLlSkpKSrjrrru4//77ASgpKeGjjz7ijz/+IDIykqSkJH788UcWLVoEWKr3LlmyhIMHDxIYGMhdd90lDY+EW0hQEKIK2dnZ7Nq1ixtvvBGAgIAAJkyYQKNGjTh9+jSvv/46zZo1o3v37rb7HDp0iJSUFM6dO8f06dPp3r07jRo14uuvv+bixYu8//77FBcX8+abb9ruYzKZeOutt+jWrRuTJ08mOzubmTNnEhsbS+fOnV3+ewv/JtNHQlzlnXfeYdSoUYwdO5aIiAhbNtChQweaNGmCUqmkadOm9O7dmwMHDlS479ChQ9FqtTRr1oymTZty8uRJALZv3859991HaGgoOp2OgQMH2u5z9OhRcnNzGTJkCGq1mujoaPr378+2bdtc90sL8T+SKQhxlalTp9KxY0cOHDhASkoKeXl5hISEkJ6ezueff86pU6cwGAwYDAZ69uxZ4b6RkZG2fwcEBKDX6wG4dOkSUVFRtu+Vb/R08eJFLl26xOjRo21fM5lMtGvXzkm/oRBVk6AgRBXat29PUh/KwwAAAWlJREFUUlISy5YtY9q0acyfP58BAwbwwgsvoNVq+fTTT8nNzbXrsSIjI8nOzraVni7f0yMqKoqGDRv6de8G4Tlk+kiIatx1113s3buXEydOUFRURGhoKFqtloyMDH777Te7HycxMZFVq1aRn59PTk4O69evt32vZcuWBAUFsWrVKkpKSjCZTJw6dYqMjAxn/EpCVEsyBSGqER4eTp8+ffjmm294/PHHWbZsGUuWLKF9+/YkJiZSUFBg1+MMGTKEjz/+mAkTJlCvXj1uvvlmfv75ZwCUSiXPPfccy5YtY/z48RgMBmJjYxk+fLgTfzMhKif9FIRwgw0bNrB161ZeffVVdw9FiApk+kgIF7h06RKHDh3CZDJx7tw51qxZU2ErqxCeQqaPhHABg8HAxx9/TGZmJsHBwfTu3ZsBAwa4e1hCXEOmj4QQQtjI9JEQQggbCQpCCCFsJCgIIYSwkaAghBDCRoKCEEIIm/8PfMNVC8WV17YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# style the plot\n",
    "style.use('ggplot')\n",
    "plt.plot(randomNumber,'b',label='line one',linewidth=2)\n",
    "plt.xlabel('Range')\n",
    "plt.ylabel('Number')\n",
    "plt.title('First Plot')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Website traffic data\n",
    "web_customers = [123,541,648,720,410,1298,1401,1560,250,60,40,10]\n",
    "time_hrs = [3,4,5,6,7,8,9,10,11,12,13,14]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Style the plot\n",
    "style.use('ggplot')\n",
    "plt.plot(time_hrs,web_customers,color = 'b',linestyle = '--',linewidth = 2)\n",
    "plt.axis([3,15,10,1700])\n",
    "plt.title('Web site traffic')\n",
    "plt.xlabel('Hrs')\n",
    "plt.ylabel('Number of users')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Style the plot\n",
    "style.use('ggplot')\n",
    "plt.plot(time_hrs,web_customers,alpha = .4)\n",
    "plt.title('Web site traffic')\n",
    "# Annotate\n",
    "plt.annotate('Max',ha='center',va='bottom',xytext=(8,1500),xy=(11,1630),arrowprops = {'facecolor' : 'green'})\n",
    "# Set label\n",
    "plt.xlabel('Hrs')\n",
    "plt.ylabel('Number of users')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Matplot - SubPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the required libraries\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "%matplotlib inline\n",
    "# define temp, wind, time, humidity and precipitation data\n",
    "temp_data = [79,80,45,80,14,44,41,55,60,62,58,65,71,76,82]\n",
    "wind_data = [10,12,14,15,13,9,11,16,17,19,18,20,21,22,25]\n",
    "time_hrs = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]\n",
    "humidity_data = [78,41,46,47,72,81,43,55,40,65,58,85,42,69,87]\n",
    "precipitation_data = [26,41,48,47,42,55,49,58,60,65,74,82,81,53,87] \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2462b846c08>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAEJCAYAAABMn/TfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO3deXxU1dnA8d+dmezLZAMNu/vyYkW01qpYlWrd6lY9blVUFBdqXati69L2raKiaGsVqeAKylFBVLAuFF9q3SEottYlCIQ1ZCMh+8zc9487gyEkZCZzZ+4sz/fz4ZPkzsy9z1xy88w595zzGKZpIoQQQoj4cjkdgBBCCJGOJAELIYQQDpAELIQQQjhAErAQQgjhAEnAQgghhAMkAQshhBAOkAQshBBJyjCMiw3D8Nm0L9MwjF/asS8RHknASSZ4kezs3yqnYxRCRMYwjPGGYXQahlHQbfvnO9n+NDAHGBzPWIV9JAEnn/Iu/04Lbju0y7YfOhSXEKL/3gE8wE9CGwzDKAP+B9jQw/aRwCLTNFtN09wU51iFTSQBJxnTNDeG/gF1wc2bu2zfDGAYRqZhGH8yDGO1YRithmF8YRjGJaH9GIaRHWwxX2kYxsuGYTQbhrHKMIzTDMMoMQxDG4ax1TCMbw3D+HmX1+0bfN25hmG8axhGW/A5Z8f3TAiROkzTXA1UAmO7bD4W+AKY38N2A1jUvQs69LNhGEcYhrHMMIwWwzA+MQzj4K7HMwzjmGArui349ZiYvTnRK0nAqesZ4ETgUmB/4G7gz4ZhXNDteXcAc4FRwCLgOWA28Gpw2z+A5wzD8HZ73RTgMeDA4OtfMAzjgNi8FSHSwiJ2TLT/CP7rvv2/pmmu62U/LuAe4FpgNFAPaMMwPACGYQwCXgeWBh+/EXjYvrchwiUJOAUZhrEvcA7wC9M0F5mm+Z1pmrOBvwDXdHv6M6ZpzjJN8xvgLiAfWGGa5nOmaX4L3AkUsmPX9mOmac4xTfMr0zRvBpZjXfBCiP5ZBIw0DGNg8OdjgcXA/wH7d9v+zk72YwDXmab5T9M0/4v1IXt3YI/g41cDNcDlpmn+xzTNt4Hb7H0rIhwepwMQMRFKlisMw+i63QM0d3vuZ12+3xj8+nkP2wayvQ+6/fw+cDBCiP76R/DrWMMw/omVNP/PNM0thmF83mX7XsBvdrIfk+2v61BLeRfgK6wesY9N0+w6evo9O96AiIwk4NTkwroIfwh0dnss0O3nro+b3beZpmkGk3hfvSVGH48LIXbCNM0awzA+w+puzgSWmaa5Jfjw4i7b/cC7O9lVwDRNf9ddB7+GrmGjy7buzxFxJAk4NX2KdZENNk1zZ11V0TiM7z+xA/wYqxtaCNF/i4BfABlsf30txrqFlAF82iUx98e/gQsNw3B3SdRHRrE/0U9yDzgFmab5b6yBVE8ZhnG+YRh7GIYxyjCMywzDuNGmw1xlGIYyDGNvwzAmYw3YkoEcQkRnETACOIPtE/ASYGhw+6Ioj/EYMACYbhjGfoZhjAX+FOU+RT9IAk5d47AutLuAL4G3gQuwpjrY4WasAV2fAwo43zTNz3f+EiFEH5Zg3QLKpst9WdM0G7FGLRew8wFYfQqOnv451voBy7E+ON8QzT5F/ximKV3/InzBEdZfAj80TfNTp+MRQohkJS1gIYQQwgGSgIUQQggHSBe0EEII4QBpAQshhBAOiPc8YGluCxG+RF7cRK5lIcLX47Uc94U41q9fH+9DOq6srIyamhqnw3CcnAdLOOdh0KBBcYqm/+RaTl9yHizRXsvSBS2EEEI4QBKwEEII4QBJwEIIIYQDJAELIYQQDpAELIQQQjhAErAQQgjhAEnAQgghhAMkAQsRR0uXZiCrvwqR/NyrV0NVVVT7kAQsRJy88UY2EycW09zsdCRCiGgVPPQQrrlzo9qHJGAh4mD1aje33OLlscfqyc93OhohRFRaW8l+6y0CSkW1G0nAQsRYeztceWUx1167lYMO6nQ6HCFElLLfeYfOAw6A8vKo9hPWWtBKqeuBy7AWYF8BXAKUAy8AJcAy4EKtdUdU0QiRgv7wBy9Dhvi59FLpexYiFeTMm0fLGWeQF+V++mwBK6UGA78GDtFajwTcwLnAvcBUrfVeQD0wPspYhEg5r76azbvvZvHAAw0YiVzbSAgRFqO+nqz336ftpJOi3le4XdAeIEcp5QFygQ3AscBLwcefBk6POpoI1da6ePvtLD78MDMux3v33SwZwSrCtnKlm9/9zsu0afUUFsovjhCpIGfBAtp/8hPMgoKo99VnF7TWep1SagqwBmgF3gKWAg1aa1/waWuBwT29Xik1AZgQ3BdlZWX9CtTngxUrDD76KPTPRU0N7LGHidsN773n63snUfD7Ydy4DL76qpMhQyJ7rcfj6ff7TiXpdB5aW2HiRA933hngmGO82z2WTudBiFSTM3cuzVdcYcu++kzASqli4DRgN6ABeBE4sYen9vgRX2s9HZgeek64NSSrq10sW5bJ0qUZLFuWyeefZzBkiJ+DD25n9OhOLrusgz339FFT4+K44wbEvDbl2rVufL5d+O67BrKzI0v2UjvTkk7n4eabvYwY4efMM+vp/pZTpR6wEOnGvXYtnq+/pu2YY2zZXziDsH4KfKe13gyglJoLHA4UKaU8wVbwECDq6twffJDJs8/msmxZJk1NLkaP7mD06A6uvXYrBx7Ygde7Y44fODBAc7NBc7NBXl7suvnWrHEDsGWLDBwXOzd3bg4ffJDFG29slvu+QqSQnFdese79Ztpz2zOcBLwGOEwplYvVBT0W+BRYDJyFNRJ6HDA/2mCys01+8pN2brihid139+MKI9cZBgwZ4qeqys2++8auG7qqykrADQ2SgEXvvvnGw513FjJnTi35+Yl131cpNRR4BtgVCADTtdYPd3n8JuB+YIDWOj26KoSIQM68eWz5059s21+f2URr/RHWYKtlWFOQXFhdyrcANyilvgVKgRnRBnPQQZ2cc04re+4ZXvINGTbMv62FGitr1lifVbZskSaN6FlLi8EVVxQzaVIT++8f2zEJ/eQDbtRa7wccBkxUSu0P25LzcVgfuIUQ3Xj+8x+MxkY6Dj3Uvn2G8ySt9Z3And02rwTsiyQKQ4f6gwmyPWbHWLPGTVFRgPp6aQGLnv32t15GjuzkvPNanA6lR1rrDVgzGNBaNymlvsQaPPkfYCpwMzb0ZAmRinLmzaP1jDOIqHXYh5TIJsOG+eLSAj7ggE7pghY9mjMnh4qKDCZP3pIU932VUiOAg4CPlFKnAuu01p85G5UQCSoQIOeVV6wEbKOwWsCJbtgwPx99FNu5wFVVbs46q0UGYYkd/Pe/Hv73fwt56aVacnMT675vT5RS+cDLwHVY3dK/BY4P43W2TClMZjKFzJJu58FYsgRXSQlFY8Zstz3a85AiCdhHVVXs3kprqzX4ap99fLzzTnbMjiOST3OzwYQJxdx+eyP77JOQ9323o5TKwEq+s7TWc5VSB2BNMfxMWQvLDwGWKaUO1Vpv7Pra/k4pTCXpNJVuZ9LtPHifegr/qaeytdt7jnZKYUokYOsesBvTJCbdf+vWeSgv91NSEpBBWGIb04RbbvHywx92oFSr0+H0SSllYA2W/FJr/SCA1noFMLDLc1ZhLTubPn9dhdiZ9nZyFixg81tv2b7rlEjAXq+JxwP19S5KSgK273/NGjfDhvkoKgrIPWCxzaxZuXz5ZQavv540ueoI4EJghVJqeXDbbVrrhQ7GJERCy168mM5998U/uMfFHqOSEgkYYOhQayBWrBLw0KF+vN6A3AMWACxdmsG99xYwb14NOTmJf98XQGv9HrDTLhyt9Yj4RCNEcsiZO9f2wVchKZNNhg3zs3p1bEZCr1njYdgwv7SABWAttjF+fAkPPdTAnnv6nQ5HCBEjRmMjWUuW0HryyTHZf8pkk2HD/DEbiFVVZXVBe70mjY0GAfsb2SJJrF/v4oILSvjtbxsZOzZ2886FEM7LfuMN2g8/HLO4OCb7T5kEHOqCjgXrHrAftxvy860kLNJPfb3BBReUcsklzZx9duIPuhJCRCc3ht3PkEIJ2GoBxyYBV1VZXdCAdEOnqdZWg0suKeHoo9u58spmp8MRQsSYa+NGMlasoO2nP43dMWK25ziz1oO2vwt6yxYDnw+Ki61+ZxmIlX58PrjqqmKGDvVz++2NSbHSlRAiOjnz59P2s59BTk7MjpEymWTIEB8bNrjx2zwmxrr/69/2R7eoyJQWcBoJzfXt7IQHHmiwcxlYIUQCy5k3j5YYdj9DCiXg7Gyre3jjRnvf0po1HoYO/X6FI683QEODNIHSxb33FvDVVxlMn15vVwlQIUSC83z7Le7qajqOOCKmx0mZBAyhogz2dkOHBmCFyD3g9DFjRh4LF2bzzDN15OUlx1xfIUT0cubOpfXUU8Ed2yI/KZVJQktS2ik0BzhEEnB6mD8/m8cey2f27LqYLO4ihEhQpmlVPjrzzJgfKqUySSzmAldVubfrgi4qkkFYqW7JkkzuuMPLs8/WMmSILLQhRDrJWLoU0+Oh84ADYn6slMoksagLvGMXtAzCSmWff57Br35VzPTp9ey3X+JXNxJC2Ct33jxr7m8cpjukVCYZOtTeucCmCWvXehg69PsEbE1DkkFYqWjlSjcXX1zCffdt4Uc/6nA6HCFEvHV2kv3aazFdfKOrlErAds8Frq52kZcX2G4AjtwDTk2bNrm44IJSbryxiRNOaHM6HCGEA7KWLME/fDj+ESPicryUyiTl5X7q6ly02fT3s/sALAhNQ0qp05b2GhsNfvnLUs45p4ULLmhxOhwhhENy5s2jJQ6Dr0L6bC4qpfYB5nTZtDtwB/BMcPsIYBWgtNb19ocYPrcbBg3ys3at25YqNaE6wF1JCzi1mCbccEMRo0d3cO21W50ORwjhEKO5mexFi2i86664HbPPTKK1/kprPUprPQo4GGgB5gG3Aou01nsBi4I/O866D2xPN3SoDnBXxcWmLMSRQp54Io8NG9z84Q9bZIlJIdJY9ptv0nHIIQTKyuJ2zEgz1VigUmu9Wil1GnB0cPvTwLvALfaF1j92joSuqvJw8MHbD8bJyTHx+w3a2qzVt0TyWro0g7/8JZ/XX68hK8vpaGJPKTUUq+dqVyAATNdaP6yUuh/4OdABVAKXaK0bnItUiPjLCY1+jqNIE/C5wPPB73fRWm8A0FpvUEoN7OkFSqkJwITg8yiL8aeLffd1UVOTTVlZbtT72rjRw8iRGTvsq6QE3O4ywn0rHo8n5u87GSTSeairg2uuyWDaNB+jR8em1mdvHDwPPuBGrfUypVQBsFQp9TbwNjBJa+1TSt0LTCIBPkwLES+umhoyP/2U+mnT4nrcsBOwUioTOBXr4gyb1no6MD34o1lTUxPJyyNWWprNhx/mUFMT/e3oysqBeL111NRs3w1dUDCA775rICMjvHmiZWVlxPp9J4NEOQ+BAFxySQk/+1kzhx/eSLxDCuc8DBo0yPbjBj8whz40NymlvgQGa63f6vK0D4GzbD+4EAks+7XXaBs7FjMvL67HjaQFfCKwTGu9KfjzJqVUebD1Ww5U2x9e5OyaC9zZCdXVbgYP3nEwlyzGkdwefzyPujoXTzzR6HQojlFKjQAOAj7q9tClbD/osutr4tqblYgSqRfHSal0HoyKCjyPPILvuecifk/RnodIEvB5fN/9DPAqMA6YHPw6v99R2Gj4cHvmAq9b52bgQD8ZGTs+Zo2ElhE7yejjjzN5/PF8Fiyo6fH/Nh0opfKBl4HrtNaNXbb/FqubelZPr4t3b1YiSpReHKelynnI/Ogjii+/nPr77qNtv/2ItDss2t6ssJpxSqlc4DhgbpfNk4HjlFLfBB+bHM6+Yq2kJEBHhzW3Mxo9jYAOkbnAyam21sXEiUU88EBDjz0b6UAplYGVfGdpred22T4OOAW4QGstpZ9Eysv6xz8ovuwy6h95hLYTTnAkhrCailrrFqC027ZarFHRCcUwQitiuRk5sv9r+VZV7bgIR4jMBU4+gQD8+tdFnHFGK2PHtjsdjiOUUgYwA/hSa/1gl+0nYA26+knwWhcipWW/+ire22+n7skn6TzkEMfisLd0UIIIzQWOJgFbLeCeXy8VkZLPX/6ST2urwc03NzkdipOOAC4EViillge33Qb8GcgC3lZKAXyotb7SmRCFiK3c2bMpeOABap9/Ht/++zsaS0omYDvmAldVuXttKRUVmVRWxrZQs7DP++9n8tRTeSxcuBlPSv7Gh0dr/R7Q072ZhfGORQgn5E2bRt6TT1Lz4ov4d9/d6XBSMwEPHepn9eroEqS1DnTPvXHWPWAZhJUMNm92cc01xTz0UAPl5QGnwxFCOME0Kbj/frJff52auXMJDB7sdERAihVjCBk2zM/q1dF9tpAu6OTn98PEicWce24LP/lJet73FSLtBQIU3n47WYsWUTtvXsIkX0jRFvCwYb6o5gI3Nxs0N7sYOLDnFpMMwkoODz1UECy2kNb3fYVIXz4fRTfcgLuqitoXX8QsLHQ6ou2kaAK2FuMwTfq1wH5VlZshQ3y4esmxMg0p8S1ZksWsWbn8/e+bccvteiHST1sbxRMnYrS3Uzd7NmZOjtMR7SAls0henklursnmzf17e1YZwt7niUpFpMS2caOLa68t4i9/qe+1F0MIkbqM5mZKx40Dj4e6mTMTMvlCiiZg+H4ucH9UVXl6XYQDoLAwQGOji4D8bU84Ph9cfXUxF13UzBFHdPT9AiFESjHq6yk991x8Q4dS/+ijkJnpdEi9StkEHE1dYKsF3Psc4owMqyzh1q3SCk40Dz9cQFaWya9/vdXpUIQQceaqrqbs7LPpOOQQttx/P4l+/yllE3A0c4GrqnpfhjJEBmIlnv/8x8PTT+cydWpDol93QgibudeupeyMM2g9+WQa77ijfwOA4ixlM8jQof3vgrbmAO88AXu9pkxFSiA+H9x0UxGTJjWx665yb0CIdOL59ltKzziD5vHj2Xr99UmRfCGFE3B/qyKZZt9d0GC1gOvrk+M/OR088UQe+fkm554rSxkLkU4yVqyg9Oyzabr5ZpovvdTpcCKSktOQAIYO7d9c4Pp6Fx6P1cLdGa9XFuNIFN995+aRR/J5/fWaZPngK4SwQaic4JZ776XtxBOdDidiKZtBBg/2s2mTG1+E9Rh2tgJWV8XFcg84EZgm/OY3RVxzzVZGjEjPEoNCpKOsxYutWr6PPJKUyRdSOAFnZkJZmZ/16yNrBfc1BzhEBmElhtmzc2lrM7jssmanQxFCxEn2a69RdN111M2cScdRRzkdTr+lbBc0fD8XOJyEGtLXHOAQr9ekvl4SsJM2bHAxeXIBL75YK6OehUgTuc8/T8GUKQlRTjBaKZ1B+jMXOJwBWBBqAcsNx3C121wLwTThttu8jBvXwr779r/usxAieeQ9/jj5Dz9MzYsvJn3yhRRPwMOH+yIuSxhui1kGYfXNNOGTTzKYMKGYvfYq5447rPW57fDaa9msWuXhmmuk0IIQKS9YTjB31ixqXn45IWr52iGlu6CHDvWzeHFWRK8JZw4whKYhSQLuSWcnLFyYzd/+lk99vYvx45v53e8aufbaAXz7bRFTpjREtTpcXZ3BnXd6eeKJOrIi++9Na0qpocAzwK5AAJiutX5YKVUCzAFGAKsApbWudypOIbYTCFB4551kfvyxVU6wtNTpiGyT0hnEugcc/mcMvx82bLAqIfVFagLvqKHB4NFH8zn88IE8+2we11yzlSVLqrn00maGDfPz5ps+mpoMLrywlMbG/nff33WXl1NPbeXggzttjD4t+IAbtdb7AYcBE5VS+wO3Aou01nsBi4I/C+E8n4+i668n44svqH3xxZRKvhBmC1gpVQQ8AYwETOBS4CsS/FNzpHOBN250UVQUIDu77+cWFUlFpJDKSjczZuQzf34OY8e2MXNmPQccsGNyzM2FJ56o53e/83LmmWU8+2wt5eWRrVr1j39k8fHHmfzjH5vtCj9taK03ABuC3zcppb4EBgOnAUcHn/Y08C5wiwMhCvG9QIDiq67CaG1N2HKC0Qq3efgw8Het9VlKqUwgF7gN61PzZKXUrVifmhPqot1llwBNTS5aWw1ycvq++RjuCGiQaUimCf/6VyZ/+1s+FRUZXHBBC4sWVfe5DKTbDXffvYVHH83ntNPKeOaZurAHUW3danDrrV6mTNlCbq5NN5PTlFJqBHAQ8BGwSzA5o7XeoJQa2MtrJgATgs+jrKwsTtEmDo/Hk5bvu7t4nAfX88/jqq7Gt3gxpQla0Sja89BnAlZKFQJHARcDaK07gA6lVMJ/ana5YNAgP1VVbvbeu+8/8uGOgAar5nBHh0FHR0JXu7KdacLLL+fw+OP5dHbCZZc1M21aHZF8ODUMmDhxK+XlfpQqZdq0eg4/vO/SgZMnF3DkkR0cdZTNQ6rTjFIqH3gZuE5r3aiUCut1WuvpwPTgj2ZNTU2MIkxcZWVlpOP77i7m56Gjg4F33EHdgw/S0dgYu+NEKZzzMGjQoF4fC6cFvDuwGXhSKXUgsBS4liT51LzHHi7q64spK+u7xVRT42affQg7xuJicLvL6OvpqfSp+X//18XcuS4mT/Zz/PEmhpGL1SHSt+7nYcIE2GuvABdeWMoDD/g555zeW8//+pfBm296WLask+Li5D6XTv4+KKUysJLvLK313ODmTUqp8uB1XA5UOxKcEEG5s2bh22MPOn78Y6dDialwErAHGA1co7X+SCn1MBEM0nD6U3N5uZd//9vHj37U90pJ//1vEUcc0U5NTWtY+y4oGMh33zXgdu+81Zwqn5qfeSaXZ5/N55VXqhkwIEBtbWSv7+k8HHAAPP+8h4suKuHrr1u58srmHdZzbmuDyy8fwO9/X4/f30ayn8poPzX3l1LKAGYAX2qtH+zy0KvAOGBy8Ot82w8uRJiM5mYK/vxnap991ulQYi6cBLwWWKu1/ij480tYCTgpPjVHUhc4nDrAXaVTRaQFC7J5+OECXn65hgED7C33t99+PubPr+HCC0tZu9bDH/6wZbuVrR56qIB99vFx0kltth43DR0BXAisUEotD267DSvxaqXUeGANcLZD8QlB3vTptB9+OL6RI50OJeb6TMBa641KqSql1D5a66+AscB/gv8S/lPz0KF+Pv00vJu04c4BDkmXqUjvv5/JpEleZs+ujVnBg0GDAsybV8P48SVcfnkxf/1rAzk5Jl984WH27FzefltGPUdLa/0e0NsnxrHxjEWInrjq6sibMYOa1193OpS4CDd7XAPMUkp9DowC7sZKvMcppb4Bjgv+nHDCnQvc3g51dS7KyyNLwKk+Evrf//Zw5ZXFPPpoPSNHxnbJx8JCk1mzasnLM1GqlOpqFzfdVMRvf9vILrvY2+oWQiSe/D//mbZTT8U/YoTTocRFWNOQtNbLgUN6eCjhPzWH5gKbJjutFbt2rZvycn9Ei/qnegJes8bNRReV8qc/beHII/sepWyHzEx4+OEG7r23gDFjBnLQQZ0oFd49eSFE8nKvW0fuiy9SvXix06HETUovRQnWghlgrdJUXNz7SOhI5gCHeL1mynZB19S4OP/8Uq65pomf/zy+915dLpg0qYkf/KCT0aM7dvrBSQiRGgqmTKH5oosIDOxxQk1KSvkEbBjfd0MXF/e+dOHq1W6GD4+si7WoKBBxsYdksHWrwYUXlnDaaa1cfHGLY3GcfLIMuhIiHXi++oqsRYuofu89p0OJq9RsvnUTzkjo/rWAU68LuqMDLrushAMO6OSmm6TSkBAi9gruvZetV1+NWVjodChxlVrZoxfh1AWOZBWskFS7BxwIwHXXFZGXF+Duu7dI168QYkeBgDU53yYZn35KxooVNF98sW37TBapkz12IrwWcGRzgCG1ErBpwl13FbJxo5tHHqnHk/I3J4QQ/VHwwANkjBqFe82a6HdmmhTecw9NN95IWFVwUkxqZI8+WC3gnSfgSOcAgzXAa8uW1Ggm/vWv+bz/fhZPPhnZus5CiPTh2rSJvKeeInD++ZSdeSaer7+Oan9Zixfjqq2l9ayzbIowuaRFO6evucCNjVZRhdLSyOaapkoLeM6cHJ57LpdXXqnB65UqQ0KInhU8/DAtZ59N5h130LzLLpQqRd3TT9N54IGR7ywQsFq/t9xCuna5pcW7HjrUz7p1bgIBa4pLd2vWWN3Pkd7z9HqtlbD6mmOcyN56K4t77inkpZdq+iwlKIRIX+7vviP71VfZvGQJJUDrL36BmZ9PyYUXUv/44xEXTsiZPx8zK4u2E06ITcBJIPmbb2HIyTHxegNs3Njz262qirz7GaxFIzIzTZqbkzP7rlzp5sYbi3jyyTr23DM2S0wKIVJDwZQpNI8fT6CkZNu2tp/9jPpHH6X4iivIeued8HfW0UHB/ffTeNttydt6sUFaJGDY+Ujo/oyADknm9aBfeSWHM89s5aCDep8fLYQQni++IOv992meMGGHxzqOPJK6p56i6KabyHnllbD2lzt7Nr7dd6fj8MPtDjWpJGfm6IedjYTuzwjoEK/XTNqKSAsX5shiF0KIPhVOnkzTr3+NmZfX4+Odo0dT+8ILFP7xj+T2UUbQaG6m4OGHabw17Kq2KSttEvDORkL3ZwR0SLIOxFq50k1trYtDDonPGs9CiOSU+f77eCorabnggp0+z7fvvtS8/DL5jz5K/qOP9vq8vL/9LW3KDfYl+TJHP+1sJLTVAk6vLuiFC3M44YS2HgelCSEEYM3Tvftumn7zG2vQSx/8I0ZQM3cuOVpTcM891gIDXbjq6sh74gmabropVhEnlbT58xuqitSdaYbuAadXC3jhwmxOOkmqDAkhepf9979jtLXRevrpYb8mUF5O7dy5ZC1Zgve226yVs4Ly//IXq9zgbrvFItykkxbTkACGD/ezevWOb3fzZhe5uSb5+f2b/5qMFZHWrnVTVeXmxz+W7ud0o5SaCZwCVGutRwa3jQKmAdmAD7haa/2xc1GKhODzUXDvvTTefnvP8zd3IlBSQq3WlFx8MUXXXkvDgw/irq4mV+u0KjfYl+TKHFEoL/dTW9mz780AAB98SURBVOuivX377dG0fiHUAk6uQVgLF2Zz/PFt6Tr3Pd09BXSfeHkf8Hut9SjgjuDPIs3lvPQSgdJS2o89tl+vNwsKqH3uOVwNDZRcfjkF99yTduUG+5I2CdjjgV13tRbk6Ko/VZC6SsYu6DfeyOakk2T0czrSWi8B6rptNoFQGRovsD6uQYnE09ZGwQMP0DhpUnTzdHNyqJsxAzMnh+zFi9l61VX2xZgC0qoNFJoLvPvu3yfcaOYAQ/KVJKyudvH11xkceWR7308W6eI64E2l1BSsD+U9Ts5USk0AJgBorSkrK4tfhAnC4/Gkxft2TZ2K6+CD8faySlXE5+GFF/BVV1NaXm5ThIkh2t+HtErAPc0Frqpyc+CB/V+IItlawG+8kc2xx7aRleV0JCKBXAVcr7V+WSmlgBnAT7s/SWs9HZge/NGsqamJY4iJoaysjFR/30ZjIwPvv5+aF1/E18t77dd5yMiAFDt34ZyHQYMG9fpY8mQOG/Q0FziaOcCQfBWRFi7M4cQTpftZbGccMDf4/YvAoQ7GIhyW/9hjtI8di2+ffZwOJeWF1QJWSq0CmgA/4NNaH6KUKgHmACOAVYDSWtfHJkx7DBvm5803t685Gc0cYEiuFnBdncFnn2VwzDHS/Sy2sx74CfAucCzwjaPRCMe4qqvJe+YZNr/5ptOhpIVIuqCP0Vp3bWvfCizSWk9WSt0a/PkWW6OzWfcuaJ8PNm50M2RIdIOwkmUa0ltvZTNmTDs5OVJyMF0ppZ4HjgbKlFJrgTuBy4GHlVIeoI3gfV6RfgoeeoiWs8/GP2SI06GkhWjuAZ+GdSEDPI316TnBE7B/uwS8fr2bsjJ/OAu89Co/36SlxaCz07rFkcgWLMjhF7+QxTfSmdb6vF4eOjiugYiE4161alu5QREf4SZgE3hLKWUCjwcHY+yitd4AoLXeoJTqcXJXIo2cLC2F9nYXWVllFBTAihUGe+zhijqmoiLweMrobTeJMHJyyxb45JMM5sxxUVjY84LqsZYI5yERyHkQiajg/vt3KDcoYivcBHyE1np9MMm+rZT6b7gHSLSRk0OGDKCiooH99/fxxRe57LprJjU1DVHts6BgICtX1mMYPXdlJ8LIyblzc/jRj3Lo6KhzbCBiIpyHRBDtyEkh7BYqN7jlPlmDJZ7CunmptV4f/FoNzMMaJblJKVUOEPxaHasg7dS1LnC0c4BDiosTfyCWtfiGdD8LIXbUV7lBERt9Zg2lVJ5SqiD0PXA88AXwKtb0BYJf58cqSDt1HYgVTR3grrzexB6I1dJi8M9/ZnH88TL9SAixvXDLDQr7hZM1dgHeU0p9BnwMLNBa/x2YDBynlPoGOC74c8LrOhBr9WoPw4dHn4ATfSrS4sVZHHRQB8XFMvpZCNFFhOUGhb36vAestV4JHNjD9lpgbCyCiqVhw/z861/WMlDRzgEOSfTFOBYuzJbFN4QQO8hesCDicoPCPonbbIuRUF3g1laDpiYXu+wS6PtFfUjk9aDb22Hx4mxOOEESsBDie1nvvot30iS23H13xOUGhT3S7qwPG2YtR1lV5WbQIL8tv3dFRQHq6xPzVC5ZksW++3YycGD0HzSEEKkh+/XXKbr2WupmzqTjUFl51CmJmTViqKDAJDMTKioybBkBDYk9CGvhwhwpPSiE2CbnhRfw3nEHtbNm0fnDHzodTlpLzKwRY8OG+fjXv7JsGQENiTsNqbMT3n47ixNPlOlHQgjI+9vfKHjwQavS0ciRToeT9hIva8TB0KHWQKxoqiB1laiDsD74IJMRI/wMHizdz0KkNdOk4IEHyHvmGWrnzcO/xx5ORyRI0wQ8fLiPjRvtWYQDEncQlnQ/CyEIBCi8806y//53aubOxT94sNMRiaBoijEkrVDXs30t4MRLwH4//P3v2bzyiiz9KETa8vko+s1v8KxcSc1LL2F6vU5HJLpIywQcSrx2zAGG7wdhmSYYCdIT/emnmQwYEGDECHs+ZAghkkx7O8W/+hVGczO1zz+PmZvrdESim8RqtsXJ8OE+CgsDFBXZszJUdja4XCatrQmSfYEFC2TtZyHSldHSQsnFFwNQ9+STknwTVFq2gEeM8PPaazW2tlaLikwaGgxyc51f7tE0reILzz1X53QoIsEopWYCpwDVWuuRXbZfA/wK8GEtN3uzQyGKKBlbtlB60UX49tiDhvvuA09a/plPCmnZAjYM2HNPe7qfQxLpPvDy5Rnk5Jjsvbe971GkhKeAE7puUEodA5wG/EBr/T/AFAfiEjZwbd5M2Vln0TFqFA1TpkjyTXCJkTFSQFGRfYtxzJmTw5QpBbS39+/1Cxdmc9JJbQlzP1okDq31EqB718hVwGStdXvwOUlRWlRsz71uHWVnnEHrSSfReNddsrxkEpCPRzaxcyrSwoU5rFrlZuHCbB58sIFRozrDfq1pWq+fNq3ellhEWtgbGKOU+hPQBtyktf6k+5OUUhOACQBaa8rKyuIbZQLweDwJ+749F1xA4NJLyb75ZrJjfawEPg/xFO15kARsE+sesD0JuLLSw8yZdfz73xmMG1fCOee0cMMNTWSHcVV9+aUHvx9Gjgw/aYu05wGKgcOAHwJaKbW71nq7AQ1a6+nA9OCPZk1N+k1xKysrIyHft8/Hrh9/zOZHH8WMQ3wJex7iLJzzMGjQoF4fkz4Km1hTkaLv8+3ogPXr3Qwf7uOMM1p5553NfPedh5/9bABLl2b0+frQ4hvS/SwisBaYq7U2tdYfAwFAmjdJxPPVV/gHDZJ5vklGErBN7KqItGaNm/JyP1lWyWIGDAgwfXo9N97YxPjxJfzxj4W07mR2kXX/V6YfiYi8AhwLoJTaG8gEpHmTRDIrKug86CCnwxARkgRsE7sGYa1c6WH33bcfvWwYcOqpbSxatJl169wcf/xAPvkkc4fXfvutmy1bXIweLd3PomdKqeeBD4B9lFJrlVLjgZnA7kqpL4AXgHHdu59FYstYvpyOUaOcDkNESO4B28SuaUiVlTsm4JDS0gDTptWzcGE2V1xRzM9/3sqttzaRk2P9rVy4MIcTT2yVwY+iV1rr83p56JdxDUTYKrOigpaLLnI6DBEh+VNtE7sqIq1c6WGPPXY+f/ekk9p4551qamtd/PSnA/jwQ6s1vHBhNieeKMUXhEgnxtatuFevpnPffZ0ORURIWsA2sWsaUmWlh9NP7/sebkmJySOPNPDWW1lMnFjMmDHtrF/v5kc/6og6BiFE8sj4/HN8++8PmTvelhKJLewErJRyA58C67TWpyildsO6X1QCLAMu1Fqn7V//eHRB9+T449s59NBqfv97L+ed1yIL3wiRZjIrKuiQAVhJKZKMcS3wZZef7wWmaq33AuqB8XYGlmxCFZGisWWLQWurwa67BiJ6XVGRydSpDUya1BTV8YUQySdj+XIZAZ2kwsoYSqkhwMnAE8GfDaxpCy8Fn/I0cHosAkwWhYUmW7ca+KOo/hcaAS1zeIUQ4cpctkxawEkq3A7Lh4CbgYLgz6VAg9Y61Fe6Fhjc0wvTafm6wkLIyCijpGT77eEuV1Zd7WK//YyUPUeyfJ1FzoOwi2vDBujowD9smNOhiH7oMwErpUKly5YqpY4Obu6pjdbjvMF0Wr6usHAglZX1BALbN4PDXbZt+fIChgyBmprU7EqW5ess0S5fJ6KX88or4PPRetZZTocSlczly+kcNQrpNktO4XRBHwGcqpRahTXo6lisFnGRUiqUwIcA62MSYRKJdiBWOFOQhBDRyZsxg8I//pHCu+7CqEvumtkZFRV0jB7tdBiin/rMFlrrSVrrIVrrEcC5wD+01hcAi4HQx8dxwPyYRZkkoh2IFekIaCFEBEyT/KlTyZs5k5r582k75RQK/vpXp6OKSmZFhdUCFkkpmmG7twA3KKW+xbonPMOekJJXNBWRAgH47ju3JGAhYsE0Kfz978lZsICaefPwDxlC03XXkfvCC7jWrXM6uv7x+8n4/HNZgjKJRTRrVGv9LvBu8PuVwKH2h5S8rC7o/t2L2bDBTVGRSX6+LMErhK38frw330zG119T89JLmEVFAAR23ZXmX/6SgqlT2TJlisNBRs7zzTcEysowi4udDkX0kyxFaaNoVsOqrPSw227S+hXCVu3tFF91FZ61a6l94YVtyTdk69VXk/3WW3i++cahAPsvY/lyuf+b5CQB2yiaikiVlW4ZgCWEjYyWFkouvRQCAWqfeQYzL2+H55heL1uvuoqC++5zIMLoZC5bJgtwJDlJwDaKZhS0jIAWwj7Gli2UnH8+gQEDqJ82jW0FtnvQfPHFZFZUkLFsWRwjjF6mlCBMepKAbWQNwurfPWAZAS2EPVw1NZSdfTadBxxAw4MP0ucC6Tk5NN1wA4V33w1mcozBMFpbca9cSef//I/ToYgoSAK2UTTTkCorpQUsRLRc69ZRdsYZtB1/PI1/+APhFsduUQpXdTVZ//d/MY7QHhmff45v33132rIXiU9q59iov13Qra2webOboUOjWEhaiDAopWYCodXtRnZ77CbgfmCA1jrplixzV1ZSev75NF96Kc1XXBHZiz0emm65hYJ77qH9qKPCTtxOyaiokO7nFJDYv2VJpr+DsFat8jB0qE9KCYp4eAo4oftGpdRQ4DhgTbwDsoPn3/+m7Oyz2XrddZEn36C2k04Cj4fs116zOTr7ZVZUyACsFCAJ2Eb9XYhDup9FvGitlwA9rb84FavgSnLcBO0iY9kySs8/ny2//z0t553X/x0ZBo2TJlF4333QkdilzTNkAFZKkDaXjbKzTUzT6lLOyQn/dTICWjhJKXUqsE5r/ZlSamfPS7zKZp2dZFx/Pb5HHiH/jDPIj3Z/p5+Oa/p0Br72GoEeWtIJUclq0ybczc0UH3qoY0UYEuI8JIBoz4MkYBsZxvcDsXJyAmG/rrLSw2GHJfYnbpGalFK5wG+B4/t6biJWNst99llydt2V2jFjwKZ4Mm66iZKLL6b2xBMxc3O3eywRKnplLVpE3oEHUldb61gMiXAeEkG0lc2kC9pm/RmIJV3QwkF7ALsBnwUrng0BlimldnU0qjAYra0UPPQQjZMm2brfzh/8gI5DDyXviSds3a9dpABD6pAWsM0iHYhlmtIFLZyjtV4BDAz9HEzChyTDKOi8GTPoOPjgmCSjxptvpuzUU2n+5S8xS0ps3380Misq2HrZZU6HIWwgLWCbeb2RDcSqq3NhGFBSEn6XtRD9pZR6HvgA2EcptVYpNd7pmPrDaGgg7/HHabz55pjs37/77rSdfHLilSsMBMj47DMZAZ0ipAVss0grIoWKMDg0lkKkGa31TocJB+t+J7z8v/6VthNPxL/nnjE7RtP11zNw7Fi2XnopgcGDY3acSHhWriRQVESgtNTpUIQNpAVss0grIq1cKUUYhIiEa8MG8mbPpun662N6nK7lChNFxrJldEjrN2VIArZZpIOwZACWEJEpmDqV5vPPJ1BeHvNjbb36arLffDNhyhVmLl8uA7BSiCRgm0U6CEuKMAgRPve335L9xhtsnTgxLsczvV6aE6hcYUZFhbSAU4gkYJtFWhFJWsBChK/wvvtovuIKzKKiuB1z6yWXkLlsmfPlCtva8HzzDZ0jR/b9XJEUJAHbLJIWsM8HVVUeRoyQBCxEXzKWLydz6VKax8d54HaClCvM+OILfHvuGdkyeyKhSQK2WSSDsKqq3AwY4JfrSYgwFN5zD03XXYfpwAXTcs45uDdtwnj77bgfO0QKMKSePqchKaWygSVAVvD5L2mt71RK7Qa8AJQAy4ALtdZpv55iJIOwpPtZiPBkLVmCe906Ws4915kAPB4ab7qJovvvh+efdySEjOXLrVKJImWEkynagWO11gcCo4ATlFKHAfcCU7XWewH1QFJO6LdbJBWRZAUsIcIQCFBwzz3WohsZGY6F0X7ccRhLl2Js3erI8TMrKugcPdqRY4vY6DNTaK1NrXXoNy4j+M8EjgVeCm5/Gjg9JhEmmcLCAE1NBoEwFraSEdBC9C379dcBaDvlFEfjMHNzMQ85hMwPP4z7sV11dbjq6vDtsUfcjy1iJ6yVsJRSbmApsCfwV6ASaNBah7LHWqDHpWISsoRZjOXnQ2ZmGaGBmr2VrKqq8nDeeZmUleXu8FgqkhJmFjkPEejspPC++9hy993gcn7ISmDsWLL++U/af/rTuB43o6KCzgMPTIhzIOwTVgLWWvuBUUqpImAesF8PT+txeGAiljCLtcLCgVRW1jN8uB/ovWTVV1/twoABddTU+OMdoiOkhJkl2hJm6ST3hRfwDx6cMPc+zWOPJcuBQgiZMv83JUX0cUpr3QC8CxwGFCmlQgl8CLDe3tCSVzhTkbZuNWhoMCgvT4/kK0SkYlVuMBrm6NG4N2zAtWlTXI+bsXy5jIBOQX0mYKXUgGDLF6VUDvBT4EtgMXBW8GnjgPmxCjLZhFMRaeVKD7vt5pceJSF6Ectyg/3mdtN++OFkvfde/I5pmlYLOJHOg7BFOH/+y4HFSqnPgU+At7XWrwO3ADcopb4FSoEZsQszuRQVBaiv3/lqWDICWojexbrcYDTax4wh65//jNvx3KtWEcjNJbDLLnE7poiPPu8Ba60/B3bo+9BarwQOjUVQyc7r7bsLWkZAC9G7eJQb7K/2MWMo+POfrVWx4lBHVBbgSF3SARoDxcV9L8ZRWSllCIXoSbzKDfaXf7fdMN1uPN9+G5fjZVRU0CHzf1NSWKOgRWSKigLU1Lh3+pyVKz1cfnlznCISwqKUmgmcAlRrrUcGt90P/BzowJpieElwwKUj4llusF8MY1s3tG+vvWJ+uMyKChpPPjnmxxHxJy3gGLAGYfXeNWWaVgKWLmjhgKeAE7ptexsYqbX+AfA14Niw423lBq++2qkQwtJ+1FFkxuM+cHs7nv/+l84f/CD2xxJxJwk4BvqahrRxo4ucHBOv17nKKiI9aa2XAHXdtr3VZVGdD7GmFTpiW7nB4mKnQghLxxFHkPXhh1ZJsxjK+PJLq8s7Nz0W60k30gUdA31VRJIR0CKBXQrM6emBWK9qZ3z6KZ6KCtzPPUd2giacbauYlZXBiBEM+O47zB//OGbHc339NcZhhyXcymmympsl2vMgCTgG+qqIJCOgRSJSSv0W8AGzeno81qvald5yC1t+/WtaWlqgpcXWfdul6ypmhT/+MYHXX2drDO8DF/3zn3QcfjgtCbaCnKxqZ4l2VTvpgo6BcBKwtIBFIlFKjcManHWB1jru90YynS432A/tRx0V8/nAmcuXywIcKUwScAxYJQl7H4QlXdAikSilTsBaWOdUrXX8m56BAIUJUG4wUh2HHkrGihUYzbGZzWA0NOCqrsa3994x2b9wnnRBx0BuronPZ9DeDllZOz5ujYCWNaBF/CmlngeOBsqUUmuBO7FGPWcBbyulAD7UWl8Zr5iyFywA03S83GCkzNxcOg88kMwPPohJdaTMzz6j84ADwL3zKY0ieUkCjgHD+H41rIEDty8M3N4OGza4GTZMWsAi/rTW5/Ww2bllZDs7Kbz33oQpNxip0HzgWCTgjGXLpAJSiku+3/gk0dt94DVrPAwa5Ccz04GghEgwuXPm4B80iPYxY5wOpV/ajzoqZoUZZAnK1CcJOEa8XrPHucAyAloIi9HaSsHUqTTedltc1lSOhc4f/MAqT1hdbe+OTZMMGYCV8iQBx0hvFZFkBLQQlryZM+kYPTqxyg1GKlSe0ObR0O6qKsjIILCTKSwi+UkCjpHeVsOSBCxEsNzgtGk03nKL06FErf3II21PwBkVFXL/Nw1IAo6R3u4Br1wpVZCEyH/0UdpOOCEhyw1Gatt8YNO+6dOZFRXJ3TMgwiIJOEZ21gKWe8Ainbk2bCBv1qyELTcYqW3lCSsrbdtnprSA04Ik4BjpqSJSfb1BR4exw9QkIdJJwdSptJx3Xurc3wyVJ1yyxJbduSsrca9cKS3gNCAJOEZ66oIOrYCVpAM+hYhaqNxg08SJTodiq44xY2wrT7itIlReni37E4lLEnCM9NQFLd3PIt0V3n8/zRMmJHy5wUi1H3mkLeUJMz77jMxPP6V5/HibIhOJTBJwjHi9Aerrd0zAMgBLpKttyeWyy5wOxXaBsjL8Q4aQsXx5VPspvOcemq67DjMnx6bIRCLrcylKpdRQ4BlgVyAATNdaP6yUKsGqGzoCWAUorXV97EJNLj21gFeu9HDKKa0ORSSEswrvuYema69N2eQSWpay85BD+vX6zCVLcK9dm1QVoUR0wmkB+4Abtdb7AYcBE5VS+wO3Aou01nsBi4I/i6CeKiJJFSSRrjKXLMFdVUXLeT0tRZ0aQgm4X0yTwsmTk64ilIhOnwlYa71Ba70s+H0T8CUwGDgNeDr4tKeB02MVZDLyegM0Nrq2TQ30+2HVKqmCJNKQaSZlucFIdfzoR/0uT5i9YAEEAklXEUpEJ6JqSEqpEcBBwEfALlrrDWAlaaXUwF5eMwGYEHweZWVlUQWcTLKzISurDI/HQ2trGWVlMGxYqdNhOcbj8aTV/39v0u08ZL/+ulVu8Oc/dzqUmNpWnvDDD2kfOzb8F/p8VkWoP/0pKStCif4LOwErpfKBl4HrtNaNwbqhfdJaTwemB380a2pqIg4yWXm9A6msrKewsJhPP21i+PB8ampqnQ7LMWVlZaTT/39vwjkPg1Jljmyo3GCaJJfQfOBIEnDunDn4y8uTtiKU6L+wErBSKgMr+c7SWs8Nbt6klCoPtn7LAZvLgSS/oiKTLVus+8AyAlokAqXUTOAUoFprPTK4LWYDKnO1tsoNHnWUHbtLeO1jxlD0m9+E/XyjtZWCBx+kbsaMpK0IJfqvz4+kSikDq2D3l1rrB7s89CowLvj9OGC+/eElt65TkWQAlkgQTwEndNsWkwGVoeTSOGlS2iSXzgMPjKg8Yd6TTyZ/RSjRb+G0gI8ALgRWKKVCk9xuAyYDWik1HlgDnB2bEJNX16lIlZUexo5tczgike601kuCYzm6Og04Ovj908C7QNRlirYll3Ra0zhUnvC992g988ydPtVoaCDvsceomTcvTsGJRNNnAtZavwf09vE1gpEG6afrcpSVlVIFSSSssAZURqSjg7wZM6idMyfqXSWb9iOPJGvJkj4TcP5jj6VMRSjRPxGNghaRCSXglhaoq3MzZIhMQRLJK9IZDf6KCopKSuIRWtyENYL91FPJePRRPKWlvXe9r19PxuzZdH7ySVKOiE+3kfy9ifY8SAKOodBiHN9+azBsmA+32+mIhOhRWAMq+zWjIcVGvYc1kr+4mIFAw0cf4euldev93e/oOOccGrOzk/IcyYwGS7QzGlJ/XoCDvF6rBfz114Z0P4tEJgMq7RQsT9hbdSR3ZWVKVoQSkZMEHEOhLuhvvkESsEgISqnngQ+AfZRSa4ODKCcDxymlvgGOC/4sotCxk/rAhffdl5IVoUTkpAs6hrq2gA85RBKwcJ7WurfFmGVApY3ajzwS76RJVnlCz/d/ZkMVoRoeesjB6ESikBZwDBUXW9OQvv7akDrAQqSR3soTpnpFKBEZScAx5PWa1Ncbcg9YiDTUvTpSOlSEEpGRBBxDRUUBNm504/FASYnpdDhCiDjaLgFLuUHRA0nAMZSfb2IYsPfeknyFSDddyxNuKzeY4hWhRGQkAceQYVgDsSQBC5F+tpUn/Ne/KLz3XpomTUqLilAifPLbEGNFRSZ77SUJWIh01D5mDN7bb7fKDaZJRSgRPknAMVZUJC1gIdJV+5gxeNauTauKUCJ8koBj7K67tnD88ZKAhUhHnaNGUfPyy+lVEUqETRJwjB18cCe5uU5HIYRwhMtFx2GHOR2FSFCSgIUQQggHSAIWQgghHCAJWAghhHCAJGAhhBDCAZKAhRBCCAdIAhZCCCEcIAlYCCGEcIAkYCGEEMIBhmnGdZUmWRJKiPAl8tqFci0LEb4er+V4t4CNdPynlFrqdAyJ8E/OQ8TnIZE5fh6d+Ce/w3Ie+nkeeiRd0EIIIYQDJAELIYQQDpAEHB/TnQ4gQch5sMh5SF7yf2eR82CJ6jzEexCWEEIIIZAWsBBCCOEIScBCCCGEAzxOB5DKlFKrgCbAD/i01oc4G1F8KKVmAqcA1VrrkcFtJcAcYASwClBa63qnYoyHXs7DXcDlwObg027TWi90JkIRCbme5XrG5utZEnDsHaO1rnE6iDh7CngEeKbLtluBRVrryUqpW4M/3+JAbPH0FDueB4CpWusp8Q9H2ECuZ4tcz9/r9/UsXdDCdlrrJUBdt82nAU8Hv38aOD2uQTmgl/MgRFKR69kSi+tZEnBsmcBbSqmlSqkJTgfjsF201hsAgl8HOhyPk36llPpcKTVTKVXsdDAibHI9f0+u5+/1+3qWBBxbR2itRwMnAhOVUkc5HZBw3GPAHsAoYAPwgLPhiAjI9Sy6i+p6lgQcQ1rr9cGv1cA84FBnI3LUJqVUOUDwa7XD8ThCa71Ja+3XWgeAv5HevxNJRa7n7cj1TPTXsyTgGFFK5SmlCkLfA8cDXzgblaNeBcYFvx8HzHcwFseE/mgFnUF6/04kDbmedyDXM9Ffz7ISVowopXbH+pQM1mjz2VrrPzkYUtwopZ4HjgbKgE3AncArgAaGAWuAs7XWKT1AqZfzcDRWd5WJNX3jitC9NJG45HqW6zkW17MkYCGEEMIB0gUthBBCOEASsBBCCOEAScBCCCGEAyQBCyGEEA6QBCyEEEI4QBKwEEII4QBJwEIIIYQD/h80WNpEYEpGLgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw subplots for (1,2,1) and (1,2,2)\n",
    "plt.figure(figsize=(8,4))\n",
    "plt.subplots_adjust(hspace=.25)\n",
    "plt.subplot(1,2,1)\n",
    "plt.title('Temp')\n",
    "plt.plot(time_hrs,temp_data,color = 'b',linestyle='-',linewidth=1)\n",
    "plt.subplot(1,2,2)\n",
    "plt.title('Wind')\n",
    "plt.plot(time_hrs,wind_data,color = 'r',linestyle='-',linewidth=1)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw for (2,1,1) and (2,1,2)\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.subplots_adjust(hspace=.25)\n",
    "plt.subplot(2,1,1)\n",
    "plt.title('Humidity')\n",
    "plt.plot(time_hrs,humidity_data,color='b',linestyle = '-',linewidth='1')\n",
    "plt.subplot(2,1,2)\n",
    "plt.title('Precipitation')\n",
    "plt.plot(time_hrs,precipitation_data,color='r',linestyle='-',linewidth='1')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw subplots for (2,2,1),(2,2,2),(2,2,3) and (2,2,4)\n",
    "plt.figure(figsize=(9,9))\n",
    "plt.subplots_adjust(hspace=.3)\n",
    "plt.subplot(2,2,1)\n",
    "plt.title('Temp(F)')\n",
    "plt.plot(time_hrs,temp_data,color='b',Linestyle='-',linewidth='1')\n",
    "plt.subplot(2,2,2)\n",
    "plt.title('Wind(MPH)')\n",
    "plt.plot(time_hrs,wind_data,color='g',linestyle='-',linewidth='1')\n",
    "plt.subplot(2,2,3)\n",
    "plt.title('Humidity(%)')\n",
    "plt.plot(time_hrs,humidity_data,color='r',linestyle='-',linewidth='1')\n",
    "plt.subplot(2,2,4)\n",
    "plt.title('Precipitation(%)')\n",
    "plt.plot(time_hrs,precipitation_data,color='y',linestyle='-',linewidth='1')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'data': array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 1.5300e+01, 3.9690e+02,\n",
      "        4.9800e+00],\n",
      "       [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9690e+02,\n",
      "        9.1400e+00],\n",
      "       [2.7290e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9283e+02,\n",
      "        4.0300e+00],\n",
      "       ...,\n",
      "       [6.0760e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
      "        5.6400e+00],\n",
      "       [1.0959e-01, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9345e+02,\n",
      "        6.4800e+00],\n",
      "       [4.7410e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
      "        7.8800e+00]]), 'target': array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\n",
      "       18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\n",
      "       15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\n",
      "       13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\n",
      "       21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\n",
      "       35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\n",
      "       19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\n",
      "       20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\n",
      "       23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\n",
      "       33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\n",
      "       21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\n",
      "       20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\n",
      "       23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\n",
      "       15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\n",
      "       17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\n",
      "       25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\n",
      "       23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\n",
      "       32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\n",
      "       34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\n",
      "       20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\n",
      "       26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\n",
      "       31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\n",
      "       22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\n",
      "       42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\n",
      "       36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\n",
      "       32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\n",
      "       20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\n",
      "       20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\n",
      "       22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\n",
      "       21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\n",
      "       19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\n",
      "       32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\n",
      "       18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\n",
      "       16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\n",
      "       13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3,  8.8,\n",
      "        7.2, 10.5,  7.4, 10.2, 11.5, 15.1, 23.2,  9.7, 13.8, 12.7, 13.1,\n",
      "       12.5,  8.5,  5. ,  6.3,  5.6,  7.2, 12.1,  8.3,  8.5,  5. , 11.9,\n",
      "       27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3,  7. ,  7.2,  7.5, 10.4,\n",
      "        8.8,  8.4, 16.7, 14.2, 20.8, 13.4, 11.7,  8.3, 10.2, 10.9, 11. ,\n",
      "        9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4,  9.6,  8.7,  8.4, 12.8,\n",
      "       10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n",
      "       15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\n",
      "       19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\n",
      "       29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\n",
      "       20.6, 21.2, 19.1, 20.6, 15.2,  7. ,  8.1, 13.6, 20.1, 21.8, 24.5,\n",
      "       23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9]), 'feature_names': array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
      "       'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7'), 'DESCR': \".. _boston_dataset:\\n\\nBoston house prices dataset\\n---------------------------\\n\\n**Data Set Characteristics:**  \\n\\n    :Number of Instances: 506 \\n\\n    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\\n\\n    :Attribute Information (in order):\\n        - CRIM     per capita crime rate by town\\n        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\\n        - INDUS    proportion of non-retail business acres per town\\n        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\\n        - NOX      nitric oxides concentration (parts per 10 million)\\n        - RM       average number of rooms per dwelling\\n        - AGE      proportion of owner-occupied units built prior to 1940\\n        - DIS      weighted distances to five Boston employment centres\\n        - RAD      index of accessibility to radial highways\\n        - TAX      full-value property-tax rate per $10,000\\n        - PTRATIO  pupil-teacher ratio by town\\n        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\\n        - LSTAT    % lower status of the population\\n        - MEDV     Median value of owner-occupied homes in $1000's\\n\\n    :Missing Attribute Values: None\\n\\n    :Creator: Harrison, D. and Rubinfeld, D.L.\\n\\nThis is a copy of UCI ML housing dataset.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/housing/\\n\\n\\nThis dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\\n\\nThe Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\\nprices and the demand for clean air', J. Environ. Economics & Management,\\nvol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\\n...', Wiley, 1980.   N.B. Various transformations are used in the table on\\npages 244-261 of the latter.\\n\\nThe Boston house-price data has been used in many machine learning papers that address regression\\nproblems.   \\n     \\n.. topic:: References\\n\\n   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\\n   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\\n\", 'filename': 'C:\\\\Users\\\\Hiteash\\\\anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\datasets\\\\data\\\\boston_house_prices.csv'}\n"
     ]
    }
   ],
   "source": [
    "# Histogram and Scatter Plots\n",
    "from sklearn.datasets import load_boston\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "%matplotlib inline\n",
    "\n",
    "boston_real_state_data = load_boston()\n",
    "print(boston_real_state_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define axis for the data\n",
    "x_axis = boston_real_state_data.data\n",
    "y_axis = boston_real_state_data.target\n",
    "# Histogram plot\n",
    "style.use('ggplot')\n",
    "plt.figure(figsize=(7,7))\n",
    "plt.hist(y_axis,bins=50)\n",
    "plt.xlabel('price in 1000s USD')\n",
    "plt.ylabel('Number of Houses')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot\n",
    "style.use('ggplot')\n",
    "plt.figure(figsize=(7,7))\n",
    "plt.scatter(boston_real_state_data.data[:,5],boston_real_state_data.target)\n",
    "plt.ylabel('price in 1000s')\n",
    "plt.xlabel('Number of Houses')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "URLError",
     "evalue": "<urlopen error [Errno 11001] getaddrinfo failed>",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mgaierror\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mdo_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m   1318\u001b[0m                 h.request(req.get_method(), req.selector, req.data, headers,\n\u001b[1;32m-> 1319\u001b[1;33m                           encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[0m\u001b[0;32m   1320\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# timeout error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1251\u001b[0m         \u001b[1;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1252\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1253\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_send_request\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1297\u001b[0m             \u001b[0mbody\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'body'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1298\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1299\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mendheaders\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1246\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1247\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1248\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_send_output\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1025\u001b[0m         \u001b[1;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1026\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1027\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    965\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 966\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    967\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1413\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1414\u001b[1;33m             \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1415\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    937\u001b[0m         self.sock = self._create_connection(\n\u001b[1;32m--> 938\u001b[1;33m             (self.host,self.port), self.timeout, self.source_address)\n\u001b[0m\u001b[0;32m    939\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetsockopt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msocket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIPPROTO_TCP\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTCP_NODELAY\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\socket.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address)\u001b[0m\n\u001b[0;32m    706\u001b[0m     \u001b[0merr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 707\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mgetaddrinfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mport\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSOCK_STREAM\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    708\u001b[0m         \u001b[0maf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msocktype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcanonname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msa\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\socket.py\u001b[0m in \u001b[0;36mgetaddrinfo\u001b[1;34m(host, port, family, type, proto, flags)\u001b[0m\n\u001b[0;32m    751\u001b[0m     \u001b[0maddrlist\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 752\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32min\u001b[0m \u001b[0m_socket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetaddrinfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mport\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfamily\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    753\u001b[0m         \u001b[0maf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msocktype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcanonname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msa\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mgaierror\u001b[0m: [Errno 11001] getaddrinfo failed",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mURLError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-c9996ebe59fb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'matplotlib'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'inline'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mflight_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_dataset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'flight'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[0mflight_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\seaborn\\utils.py\u001b[0m in \u001b[0;36mload_dataset\u001b[1;34m(name, cache, data_home, **kws)\u001b[0m\n\u001b[0;32m    434\u001b[0m                                   os.path.basename(full_path))\n\u001b[0;32m    435\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcache_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 436\u001b[1;33m             \u001b[0murlretrieve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfull_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcache_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    437\u001b[0m         \u001b[0mfull_path\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache_path\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    438\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36murlretrieve\u001b[1;34m(url, filename, reporthook, data)\u001b[0m\n\u001b[0;32m    245\u001b[0m     \u001b[0murl_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msplittype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    246\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 247\u001b[1;33m     \u001b[1;32mwith\u001b[0m \u001b[0mcontextlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclosing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murlopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    248\u001b[0m         \u001b[0mheaders\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    249\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[0;32m    220\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    221\u001b[0m         \u001b[0mopener\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 222\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    223\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    224\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0minstall_opener\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopener\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[0;32m    523\u001b[0m             \u001b[0mreq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmeth\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    524\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 525\u001b[1;33m         \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    526\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    527\u001b[0m         \u001b[1;31m# post-process response\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_open\u001b[1;34m(self, req, data)\u001b[0m\n\u001b[0;32m    541\u001b[0m         \u001b[0mprotocol\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    542\u001b[0m         result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[1;32m--> 543\u001b[1;33m                                   '_open', req)\n\u001b[0m\u001b[0;32m    544\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    545\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[1;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[0;32m    501\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    502\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 503\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    504\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    505\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mhttps_open\u001b[1;34m(self, req)\u001b[0m\n\u001b[0;32m   1360\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mhttps_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1361\u001b[0m             return self.do_open(http.client.HTTPSConnection, req,\n\u001b[1;32m-> 1362\u001b[1;33m                 context=self._context, check_hostname=self._check_hostname)\n\u001b[0m\u001b[0;32m   1363\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1364\u001b[0m         \u001b[0mhttps_request\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAbstractHTTPHandler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdo_request_\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mdo_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m   1319\u001b[0m                           encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[0;32m   1320\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# timeout error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1321\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1322\u001b[0m             \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1323\u001b[0m         \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mURLError\u001b[0m: <urlopen error [Errno 11001] getaddrinfo failed>"
     ]
    }
   ],
   "source": [
    "# Heat map Plot\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "flight_data = sns.load_dataset('flights')\n",
    "flight_data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'flight_data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-a5dc51b2ef01>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mflight_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mflight_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpivot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'month'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'year'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'passengers'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mflight_data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'flight_data' is not defined"
     ]
    }
   ],
   "source": [
    "flight_data = flight_data.pivot('month','year','passengers')\n",
    "flight_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.heatmap(flight_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie Chart Plot\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "job_data = ['40','20','17','8','10','5']\n",
    "labels = 'IT', 'Finance', 'Marketing', 'Admin', 'HR', 'Operations'\n",
    "explode = (0.05,0,0,0,0,0)\n",
    "plt.pie(job_data,labels=labels,explode=explode)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "(unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape (<ipython-input-6-836f83e3e88a>, line 7)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-6-836f83e3e88a>\"\u001b[1;36m, line \u001b[1;32m7\u001b[0m\n\u001b[1;33m    df_auto_dataset = pd.read_csv ('C:\\Users\\Hiteash\\Downloads\\airtravel.csv')\u001b[0m\n\u001b[1;37m                                  ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape\n"
     ]
    }
   ],
   "source": [
    "# Project\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "df_auto_dataset = pd.read_csv('C:\\Users\\Hiteash\\Downloads\\airtravel.csv')\n",
    "df_auto_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def origin(num):\n",
    "    if num==1:\n",
    "        return 'USA'\n",
    "    elif num==2:\n",
    "        return 'Europe'\n",
    "    else:\n",
    "        return 'Asia'\n",
    "df_auto_dataset['origin'] = df_auto_dataset['origin'].apply(origin)\n",
    "\n",
    "df_auto_dataset.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.pairplot(df_auto_dataset['age','weight','origin'],hue='origin',size=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "cause = 'Chronic Diseases','Unintentional injuries','Alzeimers','Influenza' and 'Neumonia','Spsis','Others'\n",
    "percentile = [62,5,4,2,1,10]\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "explode = (0.05,0,0,0,0,0)\n",
    "plt.pie(percentile,labels=cause,explode=explode,autopct='%1.1f%%',startangle=70)\n",
    "plt.axis('equal')\n",
    "plt.title('Ohio state - 2012 : Leading Cause of Death')\n",
    "plt.show()\n"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}