part5-DataViz.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all packages and set plots to be embedded inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load in the dataset into a pandas dataframe\n",
    "diamonds = pd.read_csv('diamonds.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert cut, color, and clarity into ordered categorical types\n",
    "ordinal_var_dict = {'cut': ['Fair','Good','Very Good','Premium','Ideal'],\n",
    "                    'color': ['J', 'I', 'H', 'G', 'F', 'E', 'D'],\n",
    "                    'clarity': ['I1', 'SI2', 'SI1', 'VS2', 'VS1', 'VVS2', 'VVS1', 'IF']}\n",
    "\n",
    "for var in ordinal_var_dict:\n",
    "    pd_ver = pd.__version__.split(\".\")\n",
    "    if (int(pd_ver[0]) > 0) or (int(pd_ver[1]) >= 21): # v0.21 or later\n",
    "        ordered_var = pd.api.types.CategoricalDtype(ordered = True,\n",
    "                                                    categories = ordinal_var_dict[var])\n",
    "        diamonds[var] = diamonds[var].astype(ordered_var)\n",
    "    else: # pre-v0.21\n",
    "        diamonds[var] = diamonds[var].astype('category', ordered = True,\n",
    "                                             categories = ordinal_var_dict[var])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cuberoot_trans(x, inverse = False):\n",
    "    \"\"\" quick function for computing cube root and cube operations \"\"\"\n",
    "    if not inverse:\n",
    "        return x ** (1/3)\n",
    "    else:\n",
    "        return x ** 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bar Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZUAAAEKCAYAAADaa8itAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAFTlJREFUeJzt3Xu0ZGV95vHvwy2gaABpGOQy7ZhORkIiSg9iyFLUtbi4EiFGFDJKe4mdsNAZM5rI5AajkonLpUwAJUNic8maYBgMgoSk7SEKRkVpELmpoUMQOxAuaWJADQT4zR/7PXbZnNN9uvutU+c0389atWrXb7+197t3nTpP7UvtSlUhSVIP2026A5KkbYehIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1M0Ok+7AXNtzzz1r8eLFk+6GJC0oN9xww4NVtWhT7Z52obJ48WJWr1496W5I0oKS5FuzaefuL0lSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSN0+7b9RL0tY6592fnnQXxuIdH/75rZ6GWyqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSN2MLlST7J/lskq8nuS3Jf231PZKsSnJHu9+91ZPkrCRrktyc5MUj01rW2t+RZNlI/ZAkt7TnnJUk41oeSdKmjXNL5XHg3VX1AuAw4JQkBwKnAldX1RLg6vYY4BhgSbstB86FIYSA04CXAIcCp00FUWuzfOR5R49xeSRJmzC2UKmqe6vqxjb8MPB1YF/gWODC1uxC4Lg2fCxwUQ2uA3ZLsg9wFLCqqtZV1UPAKuDoNu7ZVfWlqirgopFpSZImYE6OqSRZDLwI+DKwd1XdC0PwAHu1ZvsC3x552tpW21h97TR1SdKEjD1UkuwKfBJ4V1X9y8aaTlOrLahP14flSVYnWf3AAw9sqsuSpC001lBJsiNDoPyfqvrzVr6v7bqi3d/f6muB/Ueevh9wzybq+01Tf4qqOq+qllbV0kWLFm3dQkmSZjTOs78CfBz4elV9ZGTUFcDUGVzLgMtH6ie1s8AOA77Tdo+tBI5Msns7QH8ksLKNezjJYW1eJ41MS5I0AeP85cfDgTcBtyS5qdV+E/h94JIkbwPuBo5v464CXg2sAb4HvAWgqtYleT9wfWv3vqpa14ZPBi4AdgH+st0kSRMytlCpqr9h+uMeAK+apn0Bp8wwrRXAimnqq4GDtqKbkqSO/Ea9JKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEndGCqSpG7GFipJViS5P8mtI7XTk/xDkpva7dUj4/57kjVJvpnkqJH60a22JsmpI/XnJflykjuS/FmSnca1LJKk2RnnlsoFwNHT1M+sqoPb7SqAJAcCJwA/2Z7zsSTbJ9ke+ChwDHAgcGJrC/DBNq0lwEPA28a4LJKkWRhbqFTVtcC6WTY/FvhEVT1aVX8PrAEObbc1VXVnVT0GfAI4NkmAVwKXtudfCBzXdQEkSZttEsdU3pHk5rZ7bPdW2xf49kibta02U/05wD9X1eMb1CVJEzTXoXIu8HzgYOBe4MOtnmna1hbUp5VkeZLVSVY/8MADm9djSdKszWmoVNV9VfVEVT0J/BHD7i0YtjT2H2m6H3DPRuoPArsl2WGD+kzzPa+qllbV0kWLFvVZGEnSU8xpqCTZZ+ThLwBTZ4ZdAZyQ5EeSPA9YAnwFuB5Y0s702onhYP4VVVXAZ4HXtecvAy6fi2WQJM1sh0032TJJLgaOAPZMshY4DTgiycEMu6ruAn4FoKpuS3IJcDvwOHBKVT3RpvMOYCWwPbCiqm5rs3gv8IkkHwC+Cnx8XMsiSZqdsYVKVZ04TXnGf/xVdQZwxjT1q4CrpqnfyfrdZ5KkecBv1EuSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1M3YTimWtG255mUvn3QXxuLl114z6S5sU9xSkSR1Y6hIkroxVCRJ3RgqkqRuDBVJUjeGiiSpG0NFktSNoSJJ6sZQkSR1Y6hIkroxVCRJ3cwqVJJcPZuaJOnpbaMXlEyyM/AMYM8kuwNpo54NPHfMfZMkLTCbukrxrwDvYgiQG1gfKv8CfHSM/ZIkLUAbDZWq+gPgD5K8s6rOnqM+SZIWqFn9nkpVnZ3kZ4DFo8+pqovG1C9J0gI0q1BJ8ifA84GbgCdauQBDRdu0w88+fNJdGIsvvPMLk+6CtlGz/eXHpcCBVVXj7IwkaWGb7fdUbgX+3Tg7Ikla+Ga7pbIncHuSrwCPThWr6jVj6ZUkaUGabaicPs5OSJK2DbM9++uacXdEkrTwzfbsr4cZzvYC2AnYEfhuVT17XB2TJC08s91Sedbo4yTHAYeOpUeSpAVri65SXFWfAl7ZuS+SpAVutru/XjvycDuG7634nRVJ0g+Z7dlfPz8y/DhwF3Bs995Ikha02R5Tecu4OyJJWvhm+yNd+yW5LMn9Se5L8skk+427c5KkhWW2B+rPB65g+F2VfYFPt5okST8w21BZVFXnV9Xj7XYBsGiM/ZIkLUCzDZUHk7wxyfbt9kbgn8bZMUnSwjPbUHkr8HrgH4F7gdcBGz14n2RFOwZz60htjySrktzR7ndv9SQ5K8maJDcnefHIc5a19nckWTZSPyTJLe05ZyUJkqSJmm2ovB9YVlWLqmovhpA5fRPPuQA4eoPaqcDVVbUEuLo9BjgGWNJuy4FzYQgh4DTgJQzf4D9tKoham+Ujz9twXpKkOTbbUPnpqnpo6kFVrQNetLEnVNW1wLoNyscCF7bhC4HjRuoX1eA6YLck+wBHAauqal2b/yrg6Dbu2VX1pfbDYReNTEuSNCGzDZXtRrYQprYgZvvFyVF7V9W9AO1+r1bfF/j2SLu1rbax+tpp6pKkCZptMHwY+GKSSxkuz/J64IyO/ZjueEhtQX36iSfLGXaVccABB2xJ/yRJszCrLZWqugj4ReA+4AHgtVX1J1swv/variva/f2tvhbYf6TdfsA9m6jvN019pv6fV1VLq2rpokWeCS1J4zLrqxRX1e1VdU5VnV1Vt2/h/K4Aps7gWgZcPlI/qZ0FdhjwnbZ7bCVwZJLd2+63I4GVbdzDSQ5rZ32dNDItSdKEbMlxkVlJcjFwBLBnkrUMZ3H9PnBJkrcBdwPHt+ZXAa8G1gDfo52uXFXrkrwfuL61e187SQDgZIYzzHYB/rLdJEkTNLZQqaoTZxj1qmnaFnDKDNNZAayYpr4aOGhr+ihJ6muLfqRLkqTpGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHUzkVBJcleSW5LclGR1q+2RZFWSO9r97q2eJGclWZPk5iQvHpnOstb+jiTLJrEskqT1Jrml8oqqOriqlrbHpwJXV9US4Or2GOAYYEm7LQfOhSGEgNOAlwCHAqdNBZEkaTLm0+6vY4EL2/CFwHEj9YtqcB2wW5J9gKOAVVW1rqoeAlYBR891pyVJ600qVAr4TJIbkixvtb2r6l6Adr9Xq+8LfHvkuWtbbaa6JGlCdpjQfA+vqnuS7AWsSvKNjbTNNLXaSP2pExiCaznAAQccsLl9lSTN0kRCparuaff3J7mM4ZjIfUn2qap72+6t+1vztcD+I0/fD7in1Y/YoP65GeZ3HnAewNKlS6cNHq139/t+atJdGIsDfveWSXdB2ubN+e6vJM9M8qypYeBI4FbgCmDqDK5lwOVt+ArgpHYW2GHAd9rusZXAkUl2bwfoj2w1SdKETGJLZW/gsiRT8//TqvqrJNcDlyR5G3A3cHxrfxXwamAN8D3gLQBVtS7J+4HrW7v3VdW6uVsMSdKG5jxUqupO4IXT1P8JeNU09QJOmWFaK4AVvfsoSdoy8+mUYknSAmeoSJK6MVQkSd0YKpKkbgwVSVI3hookqRtDRZLUjaEiSerGUJEkdWOoSJK6MVQkSd0YKpKkbgwVSVI3hookqRtDRZLUjaEiSerGUJEkdWOoSJK6MVQkSd0YKpKkbgwVSVI3hookqRtDRZLUjaEiSerGUJEkdWOoSJK6MVQkSd3sMOkOzBeH/PpFk+7CWNzwoZMm3QVJTyNuqUiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEndGCqSpG4WfKgkOTrJN5OsSXLqpPsjSU9nCzpUkmwPfBQ4BjgQODHJgZPtlSQ9fS3oUAEOBdZU1Z1V9RjwCeDYCfdJkp62Fnqo7At8e+Tx2laTJE1AqmrSfdhiSY4HjqqqX26P3wQcWlXv3KDdcmB5e/gTwDfntKNPtSfw4IT7MF+4LtZzXaznulhvvqyLf19VizbVaKH/SNdaYP+Rx/sB92zYqKrOA86bq05tSpLVVbV00v2YD1wX67ku1nNdrLfQ1sVC3/11PbAkyfOS7AScAFwx4T5J0tPWgt5SqarHk7wDWAlsD6yoqtsm3C1Jetpa0KECUFVXAVdNuh+bad7sipsHXBfruS7Wc12st6DWxYI+UC9Jml8W+jEVSdI8YqiMQZInktw0clu8kbbPTXLp3PVu/JLsneRPk9yZ5IYkX0ryCx2m+7kkc34WTJvvURvU3pXkY2OY165Jzk3yd0m+2tbf2ztN+4Ikr+sxrQ2mO/X3fmuS/5vkGWOYxzb1PknyyAz1LX6Nkpye5D1b17OtZ6iMx/er6uCR210zNayqe6rqKX9ESRbk8a4kAT4FXFtV/6GqDmE4K2+/yfZsq1zMsAyjTmj1Tcpgtu+1PwYeApZU1YuAo4E9ZtvRCZn6ez8IeAz41dGRm7n805rpfaL5x1CZI0kWJ/l8khvb7WdG6re24Te3T3qfBj4z0Q5vuVcCj1XVH04VqupbVXV2kp2TnJ/klvYp/BUAG6nvkuQTSW5O8mfALpNZJC4Ffi7Jj7R+LQaeC/xNe/zrSa5v/fwfU22SfL1tzdwI/E6SM6cmmOTtST4yOpMkz2e49NBvV9WTAFX1QFV9sI1Pkg+1LYJbkrxhFvVzktye5C+Avca3in7g88CPTbP8+yc5sm213tj+zndt/bwrye+1cauTvDjJyra19qutzYbvk3NG1tuVSY5ow48k+WDbwvt/SQ5tW5p3JnnNHCz/ZtnYa5TkkCTXtGVZmWSfVn97+3v7WpJPjmPLcKtUlbfON+AJ4KZ2u6zVngHs3IaXAKvb8GLg1jb8ZoYvdO4x6WXYimX/L8CZM4x7N3B+G/6PwN3Azhup/zeG08QBfhp4HFg6oeX6C+DYNnwq8KE2fCTD2Tlh+JB2JfCy9ro+CRzW2j0T+Dtgx/b4i8BPbTCP10z9vczQh18EVjGcPr93W0/7bKT+2pH6c4F/Bl43hnXzSLvfAbgcOHma5d8TuBZ4Znv8XuB32/BdwMlt+EzgZuBZwCLg/hneJ+eMzP9K4Ig2XMAxbfgyhg9nOwIvBG6a9PtjmnU27WvU+vxFYFFr94aR98JzRqbzAeCdbfh04D2TXrYFuYtlAfh+VR28QW1H4JwkBzOEzo/P8NxVVbVurL2bQ0k+Cvwsw26RtcDZAFX1jSTfYlgPPztD/WXAWa1+c5Kb534JfmBqF9jl7f6trX5ku321Pd6V4UPD3cC3quo6gKr6bpK/Ztji+TpDuNyysRkm+S3geGCvqnouw3q6uKqeAO5Lcg3wnzZSf9lI/Z42/3HYJclNbfjzwMcZ/kH+YPmBwxiuJP6FJAA7AV8amcbUl5ZvAXatqoeBh5P8a5LdNqMvjwF/NTKtR6vq35LcwhBM881Mr9FPAAcBq9r62h64t407KMkHgN0Y/t5Wzm2XN85QmTu/BtzH8IlpO+BfZ2j33Tnr0XjcxvDJGYCqOiXJnsBq4B9meE42Mr35cs77p4CPJHkxsEtV3djqAf5nVf3v0cZtF9mGr+UfA78JfAM4f5p53A68MMl2VfVkVZ0BnJH1B3VnWk+TXn9P+RDV/hGOLn8YPjCdOMM0Hm33T44MTz3e8P/U4/zwrvudR4b/rdrH9tFpVdWTmb/HKad7jQLcVlUvnWbcBcBxVfW1JG8Gjhhf1zafx1Tmzo8C99awr/xNDJ88tkV/Deyc5OSR2tQ+32uB/wyQ5MeBAxgu7jmb+kEMu8AmoqoeAT4HrOCHD9CvBN46cnxg3yTTHruoqi8zXKvul5jmIH9VrWEI3w9k+K0gkuzM+tC4FnhDku2TLGL4lPuVTdRPaPV9gFdsxSrYWtcBhyf5MYAkz2iv9Za4Czg4yXZJ9mc4DrVQzfQafRNYlOSlAEl2TPKTbdyzgHuT7Eh7f8wn8zW5t0UfAz6Z4crKn2Xhb5FMq6oqyXHAmUl+A3iAYVnfy7Dr6A/brojHgTdX1aPtYO509XOB89tur5sY/lFO0sXAnzNyJlhVfSbJC4AvtU/njwBvZNjFOZ1LgIOr6qEZxv8y8CFgTZJ1wPcZ1h0MxwheCnyN4dPtb1TVPybZWP2VDLuB/ha4ZouWuoOqeqB9qr447YQH4LdbvzbXF4C/Z1iuWxlOBFiopn2NquqxDKcWn5XkRxn+V/8vhj0BvwN8GfhWe96zJtDvGfmNemkOJbmS4USGqyfdF2kc3P0lzYEkuyX5W4bjDwaKtlluqUiSunFLRZLUjaEiSerGUJEkdWOoSPNUu97VL026H9LmMFSk+WsxwxclpQXDs7+kOZbkJOA9DF9UvJnhi5JXVtWlbfwjVbVrkuuAFzB80e/CqjpzpmlK84XfqJfmULvUxm8Bh1fVg0n2AD4yQ/NTGa46+3Nz1kFpK7n7S5pbrwQuraoHAbalK1JLYKhIcy089aq0P7jqboYLiO00152SejFUpLl1NfD6JM8BaLu/7gIOaeOPZfjtHYCHmWcXC5Q2xVCR5lBV3QacAVyT5GsMx1P+CHh5kq8AL2H9FaxvBh5vPxv7axPpsLSZPPtLktSNWyqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEnd/H/tRikIQlusOQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.countplot(data = diamonds, x = 'cut');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = diamonds\n",
    "base_color = sb.color_palette()[0]\n",
    "sb.countplot(data = df, x = 'cut', color = base_color);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24a04f508d0>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZUAAAEKCAYAAADaa8itAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAFTtJREFUeJzt3Xu0ZGV95vHvwy2gaABpGK7TjulkJExE6EGMsxzUtbi4jBAjChmlvcROWOiMmUhkchkclUxcrMgEUBISm0vWBEMwCCEk2EMUTBSlUeSqoSVcOnS4pIkBNRDgN3/s96TL5pzu091vnToHvp+1atWu335r73fvs+s8tS9VlapCkqQetpl0ByRJzx6GiiSpG0NFktSNoSJJ6sZQkSR1Y6hIkroxVCRJ3RgqkqRuDBVJUjfbTboDc2333XevxYsXT7obkrSg3HjjjQ9X1aJNtXvOhcrixYtZtWrVpLshSQtKkntm087DX5KkbgwVSVI3hookqRtDRZLUjaEiSerGUJEkdWOoSJK6MVQkSd0YKpKkbp5zn6ifySGnXDTpLozFjWecOOkuSHoOcU9FktSNoSJJ6sZQkSR1Y6hIkroxVCRJ3RgqkqRuDBVJUjeGiiSpG0NFktSNoSJJ6mZsoZJkvySfT3JHktuS/LdW3y3JyiR3tvtdWz1JzkqyOsnNSQ4emday1v7OJMtG6ockuaU956wkGdfySJI2bZx7Kk8Cv1RVLwUOA05OcgBwKnBNVS0BrmmPAY4GlrTbcuBcGEIIOA14BXAocNpUELU2y0eed9QYl0eStAljC5WqWltVX2vDjwJ3APsAxwAXtmYXAse24WOAi2pwPbBLkr2AI4GVVbWuqh4BVgJHtXEvrKovV1UBF41MS5I0AXNyTiXJYuDlwFeAPatqLQzBA+zRmu0D3DfytDWttrH6mmnqkqQJGXuoJNkZ+Azw/qr6p401naZWW1Cfrg/Lk6xKsuqhhx7aVJclSVtorKGSZHuGQPm/VfUnrfxAO3RFu3+w1dcA+408fV/g/k3U952m/gxVdV5VLa2qpYsWLdq6hZIkzWicV38F+BRwR1V9fGTUFcDUFVzLgMtH6ie2q8AOA77TDo9dDRyRZNd2gv4I4Oo27tEkh7V5nTgyLUnSBIzzlx9fBbwduCXJTa32K8BvApckeTdwL3BcG3cV8HpgNfA94J0AVbUuyUeAG1q7D1fVujZ8EnABsBPw5+0mSZqQsYVKVf0V05/3AHjdNO0LOHmGaa0AVkxTXwUcuBXdlCR15CfqJUndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHUztlBJsiLJg0luHal9KMnfJbmp3V4/Mu5/JFmd5FtJjhypH9Vqq5OcOlJ/cZKvJLkzyR8l2WFcyyJJmp1x7qlcABw1Tf3Mqjqo3a4CSHIAcDzw4+05n0yybZJtgU8ARwMHACe0tgAfa9NaAjwCvHuMyyJJmoWxhUpVXQesm2XzY4BPV9XjVfW3wGrg0HZbXVV3VdUTwKeBY5IEeC1waXv+hcCxXRdAkrTZJnFO5b1Jbm6Hx3ZttX2A+0barGm1meovAv6xqp7coC5JmqC5DpVzgZcABwFrgd9q9UzTtragPq0ky5OsSrLqoYce2rweS5JmbU5DpaoeqKqnqupp4PcYDm/BsKex30jTfYH7N1J/GNglyXYb1Gea73lVtbSqli5atKjPwkiSnmFOQyXJXiMPfxqYujLsCuD4JD+U5MXAEuCrwA3Aknal1w4MJ/OvqKoCPg+8uT1/GXD5XCyDJGlm2226yZZJcjFwOLB7kjXAacDhSQ5iOFR1N/DzAFV1W5JLgNuBJ4GTq+qpNp33AlcD2wIrquq2NosPAp9O8lHg68CnxrUskqTZGVuoVNUJ05Rn/MdfVacDp09Tvwq4apr6Xaw/fCZJmgf8RL0kqRtDRZLUjaEiSerGUJEkdWOoSJK6MVQkSd2M7ZJiLVyHnHLRpLswFjeeceKkuyA967mnIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSuplVqCS5ZjY1SdJz20a/UDLJjsDzgN2T7AqkjXohsPeY+yZJWmA29S3FPw+8nyFAbmR9qPwT8Ikx9kuStABtNFSq6reB307yvqo6e476JElaoGb1eypVdXaSnwQWjz6nqp6dP7whSdoiswqVJH8AvAS4CXiqlQswVPSs5g+WSZtntr/8uBQ4oKpqnJ2RJC1ss/2cyq3AvxlnRyRJC99s91R2B25P8lXg8aliVb1xLL2SJC1Isw2VD42zE5KkZ4fZXv117bg7Ikla+GZ79dejDFd7AewAbA98t6peOK6OSZIWntnuqbxg9HGSY4FDx9IjSdKCtUXfUlxVnwVe27kvkqQFbraHv9408nAbhs+t+JkVSdIPmO3VXz81MvwkcDdwTPfeSJIWtNmeU3nnuDsiSVr4ZvsjXfsmuSzJg0keSPKZJPuOu3OSpIVltifqzweuYPhdlX2AP201SZL+1WxDZVFVnV9VT7bbBcCiMfZLkrQAzTZUHk7ytiTbttvbgH8YZ8ckSQvPbEPlXcBbgL8H1gJvBjZ68j7JinYO5taR2m5JVia5s93v2upJclaS1UluTnLwyHOWtfZ3Jlk2Uj8kyS3tOWclCZKkiZptqHwEWFZVi6pqD4aQ+dAmnnMBcNQGtVOBa6pqCXBNewxwNLCk3ZYD58IQQsBpwCsYPsF/2lQQtTbLR5634bwkSXNstqHyE1X1yNSDqloHvHxjT6iq64B1G5SPAS5swxcCx47UL6rB9cAuSfYCjgRWVtW6Nv+VwFFt3Aur6svth8MuGpmWJGlCZhsq24zsIUztQcz2g5Oj9qyqtQDtfo9W3we4b6TdmlbbWH3NNHVJ0gTNNhh+C/hSkksZvp7lLcDpHfsx3fmQ2oL69BNPljMcKmP//fffkv5JkmZhVnsqVXUR8DPAA8BDwJuq6g+2YH4PtENXtPsHW30NsN9Iu32B+zdR33ea+kz9P6+qllbV0kWLvBJaksZl1t9SXFW3V9U5VXV2Vd2+hfO7Api6gmsZcPlI/cR2FdhhwHfa4bGrgSOS7NoOvx0BXN3GPZrksHbV14kj05IkTciWnBeZlSQXA4cDuydZw3AV128ClyR5N3AvcFxrfhXwemA18D3a5cpVtS7JR4AbWrsPt4sEAE5iuMJsJ+DP202SNEFjC5WqOmGGUa+bpm0BJ88wnRXAimnqq4ADt6aPkqS+tuhHuiRJmo6hIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSNxMJlSR3J7klyU1JVrXabklWJrmz3e/a6klyVpLVSW5OcvDIdJa19ncmWTaJZZEkrTfJPZXXVNVBVbW0PT4VuKaqlgDXtMcARwNL2m05cC4MIQScBrwCOBQ4bSqIJEmTMZ8Ofx0DXNiGLwSOHalfVIPrgV2S7AUcCaysqnVV9QiwEjhqrjstSVpvUqFSwOeS3JhkeavtWVVrAdr9Hq2+D3DfyHPXtNpMdUnShGw3ofm+qqruT7IHsDLJNzfSNtPUaiP1Z05gCK7lAPvvv//m9lWSNEsTCZWqur/dP5jkMoZzIg8k2auq1rbDWw+25muA/Uaevi9wf6sfvkH9CzPM7zzgPIClS5dOGzySNu6QUy6adBfG4sYzTpx0F55V5vzwV5LnJ3nB1DBwBHArcAUwdQXXMuDyNnwFcGK7Cuww4Dvt8NjVwBFJdm0n6I9oNUnShExiT2VP4LIkU/P/w6r6iyQ3AJckeTdwL3Bca38V8HpgNfA94J0AVbUuyUeAG1q7D1fVurlbDEnShuY8VKrqLuBl09T/AXjdNPUCTp5hWiuAFb37KEnaMvPpkmJJ0gJnqEiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEndGCqSpG4MFUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEndbDfpDkjSQnPIKRdNugtjceMZJ271NNxTkSR1Y6hIkroxVCRJ3RgqkqRuDBVJUjeGiiSpG0NFktSNoSJJ6sZQkSR1Y6hIkroxVCRJ3Sz4UElyVJJvJVmd5NRJ90eSnssWdKgk2Rb4BHA0cABwQpIDJtsrSXruWtChAhwKrK6qu6rqCeDTwDET7pMkPWct9FDZB7hv5PGaVpMkTUCqatJ92GJJjgOOrKqfa4/fDhxaVe/boN1yYHl7+GPAt+a0o8+0O/DwhPswX7gu1nNdrOe6WG++rIt/W1WLNtVoof9I1xpgv5HH+wL3b9ioqs4DzpurTm1KklVVtXTS/ZgPXBfruS7Wc12st9DWxUI//HUDsCTJi5PsABwPXDHhPknSc9aC3lOpqieTvBe4GtgWWFFVt024W5L0nLWgQwWgqq4Crpp0PzbTvDkUNw+4LtZzXaznulhvQa2LBX2iXpI0vyz0cyqSpHnEUOkkyWMz1C9I8uYtnOaHknxg63rWT5KnktyU5NYkf5zkeWOYx95JLu093a2R5AtJjtyg9v4knxzDvHZOcm6Sbyf5epIbk7yn07S3eFvsMO89k/xhkrvaMn05yU93mO4XkiyYK6OmM/K6mrot3kjbeff62JChos3x/ao6qKoOBJ4AfmF0ZAZbtU1V1f1VNZF/fBtxMcOVhaOOb/VN2sz18vvAI8CSqno5cBSw22w7Oh8lCfBZ4Lqq+ndVdQjD+tt3sj2bN6ZeV1O3u2dqONPrI8m8OT9uqHTW/oGck+T2JH8G7DEy7pAk17Z3alcn2avV35PkhiTfSPKZcewBjMEXgR9JsjjJHe1d+9eA/ZIc0d6Jfq3t0ewMkOTuJL/Rxq1KcnBbD99O8gutzeIkt7bhdyQ5Z2qGSa5McngbfizJx9q6/H9JDm3vWu9K8sbOy3op8IYkPzTVR2Bv4K/a41Pa3+/mJP9rZDlG18uvJzlzZFnek+TjozNJ8hKGrx76tap6GqCqHqqqj7XxSXJG21O8JclbZ1GfdlucY68Fnqiq35kqVNU9VXV2kh2TnN/6/fUkr2l9n6m+U5JPt3X9R8BOk1mk8Wrbzxfba+hrSX5ypD76+vjjJH8KfG6iHR5VVd463IDH2v2bgJUMlzjvDfwj8GZge+BLwKLW7q0Ml0ADvGhkOh8F3teGPwR8YNLLNs0ybgdcDpwELAaeBg5r43YHrgOe3x5/EPifbfhu4KQ2fCZwM/ACYBHwYKsvBm5tw+8AzhmZ/5XA4W24gKPb8GUML6rtgZcBN41h2f8MOKYNnwqc0YaPYLg6Jwxv0q4EXj3Nenk+8G1g+/b4S8B/2GAebwQu20gffmZk29oTuBfYayP1abfFCWw3/xU4c4ZxvwSc34b/fev7jhup//eR181PAE8CSyf92tjK9fMUcFO7XdZqzwN2bMNLgFUzvD7WALtNehlGb/Nml+lZ5NXAxVX1FHB/kr9s9R8DDgRWDkcD2BZY28YdmOSjwC7Azgyfu5mPdkpyUxv+IvAphn9W91TV9a1+GMM3Rv91W84dgC+PTGPqw6m3ADtX1aPAo0n+Ockum9GXJ4C/GJnW41X1L0luYXjh9TZ1COzydv+uVj+i3b7eHu/M8E/gXkbWS1V9t20Lb0hyB0O43LKxGSb5VeA4YI+q2hv4T6zfth5Ici3wHzdSn2lbnKgkn2Do8xMM/xTPBqiqbya5B/jRNn66+quBs1r95iQ3z/0SdPf9qjpog9r2wDlJDmIInR+d4bkrq2rdWHu3mQyV8ZjuOu0At1XVK6cZdwFwbFV9I8k7gMPH17Wt8oyNvwXHd0dLDBv6CTNM4/F2//TI8NTjDbfHJ/nBQ7Q7jgz/S7W3a6PTqqqnM57jy58FPp7kYGCnqvpaqwf431X1u6ON2yGy7/KDfh/4FeCbwPnTzON24GVJtqmqp6vqdOD0rL8IJDP0baY6TL8tzrXbGPamAKiqk5PsDqwC/m6G58z3ZRq3XwQeYNjz3gb45xnabbiNTZznVPq7Djg+ybbtnMlrWv1bwKIkrwRIsn2SH2/jXgCsTbI98F/mvMd9XQ+8KsmPACR5XpKZ3mVtyt3AQUm2SbIfw/mGiaiqx4AvACv4wRP0VwPvGjlvtE+Sac9dVNVXGL6r7meZ5iR/Va1m+Ef70Qy/FUSSHVn/D/Y64K1t21rE8K79q5uoT7ctzrW/BHZMctJIbeq84XW0bb5tJ/szvFZmUz+Q4RDYs9EPA2trOLf2doYjGwuCeyr9XcZwYvIW4G+AawGq6okMl3OeleSHGdb9/2F4F/frwFeAe9rzXjCBfndRVQ+1va2Lp05sA7/GsC42118Df8uwTm5lOOE9SRcDf8LIlWBV9bkkLwW+3PbaHgPexnDIYjqXAAdV1SMzjP854AxgdZJ1wPcZzkvBsG29EvgGw7v1X66qv0+ysfoztsW5VlWV5FjgzCS/DDzE8A77gwyHE3+nHbZ8EnhHVT3eLnCYrn4ucH477HUTQ3g+G30S+EyGb2L/PPNwj2QmfqJemkNJrmQ4aX3NpPsijYOHv6Q5kGSXJH/DcF7KQNGzlnsqkqRu3FORJHVjqEiSujFUJEndGCrSPNW+5+lnJ90PaXMYKtL8tZjhg5LSguHVX9IcS3Ii8AGGDyrezPBBySur6tI2/rGq2jnJ9cBLGT4AemFVnTnTNKX5wk/US3OofTXPrwKvqqqHk+wGfHyG5qcyfEv1G+asg9JW8vCXNLdeC1xaVQ8DzLdvmJW2lqEiza3wzG/Z/ddvY87wBWI7zHWnpF4MFWluXQO8JcmLANrhr7uBQ9r4Yxh+SwPgURbwl4vquclQkeZQVd0GnA5cm+QbDOdTfg/4z0m+CryC9d9IezPwZIafmf7FiXRY2kxe/SVJ6sY9FUlSN4aKJKkbQ0WS1I2hIknqxlCRJHVjqEiSujFUJEndGCqSpG7+P1TmKQik9NpuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_color = sb.color_palette()[0]\n",
    "cat_order = df['cut'].value_counts().index\n",
    "sb.countplot(data = df, x = 'cut', color = base_color, order = cat_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24a04e32828>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_color = sb.color_palette()[0]\n",
    "sb.countplot(data = df, y = 'cut', color = base_color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4]), <a list of 5 Text xticklabel objects>)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_color = sb.color_palette()[0]\n",
    "sb.countplot(data = df, x = 'cut', color = base_color)\n",
    "plt.xticks(rotation = 90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Proportion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'proportion')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get proportion taken by most common group for derivation\n",
    "# of tick marks\n",
    "n_points = df.shape[0]\n",
    "max_count = df['cut'].value_counts().max()\n",
    "max_prop = max_count / n_points\n",
    "\n",
    "# generate tick mark locations and names\n",
    "tick_props = np.arange(0, max_prop, 0.05)\n",
    "tick_names = ['{:0.2f}'.format(v) for v in tick_props]\n",
    "\n",
    "# create the plot\n",
    "base_color = sb.color_palette()[0]\n",
    "sb.countplot(data = df, x = 'cut', color = base_color)\n",
    "plt.yticks(tick_props * n_points, tick_names)\n",
    "plt.ylabel('proportion')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create the plot\n",
    "base_color = sb.color_palette()[0]\n",
    "sb.countplot(data = df, x = 'cut', color = base_color)\n",
    "\n",
    "# add annotations\n",
    "n_points = df.shape[0]\n",
    "cat_counts = df['cut'].value_counts()\n",
    "locs, labels = plt.xticks() # get the current tick locations and labels\n",
    "\n",
    "# loop through each pair of locations and labels\n",
    "for loc, label in zip(locs, labels):\n",
    "\n",
    "    # get the text property for the label to get the correct count\n",
    "    count = cat_counts[label.get_text()]\n",
    "    pct_string = '{:0.1f}%'.format(100*count/n_points)\n",
    "\n",
    "    # print the annotation just below the top of the bar\n",
    "    plt.text(loc, count+1, pct_string, ha = 'center', color = 'b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show NA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "na_counts = df.isna().sum()\n",
    "base_color = sb.color_palette()[0]\n",
    "sb.barplot(na_counts.index.values, na_counts, color = base_color);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>species</th>\n",
       "      <th>generation_id</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>base_experience</th>\n",
       "      <th>type_1</th>\n",
       "      <th>type_2</th>\n",
       "      <th>hp</th>\n",
       "      <th>attack</th>\n",
       "      <th>defense</th>\n",
       "      <th>speed</th>\n",
       "      <th>special-attack</th>\n",
       "      <th>special-defense</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>bulbasaur</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>6.9</td>\n",
       "      <td>64</td>\n",
       "      <td>grass</td>\n",
       "      <td>poison</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>45</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>ivysaur</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>142</td>\n",
       "      <td>grass</td>\n",
       "      <td>poison</td>\n",
       "      <td>60</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>60</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>venusaur</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>236</td>\n",
       "      <td>grass</td>\n",
       "      <td>poison</td>\n",
       "      <td>80</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>80</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>charmander</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>8.5</td>\n",
       "      <td>62</td>\n",
       "      <td>fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>65</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>charmeleon</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>142</td>\n",
       "      <td>fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58</td>\n",
       "      <td>64</td>\n",
       "      <td>58</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id     species  generation_id  height  weight  base_experience type_1  \\\n",
       "0   1   bulbasaur              1     0.7     6.9               64  grass   \n",
       "1   2     ivysaur              1     1.0    13.0              142  grass   \n",
       "2   3    venusaur              1     2.0   100.0              236  grass   \n",
       "3   4  charmander              1     0.6     8.5               62   fire   \n",
       "4   5  charmeleon              1     1.1    19.0              142   fire   \n",
       "\n",
       "   type_2  hp  attack  defense  speed  special-attack  special-defense  \n",
       "0  poison  45      49       49     45              65               65  \n",
       "1  poison  60      62       63     60              80               80  \n",
       "2  poison  80      82       83     80             100              100  \n",
       "3     NaN  39      52       43     65              60               50  \n",
       "4     NaN  58      64       58     80              80               65  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon = pd.read_csv('pokemon.csv')\n",
    "pokemon.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Script with solutions for all workspace assignments in the Univariate\n",
    "Exploration of Data lesson.\n",
    "\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "\n",
    "\n",
    "def bar_chart_solution_1():\n",
    "    \"\"\"\n",
    "    Solution for Question 1 in bar chart practice: create a bar chart of\n",
    "    Pokemon species introduced by generation.\n",
    "    \"\"\"\n",
    "    sol_string = [\"I used seaborn's countplot function to generate this chart.\",\n",
    "                  \"I also added an additional argument so that each bar has the same color.\"]\n",
    "    print((\" \").join(sol_string))\n",
    "\n",
    "    # data setup\n",
    "    pokemon = pd.read_csv('./pokemon.csv')\n",
    "\n",
    "    base_color = sb.color_palette()[0]\n",
    "    sb.countplot(data = pokemon, x = 'generation_id', color = base_color)\n",
    "\n",
    "\n",
    "def bar_chart_solution_2():\n",
    "    \"\"\"\n",
    "    Solution for Question 2 in bar chart practice: create a sorted bar chart of\n",
    "    relative type frequencies.\n",
    "    \"\"\"\n",
    "    sol_string = [\"I created a horizontal bar chart since there are a lot of\",\n",
    "                  \"Pokemon types. The unique() method was used to get the\",\n",
    "                  \"number of different Pokemon species. I also added an xlabel\",\n",
    "                  \"call to make sure it was clear the bar length represents\",\n",
    "                  \"a relative frequency.\"]\n",
    "    print((\" \").join(sol_string))\n",
    "\n",
    "    # data setup\n",
    "    pokemon = pd.read_csv('./pokemon.csv')\n",
    "    pkmn_types = pokemon.melt(id_vars = ['id','species'], \n",
    "                          value_vars = ['type_1', 'type_2'], \n",
    "                          var_name = 'type_level', value_name = 'type').dropna()\n",
    "\n",
    "    # get order of bars by frequency\n",
    "    type_counts = pkmn_types['type'].value_counts()\n",
    "    type_order = type_counts.index\n",
    "\n",
    "    # compute largest proportion\n",
    "    n_pokemon = pkmn_types['species'].unique().shape[0]\n",
    "    max_type_count = type_counts[0]\n",
    "    max_prop = max_type_count / n_pokemon\n",
    "\n",
    "    # establish tick locations and create plot\n",
    "    base_color = sb.color_palette()[0]\n",
    "    tick_props = np.arange(0, max_prop, 0.02)\n",
    "    tick_names = ['{:0.2f}'.format(v) for v in tick_props]\n",
    "\n",
    "    base_color = sb.color_palette()[0]\n",
    "    sb.countplot(data = pkmn_types, y = 'type', color = base_color, order = type_order)\n",
    "    plt.xticks(tick_props * n_pokemon, tick_names)\n",
    "    plt.xlabel('proportion')\n",
    "\n",
    "\n",
    "def histogram_solution_1():\n",
    "    \"\"\"\n",
    "    Solution for Question 1 in histogram practice: create a histogram of\n",
    "    Pokemon special defense values.\n",
    "    \"\"\"\n",
    "    sol_string = [\"I've used matplotlib's hist function to plot the data.\",\n",
    "                  \"I have also used numpy's arange function to set the bin edges.\",\n",
    "                  \"A bin size of 5 hits the main cut points, revealing a smooth,\",\n",
    "                  \"but skewed curves. Are there similar characteristics among\",\n",
    "                  \"Pokemon with the highest special defenses?\"]\n",
    "    print((\" \").join(sol_string))\n",
    "\n",
    "    # data setup\n",
    "    pokemon = pd.read_csv('./pokemon.csv')\n",
    "\n",
    "    bins = np.arange(20, pokemon['special-defense'].max()+5, 5)\n",
    "    plt.hist(pokemon['special-defense'], bins = bins)\n",
    "\n",
    "\n",
    "def scales_solution_1():\n",
    "    \"\"\"\n",
    "    Solution for Question 1 in scales and transformation practice: create a\n",
    "    histogram of Pokemon heights.\n",
    "    \"\"\"\n",
    "    sol_string = [\"There's a very long tail of Pokemon heights. Here, I've\",\n",
    "                  \"focused in on Pokemon of height 6 meters or less, so that I\",\n",
    "                  \"can use a smaller bin size to get a more detailed look at\",\n",
    "                  \"the main data distribution.\"]\n",
    "    print((\" \").join(sol_string))\n",
    "\n",
    "    # data setup\n",
    "    pokemon = pd.read_csv('./pokemon.csv')\n",
    "\n",
    "    bins = np.arange(0, pokemon['height'].max()+0.2, 0.2)\n",
    "    plt.hist(data = pokemon, x = 'height', bins = bins)\n",
    "    plt.xlim((0,6))\n",
    "\n",
    "\n",
    "def scales_solution_2():\n",
    "    \"\"\"\n",
    "    Solution for Question 2 in scales and transformation practice: create a\n",
    "    histogram of Pokemon weights.\n",
    "    \"\"\"\n",
    "    sol_string = [\"Since Pokemon weights are so skewed, I used a log transformation\",\n",
    "                  \"on the x-axis. Bin edges are in increments of 0.1 powers of ten,\",\n",
    "                  \"with custom tick marks to demonstrate the log scaling.\"]\n",
    "    print((\" \").join(sol_string))\n",
    "\n",
    "    # data setup\n",
    "    pokemon = pd.read_csv('./pokemon.csv')\n",
    "\n",
    "    bins = 10 ** np.arange(-1, 3.0+0.1, 0.1)\n",
    "    ticks = [0.1, 0.3, 1, 3, 10, 30, 100, 300, 1000]\n",
    "    labels = ['{}'.format(val) for val in ticks]\n",
    "\n",
    "    plt.hist(data = pokemon, x = 'weight', bins = bins)\n",
    "    plt.xscale('log')\n",
    "    plt.xticks(ticks, labels)\n",
    "    plt.xlabel('Weight (kg)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I used seaborn's countplot function to generate this chart. I also added an additional argument so that each bar has the same color.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart_solution_1()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I created a horizontal bar chart since there are a lot of Pokemon types. The unique() method was used to get the number of different Pokemon species. I also added an xlabel call to make sure it was clear the bar length represents a relative frequency.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart_solution_2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I've used matplotlib's hist function to plot the data. I have also used numpy's arange function to set the bin edges. A bin size of 5 hits the main cut points, revealing a smooth, but skewed curves. Are there similar characteristics among Pokemon with the highest special defenses?\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAADg1JREFUeJzt3X+MZeVdx/H3R35YQ9sAZXazYVmHmo1STfiRCcFgGgpasTTdNSkNxuikbrL+QQ3EJnZb/6gmmix/WNSoJGtBxoQWCC3uptRaskKqf0iZLdgCWwLiStdddwcLlmrSZuHrH/dsOllm5t6ZuXd+PPN+JZt7znPOzf3m4fCZZ54557mpKiRJ69+PrXYBkqThMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTh7JT/soosuqvHx8ZX8SEla9w4dOvRKVY31O29FA318fJzp6emV/EhJWveS/Mcg5znlIkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjVjRJ0U3kvE9j8x77Mjem1awEkkbhSN0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiN8sGiN8YEkSUvlCF2SGmGgS1IjDHRJaoSBLkmNMNAlqREDBXqS85M8lOTbSQ4n+fkkFyZ5NMkL3esFoy5WkjS/QUfofwZ8pap+BrgcOAzsAQ5W1XbgYLcvSVolfQM9yTuB9wJ3A1TVD6vqNWAHMNWdNgXsHFWRkqT+BhmhvxuYAf4myVNJPpvkPGBzVR0H6F43jbBOSVIfgwT62cBVwF1VdSXwvyxieiXJ7iTTSaZnZmaWWKYkqZ9BAv0ocLSqnuj2H6IX8CeSbAHoXk/O9eaq2ldVE1U1MTY2NoyaJUlz6BvoVfVfwHeS/HTXdAPwHHAAmOzaJoH9I6lQkjSQQRfn+h3gviTnAi8BH6X3w+DBJLuAl4GbR1NiexZagEuSlmqgQK+qp4GJOQ7dMNxyJElL5ZOiktQIA12SGmGgS1IjDHRJaoRfQdcQv75O2tgcoUtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpES7OtUSr8TVyfnWdpIU4QpekRhjoktQIp1zkOupSIxyhS1IjBhqhJzkCvA68AZyqqokkFwIPAOPAEeAjVfXqaMqUJPWzmCmX91XVK7P29wAHq2pvkj3d/ieGWp2GxjtkpPYtZ8plBzDVbU8BO5dfjiRpqQYN9AK+muRQkt1d2+aqOg7QvW4aRYGSpMEMOuVybVUdS7IJeDTJtwf9gO4HwG6Abdu2LaFESdIgBhqhV9Wx7vUk8DBwNXAiyRaA7vXkPO/dV1UTVTUxNjY2nKolSW/RN9CTnJfkHae3gfcDzwAHgMnutElg/6iKlCT1N8iUy2bg4SSnz/9cVX0lyZPAg0l2AS8DN4+uzNXhnSGS1pO+gV5VLwGXz9H+38ANoyhKkrR4PikqSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RG9P2SaG1s43semffYkb03rWAlkvpxhC5JjTDQJakRAwd6krOSPJXkS93+pUmeSPJCkgeSnDu6MiVJ/SxmhH4bcHjW/h3AnVW1HXgV2DXMwiRJizNQoCfZCtwEfLbbD3A98FB3yhSwcxQFSpIGM+gI/U+B3wPe7PbfBbxWVae6/aPAxXO9McnuJNNJpmdmZpZVrCRpfn0DPckHgZNVdWh28xyn1lzvr6p9VTVRVRNjY2NLLFOS1M8g96FfC3woyQeAtwHvpDdiPz/J2d0ofStwbHRlSpL66TtCr6pPVtXWqhoHbgH+sap+HXgM+HB32iSwf2RVSpL6Ws596J8AfjfJi/Tm1O8eTkmSpKVY1KP/VfU48Hi3/RJw9fBLkiQthU+KSlIjDHRJaoSBLkmNMNAlqRGuh64lW2itdHC9dGmlOUKXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcIHizQyCz145ENH0vA5QpekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1om+gJ3lbkq8n+dckzyb5w6790iRPJHkhyQNJzh19uZKk+QwyQv8BcH1VXQ5cAdyY5BrgDuDOqtoOvArsGl2ZkqR++gZ69Xy/2z2n+1fA9cBDXfsUsHMkFUqSBjLQHHqSs5I8DZwEHgX+DXitqk51pxwFLh5NiZKkQQwU6FX1RlVdAWwFrgYum+u0ud6bZHeS6STTMzMzS69UkrSgRd3lUlWvAY8D1wDnJzm9WuNW4Ng879lXVRNVNTE2NracWiVJCxjkLpexJOd32z8B/CJwGHgM+HB32iSwf1RFSpL6G2Q99C3AVJKz6P0AeLCqvpTkOeD+JH8EPAXcPcI6l8V1udce/5tIw9c30Kvqm8CVc7S/RG8+XZK0BvikqCQ1YsN/Bd1Cv/pL0nriCF2SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY3oG+hJLknyWJLDSZ5NclvXfmGSR5O80L1eMPpyJUnzGWSEfgr4eFVdBlwD3JrkPcAe4GBVbQcOdvuSpFXSN9Cr6nhVfaPbfh04DFwM7ACmutOmgJ2jKlKS1N+i5tCTjANXAk8Am6vqOPRCH9g07OIkSYMbONCTvB34AnB7VX1vEe/bnWQ6yfTMzMxSapQkDWCgQE9yDr0wv6+qvtg1n0iypTu+BTg513ural9VTVTVxNjY2DBqliTNYZC7XALcDRyuqs/MOnQAmOy2J4H9wy9PkjSoswc451rgN4BvJXm6a/sUsBd4MMku4GXg5tGUKEkaRN9Ar6p/BjLP4RuGW44kaal8UlSSGjHIlIu0osb3PDLvsSN7b1rBSqT1xRG6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhrh4lxaVxZauAtcvEsbmyN0SWqEgS5JjTDQJakRBrokNcJAl6RGNHGXS787H6Tl8M4arReO0CWpEQa6JDWi75RLknuADwInq+rnurYLgQeAceAI8JGqenV0ZUqDWWh6xKkRtW6QEfq9wI1ntO0BDlbVduBgty9JWkV9A72qvgZ894zmHcBUtz0F7BxyXZKkRVrqXS6bq+o4QFUdT7JpvhOT7AZ2A2zbtm2JHyctn9Mxat3I/yhaVfuqaqKqJsbGxkb9cZK0YS010E8k2QLQvZ4cXkmSpKVYaqAfACa77Ulg/3DKkSQt1SC3LX4euA64KMlR4NPAXuDBJLuAl4GbR1mktNH4dKqWom+gV9WvzXPohiHXIklaBp8UlaRGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEevmK+j8mjlJWpgjdElqhIEuSY0w0CWpEQa6JDXCQJekRqybu1ykUVrOXVR+tZ3WCkfoktQIA12SGuGUizRCTsdoJTlCl6RGGOiS1AgDXZIaYaBLUiMMdElqxLICPcmNSZ5P8mKSPcMqSpK0eEu+bTHJWcBfAr8EHAWeTHKgqp4bVnGSNJ+1dkvoWqhnOSP0q4EXq+qlqvohcD+wYzhlSZIWazmBfjHwnVn7R7s2SdIqWM6Topmjrd5yUrIb2N3tfj/J88v4zPXkIuCV1S5iDdvw/ZM7Fjy8YP/0ee9GMW8frbX+GUI9PznIScsJ9KPAJbP2twLHzjypqvYB+5bxOetSkumqmljtOtYq+2dh9k9/9tFbLWfK5Ulge5JLk5wL3AIcGE5ZkqTFWvIIvapOJfkY8A/AWcA9VfXs0CqTJC3KslZbrKovA18eUi2t2XDTTItk/yzM/unPPjpDqt7yd0xJ0jrko/+S1AgDfQiSHEnyrSRPJ5nu2i5M8miSF7rXC1a7zpWU5J4kJ5M8M6ttzj5Jz593S0h8M8lVq1f5ypinf/4gyX9219HTST4w69gnu/55Pskvr07VKyfJJUkeS3I4ybNJbuvavYYWYKAPz/uq6opZt1HtAQ5W1XbgYLe/kdwL3HhG23x98ivA9u7fbuCuFapxNd3LW/sH4M7uOrqi+xsVSd5D7y6yn+3e81fd0hstOwV8vKouA64Bbu36wWtoAQb66OwAprrtKWDnKtay4qrqa8B3z2ier092AH9bPf8CnJ9ky8pUujrm6Z/57ADur6ofVNW/Ay/SW3qjWVV1vKq+0W2/Dhym9yS619ACDPThKOCrSQ51T8YCbK6q49C7OIFNq1bd2jFfn7iMxI98rJsyuGfWNN2G7p8k48CVwBN4DS3IQB+Oa6vqKnq/9t2a5L2rXdA6M9AyEhvAXcBPAVcAx4E/6do3bP8keTvwBeD2qvreQqfO0bYh+mg2A30IqupY93oSeJjer8MnTv/K172eXL0K14z5+mSgZSRaV1UnquqNqnoT+Gt+NK2yIfsnyTn0wvy+qvpi1+w1tAADfZmSnJfkHae3gfcDz9BbBmGyO20S2L86Fa4p8/XJAeA3uzsVrgH+5/Sv1RvJGXO+v0rvOoJe/9yS5MeTXErvD39fX+n6VlKSAHcDh6vqM7MOeQ0twAeLlinJu+mNyqH35O3nquqPk7wLeBDYBrwM3FxVg/4RbN1L8nngOnor4p0APg38HXP0Sfc/71/Qu4Pj/4CPVtX0atS9Uubpn+voTbcUcAT47dOhlOT3gd+id/fH7VX19yte9ApK8gvAPwHfAt7smj9Fbx7da2geBrokNcIpF0lqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1Ij/h8y8a6Rpc26ggAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogram_solution_1()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There's a very long tail of Pokemon heights. Here, I've focused in on Pokemon of height 6 meters or less, so that I can use a smaller bin size to get a more detailed look at the main data distribution.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scales_solution_1()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Since Pokemon weights are so skewed, I used a log transformation on the x-axis. Bin edges are in increments of 0.1 powers of ten, with custom tick marks to demonstrate the log scaling.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scales_solution_2()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pie Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAASoAAADuCAYAAACONEo5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAIABJREFUeJzt3Xl8VPW9//HXZyYJO4EALogyimBSGECwiIiAS9U22ltvbetP22Jtrdq6VHvbO3XraGsbr21tXWq9aq9Rr1tdqjJtrV5lFXAFowZcIIFA2MNAIGSb7++PM9EUskwmM/M9Z+bzfDzmQTiZOedNljfnnDnn+xVjDEop5WY+2wGUUqo7WlRKKdfTolJKuZ4WlVLK9bSolFKup0WllHI9LSqllOtpUSmlXE+LSiVERFpFZEW7R6CL544Ukacyl05lO9Er01UiRKTeGDOwl+vIM8a0pCqTyh26R6WSJiIBEVkkIm/HHzPaLX8v/vGFIvIXEXkB+KfVwMqz8mwHUJ7RT0RWxD9ea4w5B9gCfMEYs09ExgKPAcd18NoTgInGmB0ZyqqyjBaVSlSDMWbyfsvygbtEZDLQCozr5LUvaUmp3tCiUr1xNbAZmIRzGmFfJ8/bk7FEKivpOSrVG4VArTEmBnwL8FvOo7KUFpXqjT8Cc0VkGc5hn+45qbTQyxOUUq6ne1RKKdfTolJKuZ4WVYaJyMEi8qiIrBGRt0RkqYick4L1zheRjq5hUsrztKgySEQE+Cuw0BhzlDFmKnAeMMpuMqXcTa+jyqxTgCZjzJ/aFhhjqoE7RaQvcA/Old0twDXGmFe7WN4P+B/gc0Al0C+z/5Tk3X3pK4XA8HaPgTgXj+YBeR/mtzY+N6CpL85FpC1AE7AD2BZ/bK0qK22wkV3ZoUWVWeOBtzv53A8BjDFBESkG/iki47pYfhmw1xgzUUQmdrHejLv70lcGAROAIHA0cDjOXuMoYCRQ0NXrC2OyGJjZ1XMCochePiuudcAqnMJeBVRWlZXu7t2/QrmJFpVFInI3zi9kE1AD3AlgjFklItU41ybN7GT5LOCO+PJ3ReTdzP8LoLK45BCcvb3jPj7qK8PWHfGFs4HRGdh0f+CI+GPK/p8MhCIbcErrfeA1YFFVWenGDORSaaBFlVnvA19t+4sx5ociMhx4E9jQyWuki/Vl/CK4yuKSIpxD2FOB03D2mAAYvGvtCjJTUok4LP44FbgSIBCKfAIsAhbiFNfH9uKpntCiyqxXgF+JyGXGmHviy/rH/1wIXAC8Ej+0OwJYncDyV0VkAjAxHYEri0vycPbeTscppmPp5E2YoTs/CqQjQwqNiT8uBAiEIhuBfwBPAy9XlZU22YumuqJXpmeYiBwK3A4cD2zFue3kT8Bz8T+ncuDJ9I6Wtz+ZvgJnz+ZKY8ybvc1YWVziA07CeUfyq8CIRF/76qw7Nhif/7DebH+zP7b4oUGNXZ6jSoMoMA+ntP6hJ+vdRYtKfaqyuOR4nHL6Gs5hU4+9Nv3m5fv6Dju+NzksFVV7e4C/A48A86rKSlstZlHooV/OqywuGQjMxXl3saS36xu8q6phX99hvc5l2QDg3PhjfSAUuQ+4r6qsdJPdWLlLL/jMUZXFJeMqi0vuwDmJfxcpKCmAorpV/bt/lqccDtwMrAuEIk8EQpHZtgPlIt2jyjGVxSVfBK7COTne1TuKSRlat7pX56dcLB/4OvD1QCjyPs55xvKqslKdrCIDdI8qR1QWl5xdWVzyBvA34AzSUFIA/fZtPwxj6tKxbhcZD9wPrAqEInMDoYgOGJhmWlRZrrK45MzK4pI3gefpeOKFlMtvrq/KxHZcYAzwIPBBIBS5IBCK6O9TmugXNktVFpfMqCwuWYDz7tXUTG57YP2GXZncnguMw3mH8L1AKPL1QCiSlr3VXKZFlWUqi0sOriwueRhYgnOhZsYV1a3K1XOfJcATwOJAKJKWC3BzlRZVlqgsLvFXFpdcgXPV+jdtZimqW3Wwze27wAzgrUAo8rtAKNKr2aWVQ4sqC1QWl0wH3sC5SbnQchwG1tcciTGdTZ2VK/JwphNbFQhFvmY7jNdpUXlYZXHJoMrikntxRgc41naeNoLx+1sb19jO4RKHAU8GQpG/B0KRMbbDeJUWlUfF96JWAN8nTZca9Eb/vZu2287gMmcCKwOhyPdtB/EiLSqPiZ+L+jnOcCVH2c7TmaE7P9KbSA80ALg3EIo8FwhFEr7RW2lReUplcclROAUVxuV3FRTVrSqyncHFvgy8GwhFTrEdxCu0qDyisrjkPJxDvRNsZ0lEYXTNkThTvauOHQK8FAhFbtILRbunXyCXqywu8VUWl/waeAwYZDtPovyxpgFiWqps53A5H3Aj8HIgFNE90C5oUblYZXHJIJzptUK2sySjX8N2HRYlMScDywKhyFjbQdxKi8qlKotLxgDLgLNtZ0lW4a41OrRv4sYCSwOhiJW7CdxOi8qFKotLTgZexxlm2LOKdlQOtp3BY4bhnLf6lu0gbqNF5TKVxSXfAF4EPH/OYujOj46wncGDCoCHAqHIL/Tm5s9oUblIZXHJpcCjOIO0eV5B8+7hmJiep0rO9UC5jnXl0KJyiRn3TLim2cd/kmXfkz6N0fW2M3jYt3D2rnK+rLLql8KrguXBG3b3l99efYk/v1U6nYjUkwbtrt5jO4PHnY+WlRaVbcHy4C9wJg9gyxA57Cff9TfHhC2WY6VMUd2qvrYzZIGcLystKouC5cEbcc5FfKpmhASuneuPGsiKcceL6laPtJ0hS+R0WWlRWRIsD14E3NTR59YcKmNvOt9fa2B3hmOlXL+GLYdjTNR2jizRVlY593ubc/9gNwiWB88E7u3qOR+Mls/deq5vjQFPTy0uIHkte6ts58gi5wP/ZTtEpmlRZViwPDgF+AsJjH7w9ljfpDu+7HvfgKev8B5Yv2Gn7QxZ5seBUOQS2yEySYsqg4LlwQAQARIeR3vJeN9x95/he9tAa9qCpdnQnatdPSSNR90VCEVOtx0iU7SoMiRYHizCmbrqkJ6+9qUpvumPz/ItNeDJweiKdqwabjtDFsoD/hIIRSbYDpIJWlQZECwPFgDPAcXJruPZE30zXzheFqUuVeYMql93FMZ4+vDVpQYD8wKhSNbP+qNFlRm3AjN7u5JHTvHPemWizO99nMzymVi+L9akkz2kx2jg+UAoUmA7SDppUaVZsDx4FvCjVK3vT6X+Oa+P815Z9d+7ZZvtDFlsGvBr2yHSSYsqjYLlwVHAg6le72++6p/z/hGyINXrTach0Y89+2aAR1wdCEXOtB0iXbSo0iRYHvQD/4szxlDK3XS+b9aag/HMOathOz4YajtDlhOc0RZ6/GaNF2hRpc+NQPpGaxSRn33Hf+LGIl5L2zZSqDD6yZEY48l3LT3kIODhbBzHSosqDYLlwZPZ7x6+dDAivh9/z//5bYN4Pd3b6q281sZBYlrX2c6RA04Dfmo7RKppUaVYsDw4FHiEDH1tW/2Sf9Wl/onR/ryTie31Rt99O2ptZ8gRvwyEIp+3HSKVtKhS79dARkcMaM6Tvpdf5h+3pw8VmdxuTw3etXaf7Qw5Ig/472waaUGLKoWC5cFpwMU2tt1YIAMuv8x/xL58VtvYfiKK6lYlfOuQ6rXJwBW2Q6SKFlWKBMuDPuAeLH5N9/STwisu8w9r9uPKiyuH1n14uO0MOebmQChymO0QqaBFlTo/AKbYDhEdIMOvvMTfr8WH68Yq79u082BMbKvtHDlkEHC77RCpoEWVAsHy4CHAL23naLO9UA798ff8plVw3QwwBU279Z2/zPpaNlwIqkWVGr8FCm2HaK92mBwR+o5/TwxcdevKoPp1nh+11IPuCoQinh67Xouql4LlwTk4oy66TvXBMubGb/m3GXDNUMBFdav72M6Qg8YAV9kO0RtaVL1XZjtAVz4cJcW3fMO3zoArpq0aumNVVt7i4QE/CYQig2yHSJYWVS8Ey4NnAMfbztGdd4/yBX93jm+1gUbbWQbs3TQaY+pt58hBw4CrbYdIlhZV7/zcdoBELS/2TfnTl3wrDbTYzCEYX15Lw1qbGXLYNYFQxJM3h2tRJSlYHjwNOMF2jp54dZJv2kOn+l43ELOZY8De2qyYs9CDCoEf2w6RDC2q5Hlmb6q9yDTfjGdmyBKb468PrftQf+7suSoQinhuDHv9gUlCsDx4CikYWtiWJ2b7T3pxiiy0tf2iusq0jNGlEjIQD46uoEWVHE/uTbX35zP8s5eU2BnSePCu6qMwxuq5shx3SSAU8dR9l1pUPRQsD55IOgfEy6A/fMU/Z+WRmR/S2Gda+vhizXpC3Z7BwDdth+gJLaqey6oZam85zz/7w5Fk/DCwX8PWzZnepvoXP7AdoCe0qHogWB4sBM61nSPVrv+2/6T1w1mSyW0OiX6ikz3YFQyEIif19EUi0uE1cCLyoIgk9bshImER+Y+unqNF1TMXAP1sh0g5EfnJd/3TNxeyLFObLNpR6ap7I3PUD20HSJQWVc98z3aAdIn5xH/1Jf4pdQN4KxPbGxL96MhMbEd16d+TnbVGHHeJyAciEsGZWKLtc1NFZIGIvCUiL4rIofHlF4vIGyKyUkSeFpH+iW5PiypBwfLgFOBY2znSqcUvBVdc5i/Z3ZeV6d5WfktDocRaa9K9HdWlfJIfkfYc4BggGF/HDAARyQfuBM41xkwF/gzcEn/NM8aYzxtjJgGVwHcT3ZgWVeKydm+qvaZ86X/5Zf4jGwr4IN3b6tNYtyHd21Dd+naSr5sFPGaMaTXGbAReiS8/BpgAvCQiK3BmYxoV/9wEEVkkIhU4p1HGJ7oxLaoEBMuD/XDpUC7p0NBXBl9+mf+Qpjw+Tud2Bu+ubkjn+lVCjg6EIlOTfG1HdzcI8L4xZnL8ETTGnB7/3IPA5caYIHATkPAYWVpUifk3XDYwXrrt7i9FV1zqH9ziozpd2yja8UHC5yhUWn0jidcsBM4TEX/8HNTJ8eWrgREicgI4h4Ii0rbnNAiojR8eXtCTjWlRJeYs2wFsqBskB139fb+/VdiYjvUP3amTPbjE15N4zbPAR0AFzqQmCwCMMU04l/DcKiIrgRXEz18BNwDLgZeAVT3ZmOgs212Lzy6zBWc8n5w0aqtZ+5sHWgf6DCNSve5XZt+1A5Gi9ss2+2OLHxrU6Nl7KT1qalVZ6du2Q3RG96i6N50cLimAmhFy5HXf9tcZ2Jnqdec3707boaXqka/YDtAVLaruldoO4AafjJRxN5/v22AgpZMzDKqv2ZXK9amknWM7QFe0qLqnRRX3/mjf+P861/eJgZS9Wze0bnV+qtalemVCIBRx7TlDLaouBMuDo4BJtnO4yVtjfZPvOtv3voGmVKyvqG7VwalYj0oJ144KokXVtS/ZDuBGiyb4jvvzF3xvGej1jcUD6zcEMEavp3KHHt+knClaVF3z/Ayz6fLicb4TnpjlW9rbIY0F4/e37luTqlyqV3SPyqOm2Q7gZs+c6JsZ+bws6u16BuzdtD0VeVSvlQRCkZRfgpIKWlSdCJYHDwYOs53D7R46zT9rfrB3QxoP2fmRpCiO6j1XXr+mRdW5ZO9/yjl/PMs/542xyZdV0Y7Kou6fpTLElYd/WlSd06LqgdvO9c95/3CSGn+9cNfaozBGR/x0B92j8hgtqh666QL/rLUHs7inr/PHmvv5TEtVGiKpnhsfCEVc1wuuC+QiWlQ9JSI/u9B/Qu1Qlvb0pX0btm1KRyTVY/2A0bZD7E+LqgPB8uBBfDbYl+qBmE/811zsP277IN7oyesKo2t0nj/3KLEdYH9dFpWIzBeRM/Zb9iMR+WOqg4jIQBG5R0Q+EZF34uMtJztM6v7r7ukMGbo31Qutfsm/8lJ/cFc/3kn0NcPqKgelM5PqkWLbAfbX3R7VY8B5+y07L768W/EB4BPda7sfqAPGGmOOxbnY0ta7Qa77RnlNc570vfwH/rF7+vBeIs8fsvMj1x1u5DBv7VEBTwFniUgfABEJACPBOWEqIj+Jzyrxrojc1PYcEamM73W9DdwgIre3rTA+E8Xv2m9ERMbgXFx5vTEmBmCM2WqMuTX+eRGR20TkPRGpEJFvJLC8wxkyEhTo4fNVB/YVyMArLvOPasxjdXfPLWiuH4Zprc1ELtUtbxWVMWY78Dqf3UpyHvCEMcaIyOnAWJyCmQxMFZG2azCOAR6K7xn9BvhyfPhRgO8A/7PfpsYDK9tKqgP/Ht/GJOA04Lb48KedLe9whoweCPTw+aoT9f1kyOWX+Yua/XQ7hXufxuj6TGRS3fJWUcW1P/xrf9h3evzxDs6eUzFOcQFUG2OWARhj9uDMUHGWiBQD+caYiq42KCLXicgKEWkbAncmn814sRln2NPPd7G8sxkyEqWHISkUHSgjrrrE37fFR5fTY+lkD65RFAhFBtsO0V4iRfVX4FQRmQL0M8a0DVcqwK/bzTZxtDHmgfjn9uy3jvuBC+l4bwrgA2BS2/ksY8wtxpjJQNsXq7NbLLq69aI3N8u6dlwer9pWKIf+x/f8rTFhc2fPKdqxqk8mM6kuueqev26LyhhTD8zHmUiw/Un0F4GLRGQggIgcJiIdngsyxizH+eU/nw5OxBtjPgbeBH4pIv74+vryWREtBL4Rn/FiBM4e0+vdLO9ohoxuBcuDBdg7iZ/VNg6T0aEL/fUx6PAm5KE7V+slIe7hraKKewznPNDjbQuMMf8EHgWWxicUfApnOpzOPAksMcbUdfL57+GMTf6xiLwFvAz8Z/xzzwLvAitxDuN+aozZ1M3yA2bISFBSU1yrxFQdImN+/k3/FgPR/T/Xv2HrKIw5YLmyYrjtAO1lbBYaEZkH3G6M+b+MbDBJwfLgNJwpfVQaTfok9u61T8aOFviXuf0WnXjrypq+/XfrLDTWXVRVVtrRaRor0n5luogMEZEPgQa3l1Scq3Z5s9XKMb6Jv/+Kr9JAY/vlA+o3pHymG5UUV+1Rpb2ojDE7jTHjjDFfS/e2UkRP6GbI0hLf1Hu/6Fth4NPbZ4rqVvttZlKfctV/2Hqv34F0VpQMemWy7/hHTvYtNxADnezBRVw1l6UW1YG0qDLshem+E5+dIUsABtavP9KYVh2byr4C2wHa06I6kBaVBY/P9p/0z2Nlgc/E8vIatuy1nUe5ixbVgbSoLLn/TP/spcWyYGjd6s5upVKZ46px7LWoDqRFZdHt5/hnrxrzQUomN1XZQ4vqQFpUthiz95vRXQvuGvh/Z83yrXzXdpwcp3tULqdFZcGg1ljFcxtqt/7njp2zfUL+n/NvG1ZIvV5TpQAtqo402w6QU4xp/Oqu+gWL19WMP6q55dNRK/IkdtgLBdevshktx+kelct1di+iSrH+sdgHT27cVBPevmO2r4OfxSN8W6aH8h5daCObctd/2FpUB9phO0DWM6b5i/V75i+prhlX0tQ8pqunXuKfN22ifPJRpqKpT7nq90CL6kC6R5VGfWKxjx6p3bzmv7Zun5MHed09X4S+Txbc7OtHo15blVnbbAdoT4vqQK76nyRrGNM6Z8/e+a9V14ye1Nh0TE9e2leaxzxe8Iu30hVNdUiLyuW0qFKswJg1D2zasurOLdvmFCR5a8Yk35qTvuP/e48nNlVJ06JyOT30SxVjYsc37FvwWvX6kdP2NY7v7epuzHv4c6NlU5fjrquU6XAUVlu0qPZTMbeiEdDzIb2UZ8y6uzdvrbh/05bZfQx9U7FOEQpfKLhup59WnVU5/XSPygNc9U3yFGPMpH2NC5dU1wyb1bBvUqpXP1gaJtyb/7vFqV6vOoCrfge0qDr2se0AXuQ3ZuNtW7e//Ujt5ln9jRmQru2c6ntn1pd8y9/u/pkqSQ3Q+WxBNmhRdazSdgCvOaaxafHCdTUDztyzd2q6tyWC7878O0YNZ+fWdG8rR31YVVaamckUEqRF1TG9dSNBPmO23Lx1++tPbdw0c3DMFGZqu34xB0X6XFsNGZqdJLe47udfi6pjukeVgEBT82sL1m3IP6d+zzQb2z9Ydh73q7wH9Bab1FttO8D+tKg65rr/UdxEjNke2r5j6QsbamcMicWG2szy//yvzJgmlR/YzJCFXPfzr0XVgYq5FRuA3bZzuNGhzS3LX1m/IXbBrvoTbGcBECH/fwt+NWAADfr9Sh0tKg9x3TfLKmOiV+7YueSfNRuPH94ac9VUSvnSOvrZght1oL3UMOihn6foeaq44S2tb/5z/ca9F0d3nWg7S2fG+TaceIX/mSW2c2SBtVVlpa674FmLqnNv2g5gnTG7L94ZXfTq+g3HHdraeqjtON25Ju+pycfIurW2c3icK8tei6pzC2wHsGlIa+uKSE1t9Mq66Em2syRKhAHPFvy8qYDmxu6frTqxyHaAjmhRda4Cl92YmRHGNJwf3b1g4boNk45oaRllO05P9ZfGYx4qKFtmO4eHaVF5ScXcCgPk1DU6A1tjFc9u2LTpZzvqZovLxszuiem+ytlf889/3XYOD9paVVbqyjeRtKi6lhuHf8Y0nrO7fv7idTXjj25uPtJ2nFS4Ne++o0eyrdZ2Do9x7c3eWlRdm287QLr1i8Uqn9i4af3N23bM8WfRz4NPTNG8PtdtFmI663LiXHnYB1n0g5km75KtI34a03xG/Z75r1XXjP1cU/PRtuOkQ5HsnvyH/Ltd+8vnQq79WmlRdSF+nsq137xk9YnFPiqv3fzxbxKcYMHLzvYtnXmy752VtnN4wBbAtUPnaFF17x+2A6SMMa2z9jbMf626ZvSUxqYS23EyQQT/ffm/HTGE3TrEdNdeqCorde1hshZV954GPD/0bb4xa+/btKXy7s1bk55gwavyJDbyhYLrPrSdw+Wesx2gK1pU3aiYW7EVeMV2jqQZYz7fsG/Ba9U1h0zf1zjBdhxbDvdtO/7avEdy6nKTHtgDvGw7RFe0qBLzuO0AycgzZv2dW7at/POmLbP7GtPPdh7bLvb/bdok+Vj3rA40r6qstMF2iK5oUSXmGaDJdoieCO5rXLi4umbonL0Nk21ncQtn1uVf+HXW5QM8aTtAd7SoElAxtyKKR06q+4ypvXXLtjcfrd08a4AxA23ncZs+0jzmiYKbddblz9QDf7MdojtaVIlz/eHf2KamxQvX1fT/0p69x9nO4mYTfWtPusj/99ds53CJJ6vKSvfZDtEdLarEPY9LJyYVY7betHX7689s2DSzMIMTLHjZDXkPTwhI7XrbOVzgHtsBEqFFlaCKuRV7gKds59jf6ObmpQvWbfD/u6UJFrxKhMEvFFy/K8dnXX6zqqzUE+OuaVH1zO22A7QRY3b8dHvd0nk1tScMjcWKbOfxokHSMP6+/N+69kbcDPij7QCJ0qLqgYq5FSuAV23nOKSl5fWX129s+dau3a6YYMHLTvatmF3qW5aLJ9fr8MB51zZaVD33O2tbNiZ6ed3OxS+t3zjtoNbWg6zlyCIiyB35dx6eg7Mul7v92qn2tKh6LoKFWTqGt7S+9WLNxr2X7Nw1M9PbznZ+MQf9LbdmXTZ45CR6Gy2qHoqPqPD7jG3QmPqLdu5a9Or6DVNHtrh/ggWvOkh2HleWd3+u3GITqSor9dQV+lpUySknA+OpF7a2rnyhpnbH1XU7PTPBgpd9w//qjOm+99+3nSMDfm47QE9pUSWhYm5FA/CntG3AmIbzdu1esGjdhomBlpYj0rYd9S9EyH84v2zQQPbusp0ljZ6rKit17bhTndGiSt7vgWiqVzogFnv/6Q2bNl233dsTLHhVvrQe8deCG9+znSNNDB7cmwItqqRVzK3YBvw6ZSs0punLu+vnL6muKR6XJRMseNXRvo0zrvI/nY3XVz1TVVbqydFOtah65w9AdW9X0i8WW/XYxs3VtzgTLPhTkEv10o/ynj62WNatsZ0jhQwQth0iWVpUvVAxt2IfcF3SKzCm5bQ9e+cvqa4ZM6GpaWzqkqneEmHAMwU3NmfRrMt/qSor9ewhrRZV7z0KvNHTFxXEzCcP1m756PYt2+bkQ34acqle6i9NxzxS8KvltnOkQAMQsh2iN7Soeil+XdV/JPwCY2Iz9zbMX1q9ftTUxsacmGDBy6b5Vs86z/+K18vqV1VlpWtth+gNMTlzMW56BcuDfwX+ravn5BtTdefmrbtPbNgXzFAslQIxQ91JjX/Yt4ERXrzg9kMgWFVW6qkRavene1Sp81Og4/MZxpipDfsWLKmuOUhLynt8wtB5fa7b4iPWajtLEn7o9ZICLaqUqZhb8SHwi/2X+42p+f2WbSse3LRldj9j+luIplJgqNRPuiP/Tq9NRvtEVVmpq2eXSZQWVWrdCrzT9pfxjY2LFlfXFJ66t+FYi5lUipT6lp90iu9tr1yHtAu42naIVNFzVCkWLA9O8hsz75at2zeV6tjlWafF+GqPa7yn704GDbWdpRuXV5WV3m07RKpoUaVB402F1/Ux/NJ2DpUeNWb48pmNdxxvO0cX5lWVlZ5tO0Qq6aFfGvQxlAHLbOdQ6TFKth1/Q97DC2zn6EQt8B3bIVJNiyodwtFW4Js4c6apLHSR/+/TJ8vHGR9AsRsG+HZVWek220FSTYsqXcLRT4Af2Y6h0kOEPk8U3Jzfn31umkLttmx5l29/WlTpFI4+ADxsO4ZKjz7SctST7pl1+Q3getsh0kWLKv0uBnRW3iw1wVd10nf9f7P9/Y0C51eVlTZbzpE2+q5fJoQLDwJeB0bbjqJSzxh2ndL0m51rzUgbo7G2AF/M1kO+NrpHlQnh6BbgbPTkelYSYfDzBTfszqPFxh7N5dleUqBFlTnhaAVwPhCzHUWl3iBpGP9A/m+WZHizt1eVld6b4W1aoUWVSeHoC8DPbMdQ6THL9+7ss32vvZmhzT1PT4YX8jg9R2VDuPB/gAttx1Cp12pk6wmNd7GFoSPSuJkVwMyqstI9adyGq+gelR2XAPNth1Cp5xczItLn2nVpnHV5LXB2LpUUaFHZEY42AaXAq7ajqNQbIdGpt+b9dzpusakCTq4qK61Jw7pdTYvKlnB0L05ZvWQ7ikq9r/sXnJjiWZercUqq17MeeZGeo7ItXNgHeAb4ku0oKrWajX/9sY33FtbTf3AvV7UOmF1VVlodHuIWAAAFX0lEQVSVgliepHtUtoWjjcA5OO/iqCySL62HP19wQ2+nqFoHzMnlkgItKndwzlmdCzxtO4pKraN8tTOuzvtLskMYr8M53PP0DDKpoEXlFuFoM3Ae8ITtKCq1rvQ/O7VEqj/p4cveBqZXlZVm02zNSdOicpNwtAW4APhv21FU6ojQ/5mCn7f2oWlfgi95HphVVVZam85cXqIn090qXHgJcAdQYDuKSo03YuMWfq0pPKubp/0BuKaqrFRvtWpHi8rNwoUzgKcAL058qTrws+bvLn+s9dSOxltvBa6uKiu9M9OZvECLyu3ChYfinGQ/wXYU1XsxQ92spj801JgRI9st3o0zntQ8W7ncTs9RuV04WgvMQc9bZQWfMHRewbVb2826vBKYqiXVNd2j8pJw4feBO9HzVp73t9Zp83/Q/KMPgauqykoTPcmes7SovCZcOB14FDjSdhSVtB3AJYSjT9kO4hV66Oc14egyYCLwR5zpkZS3/B8wUUuqZ3SPysvChacAf0bHYveCRuBa4HbCUf2l6yEtKq8LFw4EbgEuR/eQ3eo54BrCUb3KPElaVNkiXDgVuBeYajuK+tQq4EeEoy/aDuJ1+j9wtghH3wKOB67CmedN2bMLZzzziVpSqaF7VNkoXDgU5xflSmCg5TS5xADlQIhwdLPtMNlEiyqbhQtHAD8Ffgj0s5wm2y3DOcxbbjtINtKiygXhwkNwpum6BOhjOU22+TtwG+Gojn+fRlpUuSRcOAq4HrgIyLecxsuagcdxCqrCdphcoEWVi8KFAZy9qwuBQ6xm8ZbdwH0410Ll3EwwNmlR5bJwYR5wFvA94EzAbzeQa23EucfyT4SjO22HyUVaVMoRLjwM+A7OYaHeRwjbcIbXeRxYSDiqA9lZpEWl/lW4UIBTge/i7G3l0uUNUeBZnHHrX44PDa1cQItKdS5cmA+cCJyBc2g4CRCrmVJvD84Y5Y8DL8anL1Muo0WlEhcuPBintM4ATgeG2w2UlEaca57mA68Cy7Sc3E+LSiUnXOgDpgAnA8fGH+Nw321ZG4HlOOW0HFhOOKoD1XmMFpVKnXDhAJyxsiYAxe0eAdJbYLuANcDa/R7vEo6uT+N2VYZoUan0Cxf2BQ4GiuKPYZ18PARowTk8awT2dfLxLtoXUji6I4P/GmWBFpVSyvXcdj5BKaUOoEWllHI9LSqllOtpUbmciLSKyAoReU9E/iIi/dOwjZEiorOiKNfSk+kuJyL1xpiB8Y//F3jLGPO7dp8XnO+j3oumspbuUXnLIuBoEQmISKWI/BF4GzhcRE4XkaUi8nZ8z6ut3KpE5Ffxz70pIlNE5EUR+URELo0/JyAi78U/vlBE7mrboIjME5E58Y/rReRWEXlLRF4WkWkiMl9E1ojIlzP9xVC5Q4vKI0QkD/gi0DZQ2zHAQ8aYY3HuV7seOM0YMwV4E7im3cvXG2NOwCm6B4FzgenAzT2MMQCYb4yZijM20y+BLwDnJLEupRKWZzuA6lY/EVkR/3gR8AAwEqg2xiyLL58OfA5Y4hwJUgAsbbeO5+N/VgADjTG7gd0isk9EhvQgSxPwj3brajTGNItIBc7V50qlhRaV+zUYYya3XxAvoz3tFwEvGWP+XyfraLvpNtbu47a/7/8z0MK/7mn3bfdxs/nspOan6zLGxOJ7fEqlhR76ZYdlwIkicjSAiPQXkXFJrqsKmCwiPhE5HJiWooxKJU3/F8wCxpitInIh8JiItM0ycz3wYRKrW4JzD10F8B7OyXqlrNLLE5RSrqeHfkop19OiUkq5nhaVUsr1tKiUUq6nRaWUcj0tKqWU62lRKaVcT4tKKeV6/x8E8G6O46OoIAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# code for the pie chart seen above\n",
    "sorted_counts = df['cut'].value_counts()\n",
    "plt.pie(sorted_counts, labels = sorted_counts.index, startangle = 90,\n",
    "        counterclock = False);\n",
    "plt.axis('square');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_counts = df['cut'].value_counts()\n",
    "plt.pie(sorted_counts, labels = sorted_counts.index, startangle = 90,\n",
    "        counterclock = False, wedgeprops = {'width' : 0.4});\n",
    "plt.axis('square');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data = pokemon, x = 'attack');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [10, 5]) # larger figure size for subplots\n",
    "\n",
    "# histogram on left, example of too-large bin size\n",
    "plt.subplot(1, 2, 1) # 1 row, 2 cols, subplot 1\n",
    "bin_edges = np.arange(0, pokemon['attack'].max()+4, 4);\n",
    "plt.hist(data = pokemon, x = 'attack', bins = bin_edges);\n",
    "\n",
    "# histogram on right, example of too-small bin size\n",
    "plt.subplot(1, 2, 2) # 1 row, 2 cols, subplot 2\n",
    "bin_edges = np.arange(0, pokemon['attack'].max()+1/4, 1/4);\n",
    "plt.hist(data = pokemon, x = 'attack', bins = bin_edges);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.distplot(pokemon['attack']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure, Axes\n",
    "\n",
    "https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html\n",
    "\n",
    "https://matplotlib.org/api/axes_api.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3, 4) # grid of 3x4 subplots\n",
    "axes = axes.flatten() # reshape from 3x4 array into 12-element vector\n",
    "for i in range(12):\n",
    "    plt.sca(axes[i]) # set the current Axes\n",
    "    plt.text(0.5, 0.5, i+1) # print conventional subplot index number to middle of Axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Descriptive Statistics, Outliers, and Axis Limits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [10, 5])\n",
    "\n",
    "# histogram on left: full data\n",
    "plt.subplot(1, 2, 1)\n",
    "bin_edges = np.arange(0, pokemon['attack'].max()+2.5, 2.5);\n",
    "plt.hist(data = pokemon, x = 'attack', bins = bin_edges);\n",
    "\n",
    "# histogram on right: focus in on bulk of data < 35\n",
    "plt.subplot(1, 2, 2)\n",
    "bin_edges = np.arange(0, 35+1, 1)\n",
    "plt.hist(data = pokemon, x = 'attack', bins = bin_edges);\n",
    "plt.xlim(0, 35); # could also be called as plt.xlim((0, 35))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24a7b8a80b8>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [0.0, 3.0, 4.5, 8.0]\n",
    "plt.figure(figsize = [12, 5])\n",
    "\n",
    "# left plot: showing kde lumps with the default settings\n",
    "plt.subplot(1, 3, 1)\n",
    "sb.distplot(data, hist = False, rug = True, rug_kws = {'color' : 'r'})\n",
    "\n",
    "# central plot: kde with narrow bandwidth to show individual probability lumps\n",
    "plt.subplot(1, 3, 2)\n",
    "sb.distplot(data, hist = False, rug = True, rug_kws = {'color' : 'r'},\n",
    "            kde_kws = {'bw' : 1})\n",
    "\n",
    "# right plot: choosing a different, triangular kernel function (lump shape)\n",
    "plt.subplot(1, 3, 3)\n",
    "sb.distplot(data, hist = False, rug = True, rug_kws = {'color' : 'r'},\n",
    "            kde_kws = {'bw' : 1.5, 'kernel' : 'tri'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Waffle Plots\n",
    "\n",
    "A lib could do it for you : https://github.com/ligyxy/PyWaffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "def percentage_blocks(df, var):\n",
    "    \"\"\"\n",
    "    Take as input a dataframe and variable, and return a Pandas series with\n",
    "    approximate percentage values for filling out a waffle plot.\n",
    "    \"\"\"\n",
    "    # compute base quotas\n",
    "    percentages = 100 * df[var].value_counts() / df.shape[0]\n",
    "    counts = np.floor(percentages).astype(int) # integer part = minimum quota\n",
    "    decimal = (percentages - counts).sort_values(ascending = False)\n",
    "\n",
    "    # add in additional counts to reach 100\n",
    "    rem = 100 - counts.sum()\n",
    "    for cat in decimal.index[:rem]:\n",
    "        counts[cat] += 1\n",
    "\n",
    "    return counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "waffle_counts = percentage_blocks(df, 'cut')\n",
    "\n",
    "prev_count = 0\n",
    "# for each category,\n",
    "for cat in range(waffle_counts.shape[0]):\n",
    "    # get the block indices\n",
    "    blocks = np.arange(prev_count, prev_count + waffle_counts[cat])\n",
    "    # and put a block at each index's location\n",
    "    x = blocks % 10 # use mod operation to get ones digit\n",
    "    y = blocks // 10 # use floor division to get tens digit\n",
    "    plt.bar(x = x, height = 0.8, width = 0.8, bottom = y)\n",
    "    prev_count += waffle_counts[cat]\n",
    "\n",
    "# aesthetic wrangling\n",
    "plt.legend(waffle_counts.index, bbox_to_anchor = (1, 0.5), loc = 6);\n",
    "plt.axis('off');\n",
    "plt.axis('square');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-54.339999999999996, 1132.34, 0.0, 1186.6799999999998)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# each box represents five full counts\n",
    "waffle_counts = (df['cut'].value_counts() / 5).astype(int)\n",
    "\n",
    "prev_count = 0\n",
    "# for each category,\n",
    "for cat in range(waffle_counts.shape[0]):\n",
    "    # get the block indices\n",
    "    blocks = np.arange(prev_count, prev_count + waffle_counts[cat])\n",
    "    # and put a block at each index's location\n",
    "    x = blocks % 10\n",
    "    y = blocks // 10\n",
    "    plt.bar(y, 0.8, 0.8, x)\n",
    "    prev_count += waffle_counts[cat]\n",
    "\n",
    "# box size legend\n",
    "plt.bar(7.5, 0.8, 0.8, 2, color = 'white', edgecolor = 'black', lw = 2);\n",
    "plt.text(8.1, 2.4,'= 5 data points', va = 'center');\n",
    "\n",
    "# aesthetic wrangling\n",
    "plt.legend(waffle_counts.index, bbox_to_anchor = (0.8, 0.5), loc = 6);\n",
    "plt.axis('off');\n",
    "plt.axis('square');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bi variate\n",
    "## Scatterplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat    cut color clarity  depth  table  price     x     y     z\n",
       "0   0.23  Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>species</th>\n",
       "      <th>generation_id</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>base_experience</th>\n",
       "      <th>type_1</th>\n",
       "      <th>type_2</th>\n",
       "      <th>hp</th>\n",
       "      <th>attack</th>\n",
       "      <th>defense</th>\n",
       "      <th>speed</th>\n",
       "      <th>special-attack</th>\n",
       "      <th>special-defense</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>bulbasaur</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>6.9</td>\n",
       "      <td>64</td>\n",
       "      <td>grass</td>\n",
       "      <td>poison</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>45</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id    species  generation_id  height  weight  base_experience type_1  \\\n",
       "0   1  bulbasaur              1     0.7     6.9               64  grass   \n",
       "\n",
       "   type_2  hp  attack  defense  speed  special-attack  special-defense  \n",
       "0  poison  45      49       49     45              65               65  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x24a7b91e400>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data = df, x = 'depth', y = 'price');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZUAAAEKCAYAAADaa8itAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAIABJREFUeJzsvXmYHPd53/l56+hruucAMAOAOERAAgWJskjRsEQ9oWgsfYSyE9HOMjax2cdMLIVILC2lZOOHdKLDS2Wz4kq2bG4chbCiWLJj0g5jm8yujohUYEi7hEWQ1EFKEAENQeKewdwzfVbVu39UVaN6pnumAczdvw+f4XT/urqmutFd33pvUVUMBoPBYFgMrJU+AIPBYDCsH4yoGAwGg2HRMKJiMBgMhkXDiIrBYDAYFg0jKgaDwWBYNIyoGAwGg2HRMKJiMBgMhkXDiIrBYDAYFg0jKgaDwWBYNJyVPoDlZtOmTXr99dev9GEYDAbDmuL555+/pKr9C23XcaJy/fXXc+zYsZU+DIPBYFhTiMhr7Wxn3F8Gg8FgWDSMqBgMBoNh0TCiYjAYDIZFw4iKwWAwGBYNIyoGg8FgWDQ6LvvLYFhNHD4+xKNHBjk9VmRHX46Dt+9m/96BBR+7lv0aDEuJdNrkx3379qlJKTa0Q3xifuXiJDVfSTkWewYKHLx9N0DTk3byZC7ApekK5VoAgAiogm0Jb+rv4hd+Yit/fPQ1psoeXhBgiaBA2hH8ACpe+LygxVfUksbHbAn/Rvi3hE35FBu70ozMVBidqZFP29ywudsIjOGqEJHnVXXfgtsZUTEY5nL4+BAff+plqp7PyEw1XFTYVEjh+YoCPVkXzw+4OFmhFgRsLaSp+IpjCxcnKy3FYLkRQKPfaceikHH49N03GWExXBHtiopxfxk6llYuosPHh/iNP32BYtWvb5uyLcSCyZKHHygI5NMO5ycqiIAtwtmJCqtERxrQxG8Fxoo1Hv7qcaDR2nr37g08Ozi66K645WC1H18nYSwVQ0cSWyKuLWRdm1LNp+Yrd9+yjT8++hrD09U5z0nZoXtKVVEEVV011sjVYEkolpu701S8gOHpKgOF0GUWvx8Pve9GgKbv1UPvu7GtE/dSn/Bb/Vu2e3yG9mjXUjHZX4aO5NEjg7i2kEs5iIS/XVv4/LdeZaJUa/qcqq/UfMULwA/WtqBAGI/xVTk/UWG8WMOS0BJLvh+PHhls+V49emRwwb8Rn/CHpsr0Zl2Gpsp8/KmXOXx8aNFex7Ucn2HxMaJi6EhOjxXJunbDWta1ma54VP01rhZXgOcrNT+g7AV4vtaTAyB8P86MFVu+V2fGigvufzlO+NdyfIbFx8RUDB3Jjr4cQ1NlcqnLX4FSzUeiFK04uL3eiQP48W0/UKbKNQoZl1LNZ3tfDoAfXZhkvFQj0NBt1pt1efOW7gX3f3qsSG/WbVhb7BN+q3/L+NgNy4uxVAwdycHbd1PzlWLVQzX8XfOVrCvYVmcISkzytVoCQ5Pl+vtx8PbdbOlOMVqs1d19gcJoscaW7tSC+97Rl6NU8xvWFvuE3+rfMk79NiwvRlQMHcn+vQM89L4bGShkmCjVGChkeOh9N/IT2/roz6dJO1b9Cn49E7/GtGPRn0+RcW0qvjJQyHD3Ldt49Mggf/Xd8/VthVB4LIFnjg8vuP/lOOG3+rc0QfqVwbi/DB3L/r0DTU88v/nEdxHAtsKTaC2Ys8m6wbWFrT1ZuiMXVbHqMVDIcPD23fWMqmSCqGtb2JYQaMBM1W+x10ZyrsWrI6G7a/emLj72i3sX/YTf6t/SsPwYUTEYZqEAElal25ZQC9avqniBUvV9VJ16Ku7B23c3BNiTlfteEGBbNoFCV8qed9/JVN89A3lKNb9tITKsXYz7y2BIEJ9M7ajfSfx7vSLATMWf4zZKZlRt7ApjJ0ooLl4QECh84LZd8+7bpPp2JsZSMRgSnBiaYqJYw7JCK8Vb68UoC+ArpByLbz5wR8N6MqNqS08WgJGZKoGG2VsfuG0X9//sDfPuezkyvwyrjyUTFRH5AvB3gCFVfVu09mfAm6NNeoFxVb1ZRK4Hfgj8KHrsqKr+k+g5Pwn8EZAFvgx8WFVVRDYAfwZcD5wCfkVVx5bq9Rg6g6oXgIAlgh8o3jp2fcVUo9qUZOV7PmUzGRWBZl2b7qxLNuVcUQDcpPp2Jkvp/voj4M7kgqr+qqrerKo3A/8F+IvEwz+OH4sFJeJzwH3Anugn3ueDwDOqugd4JrpvMFwTrh26u2p+QNUP1nzVfDuo6pzK91oQNs1M2RYTpRop2yLnWnz0yZc4cOhoWxXxycyvyVKVExenODUyw9hMZVEr6g2riyUTFVU9Aow2e0xEBPgV4LH59iEiW4FuVX1WwyZlXwJ+KXr4LuCL0e0vJtYNhqvmhs3d5NP2und7xVgCZS/gN/7TC5wdL3Jhosx0xSOXcujJuvTmUnzyrrcxU/WpBXpFrVbiVN+UbXFmvAwC23uz1AJd9FYthtXDSgXq3wNcVNUTibVdIvKiiPy1iLwnWtsGnElscyZaA9isqucBot8tbXIRuU9EjonIseHhhXPrDZ3Lu3dvYLLcORlKAvi+Uqz5OFEM6dx4malyrR7/uJaA+/69A/TmUly/MceegQLd2ZQJ2K9zVkpUDtBopZwHdqrqO4B/DvypiHRD0/qzK76EVNVDqrpPVff19/df1QEbOoNnB0fpz6ew1nfSFxAO9PIV4qhR1VNUw/XhqUo9/nGtvbVMb67OYtlFRUQc4O8RBtkBUNWKqo5Et58HfgzcQGiZbE88fTtwLrp9MXKPxW4yY0sbrpnTY8WOqaafnS6tEMaRgoCyd7lm5VpbrSxHqxbD6mElLJWfBY6rat2tJSL9ImJHt3cTBuQHI7fWlIjcGsVhfg14MnraU8C90e17E+sGw1Wzoy/HxckKjm2t+yKuVnGjWgBdiUyva221YnpzdRZL9r0RkceAZ4E3i8gZEXl/9NA9zA3Q3w58T0S+CzwB/BNVjYP8/xT4PHCS0IL5SrT+KeDnROQE8HPRfYPhmjh4+25qQYBqOJN+vQtLKz5w26566vC19tYyvbk6CzP50WCYxZ2f/WtOjRap+brmpzteLe/evZHH7rt1pQ/DsIowkx8Nhqvkwfe+hULaQQDHEuxOCLDM4tunRtuuRzEYkhhRMRhmsX/vABu7wgywqq900CDIOhnHWpLRv4b1jxEVg6EJl2aq+Ovc7+W2yJt2baErZXNhoszZ8SL3P/6iERZD25iGkoaOJNnnakdfjoO3724IHFe9YF1bKOGwLSHjCJ4fvlYlrLD3fGV4uopjC44lFKs+H3/qZR4CE1w3LIixVAwdx+w+V83cPOs5gSVtw8Z8CrGgFmhdUODy3BQFar5S8ZRAlZrvmwp4Q1sYUTF0HHHbET9QXr00w+ujRYYmyzz81eNAKDrreZhUxYdNXSn68+mm4jl7xRK4NFXlxNDU8hygYU1j3F+GjuP0WBFb4PxEBRHC8biB8srQNI88/QpPvHB23acRX5gsU6oFOJYgIlS9oGX/I9e28YKg3iLfYJgPIyqGjmNHX44XXx9DorkpEMYYXBs+/61X6S+kEa6iydwaYrzkYUe9vzKuIELY94vG1y1AEClsaoHc6oXiVIbOwLi/DB1HsmpeVQkCJUDZXEgzU/XJuvaCJ9D1QD1+opf7gM0WFMsSHFvY2JViz+bulvt65OlXOPgnz/PtUyOMTlc4NTJt0pE7FGOpGDqO/XsH2NOf59RoET9QUrbFpnwGJ0qlLdV8tvRkOT1WXPduMIDKLLdWmBkW/iiKH4Q/B2/f3dQaAfiDwz8mUMW1LXyFkekaG/Nh/MpYK52FadNi6EjiDDDXFrJuKCQ1X7n7lm088cJZXDtMtX1ttLTSh7rkxHNUYvIpi5lqKDTJs8PGnINlWXRnXTw/4OJkhVoQ4FhWXZgsAceywliVwMZ8mm8+cMdyvhzDEtFumxZjqRjWPa18/Q8RXkmfGSuyPbH+9u299XWJggwirEurJa5XSTuCqlL1lelqUI+3JBkphnEYS4SxYg2R8PlJS0c1HMXs2kIlUNPevgMxomJY1yQtkmRNSlzI18w1k1x/80e/EqXdCjW/dYbUWkUJXVye39g4s1Xhp68wPF3BtS0sEaqzEsLip1V9Je1Ypr19B2IC9YZ1zbWMwgXYtTFHEF19rzdBifH8cOJjuwSR5QbNa1piPrj/jSae0oEYS8Wwrjk9VqQ36zastTvKNs5c8gNdt4ICV5c6HQSKELq/bCvcSUBY85OyhOs35Lj/Z29Y1OOcjUlhXp0YS8WwrrnaUbax26wWKDs3ZDtivHC7WFG8RSzYlHcBARF29GXZuSFHIR1eq9728DeWrH1+O612DCuDERXDuuZqR9km3Wbd2VRH1K3MptkrtgRc2+LDd7yJd+zoI+M67BnI86b+LgKFlG2FfcMCXdKT/bW6NQ1Lx1KOE/6CiAyJyEuJtd8WkbMi8p3o5xcSj/2WiJwUkR+JyN9OrN8ZrZ0UkQcT67tE5G9E5ISI/JmIpJbqtRjWLlc7yvb0WJGsa9fvd1qDkv58ii096YY1izC7q+oFfP5br3Lw9t1884E7+MpHbuer/+yn+eYDd9CbS9GTdZf8ZD/73wfad2salpaltFT+CLizyfpnVfXm6OfLACLyVsLZ9TdGz/l3ImKLiA38AfBe4K3AgWhbgIejfe0BxoD3L+FrMaxh9u8d4LH7buWbD9xRH5F74NDRed0zs91mKbszjHoBsq7Fp+++ies35utFkHH7FtuClHO5Hf7s9265TvZX69Y0LD1L9k1R1SPAaJub3wU8rqoVVX0VOAm8M/o5qaqDqloFHgfuEhEB7gCeiJ7/ReCXFvUFGNYl7friY7fZpekyPx6amnMCW6+IwD/96TfWhfhduzaya1MXuZRNyrFwbRtUSDtWUwtkuU72V+vWNCw9K3H59SER+V7kHuuL1rYBpxPbnInWWq1vBMZV1Zu1bjDMS7u++P17B7j7lm2MztSo+jrn6ns9IsB13emGrK345F32fOByn7RN+XRTC2S5TvZX69Y0LD3LnVL8OeCThJb0J4HfAX6d5jFBpbno6TzbN0VE7gPuA9i5c+eVHbFhXXElKcbPDo6yvS9LLhV+TV46O7EuU4utqFuAAmcnKhw4dLSenht3Hrj/8RcpVn3SjrApn6E761KsenMskPk6FSw2rYpXDSvLsoqKql6Mb4vIHwL/d3T3DLAjsel24Fx0u9n6JaBXRJzIWklu3+zvHgIOQdj76xpfhmENs6Mvx9BUuS4U0No9M1uA4vbw643ka1Lg1UvT/OYT32VjV4rpqs+OvhwfuG1XvSda1rWbWiCz60Y+edfbzEm/A1lW95eIbE3c/WUgzgx7CrhHRNIisgvYA3wbeA7YE2V6pQiD+U9p2DfjvwN3R8+/F3hyOV6DYW3Trnvm8PEhJks1fnhhksHhaYYmy+uy9xfMNfHHizXGijVOjRbrcacnXjjL3bdsa+luMnUjhpgls1RE5DFgP7BJRM4AnwD2i8jNhJ/jU8BBAFV9WUT+HPgB4AEfVFU/2s+HgK8BNvAFVX05+hMPAI+LyL8GXgT+w1K9FsP6oR33THyCzEVt8Kt+wMWpysod9DIiQDlqEOkHynTFo5AJXV3PDo7Ws+dmk4xVAeRSDsWqZ1rfdyCm9b3BkODw8SHuf/xFZqoeGcemK2UzU/XX9cz6VoiAa1lc15shn3aYKNXmtLGPXV7fPjVK2hYGujMUMqHLUFWbPsewNmm39X1nJN8bDG0QWyjFql+fMTJe8ugvpOlKre/sr2aZL46EY4aHpypN405Jl1fGsagFyrnxMlPlGmDqRjoV01DSYIiIXThpx8LzFcsSApThqQqFjMNM1cdOZEqtF9KORSHjMDJTRQjbsNT8ACsaJlP2Amq+8u7dGzhw6Gg9ED9erFLzfUamPUo1P5xxL8prI0UsKxzWdddN1630yzMsM0ZUDB3DfF1tDx8f4oXXx/CDAFsknCcSABKeVDc6abb3pBmaruK3GjayBnEtuGVnH4/ddysHDh2tZ8ZNlmpcmq5Q8ZSulNMwETMOxL96aQYBHNsi5VhUvaCezJBxbAoZhydeOMvbt/eauEoHYdxfho5gvuyk+DGJphoi4RREEfAD6ifVfMalGgnKeukvmUs79cy3ZGZcIeOwpSfDdb1ZHrnnHTw7ODqnaBTCbsWWCJJwoFkCu/vz9BcypsljB2IsFUNHkMxOmirXGJ6qUPZ87n/8Rbb1ZnFtYXMhw7mJEqLhXBCA63qzDVfpVlSr4iu4tuD7umabTdoCW7szdSsizox7+KvH+dGFKTxVbIFPfeWHXJqpsqU703Q/QaAtxy2bJo+dhxEVQ0cQFzJOlWucGy8jAo4VNkZ8ZWia7b0ZurNho+tL0xWqfjiY66H33dggSFnXDkfvovjB2hUUCIXx3ES5wS2YT9mcnyyjgBsJ68nhmVA0AqXiBVT9gJRt4VihRefYQtUP6lMfky1tTLC+8zDuL0NHEDc6HJ6qXHZzqdRPjK+NlhgcnkYi183ODTlu2dnH/r0DDZ13N+XT+KrUZs10X6tMlT1+84nvcmpkmuHJMscvTjNR8vCCsBuSbVnYIgSBMlqs1cWj6ofB+1w6dJO9eXOBzd1pRIRCxjFNHjsYIyqGjqBZY8RQHIJ6NKDqB5wdK3FputxwMowFaWiyzOmxYnTCXR8oMDpT5cJEhcqsBISqH+BHrq34Ic9Xqp5ii7C5O83W7ky9yv76jXk+fMeb2LUpb5o8djDG/WXoCJo1RgyjzBYoOCiObVHxAmYqPo/c83b27x3g8PEhxmYq/Hh4ZqVfwpIxXzKbF8SpxWEtS9q1UAUvUMZmqgxPV3lnV7qhz9f9y3DMhtWLsVQMHcP+vQM8cs87uK43y5aeDL4qqmF8ZEtPlt39efZuKdCTdeuC8vGnXma8VFvpQ18xgkhAIAzsC1IXlYqvZBzL9PkyNGBExdBRJOdw2JaFZQnX9WQRgcHhaX54YZKJUq0evK75PkMd0verFZZAf95FrDC2UvMvpydsyqfNfHhDA8b9Zeg44jkcsSVS9X0uTVUhCuDnUnZooRSrlKr+ugjIXw2WwEAhTbHq051NkUu5YUGkH7rCUrbQHY0GMKnDhhhjqRg6lthqman4KOEc+ut6svWivZmqT7CuGrJcGYW0w6Z8mpQt1PwwdXjXpi4yjoVtCVt6svVtTeqwIcaIiqGj2b93gO6sy94tBXb35xuuvP1A8ddyIco1MlH2ODk0TX+UxRVnee3a1EVvzsWxxaQOG+Zg3F+Gjic5DTJZbQ+hm6dzbZUwID88HcaUkrNUHnn6FT7/rVeZqfp0pWw+cNsukzpsAIylYjDUa1guTZc5O1aKCvzCNNpOFhSAbb1ZerJuQxD+8PEhnnjhLP2FNG/ZUqC/kOaJF86a7C8DYCwVQ4cyu2Px3bds4/PfehUF0rbFpnyaS9PhHJFODdQDvDZaJG0LE4m0ajPl0TAfRlQMHUec9ZVs4/7EC2dJORY7N+QQudxx98x4sePNlYqvBFGaddy2pjeKPcWY7C9DzJK5v0TkCyIyJCIvJdY+LSLHReR7IvKXItIbrV8vIiUR+U708+8Tz/lJEfm+iJwUkUck+saLyAYR+bqInIh+9y3VazGsL5JX2tMVjwsTZc6OFxkr1hiZCeMHU+UaFyfLHR2oTxIEyseefIm3//bXODNW4uVzkwxNluuPm+wvQ8xSWip/BPxb4EuJta8Dv6Wqnog8DPwW8ED02I9V9eYm+/kccB9wFPgycCfwFeBB4BlV/ZSIPBjdf6DJ8w0dzmxX14mhKbZ0Z+Z0LK55ytBUlUotYKrsUetkv9csfIXTYyVcW3As8AK4OFVBVSlk3abZX/MNRTOsX5bMUlHVI8DorLX/pqpedPcosH2+fYjIVqBbVZ9VVSUUqF+KHr4L+GJ0+4uJdYOhTrPhXFNlj5GZypyOxdmUTX8+xUTZI+zRG05GNFzGsSxc28a1wrFcw9PVpo0j5xuKZljfrORX5tcJLY6YXSLyooj8tYi8J1rbBpxJbHMmWgPYrKrnAaLf5hLIMIekqyueWNiXcxmdqTV0LA5QNuXTbMqnAdjWm0EEasb91ZRwhLBgWcJj9906xwJp9r6bVi6dwYoE6kXkXwEe8J+ipfPATlUdEZGfBP5KRG6ExIzSy1yxT0JE7iN0obFz586rO2jDmqRZUHlTPo3nB1R9q96xeFM+Q3fWpVj1cCzhtdHSCh3x6qNVrU6g0JWymzzS/H03wfzOYNktFRG5F/g7wD+IXFqoakVVR6LbzwM/Bm4gtEySLrLtwLno9sXIPRa7yVra1ap6SFX3qeq+/v7+xX5JhlVMPAslSanms2dzd0PH4kImTIudLNWoeZ1tnoiEQuIIZBwLJxqjbEnYCj/QIPoNH7htV9N9tHrfTTB//bOsoiIidxIG09+nqsXEer+I2NHt3cAeYDBya02JyK1R1tevAU9GT3sKuDe6fW9i3WCoExc2FqvenJYiyY7F8VCpjV0pLKuZgXyZ+R9du9gC23vS7OjLsbk7Tdq18QLFEuEjP7OHj/zMnnCcchBaHR++403c/7M3NN3XfO+7YX0jkbGw+DsWeQzYD2wCLgKfIMz2SgMj0WZHVfWfiMj/CDxE6BLzgU+o6n+N9rOPMJMsSxiD+V9UVUVkI/DnwE7gdeDvq2pDYkAz9u3bp8eOHVusl2lYA8RZSGfGimxfIAvptoe/wdhMlZmq3/Tx9YhrCxu7UviBsrErxfB0hVI1oOIHBKrYAm/qz/Pge99yRdlbV/K+G1Y/IvK8qu5bcLulEpXVihEVw3wcOHSU4xcmGCt6C2+8jtjRl6VU8+nJunh+wJmxEr6CY4XZcYFCb87lM3ffZIShQ2lXVEzCpMGQ4N27NzBZ9teti2s2QlijMzJTZbrs8fpokdORoAhhMN62LGwRpiueyd4yLIhp02IwJHh2cJT+fIqpstcRLjAl7ETsVX0cK0wVrkVD6xVQBT9QLAl/t5O9ZYoeOxsjKoaOJ3kSHJ6qsKU7TX9/nu+fnVjpQ1tWwqS3ue7wmh9gW4Jjy4LZW836qn38qZd5CIywdAhGVAzrnvmunGefBC9NVzg7XkZESNtCxe+MmKMFBIRWy2xia6Y35y6YvWU6GBtMTMWwrlmoXcjsyu/NhQwAFybKbOnJrOShLyuubbVsSRPGXWgrSH96rEjWbSyINEWPnYWxVAzrmvmunAGOnRqlFigCpB2LLT0ZtvVmuDBZCYPUEjZTXO8EKLNLPsPML4uNeZfrN+bbsjSSUzRjTNFjZ2FExbDmmc+91apdyImhKf7FE99tcPeUvTCVtr+Q4padfbx79wZ+9+kTy/paVopaE+UMYywB5ycq9GTc+jyVmGbv+8Hbd/Pxp16mWPXIujalmm+KHjsM4/4yrGkWcm+1ahdS9QKmKx52lDscn1K9QBmdqXHw9t18/luvLuMrWV3IrNu1QBve11bvOzCnS8HsDsaG9Y2xVAxrmoUCw62unF1bKNUUx7aQQPFVCaLajELaZv/egY5IKYbmDSOT9yVKJx6aLHPwT57nlp19jBerLd/3Zl2LDZ2DERXDmmahbrj79w7wEMxpF/LokUFePD2G5yuBKhoJimsL/YUMBw4dJeiQbhMLvkqFc+PlehPJZwfDLks9GYedGy+fQhYKyJv6lc7AuL8Ma5qFuuEmT2TJ/lMHb99NyhK8ILRQlPCn5itnx4oMTZXZ1OXO/YOdiFC35JJMlD2GJstMlWsMDk/zwwuTTESz7GdjhnZ1DkZUDGua+brhtjqRPfL0Kzx6ZJBiQozi7C/Hgqqv5FIOW3o6K2Op1ckgiKrqm3FxqsKpkSIzVR8/gJmKx/u/9Bx3fvavGwTDDO3qHIyoGNY8OdfizFiJE0PTpGyrHhhudiKr+T5/cPjHDE2FBY4pW0jZFm/YmOOGzQUQwQsuJ9cu0AV/XeHYctUnhPhtinuGnRotNlgipn6lczCiYlizxJZILVD2DOTZ3pdtCK43O5FNFGv4QWiJpGwLQRCB4akKQNiOxLr8tZj9/PWIAFu602Rce06typXuRwA/CC2bpCVSSDucHJrm+IVJBoenmSrXTP3KOsWIimHNspBLpVm8peIHpJ3wY78pnyZAUVUqnk+x6pFPO/UpkKpa33Y9o4TvRU/WveruzHXnmIS3U7ZVt0QOHx9ieLqCFxWZ1qLW+pOlmqlfWYes/2+MYd2ykEulWbzFsSwKmTBjqTvrcl1PFssSbMtioJDhM3ffxKfvvqleZ9GsF9Z65AfnJ8mnnXkzwQrpuVZb8gQSdzUWQpGKLZFHjwzSk3XZ1pvFtS2U0NW2sStlsr/WIW2Lioi8QUR+NrqdFZHC0h2WwbAwC2V+NRsX/MH9byTl2HWhcWxhoJDh0f/5J+v1Ffv3DvDYfbfyzQfuoDvbGRlggTJvfMOx4P86cAvdGQfHErpSNv35FHY0vz62cEJBcXFsqSdMxOLfnXXZ3Z9n75Zu3tSf75g6oE6jrToVEfnHwH3ABuCNwHbg3wM/s3SHZjDMTzstQWKRSPL27b1tjbk9fHyIyVJtyV/HamGq0vokv6U7w/69AzxyzzvqXZ2zro1tCWPFGoWMw6auFBIN8xooZOrv644jph9YJ9GupfJB4G8BkwCqegJY0G4VkS+IyJCIvJRY2yAiXxeRE9HvvmhdROQRETkpIt8TkVsSz7k32v6EiNybWP9JEfl+9JxHRKSDcnUMzSyRdlqCJC2RVtXfcRKAbRzEAOTToSDE73nKtjgxNM1YscaujTk+c/dNPPjet9CbS81xoc2X9m1Yf7RbUV9R1Wp8zhYRhzYKcYE/Av4t8KXE2oPAM6r6KRF5MLr/APBeYE/08y7gc8C7RGQD8AlgX/Q3nxeRp1R1LNrmPuAo8GXgTuArbb4mwzqgmSWyGDx6ZJCa7zNV7mwXjQApW+a4qmaqPtv7snUL8Tef+C4K9GTdpsO5mnU1MPGU9Um7ovLXIvIvgayI/BzwG8B/XehJqnpERK6ftXwXsD+6/UXgMKGo3AV8SVUVOCoivSKyNdr266qUCNUxAAAgAElEQVQ6CiAiXwfuFJHDQLeqPhutfwn4JYyoGBaBeAqkdy05tusAxxb6ulJs78vVuxO88PpYmIbck6ln3Z0dK4HA1p4sMLcH21KJv2H10a6oPAi8H/g+cJDQKvj8Vf7Nzap6HkBVz4tI/EnbBpxObHcmWptv/UyTdYPhiki2cimkHVSVCxPljhcUgI1dKVKOzbt3b6jHUgINU4PPjZfpy/lMVzzKXoAAU+UahUyY3GCKGzuTdkUlC3xBVf8QQETsaG0xPzHN4iF6FetzdyxyH6GbjJ07d17t8RnWIYePD/HhP3uRyZLXlj+3k+jOOOzalK834IxrglK2FTbiRBmaqpByrPqX8dx4met6oZBxTTC+Q2k3DPkMoYjEZIGnr/JvXozcWkS/4wZBZ4Adie22A+cWWN/eZH0OqnpIVfep6r7+/v6rPGzDeuRjT77EhBGUOWzvSfOB23YB8NEnX+KF18fw/NB0i4tGPV/D903BssLOBErYIv9Kg/GHjw9x4NBRbnv4Gxw4dNQ0mlzDtCsqGVWdju9Et6/2EuQpIM7guhd4MrH+a1EW2K3AROQm+xrw8yLSF2WK/TzwteixKRG5Ncr6+rXEvgyGtjgzVlrpQ1iVDE1X+b1nTvDqpWl6sy4icHY87EgcF43G7gLHFrb3ZtnRlyNlW1R8vaLhXKaD8fqiXffXjIjcoqovQJjKCyz4bRSRxwgD7ZtE5AxhFtengD8XkfcDrwN/P9r8y8AvACcJ3Wr/CEBVR0Xkk8Bz0XYPxUF74J8SZphlCQP0JkhvuCKMhdKcWmSFXJisMFX26E47jBZrXJgok087OLaQdiz6ci79hUz9eXEx6WP33dr231po0JphbdGuqHwE+M8iEruXtgK/utCTVPVAi4fmFE1GWV8fbLGfLwBfaLJ+DHjbQsdhMDRjoSvhZhMRO4Xk6/YCZbzk0ZdzmCz7TJRqbO/LcddN1/HEC2eveR79QoPWDGuLtkRFVZ8Tkb3Amwm/a8dVtXNKjQ3rjtjlYkHLzrwK9GYdJsvenAFVnYQlQoAyVfa5ZWdfgxXSbneC+djRZyru1xPzioqI3KGq3xCRvzfroT0igqr+xRIem8GwZMQulx0bcrw2OveKOO1YBEHAVMXvaEEBUA07OXs61wpZqP6knRHC7bTbMawdFgrU/3T0++82+fk7S3hcBsOSkmxy+IYNObpSNm4UJ/jlm7dS8QJqQeuJh+uJ5CAyx5L6XBQIxdVXxbKErd0ZHj0y2HaGVrsB+Kttt2NYnUgYyphnAxELuFtV/3x5Dmlp2bdvnx47dmylD8OwzMy+Yh4vVqn6QYPLpVj1SNkWg5dmqHRQ5aNrhxLSlbIp1cJ5MxUvoC/n1lvYT5Zq9TYsSWtivpP/gUNH57i1ilXvigP5htWBiDyvqvsW2m7BmIqqBiLyIWBdiIqhc4iF5JWLk0xXfDZ0uWzsSjM0VWaiVKtfjSdPkq6l9XqMTsEWwQ+Cer1ONaqOvzhZ4eJkBdsSXFvozblXlKFlAvCdSbvZX18XkX8B/BkwEy8mUnsNhlVF7HpxbaFcCwhUGZmukXbsehsR1xL6utINQeaPPvlSx2V8Vf2gIW6kzM3+8gKlNFEhCGCgO0whXkggrjYA304cxrB6aVdUfp3wc/Ybs9ZNJM2wKknWPlT9AFsEJZxFX8iELpyJUo2v/rNGN8yOI7mOK4i8krDR0FSFbMpuqw3L1QTgkxcDzbodG1Y/7YrKWwkF5TZCcfkm4ZAug2FVknS9xL2qxAqvyqH1FfO7d2/g2cGRZT3WtYQCr40UsSxwLIu7brpuzjZJSyOfshGRem3LQlaHKYRc+7QrKl8kHND1SHT/QLT2K0txUAbDtZJ0vWzKpzk3UYIgdHnN15fq2UHj0V0IBTKOTSHj8MQLZ3n79t76CX+2pRFaJwGfvOttbYmCicOsfdoVlTer6k2J+/9dRL67FAdkMCwGSdcLKKhSC8KbKdviY7+4t+lJ7vRYka6Ubeanz4Ml0F9IU8i4c6yIK7E0msVOTCHk2qfdhpIvRk0eARCRdwH/79IcksFw7STH3p4ZL2PbFm/YkGXnxty8grGjL0ch4zSdq2AICTRsxDlVrs2xIuL6nyTNLI1WNSzv3r3BjB5e47QrKu8C/j8ROSUip4BngZ+O5sN/b8mOzmC4BvbvHaA3l+L6jTn2DBTozqbIpRxcW3j0yGDT5xy8fTcpJzwpGmFpjReEg8xGZipMlGr1gshC2qFU85kq1xgcnub4hUlODk3XZ9zHJC2aeHqkawvPDo6aQsg1TrvurzuX9CgMhiXiSn308Tz13/jTFyh2sAvsLVsKvPdtW3h2cLRl4kLZCxiaqtKfT2ELvPj6GBUvQORyRpkAgSjD0xUOHx+qi8N8/y5m9PDapi1LRVVfm+9nqQ/SYLhadvTlKNUui8NUucbJoWmGpiotW43s3ztAzm3XiF+fnBye5ovPngIuW2zSxHTrztiMF2u8Nlqi7AUos1KUo+fMtg5n/7uAiZ2sFzr7m2NY9xy8fXfdRz9ZqnJmrETND9Ag4NunRjj4J8/zyNOvNDzn8PEhLs10dhPumq+MzNT4m1dH6oWQzTo6jRXD+fTNEMIsMce2mCjWGqzD5L+LiZ2sL4yoGNY1yWaFFyYr9eaJYlm4tkWgyh8c/nGDxfLokUETT4lICsmVvifxU0Wg4gcNVohpIrl+aTemYjCsWeIT1cE/eZ6qr+HwLQ3nhNgSBp2TKa+nx4rYFnRQT8klQ1XxVXEs64rb5hvWJsZSMax74vTVZEyg5gf4UU+rQJVvnxqtx1gKaYfACApw2dpwLEAut8Z3rcaW+c0QQsG2RPjg/jcaAekQlt1SEZE3EzamjNkNfBzoBf4xMByt/0tV/XL0nN8C3g/4wP2q+rVo/U7g9wEb+LyqfmpZXoRhVdOszb1rC5sLGV4fLdZPlNVEN+K0LfVaiSAIM5g6rrPkPNTfKlFEQAmtvKBJoMWxwjevN5diz0BhURpCmiaTa4dlFxVV/RFwM4CI2MBZ4C+BfwR8VlU/k9xeRN4K3APcCFwHPC0iN0QP/wHwc8AZ4DkReUpVf7AsL8SwKmnWkPDUSJHtvRm6syk25VNcmq7O0QsvslxcW3h9vIJqGAtYYNxQxxC/DbFLMFDFsUJhUcL3KZuyyaVs+vNpVJXpK0zJbiUcpsnk2mKl3V8/A/x4gbTku4DHVbWiqq8CJ4F3Rj8nVXVQVavA49G2hg7m4a8eZ2iyzOujRV69NMN0Ocwuem20xODwNF1ph5Qt9aCzJWE/MMe2GJ6qkHVtPFVsW8g4NlnXxu7gqL3Q2s3lB6Hyvnlzgf/4D3+KHzx0J5+5+yZmqj61QOed9jib+aZEtiqUbFXAalhZVlpU7gEeS9z/kIh8T0S+ICJ90do24HRimzPRWqv1OYjIfSJyTESODQ8PN9vEsA44fHyIV4amCVSxLaHiBVycqtQvs6t+wNmxEl6gOLaQtgXXsnBsC5Hw8ZGZ0Eqp+Uqp5lOq+fgdbK0kCxmb8R9+bR9f+cjtTXt/TVc8LkyUOTte5P7HX5xXWOYTjnZbvxhWBysmKiKSAt4H/Odo6XPAGwldY+eB34k3bfJ0nWd97qLqIVXdp6r7+vv7r+m4DauX+MQU/xdolOlFGDNJ2Vb4ARFhY1eKLT1ZApQgCH8EGJqqhkHoFX0lq4dmghJ/8Zp92WIBmCrXODdeDgXcEopVv6XFcvj4EC+8PsZrIzMMDk8zVQ5rhGLhMIWSa4uV/O68F3hBVS8CqOpFVfVVNQD+kNC9BaEFsiPxvO3AuXnWDR3K6bEimwtpApRaNM0wnmLYk3XpL6RJ2UIQKGPFGlPlGqJKxQ+o+hqmGaN4AZjkr9bEYuJazHFBxQIwPFVBJEzb9oMwBtPMYklm5lkieIFybrzMVLlWFw5TKLm2WElROUDC9SUiWxOP/TLwUnT7KeAeEUmLyC5gD/Bt4Dlgj4jsiqyee6JtDR3Kjr4cjm3Rl3VDf3+C0WItrKYPlFzKJutajBZr+ApdKZverEMtCNvjd7C364rozblzXFCxAJQ9HyJxD9OKaWqxxNbl5kImfN+j/12YKNeFwxRKri1WpPhRRHKEWVsHE8v/p4jcTPixOhU/pqovi8ifAz8APOCDqupH+/kQ8DXClOIvqOrLy/YiDKuOeIbKZNnDjSwST8OTmReJjGsLm/JpLk1XcCOX2O7+PCcuTq3w0a8tHCts0bJnINOwHjfkvP/xFylW/ShLDFzbJgjC20OTZf7xHx8j69pMVzwyjsVAd4brerJcmq6EViM0CMd8hZIm3Xh1IdphOZP79u3TY8eOrfRhrEnWwpf3kadf4bPPnIgq5qGQsvEUZqo+AuzckKM763L8wiRWFITeu6Wbl85OGAvlCkjbFr4qW7rT7NjQ1TIN+Ox4MaxbUcFXRTWsc/GCUOA9P7RibMviut5MffDXQCHDY/fduuBxJNONs64dTZpUY8ksASLyvKruW2g7E480tMV8KZ+rhcPHh3jihbOkHYuULbi2RclT+gtpMo5F2rXoTsytD6IpkIYrwxJwbKEv53B+stL0MxG7rLpSDn5AJCSKr6GgCOGMezsSdkUZmixfcbzEpBuvPsw3ytAWa+HLO59/vpBxyKederfiqhde0db8gMlSNWxDYmiLWIiHp2thb69AW34mruvJEKjO6WSsUH+eAp6vFGsBKdtqy8o4fHyIA4eO8u1To5wfL9UzxsCkG6805qtkaIu1UCsQH2N31uW6niyOLfXsr0/ffROfufumhvHC/XkXyxLOjJfZ0pOlkLYX+hMGwuFc9ZHMSj1bCy5/JmLLthYobovqyZofUAtCd5hrCxnXmnfUc0zSas44FrVExhiYdOOVxoiKoS3WQq1A8hi7sy67+/Ps3JDjlp199UBvcrzwlp4cW7ozuJYwNFVhW2+25Qmw07Gk8WQRv0sBoThcmCg3DEC7//EXqfk+uZSD1yJYFS/b0e3NhUxb1m/Sat6UT0f7ujr3mWHxMaJiaIu1UCvQzjEmLa6pcphmXPYCKl7A8YvT1OYrH+9gAr1cu2PRmHathNbL6yNFvCAM3herPpemqkyVQxcZtJ7HknZtruvJ0p1127J+kwWWl6Yr+EHogit7gUk3XgUYUTG0xVqoFWjnGJPWzIWJcj3V2NA+rQpDA2BDzqU7myLtWCAwPFWpP97qnXbtywkU7Vi/O/pyjMxU6hX7KcfCtsLEjNWYkdhpmJRiQ0eRTEH98fDMSh/OukNo7O5sCaQdi7IXzNtD7A0bsji21VY68OHjQxz8k+fDHm8iqEKAsrErxa5N+bZSkQ1XTrspxWbyo6GjiIvzHj0yaERlCYiz7mL98BUqXoBtCcE8nTkvTFa4ZWdf3VV54NDRlvVQ+/cOkE/blGsBVT/MGNuUz1DIOKsqcaRTMaJi6DjioP0N/+rLVDu5BfESkXxHbQHXscinHUZnqg3WSmzRWAIDhTSP3XfrvLNTgHrxbc1XurMOm/KXK/qLVW9VJY50KkZUDB1Ls6mFhsUlbCip9GRdMrZwZiIRY4ne/u6MUxeDZGYXQC4V1hY9/NXjzFT9uth4fsDQVBWAjV3peiX9akoc6VSMqBg6gmYtZmzLQvVyN2PD4lMLlIxjcX68NKcAMiYOsEOY2dUbBe1jsq7NiaFptvdl62LTXwgtlJmKj2PV6t2MTZB+5TGiYliXJEUkn7IZmanSnXUbXCr9+RQXJit0WrLKYjOQTzE0XW35eCwmyZRiS0BEcO1wtk0sBjv6cgxNleviAdSz9WYX327Kp5ko1fjmA3cs0isxLAYmpdiw7pjdp+zUaJGxYm1OO5F82qE35y68Q0NLUrbMKyhJYumOB6fZImwupBuq6FvVGu3auPqLbw0hRlQM647Zc+prUSfc4akKU+Uag8PTvDYyw6sjRX7t1je0LMozzE/a5uoTHcLx9lycrDQIQ6taowff+5ZVX3xrCDHuL8O6Ip5TbwvY0RyVIFDEClNbz42X61MGReCPj75m4ilXSWWBNl2uFXYknv3+xvdVFU/nCkNydspsN6aIMFEyMZTVjBEVw7oizh7SgHBSvYBtxe3WFcsGVOq9ps5PlFb6kNclAiCCziPZliUMdKV59MggH33yJXb05djSneKZ48PMVH1StpByLDbl0/Rm3SjDK+CTd73NiMkqxri/DOuKeE69r0q55lOq+fhR0pFthWLi2EJf1uXiZNnUqSwRhYwTWoOEUyJjF2N837agkHYoe0E99vXD8xP85XfOM1P1cCwo1wImSh7TZW/VjlswzGXFREVETonI90XkOyJyLFrbICJfF5ET0e++aF1E5BEROSki3xORWxL7uTfa/oSI3LtSr8ewOtjRl6PqB2FGVyJYYkk8mCtsPnhpptoyxdVwbVjAI/e8g10bcxBNeUw7Fv35FLmUDQhdKYeNXSl6sm59Rs9k2QPigsjLp6bh6cu1LWF68RQHDh3ltoe/wYFDR1fVoDjDylsq/4Oq3pzoJ/Mg8Iyq7gGeie4DvBfYE/3cB3wOQhECPgG8C3gn8IlYiAxrg3jY0mKdIA7evpvRmRqWJaRti7QdNhtEoeL5BAEL9qEyXB1CbIkI7//Sc5waKbKpy8WJYlu5lM2WngzX9WZ55J53MF31G9KE43+TOMNbpHEdYGSmwlTZW9UTSDudlRaV2dwFfDG6/UXglxLrX9KQo0CviGwF/jbwdVUdVdUx4OvAnct90IarYylGFMd9oSwRKl7YG8oLQqvF19DtYlga4oFo1UDracMTJZ8NORfHFi5MVho6R8+e0ROPsonFxI5uCNQzvkZnavTl3FU9gbTTWclAvQL/TUQUeFRVDwGbVfU8gKqeF5E4GrcNOJ147plordV6S4LI1y4SfmgtEaw2BjM1q8g2wcJrY76WHNfyXofpqNOkHAuR0DcfX+36xuO1LHhBXOAIM1WfN/XnmSjVGjoIH7x9Nx9/6mWKVS+c2JlxGC95iEAQZlogwMZ8ihND0wD4QRC21U+w2iaQdjorKSp/S1XPRcLxdRE5Ps+2zc76Os9645NF7iN0m3Hd9h2cGy/NfjycbBcJjB3ft0LRefbkJf6Prx7HtYXujMPFyRIfe/Il/je9kTvesrntF2xopFlLDs8PODVS5PqNuTkNBdsVFlVFVal6c3OPjNdr+aj5AWnXouoHTQsVkx2jz4wVecvWnobsr66Uzc/8RD/Pvz5BT9Tv6/RYiddGS2ScClt6MhQyrimCXGWsmKio6rno95CI/CVhTOSiiGyNrJStQOwHOQPsSDx9O3AuWt8/a/1wk791CDgE8BM33zLnvKKq+Ao+Ck1y7//wm68igGtZeL7iWBY13+eRZ06yqz8/V5Cs8P5lSyjMPLKi++HtUMw6mWYtOS5OVppaL48eGWxbVC7NVEO/fOyDMawISniRYIlwZqzEeLHKgUNHm1qe8T/TXTdv57P31PNwOHDoKK4dNqU8O16uW5xlL+D0aJGB7jSubZsiyFXEioiKiHQBlqpORbd/HngIeAq4F/hU9PvJ6ClPAR8SkccJg/ITkfB8Dfg3ieD8zwO/tdjHe36yRHem8a3KuBYXJksLCtJ8iMwSIUvmuOUsWb+CNNv9Uar51IKA7b3Zhu2u1L1R9QJsW3Cs0E1SrvlGW1aIuD4IlJLAqZHphlb2rdrcx6ITW7Mnh6bxErEaCGNkEyWPf/c/vd24olcRK2WpbAb+MjoxOsCfqupXReQ54M9F5P3A68Dfj7b/MvALwEmgCPwjAFUdFZFPAs9F2z2kqqPz/eGZiscLr42Rdi3Sjk3asci4NinHIuNYkR++8YS9tTvLyEylIVOlXAvY0p2dvfsrIq4objmfdR6shBW0VgVptvtje18O15I5c+Kv1L3h2kKpRlhJvzpeakfj2OFnz1cYma6xMU89sL6QVRpbs5Uo/Tv5yXAs8ANl/96BtmOeJja69HTcOOH01j269d7fm38bx4p+bNKuFdY1TFewRHAsiUxw5abtvezYkCPtWmScSJjcy89LO8l1+/J+XZuMY+GsUCpSM0Gy5LIISUKAko/bbSQ0XCvJIU1166WNEbNJDhw6yquXppkqe1Qj90vF1KSsGK4l9c96OAIYNubDGSjFikctUFK2RX8hTT7tNHQejj8Pr4+2tlT3bs7Xu1DP/swATbtVX+1nq5Npd5xwx4lKJhKV1fCqbUvmCFj9tmPVxSoWoqQopeuWlR0J2eV9ZByblBs+no5EbbEEocEqimJIDSIUC9Ds2NIV/P34ajK2Xq70arKZMA0Oz6yKf/NOJRXVCylKzQ+4YaDA4KWZUGSscM68H2jdko5HC8dWyK9/8bmWtUUZN4x1bu/LUsiEiR/FqkfKtuqDvbKuzcnh6abbDRQyZq59G5gZ9S3Ys7nAX/3z26n5YWpxxQuoekH9dsXzKdeCaN2n7AVU6o+F21W9IFz3fCrRtsnb5frt8EqoFX6gFKs+xaoP1Jb0dbu2JNx8l0WrUdBCCyrpGkxaVs0ELL4dW2iuLS3da3WLKHbNtRCkd+3eyLvftLEtQWrlzki61fJpxwjKClP1A1y97No8NTJDLmUxXfHRILRgvOixN2zIzImv9OVcxoo1dNZANUsui9HwVKUuFs0Ge7XazqQjLy4dJyoQXgmlnLBZ3WLx7cFRHn/uNOcnS+zozXHPT+3gnbs34AdK1U8IUy0UnUYhu/x4ORazWnsCdlkE/XmrxGu+UvO9RXu9rRCY5Q68HK9KWlvxeuwibIxxXRawlGORde3wJ2WTTTnkXJts2uLY4BgPf+1HuLZQSDtcmCzx0Sdf4hP+W9m/d4D33NCPbQnv/b0jS/66DQtTiwLtm/IuY0UPz/fpy7nMVH2K1SCsyBfozqYAGuIrN2zurrs0Z6p+eBECpKM4Z80PC11jmg32StlW0+1MOvLi0pGisth8e3CU3//GCRwrrGMZmanw+984wYfZwzt3byBr2XOm1i02Gl3pVRJiVW2wrGaJUlLUWohVUgRjy6sc7bflcUAkiEsfw4jtF8sSLOIEBOUjf/Ydbryum5mqz7nxEmPFpbUCDfMTZ2xlXIvNhQzdWZdidZqy53Npplp3zdoWpJ3L35OkFRFnCm7pcTg/XqpbPJvyaQDOjpdwLEFV67GSeLBXbKlsyqebbmfSkReXjhMVSyATneCTF/azY0vNQk3JtbisThUeP3YaxxKyKRuUuh//8edO887dGxb7JTQlHs3q2hb59NL+swaq1GILqqlrMLbEZotV7E5MiNYCIujNY37VU0sDjbK5w5Wq7/PtU2NL+h4Y2kcJM7Xe1J+vu0YdS+rdDdQPv01eAF2WMDg8TdUPsEXYtamrvp+ca/HqSJEgaruzsStFIeNQqvn05lz6o/HCcRwOaEhZd2xpup0J0i8uHScqrm1xXe+1pQLP5tJ0hd6s2xBLSDkWIzOVhi8FzBameE2brMX3E4/NOr82E7nG5y68/+TifPu4vD5LfFscz+z9tbjZUqjj7bwgFKGqF1ZlxyJVqvr87//PD7g4UQnThqPL4UChN+viOhbFqoctFhNlY6msNEEAL5+bBFoP7QKYKHtRqjDUUE4MTfHPHn+B51+fwLWFPQN5SjWfiVKNvlyqLg4f+8W3NhWH2SnrrbYzLB4dJypLQbPK8NhXOzto3TyGbYoproZ/+8wJhqeqUcA/FCNflc3daaarPgOFLkSE75+dWOlD7XhaOUMtwguwQLU+2yb2rsYRz7/8znm2dKcoZDKRG81GVenOujz5oduA8HtVi0yf5LfpPTf0854b+hvW4wuj1VKvtd4wotIm8xVNNa0MN77aJWe66rOtN8Ol6SpVPyBlW2zJp5mp+g1Cn7aFihnGtSoJCMc8N/vXCer/g5HpKiJWPXYmovx4aIrnTo00FDHPlzqfTKbZ2p2tJ9PAXIGZvZfZ+iOJLVppU3I93r80eazV/qRxg3n/bnLb5Gtp9veS+6jvt8m+kvtxrqAkoOPqVPbt26fHjh27oue0U5B3rbUVhivnwKGjcyzEuO4gFnrXlrAR4Wip3prdsH5x4tqvZF2XY1P1As5NlLCEegGzAj/1hg3s6s8lasNap9XXi5mjVHqrgyydfMZhc3fWFD8242pEZb6TlymaWjkWEvuk0OfTDlPlGmfGyyt92IYrJM4ei5L7Vs2FQaouWnM7Zsyu35pTF9amgKVdC8dqXfu1XFyJqBj3Vxs0a9FuiqZWnma9w5IW4v69Aw3W4oFDR42orDIWaiTtCGzuyfKLP7GFbxwf4vXRIqrKtt4s/+Bdb+DGbT0NtVqVWdmF8fqf/s3rpBwL1TB7Mf5d8wPevKUwJ1Mxzj6c79iqUcbi1CK/J7OxhDkCFbeEStULlufWgDUWLM/q0tGsC8cidd4wotIG8wXiDSvLbOGYzeHjQ3z0r77P2fHyqrnCNYAtsLUnw8WpyrxdJ1KOTbnmceSVYSZKNXZsyNWt0j/+m9f57b/7Vt5zQz9+oPVEjUAVDS7fDgLl+dfGuDRdIes69SzDUtVnY1ea3/3Vm5r+bVWl5uvlFPcmKfTJ4uXGFPtkzdflIuVqk7T6cm3+zhuBhuebsKBz6TtvNOtd2JVuv87OiEobmED82uPw8SE+9ZUfcvzi9EofiiEizvTyI5f7hYky3gJKv7U309C3qyt9eYxwserxh998lTvespmFaovvv2MPH3/qZbwgqH+HAT50xxvZ1pclmCVCQdSLLBxtEd4P1xV/vtYVV0mgoXjVraQFBKxZgXJcG1b1fEpRGn6yHiwWvvmOP+68MV25+tdiRKUNFnKzGFaeZHZe3I12qrL0bWkMCxPPSylW/XqLlPmuzAFcC2zbWrS+XYv9HQ4CTYhQoyD5kSjFApTcplUM25KwN1/GtenBbbrNYuH5QROxauxdmBSoci1AUT7+cHv7N6LSJgu5WQwrRzJg35t161e1QQGtZx0AABYQSURBVIcloaxWar6ypScsOJ4q1zg1srAQBEBv2q5X18eici19uxbzOxy2Brry+INGQuNHcZ3ZotNgKdVvh/cXK6nKscOxG13p9p+Tzzh8vN39X9VRGQzXyOHjQzz81ePhRL9oxsaegQIP3Lm37S9+bJ288PoYAmzpySAi1PylcVEYrp4rKUDNuTYbulwuTFbCMQsQXfmHUyQnS1Uc21qTLmgRwbHlqk68dfdbE0EKtNFyiq2kOM60nFm+RlQMy87h40P85hPfZXSmSuwF8RR+dGGK33ziu3z67psWFJbYOpku1+oDuNq5Ajasft6wMcdUqQqEJ87Z/d/OjJe5YSDPx36x/QuQ9UBsHV3NSVu10S0XJO8nxGoxBMmIimHZefTIIFNlj4CoejdqsaICU+XGcbKzSVonzU44hrXNQD5F1Q84P1VhQ85lZCbMdgrn7wAi7NyQozeXWjJBWY8jh2ML6WrQyDJql2WfZysiO0Tkv4vID0XkZRH5cLT+2yJyVkS+E/38QuI5vyUiJ0XkRyLytxPrd0ZrJ0XkweV+LYar4/RYES8I5hQoqIYNJFsFX2PrZGiqjB/M38HYsPZwLGFzTza8gg7g0nQ4lMuNWuJbYpGyrSWtEUt+xnqzbn1Y2OHjQ0vy95aDR55+hbf/9td447/8Mm//7a/xyNOvXNHz5Qont66EpeIB/6uqviAiBeB5Efl69NhnVfUzyY1F5K3APcCNwHXA0yJyQ/TwHwA/B5wBnhORp1T1B8vyKgxXzY6+HJemKvjaKCwi4FhWy+Dro0cGce0wnTTt2HhV/6r+/kIFd4aVIe1YTJVrnBsvY1uXG0vWAlD1EUvYlM8sao3YbKtkvFitf8aAeuryfNbzauaRp1/h979xMmpPEyY3/P43TgJw/8/esMCzr45lt1RU9byqvhDdngJ+CGyb5yl3AY+rakVVXwVOAu+Mfk6q6qCqVoHHo20Nq5yDt++mkHGwCE/usWktCoWM0zL4enqsWB921l+4gtSVWRhBWX0I4b/p8FQ4ysCWsCI8E01nDYDrejI4ttQD9IePD3Hg0FFue/gbHDh09IqtiWZWyStD03h+Y0/ltdw94/PfejUSFAtLrOh3uL5ULLuoJBGR64F3AH8TLX1IRL4nIl8Qkb5obRtwOvG0M9Faq3XDKmf/3gE+ffdN3LC5gGtJZKHAm7cU5g3S7+jL1YvWChl3Tuscw9pFCS8aZqrRwDY/oDvjsGdzgTdsyOLaFoHCQCHDQ++7EeCa3VRJyzcuqHRt4eJkY+XfWu6eEY9eTmJJuL5UrJioiEge+C/AR1R1Evgc8EbgZuA88Dvxpk2ervOsN/tb94nIMRE5Njw8fM3Hbrh29u8d4CsfuZ0T/+YX+I/3/hQ/df1Gpiqhm6HVieHg7bup+Uqx6qGqbMyn6Mk6ZN0VvTYyXCNONA46NhCEcLTweMljqlzDsS1u2dnHNx+4g8fuu5X9ewdaCsKjRwbb/rtJyzdmcyFNLQjqn7Fi1VuTqcsxXSmb2aHHQMP1pWJFvo0i4hIKyn9S1b8AUNWLquqragD8IaF7C0ILZEfi6duBc/Osz0FVD6nqPlXd19/fv7gvxnBNXElgdP/eAR56340MFDJMlGoMFDL8/q++gx9+8r380T/8qTlXZIa1gRdofYhX/G9oS+gcvTBRbjipxy6vb58a5fx4ianEVM8rdVMlLd8Yx7bY059v+IwlR1ysNT5w2y6CKAEm0CD6Ha4vFcseqJewh/N/AH6oqr+bWN+qqueju78MvBTdfor/v71zj5GrvA7479w7j33a68cuYMBgg8Ehf/ByeJTUdQltIaHQSCCgTZRWRRAVatE2tCR9iCIhkTZtEqqWlLoE1DYQcBMFUUJaCg4gtQlPJ5gam9gmLGCvH4vXs6+Zuff0j+/OMDue2Z1Zz+zc2T0/yZqd6zvfnvvtvXO+8/jOgW+JyN/iAvVrgB/jFjRrRGQV8C4umP+bc3MVRqMoXXHCzIHR6XZFe8JRqzKjvVDXfp6EL2Sj3vWlrQwKlRM6Eh7ZIOS9DyZY0efcofW6qarV9JtPLYcLwfhNL+xmNBvQnfK58eOrmhakh9Zkf10CfBb4qYi8Fh37EnCDiJyDc2HtAW4GUNVtIvIo8AYuc+wWVQ0ARORW4AeADzygqtvm8kKM6akl378RbQW2bB9i4yOvElTrWWu0DYW+Kav7e4o9iwr3TOkCZHlPmvcOj6MoQyMT+J7U7aZaKDX9Nl52RlOVSDlzrlRU9QUqx0OenOYzdwN3Vzj+5HSfM1pHeT2uglvrLpjy0B5LW4FCJeKd+zMEodvPkDPF0vaECm+8f5iE53H12SuKx0sXIIui1wOZSSbyrpV00lP+7Huvc/JztSuHeuqBzcdNkc3AIpxGU6g1kFoefK81MFpQWnsOjeGLyyDLhdVvaIu3NI9ZbtSeFk+EMAz5+jM7ufyrP2TL9iF60wneGsqwfe8Iu/ZnEHH13s4Y6GE0G5ALddq43LGkIM/HTZHNwpSK0RQqZdZUcmtVCr7XEhgtKC3X/+HDMhLVDBWLtTSPUKG3jiZOUNlVUUo+UDzPwxdhz6Exbt+8lcHhMfKhIkAuCBkcHmdkPIeqzriAOVal0Ihss4WC1f4ymkI9bq3ZlCR/Z3gMX1wxvIIfvrQ+kQXt5w4FjkzWt+9hpj+N4qwVxRWVPDKRB4ET+zo5kJkkG4QkfGFZd4pMNpgxLldvQkg51lK8dsxSMZrCbN1atXLyki72jUziR3dwecG7pCck7O6OLTNZKp44ZTKZD8kHykQ+JBcoizqTrO7vYe3xizi937m9KqUGly9garWcq1HL7zAc9tgZTWG2bq1KVPKF37x+NbmwelR+MtBi7SgjnpTHuaa8VdeQS6G4OAhDnbIvpfClXssC5liVQrMXSfMJc38ZTaPcrVVQDvVkz1TNIrvqo6zp7+FN60HfFpS7I1MJjyVdSfKh0pHw2J9x/VMGetOoKoMfTLj9Kh544uF7ioZuM2RPOlHcU1K4hyqlBgPF+60n5TMy7hRS6Z6UWpVC3NOP45SZJnPZESwOrFu3Tl966aVWi7FgKNzsO/aNkJkMWNqdZFl3uvhQz2S93HD//x4VmynsX7h5/Wp++8EX5+IyjGMk5bv4iNvdKCzuTLJmoLf4pV7+hfhHj73meqQHLl14eU8aUPaOTDLQm57xS710MVJQIofHc/T3pMlM5mOnFI6FStday7NVLyLysqqum+k8s1SMplF6s0/kQkJVDmZypBM+vR3JmgKl0wVIN6wdsIB8m5ANnEVyfF8nvicM9Hbw8E0XFbuAHpnIkw9DDhyZ5PbNWxno7SAbhEctJs5buYSHb7qo6u+p1mK6ME5fV4rv37a+2Zc7pxxrEkKjMaVi1E2tpnbpzZ4NQvwom2f/kUl6O5J0Jn12Dh2p6BIr/I59IxO8OzyORA53VZcZ1JX0uffpHaZU2ojJqKzKCYvTDA6PsWX7EL/3b68wlgsQIBlZM8NjOdIJD8/zjiqhMp27qlRBFVpMDw6Pc9ISivfbfMzWiltmmgXqjbqoJ9+/NOMm5XsFzwfZqJ7KwdFJjkzkjxrr3qd3uI2NBzMEUf0n11v7w1TUiVzA3z37VrHfhhF/VN3+krcPjXMwk+X2zVsZi4LnirNmsvkQUPZnsnUnenz5qe0Mj+WidGR3LB/FYWD+ZmvFLTPNLBWjLuoxtUv3qhRqNRG6dN+xbJ5DozmWdCWPGmvTC7vp701zMJPH94UwONoUSfoek4FLM61krRS6O/oCFT5utIjCnyIXBAyPHZ2ep7iOjyJa9/6lXQdG8cTtb/FFCKN48UQ+nNfZWtUKY7bqWm2ZZ9RFPfn+pWmYvR0JlnWn8EToSicY6O2gJ+1HAdipY41mAzqTfnGPQiWkJP+0UNl2yjgpn+U9KbrTtm6KG0kPEJm2dM6xVn5J+J5rABe9b/cS9tPRyPT9RmBPnFEX9e6UL03DXLW8h3tK4i+VMrvGc64893guKK40K1H6X+VnecApS7vo60rx4z2HZnOZRhMJ1Vmr+Wn2GVX6288Uy1u1rIu39o8SBiFBVGkB4KS+jmmD+/OB2VSlaBZmqRh1Ue8msA1rB3j4poumdO2baawbP76KXGShVFMrgWpxU5wnbmXrCSQEkgmPHUMZdh/IsNBS5uNO0hMSvkcQhlU3p3qA7039aqollnfHFR+hK+kVFYpEY03kQyv8OIeYUjHqopGmdrWxNl52Bndd9VF60onIR+7OL7gyROD0/m42XrqGpC/FAL5GRcDCMMRDOTiaxZNjdaQYjcDDZXe5v4dO26IgxFkdpdRS0HHD2gFW9HWSTnrRuT4nL+1icWfSCj/OIeb+MuqmkaZ2tbE2rB3g3uvPnXZT15btQ3QkPPJBULRowtAFWJK+BwrhjKULjWZxytIuDmQmyUXlVlYsdsUgJ/OKCIi6XvTlCkZwVkcptabNZrIBp/f3ICWLCVWdl6nEccUsFSO2zGQV/eNzuxhY1MHKpV10p3wSvpBKeqQTzgUi4txkxtyT9oVFnUmW96QJFXwRejsSHL+4gxV9nZw50EMq2ouS9D4M2nsCZx7XUzGTsJa02bil1y5EzFIxYs10VlFh9SopKXYCVFX2jkxwZCJPoGobI5tA0he3CVU/3ENUSOEGpxiWdKdQVRK+0NeVpL8nzeHx3JS6XLdv3srwWC6KhTk3Zl9X8igrBWpPm41beu1CpO2ViohcDnwd16d+k6re02KRjDmiWibamoFeLl69lL/f8jPKQ/0pXwhUrZ/9LPA9twdkcUeCbKB0p33SCZ+9hyeKO9i7Uj6fX7+a/9l1qFh48c8/dVbFhcFfX3M2X35qO7sOjAJw+vIu7rjiI1XdobUUdIx74ceFQFsXlBQRH9gB/AowCLwI3KCqb1T7jBWUnD/MVEivtIe9L27PQl6VhOfR350knUrQlUowMp7jQGaS0Wx9jaYWEst7UsUCkIW5nYsihkZ8WCgFJS8A3lLVXQAi8ghwNVBVqRjzh5lWpQXXWWF/Q3lZ9IKbpLcjQSJqTXz+ysU8tW0f47kQAZb1pMhM5JjIt+/iqxZ8T0h6oAidSY9c4Bpk+Z5wy4bT2HjZGVPON4vAqEa7WyrXAJer6o3R+88CF6rqrWXn3QTcBLBy5crz33777TmX1YgflZRNpS/FWs8DuPfpHWx6YTej2YB0wmNpZwLxPLpTPiLC4PAYmcmguI+iJ+2TzYdMBi6x4MTFHXzs1CX89/b9jGYDPJRAay+aefGqJRy/uIPHf7KXIFR8TzihN8X7R7IE0SAdCTh35bKK11HPtRoLi1otlXZXKtcCv1amVC5Q1d+v9hlzfxmGYdRPrUql3VOKB4GTS96fBLzXIlkMwzAWPO2uVF4E1ojIKhFJAdcDj7dYJsMwjAVLWwfqVTUvIrcCP8ClFD+gqttaLJZhGMaCpa2VCoCqPgk82Wo5DMMwjPZ3fxmGYRgxoq2zv2aDiOwHWplTvBw40MLfXw/tIqvJ2VhMzsYyX+Q8RVX7ZxpkwSmVViMiL9WSlhcH2kVWk7OxmJyNZaHJae4vwzAMo2GYUjEMwzAahimVuef+VgtQB+0iq8nZWEzOxrKg5LSYimEYhtEwzFIxDMMwGoYplTlARHwReVVEnojePygiu0XktejfOTGQcY+I/DSS56Xo2FIR+S8R2Rm9LompnHeKyLsl8/nJGMjZJyKbRWS7iPyfiFwc0/msJGcc5/PMEnleE5EREbktbnM6jZxxnNM/EJFtIvK6iDwsIh1RyasfRfP57aj8VX3jmvur+YjIHwLrgEWqeqWIPAg8oaqbWyvZh4jIHmCdqh4oOfZXwCFVvUdE7gCWqOqftErGSKY9HC3nnUBGVb/SKrnKEZGHgOdVdVP0YHYBXyJ+81lJztuI2XyWEjXnexe4ELiFmM1pgTI5f4cYzamInAi8AJylquMi8iiuMsknge+o6iMi8g1gq6reV8/YZqk0GRE5CfgUsKnVssyCq4GHop8fAn6jhbK0DSKyCFgP/DOAqmZV9QNiNp/TyBl3PgH8TFXfJmZzWkapnHEkAXSKSAK3mHgfuBQoLHZnNZ+mVJrP14A/Bsq7ot8tIj8Rka+KSLoFcpWjwH+KyMtRUzOA41T1fYDoNQ7dmirJCXBrNJ8PtNoFAqwG9gPfjNyem0Skm/jNZzU5IV7zWc71wMPRz3Gb01JK5YQYzamqvgt8Bfg5TpkcBl4GPlDVfHTaIHBivWObUmkiInIlMKSqL5f91xeBtcDHgKVAHMz1S1T1POAK4BYRWd9qgapQSc77gNOAc3APyN+0UD5wK8DzgPtU9VxgFLijtSJVpJqccZvPIpGL7irgsVbLMh0V5IzVnEZK7WpgFbAC6MY9U+XUHR8xpdJcLgGuiuIAjwCXisi/qur76pgEvglc0EohAVT1veh1CPguTqZ9InICQPQ61DoJHZXkVNV9qhqoagj8E62fz0FgUFV/FL3fjPvyjtt8VpQzhvNZyhXAK6q6L3oftzktMEXOGM7pZcBuVd2vqjngO8AvAH2ROwxm2fTQlEoTUdUvqupJqnoqzhR+RlU/U/IQCM5n+XoLxUREukWkt/Az8KuRTI8Dn4tO+xzwvdZI6KgmZ2E+Iz5Ni+dTVfcC74jImdGhTwBvELP5rCZn3OazjBuY6lKK1ZyWMEXOGM7pz4GLRKQr+h4q3KPPAtdE58xqPi37a44QkQ3AF6Lsr2eAfkCA14DPq2qmhbKtxq36wblEvqWqd4vIMuBRYCXuJrxWVQ+1SMzp5PwXnFtBgT3AzQU/e6sQlya+CUgBu3DZPx4xmk+oKue9xGw+AUSkC3gHWK2qh6NjsbpHI5kqyRnHe/QvgeuAPPAqcCMuhvIIzi3/KvCZyKNS+7imVAzDMIxGYe4vwzAMo2GYUjEMwzAahikVwzAMo2GYUjEMwzAahikVwzAMo2GYUjGMJhNVqP3CLD53Tmk129mOYxhziSkVw4gv5+CqxhpG22BKxTCagIj8qYi8KSJPA2dGx04TkaeiYpjPi8ja6PiDIvKN6NgOEbkyqh11F3Bd1H/jumjos0Rki4jsEpGNrbk6w6hOYuZTDMOoBxE5H1eW51zcM/YKrgLs/bjqCTtF5ELgH3ClxgFOBX4JV3TwWeB04C9wvWNujca9E1eI9JeBXuBNEbkvqt1kGLHAlIphNJ5fBL6rqmMAIvI40IEr2PeYK7UEQGnLg0ejYoM7RWQXTnlU4j+ishmTIjIEHIcrDGkYscCUimE0h/L6Rx6uV0W11tHl51ern1RahynAnmEjZlhMxTAaz3PAp0WkM6qq/OvAGLBbRK4FV6FaRM4u+cy1IuKJyGm45llvAkdwbi7DaBtMqRhGg1HVV4Bv4ypQ/zvwfPRfvwX8rohsBbbhmiQVeBP4IfB9XNxlAhdbOassUG8YscaqFBtGixGRB4EnVHXzTOcaRtwxS8UwDMNoGGapGIZhGA3DLBXDMAyjYZhSMQzDMBqGKRXDMAyjYZhSMQzDMBqGKRXDMAyjYZhSMQzDMBrG/wPGj7DuXdGuVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.regplot(data = df, x = 'depth', y = 'price');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x24a04cf3710>,\n",
       "  <matplotlib.axis.YTick at 0x24a04cecd30>,\n",
       "  <matplotlib.axis.YTick at 0x24a04ce6550>,\n",
       "  <matplotlib.axis.YTick at 0x24a04c64f60>,\n",
       "  <matplotlib.axis.YTick at 0x24a7c14f278>,\n",
       "  <matplotlib.axis.YTick at 0x24a7b975898>],\n",
       " <a list of 6 Text yticklabel objects>)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def log_trans(x, inverse = False):\n",
    "    if not inverse:\n",
    "        return np.log10(x)\n",
    "    else:\n",
    "        return np.power(10, x)\n",
    "\n",
    "sb.regplot(df['depth'], df['price'].apply(log_trans));\n",
    "tick_locs = [10, 20, 50, 100, 200, 500]\n",
    "plt.yticks(log_trans(tick_locs), tick_locs);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.regplot(data = pokemon, x = 'attack', y = 'defense', fit_reg = False,\n",
    "           x_jitter = 0.2, y_jitter = 0.2, scatter_kws = {'alpha' : 1/3});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heat Maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [12, 5])\n",
    "\n",
    "# left plot: scatterplot of discrete data with jitter and transparency\n",
    "plt.subplot(1, 2, 1)\n",
    "sb.regplot(data = pokemon, x = 'attack', y = 'defense', fit_reg = False,\n",
    "           x_jitter = 0.2, y_jitter = 0.2, scatter_kws = {'alpha' : 1/3});\n",
    "\n",
    "# right plot: heat map with bin edges between values\n",
    "plt.subplot(1, 2, 2)\n",
    "bins_x = np.arange(0.5, 10.5+1, 1)\n",
    "bins_y = np.arange(-0.5, 10.5+1, 1)\n",
    "plt.hist2d(data = pokemon, x = 'attack', y = 'defense',\n",
    "           bins = [bins_x, bins_y])\n",
    "plt.colorbar();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins_x = np.arange(0.5, 10.5+1, 1)\n",
    "bins_y = np.arange(-0.5, 10.5+1, 1)\n",
    "plt.hist2d(data = df, x = 'disc_var1', y = 'disc_var2',\n",
    "           bins = [bins_x, bins_y], cmap = 'viridis_r', cmin = 0.5)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hist2d returns a number of different variables, including an array of counts\n",
    "bins_x = np.arange(0.5, 10.5+1, 1)\n",
    "bins_y = np.arange(-0.5, 10.5+1, 1)\n",
    "h2d = plt.hist2d(data = df, x = 'disc_var1', y = 'disc_var2',\n",
    "               bins = [bins_x, bins_y], cmap = 'viridis_r', cmin = 0.5)\n",
    "counts = h2d[0]\n",
    "\n",
    "# loop through the cell counts and add text annotations for each\n",
    "for i in range(counts.shape[0]):\n",
    "    for j in range(counts.shape[1]):\n",
    "        c = counts[i,j]\n",
    "        if c >= 7: # increase visibility on darkest cells\n",
    "            plt.text(bins_x[i]+0.5, bins_y[j]+0.5, int(c),\n",
    "                     ha = 'center', va = 'center', color = 'white')\n",
    "        elif c > 0:\n",
    "            plt.text(bins_x[i]+0.5, bins_y[j]+0.5, int(c),\n",
    "                     ha = 'center', va = 'center', color = 'black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Violin Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>make</th>\n",
       "      <th>model</th>\n",
       "      <th>year</th>\n",
       "      <th>VClass</th>\n",
       "      <th>drive</th>\n",
       "      <th>trans</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displ</th>\n",
       "      <th>pv2</th>\n",
       "      <th>pv4</th>\n",
       "      <th>city</th>\n",
       "      <th>UCity</th>\n",
       "      <th>highway</th>\n",
       "      <th>UHighway</th>\n",
       "      <th>comb</th>\n",
       "      <th>co2</th>\n",
       "      <th>feScore</th>\n",
       "      <th>ghgScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32204</td>\n",
       "      <td>Nissan</td>\n",
       "      <td>GT-R</td>\n",
       "      <td>2013</td>\n",
       "      <td>Subcompact Cars</td>\n",
       "      <td>All-Wheel Drive</td>\n",
       "      <td>Automatic (AM6)</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>6</td>\n",
       "      <td>3.8</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>16.4596</td>\n",
       "      <td>20.2988</td>\n",
       "      <td>22.5568</td>\n",
       "      <td>30.1798</td>\n",
       "      <td>18.7389</td>\n",
       "      <td>471</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32205</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>CC</td>\n",
       "      <td>2013</td>\n",
       "      <td>Compact Cars</td>\n",
       "      <td>Front-Wheel Drive</td>\n",
       "      <td>Automatic (AM-S6)</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>21.8706</td>\n",
       "      <td>26.9770</td>\n",
       "      <td>31.0367</td>\n",
       "      <td>42.4936</td>\n",
       "      <td>25.2227</td>\n",
       "      <td>349</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32206</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>CC</td>\n",
       "      <td>2013</td>\n",
       "      <td>Compact Cars</td>\n",
       "      <td>Front-Wheel Drive</td>\n",
       "      <td>Automatic (S6)</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>17.4935</td>\n",
       "      <td>21.2000</td>\n",
       "      <td>26.5716</td>\n",
       "      <td>35.1000</td>\n",
       "      <td>20.6716</td>\n",
       "      <td>429</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32207</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>CC 4motion</td>\n",
       "      <td>2013</td>\n",
       "      <td>Compact Cars</td>\n",
       "      <td>All-Wheel Drive</td>\n",
       "      <td>Automatic (S6)</td>\n",
       "      <td>Premium Gasoline</td>\n",
       "      <td>6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>16.9415</td>\n",
       "      <td>20.5000</td>\n",
       "      <td>25.2190</td>\n",
       "      <td>33.5000</td>\n",
       "      <td>19.8774</td>\n",
       "      <td>446</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32208</td>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>Malibu eAssist</td>\n",
       "      <td>2013</td>\n",
       "      <td>Midsize Cars</td>\n",
       "      <td>Front-Wheel Drive</td>\n",
       "      <td>Automatic (S6)</td>\n",
       "      <td>Regular Gasoline</td>\n",
       "      <td>4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "      <td>24.7726</td>\n",
       "      <td>31.9796</td>\n",
       "      <td>35.5340</td>\n",
       "      <td>51.8816</td>\n",
       "      <td>28.6813</td>\n",
       "      <td>310</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id        make           model  year           VClass  \\\n",
       "0  32204      Nissan            GT-R  2013  Subcompact Cars   \n",
       "1  32205  Volkswagen              CC  2013     Compact Cars   \n",
       "2  32206  Volkswagen              CC  2013     Compact Cars   \n",
       "3  32207  Volkswagen      CC 4motion  2013     Compact Cars   \n",
       "4  32208   Chevrolet  Malibu eAssist  2013     Midsize Cars   \n",
       "\n",
       "               drive              trans          fuelType  cylinders  displ  \\\n",
       "0    All-Wheel Drive    Automatic (AM6)  Premium Gasoline          6    3.8   \n",
       "1  Front-Wheel Drive  Automatic (AM-S6)  Premium Gasoline          4    2.0   \n",
       "2  Front-Wheel Drive     Automatic (S6)  Premium Gasoline          6    3.6   \n",
       "3    All-Wheel Drive     Automatic (S6)  Premium Gasoline          6    3.6   \n",
       "4  Front-Wheel Drive     Automatic (S6)  Regular Gasoline          4    2.4   \n",
       "\n",
       "   pv2  pv4     city    UCity  highway  UHighway     comb  co2  feScore  \\\n",
       "0   79    0  16.4596  20.2988  22.5568   30.1798  18.7389  471        4   \n",
       "1   94    0  21.8706  26.9770  31.0367   42.4936  25.2227  349        6   \n",
       "2   94    0  17.4935  21.2000  26.5716   35.1000  20.6716  429        5   \n",
       "3   94    0  16.9415  20.5000  25.2190   33.5000  19.8774  446        5   \n",
       "4    0   95  24.7726  31.9796  35.5340   51.8816  28.6813  310        8   \n",
       "\n",
       "   ghgScore  \n",
       "0         4  \n",
       "1         6  \n",
       "2         5  \n",
       "3         5  \n",
       "4         8  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuel_econ = pd.read_csv('./fuel_econ.csv')\n",
    "fuel_econ.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.violinplot(data = fuel_econ, x = 'cylinders', y = 'co2');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_color = sb.color_palette()[0]\n",
    "sb.violinplot(data = fuel_econ, x = 'cylinders', y = 'co2', color = base_color,\n",
    "              inner = None);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Box Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-13.897395923551887, 758.0941368076467)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmcAAAFACAYAAAD589sCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAIABJREFUeJzt3Xuc3HV99/3XZ2b2mM1xcyBsAgE2CooikovqpSJGUJCt4N3aWtuSW23p/XgYpPaq1lMLVrBe7XXXEtrLXjxqa9KK3JbUagMGI1SpVamJHAIEyCaGZDenzSbZZM87M5/7j/nNZPaQzW6yM9/fzr6fj8c+9neYwyfJ5ruf+XxP5u6IiIiISDwkQgcgIiIiIqcoORMRERGJESVnIiIiIjGi5ExEREQkRpSciYiIiMSIkjMRERGRGFFyJiIiIhIjSs5EREREYkTJmYiIiEiMpEIHcC4WLlzoK1asCB2GiJTRtm3bjrj7otBxnCu1XyIzz0Tbr2mdnK1YsYKtW7eGDkNEysjMXgkdw1RQ+yUy80y0/VK3poiIiEiMKDkTERERiRElZyIiIiIxouRMREREJEaUnImIiIjEiJIzERERkRhRciYiIiISI0rORERERGJEyZmIiIhIjCg5ExEREYkRJWciUnIPPPAA99xzT+gwRESmBSVnIlJy999/P1u2bAkdhojItKDkTKQCfe5zn+Ohhx4KHYaIiJwFJWciFehHP/oRf/3Xfx06DBEROQtKzkRERERipGTJmZm92syeLvo6YWa/b2YLzGyLme2Mvs+PHm9mts7MWs3sWTN7Y6liE5Ew3D10CBOi9ktEQipZcubuL7n7G9z9DcBVQC/wLeBTwGPuvhJ4LDoHuBFYGX3dBnylVLGJSBiDg4OhQ5gQtV8iElK5ujXfCexy91eAm4H10fX1wC3R8c3ABs/5KTDPzJaWKT4RKYO+vr7QIZwNtV8iZdTZ2cnHPvYxOjs7Q4cSTLmSsw8A34iOl7j7AYDo++LoehOwr+g5bdG1YczsNjPbamZbOzo6ShiyiEy1aZqcqf0SKaP169ezfft2NmzYEDqUYEqenJlZNfBe4J/P9NAxro0aoOLu97v7KndftWjRoqkIUURKqHic2XRLztR+iZRXZ2cnjzzyCO7OI488MmOrZ6kyvMeNwM/d/VB0fsjMlrr7gajsfzi63gYsL3reMmB/GeITkRIqHmfW398fMJKzovZLpIzWr19POp0GYGhoiA0bNvDxj3+85O9733330draOua99vZ2AJqaRhXDAWhubub222+f0njK0a35G5zqEgD4DrAmOl4DfLvo+q3RrKc3AV357gMRmb4GBgYKx9MwOVP7JVJGjz766LDzzZs3B4rklL6+vrJX/UtaOTOzeuB64PeKLn8J+KaZfQTYC7w/uv4I8B6gldzMqA+VMjYRKY/i5Kz4OO7UfomUXyaTGfe8VMarfN1xxx0A3HvvvWWJBUqcnLl7L9A44lonudlPIx/rwEdLGY+IlF9xt+Z0WUoD1H6JhJDv0jzd+UyhHQJEpKSKG9ehoaGAkYhI3DU0NIx7PlMoORORkipOyMrVRSEi09Ndd9017Pzzn/98mEACU3ImIiWlypmITNSqVasK1bKGhgauuuqqwBGFoeRMREqquFo2U8ePiMjE3XXXXSQSiRlbNYPyrHMmIjNY8SQAVc5E5ExWrVrF448/HjqMoFQ5E5GSKl7bbBqucyYiUnZKzkSkpHp6esY8FhGRsSk5E5GSOn78+JjHIiJj2bp1K6tXr2bbtm2hQwlGyZmIlNSRI0cA8EQVHR0dgaMRkbi76667yGaz3HnnnaFDCUbJmYiU1P79+yGRxBNJ2qINhEVExrJ161a6u7sB6O7unrHVMyVnIlJSu3+xh6ylIJHk8KFD02p/TREpr5GL0M7U6pmSMxEpmYGBAfa3t0EyhSeqcHf27NkTOiwRial81ex05zOFkjMRKZlf/OIXZLNZPFEFydyyijt37gwclYjElfbWzFFyJiIl09ramjtIpnBLYanqU9dEREb43d/93WHnv/d7vxcokrCUnIlIyezevRtLVuGWAoN07Xx27doVOiwRiakHH3xw2PkDDzwQKJKwtH2TSIVx99AhFLzyyl6yliTZ1wmAW4oXXzoWOCoRiasDBw6Mez5TKDkTkZLZf/AAjpPI5PbUNIYYIreNU21tbdjgRGTGuO+++856SEX+eXfcccekn9vc3Mztt98+6ecpORORkuk6dhwsOfp6V5eSMxEZZenSpcOqZeeff/6UvG5raytPP7eDTP2CST83MZjrjdi2+9CknpfsPTrp98pTciZSYcwsdAgFA4MDYDWjrmsDdBEZyxe+8AV+53d+p3D+p3/6p1P22pn6BfRd+p4pe70zqXvxkbN+riYEiEjpnGb8W5zGxYlIfOzdu3fY+b59+wJFEpaSMxEpmaqqamB0IlZTM7qaJiLyxS9+cdj5PffcEyiSsJSciUjJzGqYBZ4dfX3WrADRiEjcpdPpcc9nCiVnIlIyc+fOxcbowpypq36LyPhSqdS45zOFkjMRKZkF8+ePqpwlk0kSCTU9IjLaZz7zmWHnn/3sZwNFEpZaSBEpmblz52KMSM5m6CdhETmz1atXF6plqVSKd7zjHYEjCkPJmYiUzOzZs0dVzlLJ0eueiYjk5atnM7VqBiVOzsxsnpk9ZGYvmtkOM3uzmS0wsy1mtjP6Pj96rJnZOjNrNbNnzeyNpYxNREqvpqZm1HIa06VLU+2XSBirV6/mBz/4wYytmkHpF6G9F9js7r9qZtVAPfAZ4DF3/5KZfQr4FPBHwI3Ayujrl4CvRN9FZJrKd0/U1tbS0tLCpk2byGZHz96MKbVfIhWivb2dZG/XOS0MO1nJ3k7a289utmnJPsKa2RzgGuCrAO4+6O7HgZuB9dHD1gO3RMc3Axs856fAPDNbWqr4RKT08olYS0sLa9eu5aabbpoWU+PVfolISKWsnF0MdAD/YGZXANuAO4Al7n4AwN0PmNni6PFNQPFSwG3RtZm5Jb1IBRgYGABg06ZNuDsPP/zwdOnWVPslUkGampo4OJAq+/ZNTU1Lzuq5pWwlU8Abga+4+5VAD7kugNMZa0PAUQskmdltZrbVzLZ2dHRMTaQiUhLd3d1gSfr7+9m4cSP9/f1j7BcQS2q/RCSYUiZnbUCbuz8ZnT9ErrE7lC/3R98PFz1+edHzlwH7R76ou9/v7qvcfdWiRYtKFrzIdJXJZEKHUHDixAl8xEbs06FbE7VfIhJQybo13f2gme0zs1e7+0vAO4EXoq81wJei79+OnvIdYK2ZPUhuIG1XvvtARCZu3bp1oUMo6OzsBBv+GTCTTuPumI1VbIoHtV8ipXPffffR2tp62vvt7e1ArityLM3Nzdx+++0liS0uSj1b83bg69FMp93Ah8hV675pZh8B9gLvjx77CPAeoBXojR4rIpO0c+fO0CEUHD12DLfEqD6/7u7u3Bpo8ab2SySAvr6+0CEEV9LkzN2fBlaNceudYzzWgY+WMh6RmSBO3ZonT5wEG73obFdXV+yTM7VfIqVxpqrXHXfcAcC9995bjnBiaVpMmxKRiYtLcpbJZBgcHBjVrQnQ09MTICIRkelBm9yJVJjiAfcDAwO5VfoDGBoaOqt7IiKlkOw9elaL0Cb6TwCQrZ0z6feDs1tKQ8mZSIUprpydPHkyWHI23oD/OE8GEJHK09zcfNbPbW09mXuNiyebaC056/dVciZSYYorZydOnGDhwoVB4qiuro6SsNErm4VKGEVkZjqX2Z0hxsBpzJlIhSmunJ04cSJYHGbGrIbZeLIaT1bhySqyNblugblz5waLS0Qk7pSciVSY4uSsu7s7YCSwcOFCvLqBTH0jmfpGhhZchJkxf/78oHGJiMSZkjORClOcnIWeFdl0/lJSQ6cSxMTASRYuWkwqpREVIiKno+RMpMJks1k8Wr4i9GKOTU1N0H+iMOwsOXCSC5YvCxqTiEjcKTkTOUcnT57kgx/8ID//+c9DhwLkkrP82mL9/f1BY1m+fDlkM5hnwCE5eIJly5SciYiMR8mZyDnatWsX+/fv5x//8R9DhwJAbrH63FIVodcTKyRi2TTmWXxoQMmZiMgZaOCHyDnKJUNRxSoG3D2fmwWPKb9xsWUzeLS22ek2MxYRkRxVzkQqWOjFXhcuXEgymYRsJvcFnHfeeUFjEpFTOjs7+djHPkZnZ2foUKSIkjORCmNmpwbgJ0dvOl5OiUSCxoULwTNYVMVbsuTstjMRkam3fv16tm/fzoYNG0KHIkWUnIlUmOJV+UMnZwCLFy2OJgRkqKmtZdasWaFDEhFyVbPNmzfj7mzevFnVsxhRciZSYXKVs1xyFof1xBobF2CeBc8yb54WnxWJi/Xr1xfGpWYyGVXPYkTJmcg5Cj2ua6S4Vc7mz58PnsU8S2PjgtDhiEjk+9//fmEv3nQ6zZYtWwJHJHlKzkTOUX62poxt7ty54I55lvnz5oUOR0Qi1113XaG6nkqluP766wNHJHlKzkQqTJzWOYOiTc6zaW14LhIja9asIZHIpQHJZJJbb701cESSp+RMpMJkMhlIxGOHAGBYQqbkTCQ+GhsbueGGGzAzbrjhBhobG0OHJJHwo4VFZEql02ncEiRS1XR1dYUOR8mZSIytWbOGPXv2qGoWM0rORCrMUDoNyVo8VcXRo0dDh6PkTCTGGhsbWbduXegwZAR1a4qcozjN1uzt7cWjjc8zqToOd3SEDok5c+YUjmfPnh0wEhGR6UGVM5EK8uUvfxkAG+qDbJpdrccDRzQ8IStO1EREZGxKzkQqSGtrKwDmGSydoT+dG4MWcjHaurq6wnFDQ0OwOESkfO67775CezRZ+efdcccdk35uc3Mzt99++1m9b5woORM5R3Fa52yspTM6OjpYunRpgGhyirt9tXWTyMzQ2trK08/tIFM/+YWnE4O5NnXb7kOTel6yN/wY26mi5EykggwODo66dvDgwaDJWbHiKpqIVLZM/QL6Ln1P2d6v7sVHyvZepaYJASIV5HTJWVzU1NSEDkFEJPZKmpyZ2R4z225mT5vZ1ujaAjPbYmY7o+/zo+tmZuvMrNXMnjWzN5YyNpFKNDAQ7+Ssuro6dAgTpvZLREIpR+XsHe7+BndfFZ1/CnjM3VcCj0XnADcCK6Ov24CvlCE2kYoyODgw/IIlYpWcxWnZkQlS+yUVrbW1lZtuuumsB+9LaYTo1rwZWB8drwduKbq+wXN+Cswzs3gMlJnhnnrqKR544IHQYcgZ9PT05LZuKuKJFPva2gJFVJHUfklFufvuu+np6eHuu+8OHYoUKfWEAAe+Z2YO/B93vx9Y4u4HANz9gJktjh7bBOwrem5bdO1A8Qua2W3kPplywQUXlDh8AfjEJz5BOp3mgx/8YOhQZBzt7e2jrrklaVNydrbUfklFa21tZc+ePQDs2bOH1tZWmpubwwYVyHhLf5xpaY9SLN9R6srZW9z9jeRK/h81s2vGeexY/R2j1ihw9/vdfZW7r1q0aNFUxSnjSKfToUOQCRgrOSOR5ERXFz09PeUPaPpT+yUVbWS1TNWzsdXV1ZV9pnlJK2fuvj/6ftjMvgVcDRwys6XRp86lwOHo4W3A8qKnLwP2lzI+kakwsisxlP37R/938USqcG/lypXlDmlaU/sllS5fNTvd+UwSt4VrS5acmdksIOHuJ6PjdwF/CnwHWAN8Kfr+7egp3wHWmtmDwC8BXfnuA5E4e+ihh0KHAESzMi0Bnj11MZEs3FNyNnFqv6SSnK7LrqamhoGBgWHnY3XdVcqq+9NJKStnS4BvRbOzUsAD7r7ZzH4GfNPMPgLsBd4fPf4R4D1AK9ALfKiEsYlMmXx3YuidAjo6OnCMutpaWlpa2LRpEz3ZbOGeTIrarwrR2dnJ5z//ee68804aGxtDhxMrF1xwATt37iycX3jhhVP22u3t7SR7u8q6MGyyt5P29soYhlOy5MzddwNXjHG9E3jnGNcd+Gip4hEplXy3ZuhlIo4eOwZAS0sLa9euxd355r89CsCx6J5MjNqvyrF+/Xq2b9/Ohg0b+PjHPx46nCDGq3q9+93vZmBggBUrVnD//feXMSoZj7ZvEjlH+eQs9MSJk93deCLJpk2bcHcefvhhPFWPZWvo7e0NGptICJ2dnTzyyCOF/w+33nqrqmcjXHDBBezatYvPfe5zU/q6TU1NHBxIlX37pqamJWV7v1LS9k0i5yiflJ04eTJoHEODQ2AJ+vv72bhxI/39/ZCshkRyzG2dRCrd+vXrC/8/0+k0GzZsCBxR/NTX1/O6171uxi6hEVdKzkTOUb5ydvJkd9A4EonTdat68C5XkRA2b9487rlIXCk5EzlH+eSspydscpbbVHyMSQmZNLW1tWWPRyS0kRXj4pmJInGm5EzkHOWTs6HBQYaGhoLFMWfOHKx4GQ0AdzwzRENDQ5igRERk0jQhQOQcFS9C29PTw7x584LEsbCxcVRylj/XIGgRKbdk79GzWkoj0X8CgGztnEm/X24VnOlPyZnIOcpkTiVEfX19wZKzpUuXQnbEbgXZ3GDo8847L0BEp9x4443s3Dn2vnUiUnnOZYJBa2tuclXzxZNNtJZUzMQGJWci5yibzeCWwDwbdA/LZcuWjbpmUbK2fPnyUffK6ZOf/GRstrkSkdI7lx0F8rsU3HvvvVMVzrSjMWcyLYVejT/P3clms2C5bZJCJmcXXXRRLqZEEk9WkZ59Hp5IUltXT+hNts2MVEqfBUVEJkLJmUw7P/7xj2lpaeHw4cNnfnCJ5ZMxj/awPHHiRLBYVqxYkYslVUemvjG3+GOqmosvvkhLaciMNPLnXv8PZLpQcibTzr/+67/S09PD7t27Q4dCV1dX7iCRqwqFTM5mzZrFwkWLC+PMcCfVf5xLLr44WEwiIb397W8f91wkrpSciZyD/J6VHiVnR48eDRkOzZdcjEXJmQ314UMDhe5OkZlm5LincxkHJVJOSs5k2onLeDOAI0eO5A4SSayqhs7OzqDxXHjhhbnkzJ1E/3Egt3eeyEzU2NjI61//egCuuOIKLSkj04aSM5l28uNGstnsGR5ZeoXkzJJ4Vf2p80DyMzbNs4W1gsaaxSkyU7S25pZw2blzZ+BIRCZOyZlMO/nKWV9fX+BIoKOjAwC3BOlUPYcCT1JYunRp7iCbwQa6SSaTwWdqioSydetWent7Aejt7WXbtm2BI4LOzk4+9rGPBa+yS7wpOZNpKw775B06dAgSSTDI1szi4MFDQeMpLDbrGRKD3TQuXEgymQwak0god91117DzO++8M0wgRdavX8/27dvZsGFD6FAkxpScybQVh+Ssra0dj9Y485rZnDzRFXSts3yVzLIZEoM9LA28M4BISN3d3eOel1tnZyebN2/G3dm8ebOqZ3JaSs5k2gq5yTjkxrzt27evMFMzW5vbtmnv3r3BYqqpqSGRTIJnSKV7Wbx4cbBYREIbufBx6IWQ169fXxgrm8lkVD2T01JyJtNWOp0O+v779+9nYKAfSw+Q7O2k+sAzAOzatStoXNVVVVg2gw/0aLyZzGgj24jQbcb3v//9QgzpdJotW7YEjUfiS/upyLQVeq/GF198EQDzDGQyJHuOgBk7duygpaUlWFzV1dX0DXSDZ5WcicTIddddxyOPPEI6nSaVSnH99ddP2Wvfd999hZmpk5F/Tn4/y8lqbm7W+nEloORMpq3QydkLL7ww6ponqnj++dHXy6mqqgrzXNfJwoULg8YiIqesWbOGzZs3A5BMJrn11lun7LVbW1t5+rkdZOoXTOp5icHc7Pdtuyc/mSnZG3bR7Uqm5EymnXy3wODgYNA4duzYgSersMypsW+erOKVV/bQ19dHXV1dkLiqqqoKx1p0U2aCyVSNRlaIyln5aWxs5IYbbuDf/u3fuOGGG6b8/2emfkFuT90yqXvxkbK910yjMWcyYXFZmb87mg0Zcp2zbDbL7t2/wBNVw657ogp355VXXgkU2fBBz0rOZCZbvnz5sPM47JaxZs0aXve6101p1UwqjypnMmHZbDYWa2YdP94VfT8eLIajR48yMNAPNXOG34hmbra1tXHppZcGiGx4cjZv3rwgMYiU03iVr2uvvRbI/b+Iw+zIxsZG1q1bFzoMiTlVzmTCQo/xysfQ2ZnbIungwYPB4ihseG7D/wt5IjHsfgjFyVlNTU2wOETiIF89++xnPxs4EpGJU+VMJiwOe1keOHCAbCaDW4JX9u7D3Qt7bZZToUt11Hvb8PsBxKG6KRIXCxYsYMGCBbzjHe8IHYrIhJW8cmZmSTN7ysw2RecXmdmTZrbTzP4/M6uOrtdE563R/RWljk0mJ/QaQXBq2ne68RJ6e7oLe1uW2+kT1fCbsis5mzpqv0QkhHJUzu4AdgD5wTn/E/iyuz9oZn8LfAT4SvT9mLs3m9kHosf9ehnikwmKQ3L24osvQiLJ0MKVVB3ZyY4dO4Ksgp+Iui/JpKmtraWlpYVNmzbRnRkYfj8AJWdTSu2XTAvt7e0ke7vKOoMy2dtJe3v43wuVqKTJmZktA24C7gH+wHL9T6uBD0YPWQ/cRa5xuzk6BngI+GszM4/LFEEJvnQFwPPPP48nq0kd/QUkkjz//PO8/e1vL3sc+QTIskO0vLeFtWvX4u58898eHXY/hBDdvJVI7ZecrfGW9mhvbwegqanptM+v9IVdz7T0yZkWxq30vx8ofeXsr4BPArOj80bguLvnU+02IP8T2gTsA3D3tJl1RY8/UvyCZnYbcBvEY1r0TNLf3x/0/YeGhnjxxRdxqkj0HSNT38j2554LEku+MuaJKjZt2oS78/DDD+PJumH3Q1i5ciXPPvtssPevIGq/ZMqVajxqU1MTBwdSZV/nrKlpydS/bqA1IuOkZMmZmbUAh919m5ldm788xkN9AvdOXXC/H7gfYNWqVfpUWkahk7OXX36ZoaEhvHYWAJmGJbz88g4GBgbKPiuxkHwlk/T3nWTjxo2584a5w+8HcPvtt5+KR86K2i85F+NVdfLVoHvvvbdc4cROpVe9pkIpf4O8BXivme0BHiTXHfBXwDwzyyeFy4D90XEbsBwguj8X0N4QMdITLf4aynP5Klkyt/BrpmExmXSal156qeyxFLotR//6BcImZzIl1H6JSDAl+w3i7p9292XuvgL4APC4u/8m8O/Ar0YPWwN8Ozr+TnROdP9xjdcIr3hts5MnTwaMBLZv3w51c/BELjHKNCw5db3MTm2RNPxH1KIf2erq6jJHJFNJ7ZeIhBTi4/0fkRtc20puTMZXo+tfBRqj638AfCpAbLHws5/9LDcrMQaKE7Kurq5gcbg7zzzzLEP1p2ZmelUt1M0LMr5q9uzcMKT8BuOngsr9Pm5oaCh3SFIear9EpOTGHXNmZsuBvyA32PW7wF+4+1B071/d/ZaJvIm7/wD4QXS8G7h6jMf0A++fROwV6xOf+ATV1dV873vfCx3KsC2SQm6XtHfvXk6ePEFmxetJdZ6a5TPYsJjtzz1HNpsta1finDlzSCSTZEclZ7lK48KFC8sWi4xN7ZfMNMneo5NeSiPRfwKAbO2cMzxy7PeDqZ8QIGeeEPD3wEbgp+TW8fmhmf2yu3cCF5Y6uJksDstWwPBtiEJuSfTCCy8AkDx5gGRvJ5CbKYQl6O3pYd++fVx4Yfl+JFOpFIsXL2F/R+ew65bNTeRbunRp2WKR01L7JTNGc3PzWT2vtTXXO9J88dkkWUvO+n1lfGdKzha5+99Gx7eb2W8BT5jZexljKLRUnqNHj455XG4vvfQSlqzCBnqwzBAAqZMHydQvLNwvZ3IGsLL5Eg4eOjT8YjbNrFkNLFq0qKyxyJjUfknJnGmtrtM50xpeZ3K6Nb7OdgakZo/G05mSsyozq41K9rj7P5nZQeBRYFbJo5PgOjtzlSFPpArHIezavZt03fxRCxZ4IgmJJLt37y57TJdeein/8R//gSeqwCBT30iy7ziXXXapFoKNB7VfUjKtra08/dwOMvULJvW8xGDuc8G23YfO8MjRct2IMhOcKTn7O+CXgB/mL7j7983s/cCflzKwmSoOm4sXK07OOo6ES8727WsjW9uIDYyYMWoGtXNoa2sre0yXXXYZANmaBjxVQ9/K65n91D8Vrof0tre9jSuvvDJ0GKGp/ZKSytQvKPuirzIzjJucufuXT3P9KeD6kkQ0w/X29oYOYZgjR45AIgmWoLPzCO5e9qpQOp2m6/gxsksvIDkyOQPSVfUcGtm9WAaXXpqrkFlmCE/V5MbCufOa17ym7LGM9IUvfCF0CMGp/RKR6WpC09vMbJmZfcvMOszskJltjPadkynW3d1dOI7DMkkdHR24JXKbjQ8ODouvXLq6unB3vGrsLT08VcfRAJMV6uvraWpaVhgDl+jJ7dTz6le/uuyxyOmp/RKR6Wai2zf9A/AAp6aK/1Z0TZ8+p1jxumL9/f3B9xg7dOgwbknccgu/Hj58uLDGV7nkE0JPVkNmkNraWlpaWti0aRPdmUE8WU3PyTAVx5Urm2lrzy0Sn+w7xty581iwYHJjUKTk1H7JlGtvbyfZ21XWrsZkbyft7ekzP1CmvYkuDLXI3f/B3dPR19cATUcrgbisKwa58W9HjnSAJSFaQyxE92FhWZFEEksP0tLSwtq1a7npppuw9CCeSDI0FGbpkRUrVuTWNnMn2d/FRRetCBKHjEvtl4hMKxOtnB2JpqF/Izr/DSDc6PAKVryW2PHjx4Oul3Xs2LHc9k2pRC5BI1c5K7dT3buGp6rZtGkT7s7DDz+Mp+rBLFgX8LJlud4xy2ZIpnsK5xIrar9kyjU1NXFwIFX2CQFNTVr0dSaYaOXsw8CvAQeBA+T2jvtQqYKayeKy6CvAwYMHgdxyFW65JStCVM5SqegzhDskq+nv72fjxo309/dDshrzLMlAG42ff/75uYPsED7Yd+pc4kTtl4hMKxOtnH0BWOPuxwDMbAHwv8g1ejKFHnvsscJx6OSskIhZMre+WM2sIMlZbW1t7iA7NPYDMmlqasOMzTvvvPMAsHSuW1U7A8SS2q8pNt4CrO3t7UCusjSW0y2iKiKnTDQ5e32+YQNw96P+ir/VAAAek0lEQVRmNuMXUSqFjo4OsAR4NjbJmSdyXZrp1KxCNa2c5szJ7flmQ/1j3rd0f+Ex5TZv3rxoOQ0lZzGm9quM+vr6QodQNtrLUkploslZwszmj/jkOdHnyiSk02ncEiQSyeATAg4fPgyWJNmX+73mlmLXrvInjLNmzaK6uobBobFnZCaGelm0KEx3oplRVV3N4MAAAIsXLw4Sh4xL7dcUG6/yNVO2A9JellJKE22g/l/gx2b2ELk96X4NuKdkUc1g6XQ6VzmrqqGrqytoLLkqnhXW8TKGGEjnYiyMAysDM+O8peexq2v0ArQAqaFuzg9YsaqJkrNUVRXz588PFoecltqvCqC9LGUmmdBvWHffYGZbgdXkRh/9X+7+Qkkjm6HSmQxuCbLJmmFrnoXQceQIbomR21ly7Nixsm/sveLCC9n7s2cYnNOE9+Ym2mXqG8nWzsVPHiz7pufFqqqqAGhc0Kg9NWNI7Vdl0F6WMpNMuPwRNWZq0Eosk8lAIkkmWcWJwMlZZ+fRMZOz48ePlz05a25u5oknnmDg0l8mETWYfZe+h2RXO9UdL3HJJZeUNZ5i+SpiY6MWn40rtV+VQXtZykyhcRcx4u5kMxlIJvBkNScDbJVU7ERXV2F9s2IhJipcfvnlACS7h3/6TXYfwsyCbjaer5wlk6P/rkRk5hmvC3Yi3aya0SpKzmKksBI+BslqenvCzdYcHBxkcHAAqhtG3QvR3fra176WqupqUl1tZIu6Naq62nj1pZfS0DA6znLJJ2Xq0hSRMwm9JZ9MD0rOYqQwBd0MT1TR1x1uSnpPT08hltPeK6Oamhqu/m9X859bn+Lk634tN1FhoJtEzxHe9tb3lT2eYolAC+CKSDyp6iXnSr9VYqS3N7dUhFsCT6bo7+8Lti1Rf39/IZbT3Su31avfAQM9ha7N1NFfAHDttdcGiScvn5ypciYi082JEyd45pln2LZtW+hQpIiSsxjJJ2dgkKgim80WdXWW13jvOzR0mpX6S+zNb34z1dU1pDp3AVBzbDevfvWlp12JvFyUlInIdPWLX+Q+5H76058OHIkUU7dmjBSSMzM8WVW4VlNTU/ZYstnsae9lMpkyRnJKfX09b33rW/j3H/2Eob7XYj2dXHfdB4LEIiIyXZxugsKJEycKx4ODg3z4wx9m9uzZox6nCQrlp+QsRorHeXmyunAtxMKmhXFU2TS1tbW0tLSwadMm+vv7g46xetvb3sbjjz9OdfvPAXjrW98aLJa8pqYmdu3aFToMkYrW3t5OsrerrMtbJHs7aW9Pl+39yi1fNcvbvXs3V1xxRaBopJiSsxjpLiydkcBT1SOulVdhBmI2Tct7f5m1a9fi7mzcuLGsuwOMdNVVV+W2TDq2h/ObmmKxl+Utt9zCE088EToMEZExna7qNdZ4Xe1cEA9KzmKkUGK2BCRrhl8rs0JXqiXZtGkT7s7DDz8MQHV1dZCYILcJelPTMtra9vG6aO2z0MbrAhaRqdHU1MTBgVTZF6FtatJG41J+mhAQI1u2bAEg0XeMmn1PAgTb/Ly2tjZ3kEjS39/Pxo0bC7M0Q6/Ts2RJbnPxiy66KGgcIiIipVCyypmZ1QJPADXR+zzk7nea2UXAg8AC4OfAb7v7oJnVABuAq4BO4NfdfU+p4oujw4cPA2DZIZI9RwA4ejTM3m6FBGyMpTxCJ2eXXnop27Zt4+KLLw4ah1Sumd5+ne0m43BuG41r4LlITim7NQeA1e7ebWZVwI/M7LvAHwBfdvcHzexvgY8AX4m+H3P3ZjP7APA/gV8vYXyxM3qJCqOjoyNILKlUiurqGvp9dJfdrFmzAkR0ykc+8hFuueWWsu/vKTPKjG6/znaTcTj7jca1ybjIKSVLzjy3emp+NHtV9OXAauCD0fX1wF3kGrebo2OAh4C/NjPzUKuwBjBqbbFEslBNC6F+1iz6T47epSB0cpZIJGKVmM2gH9EZQ+2XNhmfCRKJxLAxs9rtJD5K+i9hZkkzexo4DGwBdgHH3T0/N7kNyK8g2gTsA4judwGNY7zmbWa21cy2hqoqlYK7j0rO3BIcOHAwUETQ0DALGF05C7mPZRxN49+/Mg61X1LpRk5m0uSm+ChpcubuGXd/A7AMuBq4bKyHRd/HWmZ91G89d7/f3Ve5+6o4VU/O1YkTJ0b9kvdEkgMHwyVnc+bMwcZIPObMmRMgmvhSclaZ1H6JSChlqWG6+3HgB8CbgHlmlu9OXQbsj47bgOUA0f25wIwZhDBm96Ul6ek+GWwvy7lz5sAYY85UOZOZRO2XiJRbKWdrLgKG3P24mdUB15EbJPvvwK+Sm/G0Bvh29JTvROc/ie4/Pp3Ha0zWkSNHRl3zRLJwb9myZeUOiXnz5mEjkrNEIhF0EVqRclD7FU/J3qOTHpuW6M+tFZmtnXzFPzdJQeucSfmV8rfsUmC9mSXJVei+6e6bzOwF4EEzuxt4Cvhq9PivAv9oZq3kPnHOqE0Tu7q6Rl+0ROFeqORsZOVMiZnMEGq/Yqa5ufmsntfaejL3/IvPJslactbvK3IuSjlb81ngyjGu7yY3fmPk9X7g/aWKJ+4Km54PkxvGUthzs8wWLlw46lpVVVWASOLNbKzhRjKdqf2Kn7Nd/yy/3pq2JZLpRGWQmMjP1CzeZLwnm5sUNnr9s/LIJ2eeSIEZls3S2DhqAtqMl9+HVKRSaJNxkbC0qEnMtLS0sHbtWm666SYsSs5CVWYWL85tk5StbiBTtwBw3va2twWJJc60NpCIiEwlVc5iIr+ZePEm4265LsRQXYnnnXceAOaZ3NgzzxauiUjl0ibjM8Nll13Gjh07hp1LPOgjf0zU19cDDN9kPJkadq/c5s2bR3V1DWQzuS9gyRI1niIileDuu+8e91zCUXIWE2Mu7BrNlAy16KuZcd7S83KJmeeSs/PPPz9ILCIiMrUaGxsL1bLLLrtMY4pjRMlZTMybN2/UtfwaY2PdK5em88/HPINl05iZKmciIhXk7rvv5vWvf72qZjGjMWcxsWDBgtEXs1mSqVTQFfmXLl2KZTN4NsP8BY1aSkNEpII0Njaybt260GHICKqcxcT8+fNHX/RMbpX+gOto5SpljmXTLF2qyQAiIiKlpuQsJmpqajAb/s9hnh27olZG+eU0LJtmSXQsIiIipaPkLEZGbY3kWRoDJ2fFA0TH2jFAREREppbGnMVIVVWKoaHBwrl5lrlz5waMaPhYuDG7XkXbN0lFOptNxuHsNxrXJuMipyg5i5FR2wDFIDkrfv/QscSVu4cOQWRKnctm32e/0bg2GRfJU3IWI6O6NYHZs2cHiOSU4gVwQ84aFZHyOdtNxkEbjYtMBY05i5H8+C5PVpGZtQgItwBtXnE1L9ROBSIiIjOJkrMYueaaawDI1C2g/6K3AuErZ8VqampChyAiIlLxlJzFSL5KZp7F0gPDrsXBWN2uIiIyfW3dupXVq1ezbdu20KFIESVnMVIYcO+ODfUDYbduGmnUhAUREZnW/uRP/oRsNssf//Efhw5Fiig5i5FCIuYZLB2/5ExLRoiIVI6tW7fS29sLQG9vr6pnMaLkLEYK64h5FhvqA+KVnGWz2dAhiIjIFPmTP/mTYeeqnsWHkrMYySdnFiVnDbNnx2qcl9bzEhGpHPmq2enOJRwlZzFSmJmZzWLpvtityK/kbGzq7hURkamk5CxGEolErlLmWRJD/TQuaDzzk8pI3ZpjU9IqItPR4sWLh50vWaLts+JCyVnMpFIpzLMkM/3Mnx+f8WagCpGISCX54he/OOz8nnvuCRSJjKTkLGbylTNL98dqMoCIiFSW5uZmFi5cCMDChQu1t2mMKDmLmXzlzIcGYrUALWidMxGRSvPa174WgMsvvzxwJFIsPlMBBYgSIM8A8dtoXMmZiMTRfffdR2tr65j3tm/fTjab5Vd+5VdYtmzZqPvNzc3ntNH7dNbZ2clPfvITAH7yk5/Q2dlZ2ONZwipZ5czMlpvZv5vZDjN73szuiK4vMLMtZrYz+j4/um5mts7MWs3sWTN7Y6lii7PiBChuyVmclvUQKSW1X5UjP5Gps7MzcCTxs379+sLfTyaTYcOGDYEjkrxS/rZNA//D3X9uZrOBbWa2Bfi/gcfc/Utm9ingU8AfATcCK6OvXwK+En2fUYqTs1mzZgWMZLSqqqrQIYiUi9qvaeR0la9169bxzDPPFM5f9apX8dGPfrRcYcXe97//fdLpNADpdJotW7bw8Y9/PHBUAiWsnLn7AXf/eXR8EtgBNAE3A+ujh60HbomObwY2eM5PgXlmtrRU8cVVInHqn6Suri5gJKOpciYzhdqvyvAv//Ivw87/+Z//OVAk8XTdddcV2vVUKsX1118fOCLJK8tvWzNbAVwJPAkscfcDkGsAzSy/0EoTsK/oaW3RtQMjXus24DaACy64oKRxh6DkTCRe1H6NNt4Yr/z1O+64Y8z7M3mMV9ysWbOGzZs3A7lem1tvvTVwRJJX8tmaZtYAbAR+391PjPfQMa6NWt3T3e9391XuvmrRokVTFWZsnH/++YXj2tragJGMpgkBMtOo/Zq8urq62H2wlLE1NjZyww03YGbccMMNmgwQIyUthZhZFbmG7evunq8vHzKzpdGnzqXA4eh6G7C86OnLgP2ljC+O3ve+9/Hkk08C8aucFVf1RCqd2q/TU+WrcqxZs4Y9e/aoahYzpZytacBXgR3u/pdFt74DrImO1wDfLrp+azTr6U1AV777YCYprpbFrXKm5ExmCrVfMlM0Njaybt06Vc1ippSVs7cAvw1sN7Ono2ufAb4EfNPMPgLsBd4f3XsEeA/QCvQCHyphbLFVXC1T5Wx60LZWFUntl4gEU7LkzN1/xNjjMADeOcbjHZjxc5yLEzJVzqaH5cuXU1VVxfve977QocgUUfslIiFp+l3MFCdncUuGVCEaW2NjI9/97nc1m1VERKZEvH77S+yqZcWUnJ2eEjMREZkqSs5iJm7jzIrFrZInIiJSifTbNmZUgREREZnZlJzJhKlyJiLTycg2Swtpy3Sh37YyYRpzJiLTSTabHXaeyWQCRSIyOUrOZMKUnInIdDKyUqbKmUwXGuAUQ4lEguXLl5/5gSIiMmH6gCnThZKzGFq/fn2sZ22KiEwHI7sx0+l0oEhEJkfJWQypaiYiIjJzacyZiIiISIwoORMRERGJESVnIiIiIjGi5ExERCqSltKQ6UrJmYiIVKSRszW1CK1MF0rORESkIjU0NIx7LhJXSs5ERKQiDQwMjHsuEldKzkREpCINDQ2Ney4SV0rORERERGJEyZmIiIhIjCg5ExEREYkRJWciIlKRRu5TfOGFFwaKRGRylJyJiEhF6uzsHHbe0dERKBKRyVFyJiIiFenqq68e91wkrpSciYhIRWptbR12vmvXrkCRiEyOkjMREalIbW1tw8737dsXKBKRySlZcmZmf29mh83suaJrC8xsi5ntjL7Pj66bma0zs1Yze9bM3liquEREJkJt2PS3dOnSYefnn39+oEhEJqeUlbOvATeMuPYp4DF3Xwk8Fp0D3AisjL5uA75SwrhERCbia6gNm9a08blMVyVLztz9CeDoiMs3A+uj4/XALUXXN3jOT4F5ZrYUEZFA1IZNf4cPHx52fujQoUCRiExOucecLXH3AwDR98XR9SageDBAW3RNRCRO1IaJSMnFZUKAjXHNx3yg2W1mttXMtmrNGhGJiQm1YWq/yqu+vn7cc5G4Kndydihf6o++52vObUDxUs7LgP1jvYC73+/uq9x91aJFi0oarIjICOfUhqn9Kq/e3t5xz0XiqtzJ2XeANdHxGuDbRddvjWY8vQnoyncdiIjEiNqwacTMxj0XiatUqV7YzL4BXAssNLM24E7gS8A3zewjwF7g/dHDHwHeA7QCvcCHShWXiMhEqA2b/tx93HORuCpZcubuv3GaW+8c47EOfLRUsYiITJbasOmvrq6Ovr6+Yeci00FcJgSIiIhMKa1zJtOVkjMREalIg4OD456LxJWSMxEREZEYUXImIiIVaeQYM405k+lCyZmIiFQkzdaU6UrJmYiIVKR3vetdw87f/e53B4pEZHKUnImISEVas2YNqVRuxahUKsWtt94aOCKRiVFyJiIiFamxsZFly5YBsGzZMhobGwNHJDIxSs5ERKQidXZ20tbWBkBbWxudnZ2BIxKZGCVnIiJSkdavX086nQYgnU6zYcOGwBGJTIySMxERqUiPPvrosPPNmzcHikRkcpSciYhIRdL2TTJdlWzj8+kgm82yd+9estksAIsWLWL27NmBoxIRkamQ79I83blIXM245Kyrq4sXXniBp556ih/84IccPnyocK+qupr//uY3c/XVV3P55ZezfPlyEgkVF0VEpqOGhga6u7uHnYtMBxWbnLk7HR0dvPzyy7S2ttLa2spLL++kI5+MJZJkU7VQMwe3BOBkLMETP/kZP/zhDwGoratn5cpmXrVyJZdccgkrV67koosuKqybIyIi8XXXXXfxh3/4h4Xzz3/+8wGjEZm4issy/uu//otHH32UbT9/iuPHjp66UTeXodoFZJetIjNrEZmGRdS9/D1SJw8WHpKefR4nXnsLif4uEt2HGew5wjO7D/Lc8zvwzBAAVVVVXHbZa3jrW99CS0sL9fX15f4jikiFue+++2htbR3zXnt7O319fWf92nV1dTQ1NY15r7m5mdtvv/2sXzvuVq1aVaieNTQ0cNVVV4UOSWRCKio5+9GPfsTnPvc5sARD81eQueBVZGYtJFs3H5JVE3sRM7J188jWzSO96FUMALhjAydI9nQy2NPBM7vaePbZ/81TTz/Nn33xi6X8I4nIDNDa2srTz+0gU79g1L1Efy+WHTrr1z456BwcODTqerL36BiPrjx33XUXn/zkJ1U1k2mlopKzhQsXkkpVkU5UMXDBL+FVdeM/ITNIbW0tLS0tbNq0ie7M4NiPM8Nr55KunUu68WJ45cdU93TSdP75U/+HEJEZp729HRh7U+5s7ZwSvatH71vZVq1axeOPPx46DJFJqajR7v39/SxevAgGe0n0d53x8ZYepKWlhbVr13LTTTdh6dMkZyNUn9wPwJIlSxgYGDinmEVERESKVUzl7NFHH+XP/uzPAOi/8M1kGpac8TmeqmbTpk24Ow8//DCemtj4sZ6LV1O35z/5m7/5G/7jRz9i3b33nlPsIjKzNTU10XHsxJj3Ev0nzqlb0xNVp6m+2WnHoolIWBWTnNXU1BSOaw89R/LEfrJ183NftfNyjVMiOfxJyWr6e4+ycePG3PnseaNf2B0b6iXRdzz6Okaq7yiJvtx4jdqa2lL9kWJjzpw5nDgx9i8OETl3zc3Np73X3p6eggkBY31YXTLu+4pIOBWTnF177bV8/etf58c//jHPP/88L728k4MHnsE9GsdhiWjG5jwy9QvJNiwCHz3Gw/pPkOw+RLKng2TfMVJ9x/H0qa7LhtlzWPmqS7j00uu56qqruPLKK8v1Rwzmz//8z087k0xEzl0lz5gUkckzHyNBmS5WrVrlW7duPe39gYEB9u7dyyuvvMKePXvYvXs3L+/cyZGOjtwDLBGtcRadWgKiSQG1tXU0N1/CJZdcwooVK1ixYgUXXngh8+fPx8xK+ucSkdMzs23uvip0HOfqTO2XiFSeibZfFVM5G0tNTQ0rV65k5cqVw64fP36cZ599lieffJJjx44VrqdSKd7whjdw5ZVXcuGFF2p3ABERESm7ik7OTmfevHlcc801XHPNNaFDERERERlGpSERERGRGFFyJiIiIhIjsUrOzOwGM3vJzFrN7FOh4xERmQy1YSIyFWKTnJlZEvgb4EbgNcBvmNlrwkYlIjIxasNEZKrEJjkDrgZa3X23uw8CDwI3B45JRGSi1IaJyJSIU3LWBOwrOm+Lrg1jZreZ2VYz29qRX69MRCS8M7Zhar9EZCLilJyNtbLrqBVy3f1+d1/l7qsWLVpUhrBERCbkjG2Y2i8RmYg4JWdtwPKi82XA/kCxiIhMltowEZkScUrOfgasNLOLzKwa+ADwncAxiYhMlNowEZkSsdkhwN3TZrYWeBRIAn/v7s8HDktEZELUhonIVJnWG5+bWQfwyhS81ELgyBS8zlSJWzwQv5gUz/jiFg9MXUwXuvu0H7A1he0XxO/fW/GMT/GcWdxiKmv7Na2Ts6liZlsnskt8ucQtHohfTIpnfHGLB+IZU6WI29+t4hmf4jmzuMVU7njiNOZMREREZMZTciYiIiISI0rOcu4PHcAIcYsH4heT4hlf3OKBeMZUKeL2d6t4xqd4zixuMZU1Ho05ExEREYkRVc5EREREYkTJmYiIiEiMzOjkzMyWm9m/m9kOM3vezO4IHE+tmf2XmT0TxfP5kPHkmVnSzJ4ys02hYwEwsz1mtt3MnjazrTGIZ56ZPWRmL0Y/S28OHM/Ho5+f58zsG2ZWW+b3/3szO2xmzxVd+4vo7+dZM/uWmc0rZ0yVSO3XxKj9OmM8ar9GxxC8DZvRyRmQBv6Hu18GvAn4qJm9JmA8A8Bqd78CeANwg5m9KWA8eXcAO0IHMcI73P0NMVkH515gs7tfClxBwL8rM2sCPgascvfLya1U/4Eyh/E14IYR17YAl7v764GXgU+XOaZKpPZrYtR+jU/t12hfI3AbNqOTM3c/4O4/j45PkvuhbAoYj7t7d3RaFX0FnbFhZsuAm4C/CxlHXJnZHOAa4KsA7j7o7sfDRkUKqDOzFFBPmTffdvcngKMjrn3P3dPR6U/JbQou50Dt15mp/Rqf2q+xxaENm9HJWTEzWwFcCTwZOI6kmT0NHAa2uHvQeIC/Aj4JZAPHUcyB75nZNjO7LXAsFwMdwD9EXSd/Z2azQgXj7u3A/wL2AgeALnf/Xqh4TuPDwHdDB1FJ1H6dltqv8an9Ojslb8OUnAFm1gBsBH7f3U+EjMXdM+7+BnJZ+dVmdnmoWMysBTjs7ttCxXAab3H3NwI3kuvKuSZgLCngjcBX3P1KoAf4VKhgzGw+cDNwEXA+MMvMfitUPCOZ2WfJdcd9PXQslULt19jUfk2I2q9JKlcbNuOTMzOrItewfd3d/yV0PHlRafkHjO73Lqe3AO81sz3Ag8BqM/ungPEA4O77o++HgW8BVwcMpw1oK6oQPESusQvlOuAX7t7h7kPAvwD/PWA8BWa2BmgBftO1wOKUUPs1LrVfZ6b2axLK2YbN6OTMzIxcX/sOd//LGMSzKD8DxMzqyP2gvhgqHnf/tLsvc/cV5AZlPu7uQT/FmNksM5udPwbeBTw3/rNKx90PAvvM7NXRpXcCL4SKh1x3wJvMrD76+X4nMRgMbWY3AH8EvNfde0PHUwnUfo1P7deZqf2auHK3YalSv0HMvQX4bWB7NE4C4DPu/kigeJYC680sSS5x/qa7x2L6d4wsAb6V+39LCnjA3TeHDYnbga+bWTWwG/hQqEDc/Ukzewj4ObnS+1OUe9sRs28A1wILzawNuJPczKYaYEv0b/dTd/9/yhlXBVL7Nf2o/RpHHNoviEcbpu2bRERERGJkRndrioiIiMSNkjMRERGRGFFyJiIiIhIjSs5EREREYkTJmYiIiEiMKDmT2DCzr5nZr0bHfzfZTZzNrPvMjxIRmXpqv2QqzfR1ziSm3P13Svn60QKH5u5x2nNPRCqA2i85V6qcScmZ2a1m9qyZPWNm3zKzX0TbzmBmc8xsT/686Dk/MLNV0XG3md0TPf+nZrYkun6Rmf3EzH5mZl8Y8fxPRNefNbPPR9dWmNkOM/vf5BY5XB592n3OzLab2cfL8fchItOH2i8JQcmZlJSZvRb4LLDa3a8APkJuz72bood8ANgY7aN2OrPIrcZ8BfAE8LvR9XvJbdj734CDRe/5LmAluT3r3gBcZac2F341sCHa5Hch0OTul7v764B/ONc/r4hUDrVfEoqSMym11cBD7n4EwN2PAn/HqS1CPsSZG5VBIL8NzDZgRXT8FuAb0fE/Fj3+XdHXU+Q+YV5KrrEDeMXdfxod7wYuNrP7on3TTkzqTyYilU7tlwShMWdSagYM2yPM3f8zKtG/HUi6+5k2/h3yU/uMZRj+czvW/mMG/Jm7/59hF81WAD1FcRwzsyuAdwMfBX4N+PAZ/0QiMlOo/ZIgVDmTUnsM+DUzawQwswXR9Q3kPjWeSyn+P8l1KwD8ZtH1R4EPm1lD9J5NZrZ45JPNbCGQcPeNwB8DbzyHWESk8qj9kiCUnElJufvzwD3AD83sGeAvo1tfB+Zzqqx/Nu4APmpmPwPmFr3n94AHgJ+Y2XbgIWD2GM9vAn5gZk8DXwM+fQ6xiEiFUfslodipaqtI+UTrAd3s7r8dOhYRkclQ+yWlpjFnUnZmdh9wI/Ce0LGIiEyG2i8pB1XORERERGJEY85EREREYkTJmYiIiEiMKDkTERERiRElZyIiIiIxouRMREREJEb+f0ksEhgh9G4yAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [10, 5])\n",
    "base_color = sb.color_palette()[0]\n",
    "\n",
    "# left plot: violin plot\n",
    "plt.subplot(1, 2, 1)\n",
    "ax1 = sb.violinplot(data = fuel_econ, x = 'cylinders', y = 'co2', color = base_color);\n",
    "\n",
    "# right plot: box plot\n",
    "plt.subplot(1, 2, 2)\n",
    "sb.boxplot(data = fuel_econ, x = 'cylinders', y = 'co2', color = base_color);\n",
    "plt.ylim(ax1.get_ylim()) # set y-axis limits to be same as left plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24a7c1bca90>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_color = sb.color_palette()[0]\n",
    "sb.boxplot(data = fuel_econ, x = 'cylinders', y = 'co2', color = base_color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24a06356860>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_color = sb.color_palette()[0]\n",
    "sb.violinplot(data = fuel_econ, x = 'cylinders', y = 'co2', color = base_color,\n",
    "              inner = 'quartile')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustered Bar Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.countplot(data = fuel_econ, x = 'year', hue = 'cylinders');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x24a7c305400>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sb.countplot(data = fuel_econ, x = 'year', hue = 'cylinders')\n",
    "ax.legend(loc = 8, ncol = 3, framealpha = 1, title = 'cat_var2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24a06147550>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ct_counts = fuel_econ.groupby(['year', 'cylinders']).size()\n",
    "ct_counts = ct_counts.reset_index(name = 'count')\n",
    "ct_counts = ct_counts.pivot(index = 'cylinders', columns = 'year', values = 'count')\n",
    "sb.heatmap(ct_counts)\n",
    "sb.heatmap(ct_counts, annot = True) # , fmt = 'd' => error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Faceting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x24a7c199320>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1512x216 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = fuel_econ\n",
    "bin_edges = np.arange(-3, df['co2'].max()+1/3, 1/3)\n",
    "g = sb.FacetGrid(data = df, col = 'cylinders')\n",
    "g.map(plt.hist, \"co2\", bins = bin_edges);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\dev\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x24a06165ba8>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "group_means = df.groupby(['year']).mean()\n",
    "group_order = group_means.sort_values(['co2'], ascending = False).index\n",
    "\n",
    "g = sb.FacetGrid(data = df, col = 'year', col_wrap = 5, size = 2,\n",
    "                 col_order = group_order);\n",
    "g.map(plt.hist, 'co2', bins = np.arange(5, 15+1, 1));\n",
    "g.set_titles('{col_name}');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adapted Bar Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_color = sb.color_palette()[0]\n",
    "sb.barplot(data = df, x = 'cat_var', y = 'num_var', color = base_color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sb.pointplot(data = df, x = 'cat_var', y = 'num_var', linestyles = \"\")\n",
    "plt.ylabel('Avg. value of num_var')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize = [12, 5])\n",
    "base_color = sb.color_palette()[0]\n",
    "\n",
    "# left plot: violin plot\n",
    "plt.subplot(1, 3, 1)\n",
    "sb.violinplot(data = df, x = 'condition', y = 'binary_out', inner = None,\n",
    "              color = base_color)\n",
    "plt.xticks(rotation = 10) # include label rotation due to small subplot size\n",
    "\n",
    "# center plot: box plot\n",
    "plt.subplot(1, 3, 2)\n",
    "sb.boxplot(data = df, x = 'condition', y = 'binary_out', color = base_color)\n",
    "plt.xticks(rotation = 10)\n",
    "\n",
    "# right plot: adapted bar chart\n",
    "plt.subplot(1, 3, 3)\n",
    "sb.barplot(data = df, x = 'condition', y = 'binary_out', color = base_color)\n",
    "plt.xticks(rotation = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bin_edges = np.arange(0, df['num_var'].max()+1/3, 1/3)\n",
    "\n",
    "# count number of points in each bin\n",
    "bin_idxs = pd.cut(df['num_var'], bin_edges, right = False, include_lowest = True,\n",
    "                  labels = False).astype(int)\n",
    "pts_per_bin = df.groupby(bin_idxs).size()\n",
    "\n",
    "num_var_wts = df['binary_out'] / pts_per_bin[bin_idxs].values\n",
    "\n",
    "# plot the data using the calculated weights\n",
    "plt.hist(data = df, x = 'num_var', bins = bin_edges, weights = num_var_wts)\n",
    "plt.xlabel('num_var')\n",
    "plt.ylabel('mean(binary_out)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Line Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set bin edges, compute centers\n",
    "bin_size = 0.25\n",
    "xbin_edges = np.arange(0.5, df['num_var1'].max()+bin_size, bin_size)\n",
    "xbin_centers = (xbin_edges + bin_size/2)[:-1]\n",
    "\n",
    "# compute statistics in each bin\n",
    "data_xbins = pd.cut(df['num_var1'], xbin_edges, right = False, include_lowest = True)\n",
    "y_means = df['num_var2'].groupby(data_xbins).mean()\n",
    "y_sems = df['num_var2'].groupby(data_xbins).sem()\n",
    "\n",
    "# plot the summarized data\n",
    "plt.errorbar(x = xbin_centers, y = y_means, yerr = y_sems)\n",
    "plt.xlabel('num_var1')\n",
    "plt.ylabel('num_var2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute statistics in a rolling window\n",
    "df_window = df.sort_values('num_var1').rolling(15)\n",
    "x_winmean = df_window.mean()['num_var1']\n",
    "y_median = df_window.median()['num_var2']\n",
    "y_q1 = df_window.quantile(.25)['num_var2']\n",
    "y_q3 = df_window.quantile(.75)['num_var2']\n",
    "\n",
    "# plot the summarized data\n",
    "base_color = sb.color_palette()[0]\n",
    "line_color = sb.color_palette('dark')[0]\n",
    "plt.scatter(data = df, x = 'num_var1', y = 'num_var2')\n",
    "plt.errorbar(x = x_winmean, y = y_median, c = line_color)\n",
    "plt.errorbar(x = x_winmean, y = y_q1, c = line_color, linestyle = '--')\n",
    "plt.errorbar(x = x_winmean, y = y_q3, c = line_color, linestyle = '--')\n",
    "\n",
    "plt.xlabel('num_var1')\n",
    "plt.ylabel('num_var2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bin_edges = np.arange(-3, df['num_var'].max()+1/3, 1/3)\n",
    "g = sb.FacetGrid(data = df, hue = 'cat_var', size = 5)\n",
    "g.map(plt.hist, \"num_var\", bins = bin_edges, histtype = 'step')\n",
    "g.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def freq_poly(x, bins = 10, **kwargs):\n",
    "    \"\"\" Custom frequency polygon / line plot code. \"\"\"\n",
    "    # set bin edges if none or int specified\n",
    "    if type(bins) == int:\n",
    "        bins = np.linspace(x.min(), x.max(), bins+1)\n",
    "    bin_centers = (bin_edges[1:] + bin_edges[:-1]) / 2\n",
    "\n",
    "    # compute counts\n",
    "    data_bins = pd.cut(x, bins, right = False,\n",
    "                       include_lowest = True)\n",
    "    counts = x.groupby(data_bins).count()\n",
    "\n",
    "    # create plot\n",
    "    plt.errorbar(x = bin_centers, y = counts, **kwargs)\n",
    "\n",
    "bin_edges = np.arange(-3, df['num_var'].max()+1/3, 1/3)\n",
    "g = sb.FacetGrid(data = df, hue = 'cat_var', size = 5)\n",
    "g.map(freq_poly, \"num_var\", bins = bin_edges)\n",
    "g.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## QQ Plots\n",
    "\n",
    "quantile-quantile plot, or Q-Q plot, to make a visual comparison between your data and your reference distribution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Observed Standard Scores')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate the data\n",
    "np.random.seed(8322489)\n",
    "\n",
    "n_points = 120\n",
    "unif_data = np.random.uniform(0, 10, n_points)\n",
    "\n",
    "# set up the figure\n",
    "plt.figure(figsize = [12, 5])\n",
    "\n",
    "# left subplot: plot the data\n",
    "plt.subplot(1, 2, 1)\n",
    "bin_size = 0.5\n",
    "bin_edges = np.arange(0, 10 + bin_size, bin_size)\n",
    "plt.hist(x = unif_data, bins = bin_edges);\n",
    "\n",
    "# overlay a theoretical normal distribution on top\n",
    "samp_mean = unif_data.mean()\n",
    "samp_sd = unif_data.std()\n",
    "\n",
    "from scipy.stats import norm\n",
    "x = np.linspace(0, 10, 200)\n",
    "y = norm.pdf(x, samp_mean, samp_sd) # normal distribution heights\n",
    "y *= n_points * bin_size # scale the distribution height\n",
    "plt.plot(x, y)\n",
    "\n",
    "# right subplot: create a Q-Q plot\n",
    "plt.subplot(1, 2, 2)\n",
    "\n",
    "qs = (np.arange(n_points) - .5) / n_points\n",
    "expected_scores = norm.ppf(qs)\n",
    "data_scores = (np.sort(unif_data) - samp_mean) / samp_sd\n",
    "\n",
    "plt.scatter(expected_scores, data_scores)\n",
    "plt.plot([-2.5,2.5],[-2.5,2.5],'--', color = 'black')\n",
    "plt.axis('equal')\n",
    "plt.xlabel('Expected Standard Scores')\n",
    "plt.ylabel('Observed Standard Scores')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Swarm Plots\n",
    "\n",
    "Similar to a scatterplot, each data point is plotted with position according to its value on the two variables being plotted. Instead of randomly jittering points as in a normal scatterplot, points are placed as close to their actual value as possible without allowing any overlap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Could not interpret input 'cat_var'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-61-091ac28577de>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m# left plot: violin plot\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0max1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mviolinplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'cat_var'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'num_var'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbase_color\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;31m# center plot: box plot\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\dev\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36mviolinplot\u001b[1;34m(x, y, hue, data, order, hue_order, bw, cut, scale, scale_hue, gridsize, width, inner, split, dodge, orient, linewidth, color, palette, saturation, ax, **kwargs)\u001b[0m\n\u001b[0;32m   2385\u001b[0m                              \u001b[0mbw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscale_hue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgridsize\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2386\u001b[0m                              \u001b[0mwidth\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msplit\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdodge\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlinewidth\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2387\u001b[1;33m                              color, palette, saturation)\n\u001b[0m\u001b[0;32m   2388\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2389\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\dev\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, x, y, hue, data, order, hue_order, bw, cut, scale, scale_hue, gridsize, width, inner, split, dodge, orient, linewidth, color, palette, saturation)\u001b[0m\n\u001b[0;32m    560\u001b[0m                  color, palette, saturation):\n\u001b[0;32m    561\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 562\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestablish_variables\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue_order\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    563\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestablish_colors\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    564\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestimate_densities\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscale_hue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgridsize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\dev\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36mestablish_variables\u001b[1;34m(self, x, y, hue, data, orient, order, hue_order, units)\u001b[0m\n\u001b[0;32m    153\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstring_types\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    154\u001b[0m                     \u001b[0merr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"Could not interpret input '{}'\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 155\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    156\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    157\u001b[0m             \u001b[1;31m# Figure out the plotting orientation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Could not interpret input 'cat_var'"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [12, 5])\n",
    "base_color = sb.color_palette()[0]\n",
    "\n",
    "# left plot: violin plot\n",
    "plt.subplot(1, 3, 1)\n",
    "ax1 = sb.violinplot(data = df, x = 'cat_var', y = 'num_var', color = base_color)\n",
    "\n",
    "# center plot: box plot\n",
    "plt.subplot(1, 3, 2)\n",
    "sb.boxplot(data = df, x = 'cat_var', y = 'num_var', color = base_color)\n",
    "plt.ylim(ax1.get_ylim()) # set y-axis limits to be same as left plot\n",
    "\n",
    "# right plot: swarm plot\n",
    "plt.subplot(1, 3, 3)\n",
    "sb.swarmplot(data = df, x = 'cat_var', y = 'num_var', color = base_color)\n",
    "plt.ylim(ax1.get_ylim()) # set y-axis limits to be same as left plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rug and Strip Plots\n",
    "\n",
    "You might encounter, or be interested in, marginal distributions that are plotted alongside bivariate plots such as scatterplots. A marginal distribution is simply the univariate distribution of a variable, ignoring the values of any other variable. For quantitative data, histograms or density curves are fine choices for marginal plot, but you might also see the rug plot employed. In a rug plot, all of the data points are plotted on a single axis, one tick mark or line for each one. Compared to a marginal histogram, the rug plot suffers somewhat in terms of readability of the distribution, but it is more compact in its representation of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = sb.JointGrid(data = df, x = 'num_var1', y = 'num_var2')\n",
    "g.plot_joint(plt.scatter)\n",
    "g.plot_marginals(sb.rugplot, height = 0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another supporting plot type similar to the rug plot is the strip plot. It's like a swarm plot (see the previous page) but without any dodging or jittering to keep points separate or off the categorical line. You can also think of it as a rug plot faceted by categorical levels. You can use seaborn's swarmplot function to add a swarm plot to any other plot. The inner = \"stick\" and inner = \"point\" options can also be used with the violinplot function to include a swarm plot inside of the violin areas, instead of a box plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize = [10, 5])\n",
    "base_color = sb.color_palette()[0]\n",
    "\n",
    "# left plot: strip plot\n",
    "plt.subplot(1, 2, 1)\n",
    "ax1 = sb.stripplot(data = df, x = 'num_var', y = 'cat_var',\n",
    "                   color = base_color)\n",
    "\n",
    "# right plot: violin plot with inner strip plot as lines\n",
    "plt.subplot(1, 2, 2)\n",
    "sb.violinplot(data = df, x = 'num_var', y = 'cat_var', color = base_color,\n",
    "             inner = 'stick')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stacked Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pre-processing: count and sort by the number of instances of each category\n",
    "sorted_counts = df['cat_var'].value_counts()\n",
    "\n",
    "# establish the Figure\n",
    "plt.figure(figsize = [12, 5])\n",
    "\n",
    "# left plot: pie chart\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.pie(sorted_counts, labels = sorted_counts.index, startangle = 90,\n",
    "        counterclock = False);\n",
    "plt.axis('square');\n",
    "\n",
    "# right plot: horizontally stacked bar\n",
    "plt.subplot(1, 2, 2)\n",
    "baseline = 0\n",
    "for i in range(sorted_counts.shape[0]):\n",
    "    plt.barh(y = 1, width = sorted_counts[i], left = baseline)\n",
    "    baseline += sorted_counts[i]\n",
    "\n",
    "plt.legend(sorted_counts.index)  # add a legend for labeling\n",
    "plt.ylim([0,2]) # give some vertical spacing around the bar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat1_order = ['East', 'South', 'West', 'North']\n",
    "cat2_order = ['Type X', 'Type Y', 'Type Z', 'Type O']\n",
    "\n",
    "plt.figure(figsize = [12, 5])\n",
    "\n",
    "# left plot: clustered bar chart, absolute counts\n",
    "plt.subplot(1, 2, 1)\n",
    "sb.countplot(data = df, x = 'cat_var1', hue = 'cat_var2',\n",
    "             order = cat1_order, hue_order = cat2_order)\n",
    "plt.legend()\n",
    "\n",
    "# right plot: stacked bar chart, absolute counts\n",
    "plt.subplot(1, 2, 2)\n",
    "\n",
    "baselines = np.zeros(len(cat1_order))\n",
    "# for each second-variable category:\n",
    "for i in range(len(cat2_order)):\n",
    "    # isolate the counts of the first category,\n",
    "    cat2 = cat2_order[i]\n",
    "    inner_counts = df[df['cat_var2'] == cat2]['cat_var1'].value_counts()\n",
    "    # then plot those counts on top of the accumulated baseline\n",
    "    plt.bar(x = np.arange(len(cat1_order)), height = inner_counts[cat1_order],\n",
    "            bottom = baselines)\n",
    "    baselines += inner_counts[cat1_order]\n",
    "\n",
    "plt.xticks(np.arange(len(cat1_order)), cat1_order)\n",
    "plt.legend(cat2_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat1_order = ['East', 'South', 'West', 'North']\n",
    "cat2_order = ['Type X', 'Type Y', 'Type Z', 'Type O']\n",
    "\n",
    "artists = [] # for storing references to plot elements\n",
    "baselines = np.zeros(len(cat1_order))\n",
    "cat1_counts = df['cat_var1'].value_counts()\n",
    "\n",
    "# for each second-variable category:\n",
    "for i in range(len(cat2_order)):\n",
    "    # isolate the counts of the first category,\n",
    "    cat2 = cat2_order[i]\n",
    "    inner_counts = df[df['cat_var2'] == cat2]['cat_var1'].value_counts()\n",
    "    inner_props = inner_counts / cat1_counts\n",
    "    # then plot those counts on top of the accumulated baseline\n",
    "    bars = plt.bar(x = np.arange(len(cat1_order)),\n",
    "                   height = inner_props[cat1_order],\n",
    "                   bottom = baselines)\n",
    "    artists.append(bars)\n",
    "    baselines += inner_props[cat1_order]\n",
    "\n",
    "plt.xticks(np.arange(len(cat1_order)), cat1_order)\n",
    "plt.legend(reversed(artists), reversed(cat2_order), framealpha = 1,\n",
    "           bbox_to_anchor = (1, 0.5), loc = 6);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ridgeline Plots\n",
    "\n",
    "One of the hot new visualization types from recent years is the ridgeline plot. In a nutshell, the ridgeline plot is a series of vertically faceted line plots or density curves, but with somewhat overlapping y-axes. This can be thought of as a contrast to the line plot variation seen in the \"Line Plots\" part of the lesson, where multiple lines were plotted on the same axes, with different hues. On this page, I'll walk through the creation of a ridgeline plot using some of the demonstration data shown in the \"Faceting\" page:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_means = df.groupby(['many_cat_var']).mean()\n",
    "group_order = group_means.sort_values(['num_var'], ascending = False).index\n",
    "\n",
    "g = sb.FacetGrid(data = df, col = 'many_cat_var', col_wrap = 5, size = 2,\n",
    "                 col_order = group_order)\n",
    "g.map(plt.hist, 'num_var', bins = np.arange(5, 15+1, 1))\n",
    "g.set_titles('{col_name}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_means = df.groupby(['many_cat_var']).mean()\n",
    "group_order = group_means.sort_values(['num_var'], ascending = False).index\n",
    "\n",
    "g = sb.FacetGrid(data = df, row = 'many_cat_var', size = 0.75, aspect = 7,\n",
    "                 row_order = group_order)\n",
    "g.map(sb.kdeplot, 'num_var', shade = True)\n",
    "g.set_titles('{row_name}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_means = df.groupby(['many_cat_var']).mean()\n",
    "group_order = group_means.sort_values(['num_var'], ascending = False).index\n",
    "\n",
    "# adjust the spacing of subplots with gridspec_kws\n",
    "g = sb.FacetGrid(data = df, row = 'many_cat_var', size = 0.5, aspect = 12,\n",
    "                 row_order = group_order, gridspec_kws = {'hspace' : -0.2})\n",
    "g.map(sb.kdeplot, 'num_var', shade = True)\n",
    "\n",
    "# remove the y-axes\n",
    "g.set(yticks=[])\n",
    "g.despine(left=True)\n",
    "\n",
    "g.set_titles('{row_name}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_means = df.groupby(['many_cat_var']).mean()\n",
    "group_order = group_means.sort_values(['num_var'], ascending = False).index\n",
    "\n",
    "g = sb.FacetGrid(data = df, row = 'many_cat_var', size = 0.5, aspect = 12,\n",
    "                 row_order = group_order, gridspec_kws = {'hspace' : -0.2})\n",
    "g.map(sb.kdeplot, 'num_var', shade = True)\n",
    "\n",
    "g.set(yticks=[])\n",
    "g.despine(left=True)\n",
    "\n",
    "# set the transparency of each subplot to full\n",
    "g.map(lambda **kwargs: plt.gca().patch.set_alpha(0))\n",
    "\n",
    "# remove subplot titles and write in new labels\n",
    "def label_text(x, **kwargs):\n",
    "    plt.text(4, 0.02, x.iloc[0], ha = 'center', va = 'bottom')\n",
    "g.map(label_text, 'many_cat_var')\n",
    "g.set_xlabels('num_var')\n",
    "g.set_titles('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multivariate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encoding via Shape\n",
    "\n",
    "Shape is a good encoding for categorical variables, using one shape for each level of the categorical variable. Unfortunately, there is no built-in way to automatically assign different shapes in a single call of the scatter or regplot function. Instead, we need to write a loop to call our plotting function multiple times, isolating data points by categorical level and setting a different \"marker\" argument value for each one.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "cat_markers = [['A', 'o'],\n",
    "               ['B', 's']]\n",
    "\n",
    "for cat, marker in cat_markers:\n",
    "    df_cat = df[df['cat_var1'] == cat]\n",
    "    plt.scatter(data = df_cat, x = 'num_var1', y = 'num_var2', marker = marker)\n",
    "plt.legend(['A','B'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(data = df, x = 'num_var1', y = 'num_var2', s = 'num_var3')\n",
    "\n",
    "# dummy series for adding legend\n",
    "sizes = [20, 35, 50]\n",
    "base_color = sb.color_palette()[0]\n",
    "legend_obj = []\n",
    "for s in sizes:\n",
    "    legend_obj.append(plt.scatter([], [], s = s, color = base_color))\n",
    "plt.legend(legend_obj, sizes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize = [5,5])\n",
    "\n",
    "common_size = 2500\n",
    "plt.scatter([0],[1], marker = 's', s = common_size)\n",
    "plt.scatter([1],[1], marker = '^', s = common_size)\n",
    "plt.scatter([0],[0], marker = 'o', s = common_size)\n",
    "plt.scatter([1],[0], marker = 'X', s = common_size)\n",
    "\n",
    "# a little bit of aesthetic cleaning\n",
    "plt.xlim(-0.5,1.5)\n",
    "plt.xticks([])\n",
    "plt.ylim(-0.5,1.5)\n",
    "plt.yticks([])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encoding via Color\n",
    "\n",
    "    Qualitative (all up to 6 colors): 'deep', 'pastel', 'dark', 'muted', 'bright', 'colorblind'\n",
    "    Sequential: 'rocket' (white-orange-red-purple-black), 'mako' (mint-green-blue-purple-black)\n",
    "    Diverging: 'vlag' (blue-white-red), 'icefire' (blue-black-orange)\n",
    "\n",
    "For all of these strings, appending '_r' reverses the palette, which is useful if a sequential or diverging palette is rendered counter to your expectations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = sb.FacetGrid(data = df, hue = 'cat_var1', size = 5)\n",
    "g.map(plt.scatter, 'num_var1', 'num_var2')\n",
    "g.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(data = df, x = 'num_var1', y = 'num_var2', c = 'num_var3')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = sb.FacetGrid(data = df, hue = 'cat_var1', size = 5,\n",
    "                 palette = 'colorblind')\n",
    "g.map(plt.scatter, 'num_var1', 'num_var2')\n",
    "g.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(data = df, x = 'num_var1', y = 'num_var2', c = 'num_var3',\n",
    "            cmap = 'mako_r')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Faceting for Multivariate Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = sb.FacetGrid(data = df, col = 'cat_var1', size = 4)\n",
    "g.map(sb.boxplot, 'cat_var2', 'num_var2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = sb.FacetGrid(data = df, col = 'cat_var2', row = 'cat_var1', size = 2.5,\n",
    "                margin_titles = True)\n",
    "g.map(plt.scatter, 'num_var1', 'num_var2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other Adaptations of Bivariate Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xbin_edges = np.arange(0.25, df['num_var1'].max()+0.5, 0.5)\n",
    "ybin_edges = np.arange(7,    df['num_var2'].max()+0.5, 0.5)\n",
    "\n",
    "# count number of points in each bin\n",
    "xbin_idxs = pd.cut(df['num_var1'], xbin_edges, right = False,\n",
    "                    include_lowest = True, labels = False).astype(int)\n",
    "ybin_idxs = pd.cut(df['num_var2'], ybin_edges, right = False,\n",
    "                    include_lowest = True, labels = False).astype(int)\n",
    "\n",
    "pts_per_bin = df.groupby([xbin_idxs, ybin_idxs]).size()\n",
    "pts_per_bin = pts_per_bin.reset_index()\n",
    "pts_per_bin = pts_per_bin.pivot(index = 'num_var1', columns = 'num_var2').values\n",
    "\n",
    "z_wts = df['num_var3'] / pts_per_bin[xbin_idxs, ybin_idxs]\n",
    "\n",
    "# plot the data using the calculated weights\n",
    "plt.hist2d(data = df, x = 'num_var1', y = 'num_var2', weights = z_wts,\n",
    "           bins = [xbin_edges, ybin_edges], cmap = 'viridis_r', cmin = 0.5);\n",
    "plt.xlabel('num_var1')\n",
    "plt.ylabel('num_var2');\n",
    "plt.colorbar(label = 'mean(num_var3)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_means = df.groupby(['cat_var1', 'cat_var2']).mean()['num_var2']\n",
    "cat_means = cat_means.reset_index(name = 'num_var2_avg')\n",
    "cat_means = cat_means.pivot(index = 'cat_var2', columns = 'cat_var1',\n",
    "                            values = 'num_var2_avg')\n",
    "sb.heatmap(cat_means, annot = True, fmt = '.3f',\n",
    "           cbar_kws = {'label' : 'mean(num_var2)'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = sb.barplot(data = df, x = 'cat_var1', y = 'num_var2', hue = 'cat_var2')\n",
    "ax.legend(loc = 8, ncol = 3, framealpha = 1, title = 'cat_var2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = sb.pointplot(data = df, x = 'cat_var1', y = 'num_var2', hue = 'cat_var2',\n",
    "                  dodge = 0.3, linestyles = \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_poly(x, y, bins = 10, **kwargs):\n",
    "    \"\"\" Custom adapted line plot code. \"\"\"\n",
    "    # set bin edges if none or int specified\n",
    "    if type(bins) == int:\n",
    "        bins = np.linspace(x.min(), x.max(), bins+1)\n",
    "    bin_centers = (bin_edges[1:] + bin_edges[:-1]) / 2\n",
    "\n",
    "    # compute counts\n",
    "    data_bins = pd.cut(x, bins, right = False,\n",
    "                       include_lowest = True)\n",
    "    means = y.groupby(data_bins).mean()\n",
    "\n",
    "    # create plot\n",
    "    plt.errorbar(x = bin_centers, y = means, **kwargs)\n",
    "\n",
    "bin_edges = np.arange(0.25, df['num_var1'].max()+0.5, 0.5)\n",
    "g = sb.FacetGrid(data = df, hue = 'cat_var2', size = 5)\n",
    "g.map(mean_poly, \"num_var1\", \"num_var2\", bins = bin_edges)\n",
    "g.set_ylabels('mean(num_var2)')\n",
    "g.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = sb.PairGrid(data = df, vars = ['num_var1', 'num_var2', 'num_var3'])\n",
    "g.map_diag(plt.hist)\n",
    "g.map_offdiag(plt.scatter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = sb.PairGrid(data = df, x_vars = ['num_var1', 'num_var2', 'num_var3'],\n",
    "                y_vars = ['cat_var1','cat_var2'])\n",
    "g.map(sb.violinplot, inner = 'quartile')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation Matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24a04fa3828>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.heatmap(df.corr(), annot = True, fmt = '.2f', cmap = 'vlag_r', center = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot of price vs. carat, with log transform on price axis and\n",
    "# cube-root transform on price\n",
    "diamonds['cr_carat'] = diamonds['carat'].apply(cuberoot_trans)\n",
    "\n",
    "plt.figure(figsize = [8, 6])\n",
    "\n",
    "plt.scatter(data = diamonds, x = 'cr_carat', y = 'price')\n",
    "\n",
    "plt.xlabel('cube root of carat')\n",
    "\n",
    "plt.yscale('log')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select diamonds of approximately 1 carat\n",
    "diamond_flag = (diamonds['carat'] >= 0.99) & (diamonds['carat'] <= 1.03)\n",
    "diamonds_1c = diamonds.loc[diamond_flag,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [8,6])\n",
    "\n",
    "ax = sb.pointplot(data = diamonds_1c, x = 'color', y = 'price', hue = 'cut')\n",
    "\n",
    "# special handling of tick marks following seaborn function - you might try\n",
    "# doing a log transform on the data itself instead of this workaround.\n",
    "plt.yscale('log')\n",
    "plt.yticks([3000, 5000, 8000], ['3k', '5k', '8k'])\n",
    "ax.set_yticklabels([], minor = True)\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Custom Palette"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "def plot_bar(df, title=\"\", figsize=(10,5), x_col='', y_col='', x_label='', y_label='', highlight_pos=[], highlight_neg=[]):\n",
    "    # Build a custom palette to highlight the new values\n",
    "    custom_palette = []\n",
    "    for index, row in df.iterrows():\n",
    "        if row[y_col] in highlight_pos:\n",
    "            custom_palette.append(sns.color_palette(\"Blues\")[3])\n",
    "        elif row[y_col] in highlight_neg:\n",
    "            custom_palette.append(sns.color_palette(\"Reds\")[1])\n",
    "        else:\n",
    "            custom_palette.append(sns.color_palette(\"Blues\")[1])\n",
    "    # Build the subplot\n",
    "    fig,ax=plt.subplots(1,1,figsize=figsize);\n",
    "    # Draw the plot\n",
    "    ax = sns.barplot(x=x_col, ax=ax, y=y_col, data=df, palette = custom_palette )\n",
    "    ax.set_title(title);\n",
    "    ax.set_xlabel(x_label);\n",
    "    ax.set_ylabel(y_label);\n",
    "    # Set the labels of X axis\n",
    "    vals = ax.get_xticks();\n",
    "    ax.set_xticklabels(['{:,.0%}'.format(x) for x in vals]);\n",
    "    # Put percentage on the bar\n",
    "    for index, row in df.iterrows():\n",
    "        ax.text(x=row[x_col],y=index, s=f'{row[x_col]:,.0%}', fontsize=12, color='black', ha=\"right\", va='center')\n",
    "    # Draw\n",
    "    plt.show();\n",
    "    \n",
    "df_test = pd.DataFrame({'country_code': ['US', 'UK', 'ES', 'FR', 'RU'],\n",
    "                    'score': [0.5, 0.6, 0.7,0.78,0.8]\n",
    "                    })\n",
    "plot_bar(df_test, title='Score by country',x_col='score',y_col='country_code', x_label='Score', y_label='Country',\n",
    "         highlight_pos=['US', 'FR']\n",
    "        , highlight_neg=['ES'])"
   ]
  },
  {
   "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": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}