snow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Snow and Stocks  \n",
    "\n",
    "Does snowfall in New York City affect stock prices ?  This notebook explores that quesiton.  \n",
    "\n",
    "By Fred Etter - August, 2019\n",
    "\n",
    "Import modules and libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy\n",
    "from datetime import datetime\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import ttest_ind\n",
    "from scipy import stats\n",
    "import sklearn\n",
    "from sklearn import preprocessing\n",
    "from sklearn import linear_model\n",
    "import seaborn as sns\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import normalize\n",
    "from sklearn.cluster import MeanShift, estimate_bandwidth\n",
    "from scipy.stats import ttest_1samp, wilcoxon, ttest_ind, mannwhitneyu\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in the rain and snow data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Minute</th>\n",
       "      <th>Total Precipitation (high resolution) daily sum [sfc]</th>\n",
       "      <th>Snowfall Amount (high resolution) daily sum [sfc]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22.7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Month  Day  Hour  Minute  \\\n",
       "0  2009      7   31     0       0   \n",
       "1  2009      8    1     0       0   \n",
       "2  2009      8    2     0       0   \n",
       "3  2009      8    3     0       0   \n",
       "4  2009      8    4     0       0   \n",
       "\n",
       "   Total Precipitation (high resolution) daily sum [sfc]  \\\n",
       "0                                               22.7       \n",
       "1                                               17.4       \n",
       "2                                                0.0       \n",
       "3                                                0.2       \n",
       "4                                                0.0       \n",
       "\n",
       "   Snowfall Amount (high resolution) daily sum [sfc]  \n",
       "0                                                0.0  \n",
       "1                                                0.0  \n",
       "2                                                0.0  \n",
       "3                                                0.0  \n",
       "4                                                0.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use pandas read_csv command to import the local csv file\n",
    "df = pd.read_csv('snow_and_rain.csv', skiprows=11, sep=';')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the columns and then rename them for easier navigation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Year', 'Month', 'Day', 'Hour', 'Minute',\n",
      "       'Total Precipitation (high resolution) daily sum [sfc]',\n",
      "       'Snowfall Amount (high resolution) daily sum [sfc]'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the Hour and Minute columns\n",
    "df.drop(['Hour', 'Minute'], axis=1, inplace=True)\n",
    "\n",
    "df.columns = ['Year', 'Month', 'Day', 'Rain', 'Snow']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the top 5 columns of the dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Rain</th>\n",
       "      <th>Snow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "      <td>22.7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>17.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Month  Day  Rain  Snow\n",
       "0  2009      7   31  22.7   0.0\n",
       "1  2009      8    1  17.4   0.0\n",
       "2  2009      8    2   0.0   0.0\n",
       "3  2009      8    3   0.2   0.0\n",
       "4  2009      8    4   0.0   0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop some solumns for plotting purposes and then plot the snow and rain amount in NYC for the past 10 years:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_plot = df\n",
    "df_plot['dt'] = pd.to_datetime((df.Year*10000+df.Month*100+df.Day).apply(str),format='%Y%m%d')\n",
    "df_plot.columns = ['Year', 'Month', 'Day', 'Rain', 'Snow', 'dt']\n",
    "df_plot.index = pd.to_datetime(df_plot.dt)\n",
    "df_plot.drop(['Year'], axis=1, inplace=True)\n",
    "df_plot.drop(['Month'], axis=1, inplace=True)\n",
    "df_plot.drop(['Day'], axis=1, inplace=True)\n",
    "df_plot.drop(['dt'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_plot.plot(figsize=(14, 6))\n",
    "plt.title('Snow in cm in NYC')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Cm')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot above shows the daily snow in orange and rain in blue.  Next read in S&P 500 data for the past 10 years approximately:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in all of the local csv files into pandas:\n",
    "df5 = pd.read_csv('SP500_new.csv')\n",
    "\n",
    "# Convert price to a number, with coersion to throw NA's if conversion not possible\n",
    "df5['price'] = pd.to_numeric(df5.SP500, errors='coerce')\n",
    "\n",
    "# Remove NA's\n",
    "df5.dropna(inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>DATE</th>\n",
       "      <th>SP500</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-08-07</td>\n",
       "      <td>1010.48</td>\n",
       "      <td>1010.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-08-10</td>\n",
       "      <td>1007.10</td>\n",
       "      <td>1007.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-08-11</td>\n",
       "      <td>994.35</td>\n",
       "      <td>994.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-08-12</td>\n",
       "      <td>1005.81</td>\n",
       "      <td>1005.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-08-13</td>\n",
       "      <td>1012.73</td>\n",
       "      <td>1012.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         DATE    SP500    price\n",
       "0  2009-08-07  1010.48  1010.48\n",
       "1  2009-08-10  1007.10  1007.10\n",
       "2  2009-08-11   994.35   994.35\n",
       "3  2009-08-12  1005.81  1005.81\n",
       "4  2009-08-13  1012.73  1012.73"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the S&P 500 index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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I4dmrjwV8jUVKqtw8+fnWdpohbD9YHng9rl9aoClKYqwvE/nut9aEVNZYubOIcqdM3bWnDGmROSh4FhEREWlDD83dyLyNHa/Z25pdxQAcNzgjMPbw5RM5ZkA3AG6eNYKUuGjW7ylul/kB5O0rDbw+fnBt7vK4vmkAfLKhINA0BeCDNXsBuOK4AZw4LLNF5qANgyIiIiJt6E8fbABg631nt/NMQs1evQeAvt0SuXr6QMqq3Bw3qDaQdkUZ0pNicHvaL23j4Y83MTgzibnXnxwyXrdj4Ny1ezltRDZb9pcxsEcSd543usXmoJVnERERkTZirQ37ur18sfkA33p8IfuKK3l5ST4A6Ykx3HrOSO67cGy9TnwxUVHUtEPO86qdRew6VEFplZvRfdLqnTfGcPGkfoHjq59eDEBJpZvU+JZdK9bKs4iIiEgbCd7Qtruokt7pCe0yj4Nl1cRFR/Hsgm3M27ifbz/ha3PdKy2euOiGq1G4ogzuBhqRtKZzHpofeH1qblbYa1IT6oe1xZU1pCbUL13XHAqeRURERNqIv8Qa+FZF28vEuz+gR3IsZU4TlHV7ShiQkcgnN5xy2PuiXVGBcnZtxVPn/fp2Swx7XUp8aJBsraWk0k2vtPgWnY+CZxEREZE28tRnWwOvy6rbL3gG2F9aHXK87UB5A1fWinG1/crz+j0lIcf9uodfrU+pk55RWeOluKKG1PiWXXlWzrOIiIhIG1m/tzYQLCipOsyVHVN0lGnzlefvPbM48NoVZeiRFBf2On/ZOr/dRRWUVLrrBdXNpeBZREREpI1s2FvCGGfD22Ofbm63OQSb0D894nujo6KoacOV5/JqNzsPVQSOPV5LVJ0g2a9u85Z/zd9CRY1HK88iIiIindG+kkr2Fldx3vjeAM1uE90U6/YUM+PBT0PGnv/eVAAeufyYI94f7TJs3FvK7FW7A2N/en89OTe93SrVQ77ccjDia/0rz5dP7Q/AtgNlQP10juZSzrOIiIhIG3hvla+Ocm7P1Eat9rakZxZsqzcWH+OKuOZ0tCuKA2XV/ODfX7HstjNIT4zloQ/zACgorSIrpWU2532yoYB1u4vZsLf0yBc7LjymL2t2F3P9GcMpqnDz5vJdAC1ebUMrzyIiIiJt4NbXVwPQKz2eGFfbpj8AfLqhgP8s3A7AlIHdGdcvnT9cNLZRz4gJSpkoddpgJzor6EXlNS0yz8VbD3LFE19y77vrOFTu29Q41ukg+NPThjZ4X2JsNPdeMJZuSbGMDaoFndjCK/xaeRYRERFpZQdKq4gy4LUwqEcScdFRlFd76l3n8Vpe/SqfGaN6ktaMFdMl2wqJMjChf7fAmL+W809PG8rPzxjWpOcGb8qrrPEF//ExLsqrPZRU1VYPufxfC+nXPYF7L2hccA5w0SMLAq/nrtvHkKxknvrOFOas2cvXJvaJ6BnBqRqx0S27VqyVZxEREZFW9of31uPfz2aMqbfyXOPx8q95m1m2o5AbXl7B6Q980uT32l1UwYUPf87X/vE5+4or650/d1yvJj87xlUbOlbW+IL/OCc4Da5bPT9vP89/uaPRz3/ysy31xrJT4+ieFMs3JvcLef/DOVRRuwoe6T2RUvAsIiIi0sqqnUA5K8VXZi3GZagO6jb4zIJt/PbttfzzE18FjuaUsTv/758FXt/6+iqAkNrMzQkmo121K8/+bokJMb60iGInYC1vYv3qPUWV3PnmGgBG90lleHYKANmpjc+jvuzY/oHXCp5FREREOpkqt5cYl+HVHx0PUG/leet+X2WI4LG6pdcitbe4NvB+b/Ve9hZXsv2grwFKv+4J9O8evkNfJDYGbeBbur0QgGQnRaLQyU+OpNlKOP45njW6J89cdSxDspIB6NmE4Dk5rjZtQ8GziIiISCdzqLyaMX3SAq2lY11RgdVoqG3bHZyfG5x60BjxMVFcOqU/Jw3LBOCDNXvJ2+cLeh+6dCLGhK+THIk1u4sDr2c71UP8K88Hy3zBsz8IjmtkrnGZkzP93RMG0T0pNlDPuSkrz8GfMVbBs4iIiEjnUVnj4bO8A6zcWRQYi4txBTbcARxwWmUXV9SmPPzp/fWNfi+P11JZ4yUzJY5fnz0CgN/8bxUHnMDWnzbSEhZvK6Ta7Q2slhc67+EPorslxjbqeb/873IAkuJ8wbh/5T09sXml5rRhUERERKQT+f3sdQCcNCwrMBYfExXYcAdwqKLa+W/tavNzTlm5xliRfwiAft0SyMlIAiAzJY6SSt9zW6rm8TEDfFU8lmwr5KvtvvcsdErV/fE9X9DfmBJxHq8NBPhJsb6UC//KdWNXsP386SktHTyrVJ2IiIhIK5q3cT+nDM/kX1dMCowlxLgCwfPnefsDq9AHnPSNzJQ4+nVLiOj5Hq/l4/X7WLjlIEXlNSTGujhrTC9io6M4fUQ2+YXlFFXUEGUgqYVqHn/7uAEs2VbIpY99ERh7Y/kupg/tEQiCIw1aq91ehv3mXQBS4qLp5wS9N83KJSU+mlNysw53e4PevHY6b63cRU5G03O8w1HwLCIiItKK9pdWMXVQ95CxhBgXNR7L8h2H+Oa/FgbG9zlVNo4blMGSbYVHfPbzX27n5ldXhoydMLRHYMNc7/R45qzdy7o9JQDNyncG/4q5l8TY8CHkjS+vILdnCuv2lOCNsF33nqLacnrHD8kIvM5KiefO80Y3ea5piTFcduyAJt/fEKVtiIiIiLSSGo+XQ+U19EgOzTWOdzbZbTsYvjJFn24J7DxUEVLOLpxw7bbnbdwfeL27qH6d5+Z467rp3HvBmJCSdUBIu/EkJ3B3R1gtZPP+2goe/s2HHZmCZxEREZFW4t88Vy94dtIn/Jvsgv3k1CEUObnPf56zIeTcjoPlIff4c5kbMjmn22HPN9aQrBQundKf6KBOgzkZieT2TAkc+zcQeiIMnr8KWmFPaOFW2q1BwbOIiIhIK/E3O6kXPDv5wAfDBM+DMpP56WlDAQiOP2s8Xk64/yOue35pYKzUKe82qEdS2Pf/xqR+gddvXTe9CZ8gvOA23aP7pBEdVRtS+lfL3Z7Igud5ebUr5f27h/8cHYmCZxEREZFW4q/f3CM5tGybf4V10daD9e4ZlJlEdmp8vfrE/g5+851g01pLaaWbH508mA9/eTIDnI1xd583KnCPP/d50oBujO6T1hIfCQhtPJIY6+KMkdmB4zKnw2BBaVXYlfW6Vu8sZkpOd/5x2UR+cNKgFptja1HwLCIiItKCyqvdfOOfC5i7dm9gM1/drn7x0b7g+fNNBwBYeMtpgXMDnVXkGJcJ6ThYVuUhWJXbi9trAx3+RvRMde5PDlwT7YrizWun88R3JrfIZ/MLWngmMTaaE4dlBoL2PUWVpCfGUO32smpXUQNP8KnxeKn2eJk+tAezxvRq9obGtqBqGyIiIiIt6JP1BXy55SBfbqldVc6q0yWvbm5vZlBaR0q8rxZzTHRoC29/ioZfSaXvOMVZXb73gjEMykyqV9ljTN+WW3H2C04n8X+WNKcpSo3HkpUSx6HympD5h1Ne7fuBoDE1odubVp5FREREWpB/NdkvXGAYH1MbgsVFRxEVZehZJ8COcYUGz4XltSkQq3YWsanAV6XCv/LcLSmWG8/MJbqF21GHE7wZMNGpkBHczCTT6WRY00Des9vjZcm2QioCwXPnWc/tPDMVERER6eDcHi/PfrGN3mnxzPvVqdz86gquOD6n3nVx0bUBtT+4nnP9SdQElaaLdUVR7bZYa1m5s4h/fro5cO6ch+YHVpxH9EptpU/TMG9Q8OxfeQ5uitK/exKfcaDBled/zd/Cfe+uY3Qf39w708qzgmcRERGRFvL6sl0A7CqqxBVluP+icWGviwrK7fXXRU6Oi4agohxRUfDKV/n0SY/nrx/m1XtGSZWbCyb0Ibdn2wfPnqAGKP5V47igFW//5sWGgufdhyoAWLWz2HlG5wmelbYhIiIi0kzVbi+rdhbx1OdbI7o+qLIbfdLDt+HecdAXYIYLnP3SEmMinmNLGpZdW9fZH/jGxQSvPPuC55+/uJzPg0rR+WWnhaaodKa0DQXPIiIiIs30j4/zOOeh+azcWURuzxRe+eFxh72+X7fa6hv96lTiOJzhQUEr+CputIfs1Hi+MakvUFurOrgaSHB1kbveWlPvfkNoVY26HQs7MgXPIiIiIs3k7wgIcMnkfhwzoPthrvalapw5qidQv4zd4dxy9oiQ48pqTwNXtr6rp/tqMk8f2gOAcX1rW3T3ClpZDhcYV1SHVg7JrrNZsiNT8CwiIiLSTJ/n1VbYyGmg219de4orAejXPXzaRjjj+6Zzw8zhTBnoC879+dLtYXjPFLbed3YghSMtMYYTh2UCvnJ7KU4VkOHZ9XOyi50ye9FRhpV3zAjUtu4MOk+CiYiIiEgHdcBJXbhh5nCmDekR0T27i3w5zcEpHEcSFxPFj08Zwo9OHsybK3ZzkhOsdhRPXjkZr7XEuKJYdtsMTv3Tx7i99VNLiipq6Nc9gU9+eQpRUZ0nZQMUPIuIiIg0W1pCNMcO6s6PTxkS8T2Hyn2pHv6ayJHwt+w2xvB/43o3bpJtwBVlcDn5zK4o45Tbqx887y2upHtibKcLnEFpGyIiIiLNsq+kkk0FZYG6y5Hyl3FLiIm8TFtnCzbjYsIHz3n7SkMqdnQmCp5FREREGqm82s2CTb4mIFPumQtAr7TIc5ehtsV1XITB8ys/PL5Rz+8I4qJdVLpDNzW+tHgH+0qqSEtonzJ7zaW0DREREZFGOFRezfi7PgDgr5dOAGBoVjI/OmVwk54X3Kr7cI4Z0K1Jz29PaQkx7CupDBm78eUVAIRv3N3xaeVZREREJEILNh3gkke/CBz/5PmlAPxy5nBiXE0Lq2IbuO+GmcM5NTerSc/sKLolxlJYVlvGL7it976SqvaYUrNp5VlEREQkAj97YSn/c9pvg29V1V/feUrO4es6h/OPyyby8pJ8jAmfx+zffPjcwm2kxnfOFIfuSTGBJioAxZW+7ys9MYYbZw5vr2k1i4JnERERkSNwe7whgfOTV06mosbDj577itNHZNMtKbbRz5w1phezxvQ64nWXHTug0c/uKLolxVJR46Gi2kNCrCsQSN9x7qhGdVbsSBQ8i4iIiBxGaZWbZxZsDRk7JTcrkMt75fE5bT6nzqJ7ou+HisLyahJiEwLBc1N+2OgoFDyLiIiINOCpz7Zwx5trAJjYP52iippAsJyVEs/W+85ux9l1fP4g+WBZNb3Ta4PnDAXPIiIiIl2PP3AGePI7UzptebX24g+S567dx+g+aWzZXwZAz7T49pxWs6jahoiIiEgD+jt5uXN+caIC5yYY3y895HjR1oMM7JFEj+TIuyp2NAqeRURERMLI21fK9oPl3HbOSIZkdc5ueO0t2hVFrCuKKqdRysqdRUyoE1B3NkcMno0x/YwxHxlj1hpjVhtjfuqM32GM2WmMWeb8mhV0z83GmDxjzHpjzMyg8TOdsTxjzE2t85FEREREmu/fX2wjxmU4Z9yRK2JIw+Kio6is8bXorqzxkhLfubOGI5m9G7jeWvuVMSYFWGKM+cA596C19o/BFxtjRgKXAKOA3sAcY8ww5/TfgTOAfGCRMeYNa+0aRERERDoIay0lVW4+3VDAScOyyErpvPm5HUFcjG/ludrtpaiihqio8HWtO4sjBs/W2t3Abud1iTFmLdDnMLecB7xgra0Cthhj8oApzrk8a+1mAGPMC861Cp5FRESkw/jVKyt4aXE+AP83vnc7z6bzi4t2UeX2ct3zXwGwfk9JO8+oeRqV82yMyQEmAAudoWuNMSuMMU8YY/wN1/sAO4Juy3fGGhoXERER6RDOeWheIHCGpnUOlFBxMVFU1nh4b/VeAKrc3naeUfNEHDwbY5KBV4CfWWuLgYeBwcB4fCvTf/JfGuZ2e5jxuu/zfWPMYmPM4oKCgkinJyIiItIsHq9l1c5iAC6Y2IeJ/dMZ1SetnWfV+cVFu9hUUBY49tp64V+nElHGtjEmBl/g/Jy19lUAa+3eoPOPAW85h/lAv6Db+wL+fpYNjQdYax8FHgWYNGlS5/52RUREpMMoq3IDsG5PMVv3l3PhMX1DzucXlgPw+wvHcPHdfIhwAAAgAElEQVTk/m0+v64qPiaKpdsPBY47eex85ODZGGOAx4G11toHgsZ7OfnQAF8DVjmv3wD+Y4x5AN+GwaHAl/hWnocaYwYCO/FtKvxmS30QERERkYY8/PEmfj97XcjYlIHd6efUcZ7x4CdEGd8/kg/JSm7z+XVlcdGhiQ62k0fPkaw8TwO+Baw0xixzxm4BLjXGjMeXerEVuAbAWrvaGPMSvo2AbuDH1loPgDHmWuA9wAU8Ya1d3YKfRURERKSe4sqaeoEzwAn3f8Tt547k4sn92LC3NDA+JFM1nVtSXLQr5Lhzh86RVduYT/h85XcOc889wD1hxt853H0iIiIiLe3ON3yFvS6f2p/Ljh0AwFl/mec79+Yapg7KCLk+LVGdBFtSfEzdled2mkgLUYdBERER6TL++ckmptwzJ3Ds9nh55Stf9YxfzhjOiF6p9O2WEHLPdc8vDby+94IxbTPRo0jdleejYsOgiIiISEfl8VoMUFLl5t53fekZS7YdZGCPZA6WVQFw/4VjSU+MBSA+JjSYy9vnS9lYcccMUuO16tzS/DnPMS5DjcdyyvCsdp5R8yh4FhERkU7t/tnr+Oenm0PGLnx4AQC3zMoFYEzf2pJzMa76//B+9fSBCpxbif+Hlb7dEnnh+1PpkRzXzjNqHqVtiIiISKc2b+P+Bs/97h3fSnRGcmzI+GPfnkRuT9/GwEE9krj1nJGtN8GjnH/lOdYVRXZqPK5O3p5bwbOIiIh0alVuDwCn5WZx13mj+OymU+td0y0xNHg+Y2Q2F0z0NTq+7VwFzq0pztkwGBPduYNmP6VtiIiISKdUUlnDqX/6hIKSKq44bgB3njcagOo67Z+fvHJy2FSN704fxOVTB5AYq3CoNcU7GwZN2OJtnY9WnkVERKTT2bK/jOU7iigo8W0InD40M3AuNjqKrfedzQ0zhwOQ2yt83eaoKKPAuQ2kxPu+48oaTzvPpGXofzEiIiLSblbvKsLtsYzrlx7R9bf+bxXPfrEtZGzejacEOgUG++FJg7l4cr9Ov0Gts/PXza5Q8CwiIiLSPGf/dT4AW+87G7fHS3SY9AqAtbuLueW1lSzdfihkfHh2StjAGXwrywqc25+/iklXWXlW2oaIiIi0uw17Sxjy63d54cvtYc//6pUVgcD5BycNDozPGJXdJvOTpktLcFaeqxU8i4iIiLSIGQ9+CsBNr66krMpd77x/09m8G0/hprNyAwHZsQMz6l0rHUuq83tVrpVnERERkaZxe7yc//fPwp576vOtXPnklyErlTsKy/nahD6BFI1SJ8DOSlVaRkfnT9vo5F25AxQ8i4iISJt7c8Uulu04FPbcH95bz8frCxhx22xeXLSdgpIqdhdVMiQrOXCNx+uLxLJT49tkvtJ0/n8l6Cq0YVBERETaVI3Hy89fXA7Ah9efREZSHPmHykmNj+GE+z8KufZXr6wEVgIwODMpMH7plP48/+X2LheYdUXxMV1rrVbBs4iIiLSpd1ftAeCSyf0YlOlbTU5LTONAaVXgmtNHZFFe7eHzTQcCY7k9UwOv771gDL/72ug2mrE0hzGGM0f1ZOborrG5s2v9KCAiIiId3s7CCgB+6TQx8euWGEu6UxN46qAMnvvusSHnc3okhRwb0zU61h0NHvnWMXxtQt/2nkaL0MqziIiItKkdheV0T4qtV4M5Ksqw7LYZeL0WY0KD4/9879i6jxFpFwqeRUREpM1Ya1mw6QAje6U2eE1UVG3Q/JuzR5CZEsfxg3u0xfREjkjBs4iIiLSZ9XtL2LK/jO+fOCii6797QmTXibQV5TyLiIhIm8nbVwrA+H7p7TwTkabRyrOIiIi0qsoaD796ZQWn5mZxy6u+snP+ZicinY2CZxEREWlV6/eU8PqyXby+bBcAKXHRJMcpBJHOSWkbIiIi0qoOllWHHP/7u6qcIZ2XfuwTERGRZtt2oIyM5DiiowzxMS5+P3sdD3+8icRYF8cO7B64btKAboxTvrN0YgqeRUREpFkOlVdz0h8+DhxPGtCNxdsKASiv9vDR+gIA3rpuOoOdjoIinZWCZxEREWmWx+dvCTn2B851jeqdqq6A0ukpeBYREZFmeejDvLDj//nesRw/uAd5+0rJyUhU4CxdgoJnERERaTKv15IY68JrLf+++liiXVGc//fPADhmQDcAhmQpVUO6DgXPIiIi0mRbDpRRXu3h/ovGMimnO16vDZyLi3a148xEWoeCZxEREWmy4ooaADKT4wCIijI89u1JWm2WLkvBs4iIiDRZebUHgMTY2lXmM0Zmt9d0RFqdmqSIiIhIo+0uquB376ylsNzXACVJHQPlKKH/pYuIiEij3fnGGmav3hM47pYU246zEWk7WnkWERGRRnF7vHyWtz9wnJEUS++0+HackUjbUfAsIiJylKis8XDvO2v5YvMBisp9G/3Kqtx4gipkROKTDQWUVLnp1z0BgAn901XDWY4aStsQERE5Cvxr3mZ++/ZaAP756WYAbj1nJL9/dx1XTsvhllkjInqOtZarn14MwFPfmcLfPszjJ6cNbZ1Ji3RAWnkWERHpol5eks+vXl7BK0vyA4FzsLvfWkO1x8vctXspqazhmLs/YPaqPYddiV6241Dg9eDMZB68eDwDeyS1yvxFOiKtPIuIiHRBW/aX8cv/LgfgxcU7AuO3zMolIcbFra+vDrl+w95SDpRV84N/LwFg9Z0ziXaZkEYn1W4vP3txGQB/vXRCa38EkQ5JwbOIiEgX4/VaTvnjx/XG/3DRWL4+qR8er+VgWQ0p8dF8sqGATzYUsH5PSci1o25/jxOG9uDZq48NjC3ccoBtB8oBmDqoe6t+BpGOSmkbIiIiXcya3cUA9O+eyKo7Z3LVtIEAzBrTCwBXlOGnpw/lqukDyUj2lZi75bWV9Z4zb+N+Kqo9/POTTVTWeNh1qAKAJ6+cTFaKqmvI0UkrzyIiIl3Mqp1FAPzpG+NIjovmllm5/OS0IWEbmRSUVDX4nCFZyYy4bTbga4JS5LTiPm5wRivMWqRz0MqziIhIF7OnuBKAsX3TAIh2RZGeGL6Jye6iypDj604dwubfzSIzJY68faWB8bW7iykoqSIlLpr4GFfdx4gcNRQ8i4hIh/bS4h3k3PQ2K/OL6p2zNrQqxK5DFTz88SYOllW31fQ6HLfHy0uLdjCmT1rIZr+G7AkKnh++bCLXzxhOVJRhSk5tTnPvtHheW7qT7QfL6ZES1yrzFuksFDyLiEibqKzx4PZ4j3jdgdKqQFC8uaCUG19eAcC5f5vPK0vyA9fd8cZqBt78Du8HtYi+5tkl/H72upDrurL9pVWs21NcZ6yaXUWVXDy5X0TPuH7GMABio6M4y8mJBnjo0gn0SPYFypdO6U95tYcP1+2jR7LacMvRTTnPIiLSqsqr3Vz62EKW7zjEqblZPHHl5Aav3VNUydR75zZ4/vr/Lsft9fK1CX156vOtgK/b3YxRPQHIL/RVgghON2gOt8eL22s7bJrCVU8tYkV+EevuPpNPNhTQIzmO9MQYAFLiI/sr/jvTBnLRMX055HQc9IuKMiy85TRKKmtIjovmbx/lUeX2BgJqkaOVVp5FRKRV3fLqSpY7jTU+XLcPgDeX7+Lch+YHgl23x8szC7byw+eWhH3GBRP6BF7/6pWVDPvNu4Fjf9C3dX8Zhc7rFxfvYMwd75G3r5QP1+2lsKyamghWvev6zlOLyL11dshYjcfbJmkh1W4vJZU1DZ7/YvMBVjipLHuKKrnm2SVc+PDnrNnlW4lOaETAnxIfQ7/uifXGXVGG9MRYol1RTMrpBqDgWY56Cp5FRKTVvPpVPv9btitkbNehCq57fikrdxbxzIJtAPzkhaXc9vpqlm4/FO4xfOu4AQ2+x2eb9mOtZcm2QoDAymtJpZvTH/iEq55azIS7P+CuN9c0ev7zNu4HYEV+7bx+9coKJt79wWG78LWEbz+xkDF3vM+Og+X1zj39+VYuefSLwPGhitog+7rnlwKQENuyq+X+oFnBsxztFDyLiEiTfLx+H3+ZsxFvA0Gk12u5+VVf7eAbZg7nF2f4cmuDUyp2FvrqBr+zck/9Bzi+O30gE/p3Y/PvZvGDkwaHnDtvfG8OlddQXOlmw94SYl1RfHNK/7DPeeWrxuVBf+SskvvnV1BSxX3vruPVr3YC8O6q3Y16XmO4PV6+2HwQgBPu/yikgYnb4+X2N0K7A24PE2APzkxu0TnFRftChm5JMS36XJHORsGziIg02rIdh7jyyUU8OGcDm/eXhb2msLyaKreX288dyY9PGcIpw7MA2BdUV/jtlbuZdt+HIff99vzRIce/OWck4MvBnTWmZ8i5E4ZmArBxbwnPLNjG4KxkuieF39DWP0xawuF856lFgdePfLKJyffM4ZFPNgXGrv3P0kY9rzFufGVFyPHTC7Zy6/9W8c7K3axzAumrpw8k756zSImL5rbXV4Vcn5EUS+/0hBadU6803/PiI6jgIdKVKXgWEZHDqqj2sHpXERf84zNeX+Zbdf3De+sC5/15y3X5g+TsVF8nusQ4X9D1y/8uD7lup9O1bmCPJABOyc3i4csmAjChf3rItWP6pJHkpCPcfFYuvdN9z77okQVU1HgYmpVMN6eecd2c37ob4g7n87z9EV2Xt6+UnYcqmLNmLzk3vc3uooqI3yOcVTuLqHZ7A6vbb1w7DYD/LNzOs19s40fPfUWxk6Jxxshsol1R5PZKCflsAzISWXDzac2aRzg/PHkwN5+Vy7njerf4s0U6E1XbEBGRBnm9NtBhDuCr7csY0SuVz/IOcMnkfrywaEeg61xdnzkBaJZTFzgt4fD/3P/SNceRkRRLVJShT3oCX916Rr0A2BjDOz89gRhXFL3TE8jbVxJyvntSLNOH9gCgosbDU9+ZTEp8NP9buou3VoTmXh/OPe+sDbz+z/eO5ZuPLQTgqmkDGdM3lZ+/6PsB4PQHPgFgXD9fkL8ivyiwQttY+0urOOeh+YHjy47tz7DslHrXLdh8AKj9Po8blMGirYWB8ynx0cRGt/zaWHyMi2vqpM2IHI208iwiIg2avbp+LvLctb5c4FNzfWkYP31hGef//TOsteTc9DY/em4Jc9bs5bdv+wLQkb1TAd9Gs2lDats6j3LGAW6ZlUtmShxRUSYw1j0pNuymtwEZSYGUhMyU+JBzrihDdmo8Vx6fw2/PH83Jw7M4ZkB3YqOjqHZHXm0jNd4XmF49fSBDgnKHk+JcJMfV/yHAX03E7Wn6JsK6ecz9uyeGLZH30Id5vjk6wfPPTh/Gr2eNCHyfiTFaFxNpTQqeRUSkQfe+u7bemH8jXXBO7bIdh6is8QWn76zcw3efWRw4lxhbG8wdO7A2eB7TJ8057+KCiX2bNL/UOrWMo53g+47/G8XlU2srdMRGR1HdiFJ15TUepg/pwa3njCQ56D2KK2qIdpkG7yurdkf8Hn4Hy6p5+ONNbCkIzR33Vw356Jcnh73Pv/IcFWX43omDAukxLV1lQ0RCKXgWEZGw3B4vOw76cnhfuuY4rj1lCAAHyny5zCnx0XxnWk7g+kjyfd1OZY7cninc8X+jeOWHx7PmrjObXP7MGMOqO2dy45nDAchKjQ97XYwrihqPrdfOuyFlVe5Ak5Hg1JG0hBgGZiQ1eF9T6j/f8/Zafj97HWt2h3YKTHFWv/t1Swjb1S+pTpDs/8EhUcGzSKtS8CwiIvUs2XaQIb/2NSL5+enDmDKwe6CN8yZnhTQh1kXPoGD1qzA1mv/17Ukhx/6KF5dPHUB8jItjBnRr9lyT46K5fOoAbjxzON+aGr4etL/MWiSrz0UVNeTtKyUpzhc8G1O70nzNSYPJ6ZHEl78O3ZCXk5FISlx0g5snD2djUN52n6DV/H7dfN9VtCuKhbeczpLfnB44d8nkfiHz8l3nO9bKs0jrUmKUiIiEKK1y8+PnfGXY7j5vFJc4dZPrBmuJsdGBVAGAD9aE5kefMjyT00dmh4xdOLEPKfHRnDw8s0XnnBofw49OHtLg+RgnsKzxWOIO8zeftZYznE2Ae4oq6533B9RZKfGM65ceyHVOS4ihR3JcSD3mSOU7ta7TEmK4avpAzhvfm4pqT0jHP1eUISM5jrx7zuLtlbs5d2z9ihcpTi62Vp5FWtcRV56NMf2MMR8ZY9YaY1YbY37qjHc3xnxgjNno/LebM26MMX81xuQZY1YYYyYGPesK5/qNxpgrWu9jiYhIU81Zs5c9xZU88I1xfOu4HGJc4f+qSIhxkZVam27x3uq9IecH9qjfpMMYw8xRPYlr41rBsc5nONKmwf2l1YESe5sKSg97rTtoFTs5PprcXims210ScWoIwAMfbOBgWTU3njmc5bfP4OrpA+mRHBe2VTb4VqHPG98nZGOlXy+nbF90lP5RWaQ1RfL/MDdwvbV2BDAV+LExZiRwEzDXWjsUmOscA5wFDHV+fR94GHzBNnA7cCwwBbjdH3CLiEjbydtXQpXbE/bcml3F/OzFZQBMzule7/z0Ib4ycHHRUYHKFsEGZNQGfd0SO04nulgnWD9c8JxfWM7ke+YEjmcErZr/+eLx9VJQVu+qzVFOT4ylV1oCJVVu/v3Ftojn9de5GwFIDFNVo7H8KTTFlZHXsxaRxjti8Gyt3W2t/cp5XQKsBfoA5wFPO5c9DZzvvD4PeMb6fAGkG2N6ATOBD6y1B621hcAHwJkt+mlERKSefSWVPD5/Cz95fil5+0o5/YFPA22z6/I3LAHCdqjzNzLxpwbUDZ5/dvrQwOshWS3bHro5atM2Gg6eZ6+qTTv5w0VjudXpbAhw/oQ+9VJQgvOrJ/RL50Sn2+GnGyNrsOJxNk9GGbjwmKZVGwnWzems2JhmMCLSeI3KeTbG5AATgIVAtrV2N/gCbGNMlnNZH2BH0G35zlhD4yIi0krW7Cpm1l/nBY7fWO5rFPLqVzu5fsbwkA1qACXOquWcX5yIK0xqgL9sm8tJDUiuk0Bc465NWRia3XGCZ3/TkKqglWeP13KovJoMp9LH/tLaShk9kuOIbiBdxe/u80fzf+N7892nF/N/43uTmRxHjMtE9EPDwbJqTvnjxwDcdFZuoLJGc/grloT7fRORlhNxYpQxJhl4BfiZtbb4cJeGGbOHGa/7Pt83xiw2xiwuKCiIdHoiIke1are3Xqm4nYcqQgLnui599It6Y/7Wz/4W13UdO9CXyrG/tCowNucXJ3LxpH4AWCzdnRXQAYcp6dbW/NU2lm4vDKQ1/GXuRo757RwOOJ9lT9D3F2mHvsk53Vl++wyyUuIxxpAUF01p5ZFrPX+6oSDQmTEhtmX27k/sn85NZ+Vyz/mjW+R5IhJeRH86GGNi8AXOz1lrX3WG9zrpGDj/3eeM5wP9gm7vC+w6zHgIa+2j1tpJ1tpJmZktuxtbRKSr2FxQyqy/zGPNrmJW5hdx/t8/47h7P+RQee3q6XNO7u3Fk/qx5d5Z9Z6x/WD9smp3vLkGaDh4nubkPAcbkpXCreeO5MenDOb8CX34+IaTef/nJza40bA9+Odyw8srAikrc9f6Njjm7SvFWsv/lvn+Svrnt47h+MEZ4R90BEmx0ZRVHTl4XrWzKPC6ewPfdWMZY/jBSYMbrHUtIi0jkmobBngcWGutfSDo1BuAv2LGFcDrQePfdqpuTAWKnPSO94AZxphuzkbBGc6YiIg00ryN+1mz25eSce7f5gcabFz2r4WBa3YdqsAYuO/CMRhj+PLXp3HBhD5cNW1g4JoFmw6EfX64ag5AgwFxclw0N8zMJS7aRWp8DMOyU5r60VpF8Ery2yt2A+CkHFNe46EwKE945qie9cryRSox1kVFTfjNmMGCG6L425yLSOcQyb8VTQO+Baw0xixzxm4B7gNeMsZcDWwHvu6ceweYBeQB5cB3AKy1B40xdwOLnOvustYebJFPISJylFm8rTDs+OpdxRSV15CWGMPKnUWcOjwrEAhmpcTzwMXjAXjisy0AXPrYF1wwoQ9/+sY43F5LYqyLc8b2OuL71+1u19HF1gn6r/3PV6x1Atg3lu0i08kXvudrzUt5SIgweC6qqGFyTjdunjVCTU1EOpkjBs/W2vmEz1cGOK3ugPUVuPxxA896AniiMRMUEZFad725JhD41nXTWbnc9+46dhSWU+2JZ1NBGRcd0y/stcFeXbqT288dxYuLt1Ne7SH+CGXTlt12RoMr0x1VUp2NjW85q88Ary3dycxRPQEY1ze9We+TEOOivPrIwXO128uAjEQm9lfFVpHOpuMkpImISIM+z9vPR+v3hQTON5+Vy2/OHkGP5DhiXVGM7JUKQGWNh3/N3wzA1EH1azUD5N1zVshxldvDOqc73jcmHT7gTk+MJbUFqkO0pbpVQerast/Xcjxceb7GSIh1UXmEledVO4vYuK+0zRvFiEjLUHtuEZEObmV+Ed8MymUGGJyZxDUnDQbg28flYLGsyPdtQquo8bBtv28zYEMrqXXLsJVVe9hfWs3QrGRG90lr6Y/Q7vwl9uoakJHItgPl5O0rJcZlmt3YJTHWRX5hw8FzSWUN5zw0H1BJOZHOSivPIiId3NrdodVBH7p0Au/89ITAcWx0FHHRLuKdlcxvPf4ls1fvYVzftMOmV9x/0djA6z1FlXy6oYCN+w7fkrqzamjl+aYzcwHfanByXHSTNwr6xce4yNtXyn8Wbgd87beH/+ZdlmzzbfH5x8ebAteud1b6RaRzUfAsItLB1S0pd8bI7LD/5B8fE/pHemrC4VdRg9Mz9hRXHObKzi8uTN3mz286lROGZZIQ42L93pJ6edFN4e+8eMtrvnJ4n+ftp8rtDbTy/jyvtvugv8OgiHQuCp5FRDqw91bv4W8f5dE9KZYZI7OZe/1JDW7o65kWWt/3b5dOPOLzX/z+VAAKy7p2S+dwK8q90xNIjotmjJOmcqS86EgkBP3eWGsDK/8V1R7W7ylheX5tfedHLj+m2e8nIm1PwbOISAd26/9WERsdxd+/OZFHvz2JwZkNt35OiY/hzv8bFThOiyB/1786fbDM11zloUsnNHPGnU9Gsq9JSSRttY/k+KAmMpv3lwVKVd377joKgxrYPHTpBPpnJDb7/USk7Sl4FhHpoJbtOMS+kipyMhI5LsKOd1ccn9Oo90hyWkMfcILntCOkenQVs39WmzPuD557OLWem+OU4bUNT576bCsLt9S2M/Cnadx7wRjOHde72e8lIu1D1TZERDqYDXtLqHZ7+cdHeQD8+eLGrQa/+P2pZEQYCPqD5U3ORsGGqlJ0JQtuPpVeabUl6bon+b6r2DB50c3xrNMe3a/Uads9pgtWMxE5mnT9PyVFRDqYGo+Xx+Zt5pLJ/emeFFvv/IwHP8UVZUiKdXHplP6M7J3aqOcfOyiyVWrwpXakxkfz5daDIbWiu7K6Lcb95emOVJ+5ufYVVwL1G7aISOeitA0RkTa2eGsh989ez51vrgZ8G8tqPF5ufHk5OTe9Dfj+ib+40s2E/s3reBcJf+7tuH5pR+wu2BXUDZ79n7msqmWC52HZ4XOnl+cXEeuKom+35jViEZH2peBZRKQV7TxUway/zOPKJ7+kpLKGyhoPm/f7UiRKK914vJbb31jN0F+/y0uL8+vd3xaBVlGFr9JGuFXwrii2TvDs/wHljJHZLfL8B74xPuQ4w/leF209yJCs5HrBu4h0Lvp/sIhIK3px0Q7W7C7m4/UFjLnjfXJvnc38jb5av3PX7eOqpxbxzIJtDd6fnRrf4LmW0jfdt/K8r6Sq1d+rI4hxhZaty+2Zyrq7z+TM0T1b5PmjeqcyaUC3wPFD3/TlrG87UM6IoyAtRqSrU/AsItJK3B4vT362pd74u6v2BF5/sqEg5FxuzxRumDmc284ZyXs/O/Gwpelayi9nDge6fqWNu88bRWp8dNi22C2ZrmKMCbQ4v2VWLsOzUwLnhvds/d9PEWld2rUgItIM1toGWzq/tnQnJZXuIz4jIcbFH74+llG90xjYI6mlp3hEY/qk8f0TB/H1Y/q2+Xu3pW8dl8O3jstpk/c6c3RPnvp8K6fmZoekw6QnHh2pMSJdmVaeRUQaYcv+MnJuepu3VuzijeW7GHjzO/zipWXkF4a20HZ7vNzw8op6908b4quEMTizNkju0y2Bc8b2bpfAGXwl2m6ZNYKhQSuk0jxTB2Ww9b6zGZKVjDEm8Pvt9qglt0hnp+BZRCRCxZU1nPLHjwG49j9L+cnzSwF49audIZv9Hv54E0N+/W7g+AYnLQLgmauO5anvTOaJKycHxlR9oes7frCv82C1u3XL4YlI61PahohIhOau3dvguWKnYgXAo59uCrze8NuziI2O4uwxvSitcuOKMpzsdKE7e0wv3l65mz7pCp67On+FDbdXK88inZ1WnkVEIvTKkp307ZbAI5cfEzLer3sCheW+9tZF5TUUlvsC6SuOGxDoWpfTIymwicyvoNRX3SK3p9IluroLJvYB4JTcrCNcKSIdnYJnEZEI7C+tYn7efi6Y2JczR/dkzi9ODJxLjIkOdKd7d9VuAP72zQnced7owz5zx0FfnvRJwxRQdXWj+6Sx9b6z26R6ioi0LqVtiIjUYa3lvdV7mZ9XwCnDs3hx0Q4SYn2lzCbn+Or3DslKYemtZ1Dj9fK9pxdTWeMFCFTXOHFY5hHf56+XTmDu2n2BDn8iItLxKXgWEQmy81AFzy7YxiOf+PKW//3F9pDz/o1fAN2cEmTxMS4qnJXnlTuLcEUZUuKO/Mfr5JzuTM7p3lJTFxGRNqDgWUQkyI0vL+ezvAMAnJabxdx1+wLnUg7TYOOTDQXk3PQ2xsBxgzIarP0sIiKdm3KeRUSCHCitDrx+4OLxIecaangS3ATDWrh86oDWmZyIiLQ7Bc8iIkGKgkrOpSXE8Kszc494T91guau3uRYROZopbUNEjno/fWEpry/bFTg+eXgmD37Dt+r8w2ASeHEAACAASURBVJMHE+My3D97Pa9fOy3s/ccM6BZynBKvP1pFRLoqrTyLyFGtpLImJHAGuOK4nMBmQIDvnjCI9b89kxG9Uht8zms/Oj7wOjVeK88iIl2VgmcROaqNueP9emPhGlkcaQPggIykwOtUpW2IiHRZCp5F5Kj11fbCwOvfXzgGaHrKRbfE2oA5VWkbIiJdlv6EF5Gj1p/nbARg1pieXDy5P/uKqzhjVHaTnhW8Mh3t0rqEiEhXpeBZRI5aOwvLmTEym39cdgwA1502tFnPm/OLk7RZUESki9PyiIh0eftKKhl35/vM21gQMr6/tJpeafEt9j5DspLJTm2554mISMej4FlEurz1e0ooqqjhj+9vCIzVeLwUVdSEVNUQERE5EgXPItLp/fzFZfzw30uocnvCni+r8o0XltV2D3z0080AJMa6Wn+CIiLSZSh4FpFOy1pLRbWH15bu5N1Ve3hpcX7Y6/aXVgGw/WA5H6zZi7WWP7y3HoDSBlpui4iIhKOdLSLSKW3dX8bFjy5gb3FVYOzW/62irMrND04aHBirdnv575LaoPp7zywOec4JwzJbf7IiItJlaOVZRDqdGo+XK578MiRw9rvv3XUhx9f+5yuW7zjE5VP7U7fPyUOXTmByTvfWnKqIiHQxCp5FpNO58eUVbDtQDsCw7GTOGJnNwltO45ThmaQnhnb327ivFICrpg1k8a9PDzmXFKd8ZxERaRwFzyLSYcxdu5dfvLSMsqrD5yF/ueUgAPddMIb3f34Sj317Etmp8QzLTqGiOnTTYFx0FCcPz2RQZjIZyXG8/IPjAufiYxQ8i4hI4yjnWUQ6BI/XcvXTvnzkWFcUPzt9GD0bqMFcVFHDVdMGcsmU/iHjKfHRVLm9fPuJLzlnTC9mjMpm/d4SzhrdK3DNpJzu9EiOY39pFQkKnkVEpJG08iwiHcJf5m4MvH5h0Q6m3js3cLxkWyGVNb4V5coaD6VVblIT6v/s3ystAYBPNxRw4ysreOSTzVgLxw3OCLludJ9UAJLitH4gIiKNo785RKRDWLe7uN7Y+j0lFFfW8PVHFnD51P789vwxLN5aCEC3xPrNTU4eHlo548VF2+mdFs+E/ukh4zefNYKJ/bsxNCu5BT+BiIgcDRQ8i0iH4G9r/e5PT+C8v39GtdvLzD9/Gji/fk8JAFsPlAFwam5WvWdkJMeRFOuizMl7Liyv4cwpPYlxhf4j2/CeKQzvmdIqn0NERLo2pW2ISLu7+601PPvFNoZnpzCiVyrfmNS33jX+lIznFm5nUGYSfdITwj5r5R0zue7UIYHjGSN7ts6kRUTkqKTgWUTa3eP/396dx0dV3f8ff32yk40EEvawhB1FWaK4IIIron5ra+veqtWfW7WLXdSq1W/169LWpVitW92r1lZrrQvuiIALiKgg+yKyhp0EyH5+f9ybmQxJyCSZZJLJ+/l48MjMuXfunHy4M/nMmXM/Z+YqAK70k97vj82rtU96SgLbd5exaMMuzijIIy7Oau0DEBdnnHdYPwAunziQSXWMUIuIiDSVpm2ISFR940/DyM9N49SDewEwKi+L9OQEimuUrNu5tzywAMqwBqZcdM9MYfUdJ7dQj0VEpCNT8iwiUfXE7NUAPHDumJD2hPjQkeXXvtwQuD06L7vF+yUiIlIXTdsQkaj6aMVW8rp0YliPzJD2Zy4ax6VH5zP9VxMZ0TO47emLDqXzPqsIioiItBaNPItIq5q+pJB532zn6hOGsmDdThb7VTT2dWDvzhzYuzMAK7d4S2wPyEnjqMG5de4vIiLSGpQ8i0iruuDxOd7PIwdwyn0zw3pMSXkVAGnJWhFQRESiS9M2RCQq7n9/eeD2ccO773ffm08dAcDGnaUt2icREZGGaORZRFpcRWUVf3xrCcs2FQfaqsvT/fOywxmdl1XfQwE4fWwfbv7v1wztoRUBRUQkupQ8i0iLeWLWKgqLSql0joc+WFlre0KcMbZvdr01m6tlpCTy4uWHk5+j5FlERKJLybOItIg5q7dx83+/3u8+I/t0bjBxrja2X5dIdEtERKRZNOdZRFrElc/OC7mfklj77ebkkT1bqzsiIiIRoZFnEYm4Bet2smlXKVcfP4QF63by1tebmHnNMeSkJwNw11tLuO+95XTPTIlyT0VERBpHybOIRMzfP/mGgn5d+GTVNgDOOiSPbscOrrXflccMondWJ408i4hIu6PkWUQi4pmPv+GGlxcwpm8WB+dlkRQfR25Gcp37JifEc9ahfVu5hyIiIs3X4JxnM3vMzArNbEGNtpvNbJ2Zzff/Tamx7TozW25mS8zsxBrtk/225WZ2beR/FRFpbc45lhcWcdr9s7jhZe8tYt6aHTw+azUThuRiFt7FgCIiIu1FOCPPTwB/AZ7ap/0e59yfajaY2QjgLOAAoBfwjpkN8TffDxwPrAXmmNkrzrn9X4ovIm3a9S8v4NlP1tS57dqThrVyb0RERFpegyPPzrkZwLYwj/cd4HnnXKlzbhWwHDjU/7fcObfSOVcGPO/vKyLt1L8/XxuSOM+69piQ7YO6qSaziIjEnuaUqrvSzL70p3Vk+229gW9r7LPWb6uvvRYzu8TM5prZ3M2bNzejeyLSUiqrHE/M/iakrXdWJ/5+8bgo9UhERKR1NPWCwb8CtwDO/3kX8GOgrgmOjrqTdFfXgZ1zDwMPAxQUFNS5j4hE1y2vfs0X3+7gplNHkJ+bTnpyPABHDsqJcs9ERERaVpOSZ+fcpurbZvYI8Kp/dy2QV2PXPsB6/3Z97SLSjlRVOf712VpOG9WLC48cUGv7Uz8+tN4qGyIiIu1dk6ZtmFnN4qzfBaorcbwCnGVmyWY2ABgMfArMAQab2QAzS8K7qPCVpndbRBpSVlGFc5H78qaqyvHJyq0MvfENiksrGD84t879JgzJZXjPzIg9r4iISFvS4MizmT0HTARyzGwtcBMw0cxG4U29WA1cCuCcW2hmLwBfAxXAT5xzlf5xrgTeBOKBx5xzCyP+24gIABWVVYy/8z0Ki0p5/1cT6ZGZwvNz1vDd0b1ZtKGIvC6d6JOd2qhjXvL0XN5ZVAjAuAFdmHxgj5bouoiISJtmkRyZirSCggI3d+7caHdDpN1ZtqmI4++ZEbh/6YR8HpqxkgN6ZbJw/S7MYNXtJzfqmP2vfS1we/n/nURCfHOuNxYREWlbzOwz51xBQ/vpr59IDNqxtxzwkmaAh2asBGDh+l0AhPuZedmmIqa+uwznHJ07JZKZksDjFxyixFlERDosLc8tEmPKK6v4+8deGbnJB/YIJM5NUT16nZ2WREl5Jecf0Z9Jw7pFpJ8iIiLtkYaPRGLMy5+v4+X5XjGbnPRg1YupZ48O2e/NhRuprHIUFpXUeZyikvLA7bcWbqS0oors1KQW6LGIiEj7oZFnkRhTWFQKQFZqIn2yO/HgeWNIToinoH92yH4vzVvLpU9/Bnjl5SYMCa2ecfpfZwduf7hsCwDZqYkt2XUREZE2T8mzSAv57JttPDH7G+as2sY/LzucvC6Nq24Rrm27yzj/sU+JM3j5J0eyu7QCgBm/mYSZMfnAYGXJT357LCmJ8Zxwzwe8uTBQrp0lG4sCyfODH6xg6aYilm4qBuDQ/l34dPU2APp1TWuR30FERKS9UPIsEmFVVY4731zMQx8E5xq/v6SQHx3eP3D/2217yM1IJiUxvlnPVVFZxV+nL+erdTsBGHDd6wAkJ8SRmVJ7lLh7ZgoAm3aVhrRnpSZSXlnFNS9+yUvz1oVsu3zSQD593Eueh/XIaFZ/RURE2jvNeRaJsKc//iYkcQbYtCs4r/jOaYs56g/vc96jnzT7ua558Sse+XBVrfbSiqr9Pu7W0w6kU2I8v50yDIC95ZUs2rArJHH+4/cPYta1xzBpaPACwew0zXkWEZGOTSPPIhF20yvB9X8yUxIoLq3g3UWFXDFxEJuLSvnr9BUAzP1me7OeZ8nGIl6ct5auaUncc+YoZi3fwrOfrqGopIIHzxuz38eed1g/zjusH6UVldz2+mKKSipYsbk4sH32tcfQK6tT4P5jFxSwfXd5XYcSERHpUJQ8i0RQRWVwxDc/J42nLjqUaQs2cutrizjgpjdD9s1qxsV3U99dxt1vLyXO4F+XH8GAnDQmDMnluinDG3Wc5IR4kuLjKC6t4L3Fm0lOiOPT3x5H5336dsyw7k3uq4iISCxR8iwSISXllZSWe8nzjaeM4KLxAwD4n1G9uPW1RSH7ZqcmUlxagXMOM9vvcZ1zlFZUkZIYz8695cxdvY27314KQNf0ZAbkNO8ivrLKqsBo+HdG9aqVOIuIiEiQ5jyLRMC97yxl2I3TOPuRjwHISA5+Lu2WkVJr/0Hd0imvdHwWxtSNV7/cwLAbp7Fqy27Oe/QTLnoyuGT95qLS/TwyPFNG9gjcLujfpdnHExERiWVKnqVVTF9SyK//+UVEkr22pLCohIuemMO97ywD4OsN3vLXhwzYfxJ62ujeAEx9b3m9+zjncM5x11tLAHj9qw2BqhqR9MC5YwO3B+aqFJ2IiMj+aNqGtIo/vrmEhet38eqXGxiVl8VzlxzW4GMWrNtJbkZyoLxaW7Nh514ue2YeX3y7A4BenVNYv7OE40d0r3cqxdJbT2J5YTEjemXy6IermLF0M99u21NnDeiT/vwhA3PTWb11D+DFsKbvjenNxePzI/o7DcpNj+jxREREYo1GnqVJqqpc2PuWVVSxyB+R3VteyUcrt/L1+l1UVjk27ixh9ZbddT7ulPtmMulP03HO8fma7SEX40Xby5+v4/Db3wskzo9fcAiXHj0QgJz02uXc3vrFBGZeM4mkhDhG9MoE4NxxfQF47asNtfbfVVLO4o1FdW6r9v0xfQLHaq7TRvUiPyeNbm30g4qIiEhboeRZGu3RD1cy4qZpFPsr2dWlorIK57wE++2vN7Fvrj1l6ofc+J8FHHb7u0z80/Raj3/oA+8Ctj1llXy0YivffWA2f9hn5DVa1u/Yy8//MT9wPz8njUnDugWqZxzcJ6vWY4Z0z6BPdujo8sVH5TOoWzqzlm+ptf+dbywOuX/DycEqGhkpCf7PyF3Yd8+Zo3jrFxMidjwREZFYpeRZGmX9jr3c+toiSsqrWLBuJ9MWbGBrceg85n/MWcMh//cOlzz9GcWlFfzk2XlA7RHZZz9ZU+dzlJRXcnuN5HGlPzL98IyVbWL0uXrBkykje/DH7x/E85d6U1D+5+BePH3RoZx5SF7Yx5owOJdPV22jvLKKZz9Zw3uLvSWzF67fFbLfyN6dA7cvHp9PnEHPrMiNEpsZCfF6OxAREWmI5jxL2HbuLeeIO94L3D/rYa+yRHycsfiWySTGx7FzbznXvPgV4I04//3jbwA4fkR37vjeSD5fs4MZyzbz1Eff1Ps81RcVju6bxedrdnD768Eybxt3ldQawW1NU99dxvQlhYA3cjymb3Zgm5lx1ODcRh1vVN8sHpu1itkrtvLbf3tx+96Y3sz3p4NU65qexMs/OZIq5xidl8VlE/NJTmje0t4iIiLSeBpqkrA94yfCqUmhSVtlleO6l7zEb8bSzQCc5Y++Vo8gXzohn67pyRw3ojs3n3oAPz12cODxnRJDj1foJ89HD/ES0d1llYFtu0sriZbP12zn7reXMm+Nl9gO65HR7GMO7e4d4/zHPg201Vwiu9qgbhmMystiTN9szEyJs4iISJQoeZawbNi5lz++uYQ4gznXH0fXtNApGDOWbmbn3nKueu5zAC6fOJATRgRXpetZY6nnuDjjkgn5FPTL5oBemewtr2SNX1ECgiPPdY3i7m+edUu74u/zAre7piWRmtT8L266ZSTXu+38w/sBKh8nIiLSlih5lgYVl1Zw+O3edI17zhxFWnIC3xvTO2SfwqJSZi7zLnw7oFcmedmp3HvWqMD2nvtUcUhPTuBflx/Bzf9zAAArtxQHtm3251DndenEpRNCS7FtKY5OnejdpRVs2FnCFRMH8sbPjuKZi8dF5LidOwUv+ru+xtLaA3PTuOGUEXz622N5+SdHRuS5REREpPk051nq9dK8tbw8f31gKgbAKQf1AuC6k4ZzRkEe6SkJgcS6+sLAqWePJi7OSE1KYM71x1FYVEJcXN1LUPfr6s1fXrShiIlDu3Hb64t49pM1xBl0TUvmuinDOfOQPJIT4zn6D+8z/9sdnHhAjzqP1VJWbC5m8r0zABjeM5PhPSNTHg4IiUt1FQ2AV64cT2J8nErHiYiItDFKnqVOJeWVXP3CFyFtOelJxPvJXlycMdifr3v/OWMCiTN4pduq5WYkk7ufqQndMlIY3C2dO6ct5s5pwQobOenJgefK9xfuGNmnM/+cu5bLJw4kswll2vaWVfKvz77ltNG9G1Xm7eEPVlJe6dXaK+if3cDejfeH7x9E98wUduwpC7TtOw9cRERE2gZN25Batu8uY+q7y2q152bUPQp68kE9A7fvP2cMZnWPMtdnXH7tpazrWmjksqMHsqW4lGWbimttC8cbCzZw438W8reZq1iysYjyMMrePfrhSv4x91sAZvx6Ej07d2rgEY13RkEeRw/JDZlDXd9IvYiIiESXkmepZfQtb/PAdG+RkhtOHs5l/sp5v5k8tMHHjulXe4GQhvxm8rBabX3rWK46O9VLqPeWNa3ihr9mC69+uYET753Bve8s3e/+VVWOW1/zyuQ9cO4Y+nZt2RJ5Q7tn0LdLKhcc0b9Fn0dERESaTtM2JKCisor1O0oC9w/t34WLxg/AzPjpsYPCqi7RowlzdDNTErl0Qj4PzVgZaPuhX2mipuoSeVt3N+2iwTJ/pHl5oTdyff/7K/j1icHEfcXmYpLi48jrksrPn/+cl+evB+CeMw9mysietQ8YYX27pjLjN5Na/HlERESk6ZQ8CwA795Qz+pa3Asto//fK8YzsE1zVLtyybI2dslHtuinDGd4zk6E9MujZOYWs1NrTNpITvC9Kfvb8fL4zqnet7Q2prgZS01XPfc59Z49mx54yjr3rAwBW3jYlkDgDHD2kW6OfS0RERGKTkmcBYObyLVQ5SIw3LjiiPwf2blxFib9fPI512/c2qw+njd5/QlxdeSIlsfGzjSoqq3jtqw212v/7xXruO3s0P35iTqDtvcWFIft0SaudyIuIiEjHpOS5g9taXMplz3zGnNXb6Z3ViXd/eTQpTaj0cOSgnBboXajOnRLp2TmF8U14ri/X7ayzfVC3dLbvLgusGgjwr8/WAnDCiO4hKyGKiIiIKHnu4N5ZtIk5q7cD3gIoTUmcW1NifFxYVTL29eW3O+psr6isoqQi9ALEaQs3AnDraQeqzrKIiIiEULWNDuL5T9dw8ysLKSkPJorz1mznllcXkZwQxx++fxCHtEAN40hLjLdAzeVwzV6xhZv/+3Wd23bsLaeswkvG961y0Zha0CIiItIxKHluIW8u3Mg/5qyJdjcCbnh5AU/MXs0ZD30UaLvrrSUUl1ZwwRH9OaMgr8kX+7WmxPi4QNWMandOW8yjH3qVOj5asZUlG4tCtp/zyCeB29Ul8O47ezQXHNGfHXvKKSn3jje2XzaH53cN7Ft9gaKIiIhINU3biLDFG3fxyxe+YOH6XYA3T3fygS1f5qwh1Xnxl2uDc3+3FJVx3PBuXHtS7TrLbVVSQui0jT1lFfzVr0l90fgBnP3IxwCsun1KnR8GnrloHE/MXs2UkT3ZVVIOwLuLN9V5bC1UIiIiIvvS0FqEPfPxN4HEGeCyZ+Yxe3ntEmmtqbyyKmSqw4ylm1leWMyOvWV0TUtuFyPO1RLj49i1t5yfPvc5lz49l0UbgqPMq7fuCdz+wv+QsLU4tCZ0366p/O7UEcTHGf9zcC8APvPnfCclxHHPmaNa+lcQERGRdkwjzxG0Y08Z/5y7tlb7OY9+wuo7To5Cjzzbd5cBMKxHBos3FvGjxz4NbOuc2r7m9SbGG4s3FgeqY0w+sEdg26Q/TQ/cXrBuJ6PyskJG2l+49PCQY2WkJJKfm8bHK7cCkOwvkPLp9ceyocZiMSIiIiLVNPIcIeWVVYz6/duU+heftYW5sx+v3MorX6zn3Ee9Ob+/O2VErX3i2tGoM3jzknfsKQ/cn7lsa537VV8Y+cVaL8n+8uYTOHRAl1r7HdS7M7v95b4HdUsHoFtGCgfnNX6ZcREREYl9GnmOkMdnrQrcXvC/J5KWFM+A614H4KAaK/W1ltvfWMRDHwSXu05JjOOgOhLCzUVNW+o6WjL3qYDx4rzaI/0AW4rLKCmvZOPOEnIzkms9rlqefwFhdmqiytKJiIhIg5Q8N9N/v1jPVc99Hrg/7edHkZ7shXXO9cfxs+c/p7ColBtfXsCvJw+tN4mLpLKKqpDE+Y7vjWTSsG6kJyfwqxOG8OK8dZxRkEdpRSXnjOvb4v2JpLTk8E7ZBz9YwfQlhfTonEKP/STF1f9XXdOTI9I/ERERiW1Knpvhn3O/5df/+jJw/8pJgxjWI7isdW5GMvm5acxesZXlhcVkpyZy9QlDW7RPFZVVnDz1w5C2sw4NJshXHjOYK49pv6vmZaQ0fMomJcRRVlHF4o1FLN5YxJDu6fXuW52MZ4ZxXBERERFlDM3w5EerA7dfvWo8B/auPT2je0Zw1LO8qnGLezTFK1+sZ1lhMQAP/XAsvbM6tfhztqa0pLpP2V+fOJQ/vrkEILDoSbWlm4rrPV71tI383PoTbBEREZFqumCwhsUbd/HdB2Yx+PrXedNfork+T8xaxYJ1u8hJT2bZ/51UZ+IModMMUltw6evlhUWcPPVDrn7hCwDeufpoTjygR739aq/S6xghvusHB3OuP/3kplNrXxR54ZH96z3ehME5TD17NFcfPyRifRQREZHYpZFn39biUibfG5zucMcbiznxgB517rtic3FglPOxCwpIjK//M0h6jeT5m2176t2vObYWl3Lc3TNC2qorR8Sa9DrmPA/qlk5WahIrbptCfJzxv/ssxX3ufuZ1mwXrPYuIiIg0RCPPvodnrAy5369rar373vzKQnaXVXLvmaM4qM/+S5rVHHn+12dr+bYFEujpSzZH/JhtVV3Jc/VodHw9KwK2xkWaIiIi0jF0+JHnBz9Ywd9mrmJzUSlHDOzKkYNyePCDFewpraz3MRt3ljBuQBdOG927weOnJYdO1Vi9dXdgnm04SsorGXbjNACuPWkYlx09MGR7eWUVt7+xKHD/i5tOqLWqXiypq9pGXQl1TZmdlDyLiIhIZHTYkefSikrW79jLve8sDdQ6Pn1MH34yaRCH53dlV0l5nY/btKuEZYXFjOiVWef2fSX5UzpyM7xSaBt3hq5c987Xm7hz2uLAoh41lVdW8beZwfrRd7+9tNY+9723nC3F3gqCJx3Yg86dEmP64reaiXKXtCTSkuLJ2meVxLk3HMdLVxwRuJ/SgnPNRUREpGPpECPP7y7axPQlm9lSXMqAnDTGD87h7reWMveb7QAcMbArs1ds5bgR3QFvpHLX3tDkeXdpBU/MXs2nq7YBcPah4dVHrq6wMaJnJh/v3crSTUWBbc45Ln5qbuD+NZOHhTz23neWcv/7KwL387JrV854c4F3YePK26YQV8+0hVjSKSmeSyfkM7JPZ04Y4c1JT9pnBcec9GSyNNosIiIiLSDmkueqKheSRJZVVHHRk3ND9nlg+oqQ+0/9+FCKSyvo7CdcGSkJFJVUsGjDLh75cCV3fO8gXpy3NnCR4PhBOQzpnhFWfw7P78o54/py1TGDOGXqTOas3s51L30JGJcdnR/Yb0VhaDk15xyPz1oduP+DsX2YtXxL4P7KzcWcet9MdpdVcsPJwztE4lztuinDG9wnYT8XcYqIiIg0VUwlz4/NXMXvX/2a/Nw03vvlRACG3PDGfh/TIzOFhPg4slKTAm0JcUZRaQUn/dmrvnFGQR7vLS4MbP/N5PAXOklKiOO2744EvOkD87/dwfxvdwBQWmOqxpYa85TvfmsJ0xZuZE+Zt/2ccX1JjDM27irhvneX8ePxA5i+ZDO7/e3jBnQNuz8iIiIi0nQxlTz//lWvRNnKzbv5dNW2esu1dUqMZ295JT8/bnCdZcr656SF3N9SXMpHK7bSs3MKhw/s2mCFjfqs27E35P5Ln68DoGtaEgvW7QqMmk99b3lgn0d+VMDxI7pz57TFVDm46+2lDOyWTnFpRWCf4T3DGwXvaL743Qk4Wn5hGhEREek4Yip5rmnb7lIWb/BWmsvNSOYflxxGp6R44uOM9OQEKqpcvSXMzizIo6ikgjveWAzA7a8vprSiijtPP4gJQ3Ij2k8z+PH4AfzxzSVsKiqhpDx0dbzqqSTDegQT5OLSCtbv2EtmSgLv/nKipijUo3Oq5j2LiIhIZLXb5Hnl5mJe+3IDY/tnc8TAHJ7++BvASzZ37i1n3pod9Mj0lsZ+/adHBapdhCMhPo5D+mcH7lePGI/L7xLB38BzyYR8OvnVIL7zl1m1kvPqvHji0G4M7pbOssJiVm7ezbbdZfTs3KlRv5eIiIiINE+7HbI85q4PuOvtpZzzyCd8umobz3zkJc/P/r9xHDU4h4dnrOT3r35N98zkJiWYo/OyuWJiaE3l5ITmlTz76bGDa7WlJiZw+pg+AOwqKWf5PhcO9s7yakJ37pTI21cfzXHDu/HgByt46+tNpCarBJuIiIhIa2qXyfPuGvN9Ac546COWbCrip8cM4oBenUNqMB/cxPnJcXHGL44fErj/4Hljm9bZGq4+fghzrj+OGb+eFGibtnAjnVMTuWLiQCoqHau37mbyAT04fUwfZl97DD06p4Qc4+KjghU69pbVv5CLiIiIiEReu0yeF6zbCXiLZNR0WL5XdSI1MTgbJTs1dJ/GSIyP4/1fTeSEEd0ZPzinycepKTcjmb5dU0lN8kaNL/dHt1OT4qmocuzYU86gbuncdcbB9MqqXde5Z41kurAodlcSFBER4p/QEgAAD45JREFUEWmL2l3yvLeskjMf/hiAC4/oH7JtQK5XJaPmkth1JaCNMSAnjYd/VNDgEtCNNe1nE/jtlGGcelBPADolBY/fKan+6Rj9ugYrgVwfRr1jEREREYmcdnPB4M495WzfU8buMm/Kxo8O78flEweyq6ScRz70lrDOTffmNp93WD9ufW0RABcc2T8q/W1I366pXDIhOKc6u0ZliNT9JM8Aq+84ucX6JSIiIiL1axfJ83/mr+Nnz88H4MZTRgBw1iF9SYiP47TRvQPJc3XJtpTEeP5+8Ti6ZSQHSr21dQNq1JZOi/Aot4iIiIhERoPTNszsMTMrNLMFNdq6mNnbZrbM/5ntt5uZTTWz5Wb2pZmNqfGY8/39l5nZ+Y3p5JOzVwdu3/Lq13RJSyLfn6IxtHsGh+d35aEfhl7Qd+SgHAaHuYR2W3BQnyxy0pPplBjPscO6Rbs7IiIiIlIHc27/K7CZ2QSgGHjKOXeg3/YHYJtz7g4zuxbIds5dY2ZTgKuAKcA44M/OuXFm1gWYCxQADvgMGOuc276/587MG+q6nHs3ACeP7MlrX20A4JbTDuSHh/Vr6u/cZm0uKiUtOZ7UJI08i4iIiLQmM/vMOVfQ0H4Njjw752YA2/Zp/g7wpH/7SeC0Gu1POc/HQJaZ9QROBN52zm3zE+a3gckNPXdpRXC1vZqVNUbnNa38XFuXm5GsxFlERESkDWtqtY3uzrkNAP7P6nkGvYFva+y31m+rr70WM7vEzOaa2dya7f26pgZu53VJrfU4EREREZGWFulSdVZHm9tPe+1G5x52zhU45wpqlpkbmJvOn88axfEjurebiwBFREREJLY0dY7AJjPr6Zzb4E/LKPTb1wJ5NfbrA6z32yfu0z69oSfpmpbEnqR49pRVMiAnjf45aXxnVJ0D1iIiIiIiLa6pI8+vANUVM84H/lOj/Ud+1Y3DgJ3+tI43gRPMLNuvzHGC39agxy44hJMO7KGpGiIiIiISdQ2OPJvZc3ijxjlmtha4CbgDeMHMLgLWAD/wd38dr9LGcmAPcCGAc26bmd0CzPH3+71zbt+LEOt0WH7XwLLbIiIiIiLR1GCpumgqKChwc+fObXhHEREREZFmiFipOhERERER8Sh5FhEREREJk5JnEREREZEwKXkWEREREQmTkmcRERERkTApeRYRERERCZOSZxERERGRMCl5FhEREREJk5JnEREREZEwKXkWEREREQlTm16e28w2A9+00tPlAFta6bnaA8UjSLEIUiyCFIsgxSJIsQhSLIIUi6C2HIt+zrnchnZq08lzazKzueGsZ95RKB5BikWQYhGkWAQpFkGKRZBiEaRYBMVCLDRtQ0REREQkTEqeRURERETCpOQ56OFod6CNUTyCFIsgxSJIsQhSLIIUiyDFIkixCGr3sdCcZxERERGRMGnkWUREREQkTEqeRURERETCpORZREREpJnMzKLdB2kdHSp5NrN4/6dOcMDMOtT/f310PoSqfp0ImFln/2eHf62YWQ//Z4d/vZjZAWaWEu1+tAVmdqSZDYx2P9qITtHuQFsR6/lWh/iD4L+4nwRuMLMurgNfJWlmh5rZTwGcc1XR7k80mdk4M3sEuMbMGlxRKNaZWYGZPQ38riP/MTSzODPLNLNXganQsV8rZjbazN4FbgHo4O+fB5nZTOBWoGu0+xNNZjbGzN4C3gM6R7s/0WRmh5nZi8D9ZnZCRx6AMLPD/b+rvzCzzFh9v4j55NnM8oEHgPeBfsAtZnZydHsVHWb2c+DfeB8iTvLbOtyL3Mzizex2vHI5s4AxwE1m1j26PYsOP1n8C/AQ8C7QE7jZzFKj27Po8BPlIiAR6G1mZ0LHG302zz3AU8CTzrn/F+0+tQE3AP9yzn3XObcOYndkrT5mlmhmD+G9f04F3gQm+ts61GsEwMwm4uUYLwFLgPOA7Gj2KVrMbALwF7wPVL2A68zsxOj2qmV0hBN9LLDIOfcE8EtgPnCKmeVFtVfRsRw4BbgcuA7AOVfZ0d788c77NcAP/PPi58BhdNCv3Pxk8T3gWD8efwAcUBHNfkXZMGALcC9wrpllOOeqOtJrxR8xSgc+d849BWBmAztoghTnfxtT7Jy712873syygJj+eroOycAHwFHOuVeBF4HhZpbQQb+hGQnMcc79HXga70N3cXS7FDVjgVnOuefwvqnqDpxVPeUrlsTcm6D/9cmQGk1zgD5mluec24430rgD+G5UOtiK6ojFa8CX/s/i6ukb+G/+sWyfWFQBzznnlppZsnNuPbAWyIleD1vXvueGc+4l59wOMzsemIs3+nybmQ2PWidbSc1Y1EiAlgNlwCr/3/lm1jdWv4KsVsd7xi+BcWZ2o5nNAv4IPGFmY6PTw9ZTMxZ+UlgIHGVmJ5vZy8Cv8EZef+3vE7Pnxj7nxW7n3LPOub3+/QSg0jlX0RE+WNXxGvkQ+IGZ/Q6Yh/fe+YCZ/SAqHWxFdcRiKdDZzHr6+VYx3oet70Slgy0oZk50M8sys9eAt4EzzCzd31QCzATO8O8vAb4GusbqBR91xCKtepNzrtI5VwLcBVxkZjnOuZgdYazrvPBjsAPAOVdqZhnAAGB9NPvaGuo7N2okjduBc5xzxwN78JLGmJzOUlcsaiRABcAu59xCYCFwE/BX/yvrmHnfrFbfeeGc2wXcD5yO923V2cAG4PRYvU5gP7EoAh7HG1F7zDl3IvAocJiZHRa1Dreg+l4j/pSe6tfBB8B3zSw7lkee68sxnHPzgclAf+AK59xEvEG6ybE6+LCffGspsAt40rw54HnA50CG/7iY+XYmlv4IpOHNvbrKvz3Bb98MfAyMNLNDnXOVwDrgSD+JjEV1xmKfN7bpeHG5CrwLCVu3i61m31gcVcc+44CFzrn1ZpZuZoNbs4OtrL5zw/k/5zrnXvf3fR0YjZdEx6L63jPAm9aTYWb/AH4DfAYsdc6Vx2iCUG8snHNTgUnOuRnOuVLgZbwPFx3xvHgVL0mqntM6F9gElLZi/1pTve8X/jSmOGC1v8/R0epkK6n3b4lz7lMgFy8W4E2DywB2t24XW01958UyvG+rbse/NgBYgD8nPpa+nWnXybOZ/cjMjjbvis51eBcwvIA32nyomfX2k+WP8T793ON/QjoAWGMxdEFUA7EYZ2a9/P0MvLnOeFeMX2NmO4ExsfKpsBGxSPAfkgV8a2YX4k3zGRWNfreUcONRh7F4o4wx881EI2KRjffHcCPeB4jLgaGxNJLUmPPC/wq22li8aU6VrdrhFhRGLHoDOOe+xJumcaWZ5eBdHHYgsDVKXY+4xvwt8T9IVn+DW1LdHo1+t4RGxCIZmA38xH/osXjVWGJmgC6MfKsXgHOuzDn3vj/vGbz3i2nR6XXLsfb2QcB/YfYAnsWbu7oC75PPz5xzW/x9jsSbpjHXOfd0jcfeDfTBq7rxI+fcklbufkQ1MhZznHPP+G1xQD7eV5BlwM+dc1+1/m8QOU2Nhd/+NHAu8CRwj/8Hsl1rxrmRiTcSfxte4vhL59zS1v8NIqep7xn+lKbq7elAknNuWxR+hYhpxnmRDBwO/AnvA1WHPS/89qvx3kMHA79wzn3dyt2PqGacF/H+RedPAyucczdHo/+R1Iz3iwPwpnf1AMqBK51zi1r/N4icZv5dHQ/8Ge/C60udc6tbt/ctq12NPPsvVIf3dcg659yxwBXANrxPQQA452bhfX0y1Mw6mzenFbwRg4ucc+NiIHFubCyG+bFI9UcLdgG/c84dGwOJc1NikVljntZrwBnOuQtjJHFu6rmR4s9xdcCtzrlTYyBBaup7Rppzbot5ZQ3jnHPFMZA4N/W86ORP1yhD50WG3343XtJ8Ygwkzs35W1L97cOPYyRxbsp5keW/RhYC5wMX+H9X23vi3NTzovoaq5XAjf5rZHWrdr4VtIuRZ//r9d/jVYV4HcgEvu+cO9/fbngXe53lnPvAb0vHm5ZwBN5I82jnVVVo1yIUi7HOubVR6H5ENTMWRwJ9gVHOuQ1R6H7ERSgeep3oPUPnhc4LxaLxr5Exzq/93Z4pxwhPmx95NrOj8S7WycYrH3UL3lcik8y/yM3/dPR74OYaDz0Z71PSF8DIGHmBRyoW7f6kjkAs5uPFIlYS50jFQ68TvWeAzoubazxU50WQYhG072skFhJn5RhhSmh4l6irAv7kgvOKRuOVFfsd8FdgrHlzeP+N9x/c3/+KoAQ4zjk3IzrdbhGKRZBiEUrxCFIsghSLIMUiSLEIUiyCFIswtfmRZ7xPQS9YcBnpWUBf562EFm9mVzlvDm8fvELtqwGcc/+Jwf9IxSJIsQileAQpFkGKRZBiEaRYBCkWQYpFmNp88uyc2+OcK3XBCxOOx6vdDHAh3rKgrwLP4a3uE1OlcmpSLIIUi1CKR5BiEaRYBCkWQYpFkGIRpFiErz1M2wC8Kz/xqgB0B17xm4uA3+LV2VxVPefIn5MTsxSLIMUilOIRpFgEKRZBikWQYhGkWAQpFg1r8yPPNVQBiXg1Aw/yP/3cCFQ552a6GJis3wiKRZBiEUrxCFIsghSLIMUiSLEIUiyCFIsGtItSddXM7DC8VXxmA4875/4W5S5FjWIRpFiEUjyCFIsgxSJIsQhSLIIUiyDFYv/aW/LcB/ghcLfzivZ3WIpFkGIRSvEIUiyCFIsgxSJIsQhSLIIUi/1rV8mziIiIiEg0tac5zyIiIiIiUaXkWUREREQkTEqeRURERETCpORZRERERCRMSp5FRNo4M6s0s/lmttDMvjCzq80sbp99/mxm66rbzexC/zHzzazMzL7yb99hZheY2eYa2+eb2Yjo/HYiIu2Lqm2IiLRxZlbsnEv3b3cDngVmOedu8tvigNXAeuBa59z0fR6/Gihwzm3x71/g37+ylX4FEZGYoZFnEZF2xDlXCFwCXGlm5jdPAhYAfwXOjlbfREQ6AiXPIiLtjHNuJd77dze/6WzgOeDfwClmlhjGYc7cZ9pGpxbqrohITFHyLCLSPhmAmSUBU4CXnXO7gE+AE8J4/D+cc6Nq/Nvbgn0VEYkZCdHugIiINI6Z5QOVQCFwKtAZ+MqfxZEK7AFei1oHRURimJJnEZF2xMxygQeBvzjnnJmdDVzsnHvO354GrDKzVOfcnmj2VUQkFmnahohI29epulQd8A7wFvC/ZpYKnEiNUWbn3G5gJt6I9P7sO+f5iJbqvIhILFGpOhERERGRMGnkWUREREQkTEqeRURERETCpORZRERERCRMSp5FRERERMKk5FlEREREJExKnkVEREREwqTkWUREREQkTEqeRURERETC9P8B9/ozAafasXAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df5.index = pd.to_datetime(df5.DATE)\n",
    "df5.plot(figsize=(12, 6))\n",
    "plt.title('S&P 500 Time Series')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot shows the bull market from late 2009 to June of 2019.  Notice that the chart does not have an absolute zero in this case (it is not necessary).  Next, add some columns to perform some analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For S&P 500\n",
    "df5['date_time'] = pd.to_datetime(df5['DATE'])\n",
    "\n",
    "df5['weekday'] = df5.date_time.dt.weekday\n",
    "df5['day'] = df5.date_time.dt.day\n",
    "df5['month'] = df5.date_time.dt.month\n",
    "df5['year'] = df5.date_time.dt.year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add a column for the change in price for each day:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add a column for the change in price each day\n",
    "df5['change'] = df5.price - df5.price.shift()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sum the total change for each day of the week for the S&P 500 to see the cumulative return for each day:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "52.15999999999292\n",
      "673.5500000000052\n",
      "574.7199999999998\n",
      "320.70999999999515\n",
      "348.76000000000704\n"
     ]
    }
   ],
   "source": [
    "# For S&P 500\n",
    "print(df5.loc[df5['weekday'] == 0, 'change'].sum())\n",
    "print(df5.loc[df5['weekday'] == 1, 'change'].sum())\n",
    "print(df5.loc[df5['weekday'] == 2, 'change'].sum())\n",
    "print(df5.loc[df5['weekday'] == 3, 'change'].sum())\n",
    "print(df5.loc[df5['weekday'] == 4, 'change'].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The largest difference in the above sums was between Monday and Tuesday.  Monday gained 52 points while Tuesday gained a total of 674 points.  Execute a ttest to determine if the sum of Mondays vs Tuesdays is really significantly different or if the difference was due to random chance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=-1.1291802920678604, pvalue=0.2590969855069577)\n"
     ]
    }
   ],
   "source": [
    "print(ttest_ind(df5.change.where(df5.weekday == 0).dropna(), df5.change.where(df5.weekday == 1).dropna()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pvalue above is about 0.26 which is much too high to validate a significant difference between the two days of the week.  It must be assumed that the difference is due to random chance.  If the pvalue was below 0.05, then a real difference can be concluded:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>DATE</th>\n",
       "      <th>SP500</th>\n",
       "      <th>price</th>\n",
       "      <th>date_time</th>\n",
       "      <th>weekday</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-08-07</th>\n",
       "      <td>2009-08-07</td>\n",
       "      <td>1010.48</td>\n",
       "      <td>1010.48</td>\n",
       "      <td>2009-08-07</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>2009</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-08-10</th>\n",
       "      <td>2009-08-10</td>\n",
       "      <td>1007.10</td>\n",
       "      <td>1007.10</td>\n",
       "      <td>2009-08-10</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>2009</td>\n",
       "      <td>-3.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-08-11</th>\n",
       "      <td>2009-08-11</td>\n",
       "      <td>994.35</td>\n",
       "      <td>994.35</td>\n",
       "      <td>2009-08-11</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>2009</td>\n",
       "      <td>-12.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-08-12</th>\n",
       "      <td>2009-08-12</td>\n",
       "      <td>1005.81</td>\n",
       "      <td>1005.81</td>\n",
       "      <td>2009-08-12</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>2009</td>\n",
       "      <td>11.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-08-13</th>\n",
       "      <td>2009-08-13</td>\n",
       "      <td>1012.73</td>\n",
       "      <td>1012.73</td>\n",
       "      <td>2009-08-13</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>2009</td>\n",
       "      <td>6.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  DATE    SP500    price  date_time  weekday  day  month  \\\n",
       "DATE                                                                       \n",
       "2009-08-07  2009-08-07  1010.48  1010.48 2009-08-07        4    7      8   \n",
       "2009-08-10  2009-08-10  1007.10  1007.10 2009-08-10        0   10      8   \n",
       "2009-08-11  2009-08-11   994.35   994.35 2009-08-11        1   11      8   \n",
       "2009-08-12  2009-08-12  1005.81  1005.81 2009-08-12        2   12      8   \n",
       "2009-08-13  2009-08-13  1012.73  1012.73 2009-08-13        3   13      8   \n",
       "\n",
       "            year  change  \n",
       "DATE                      \n",
       "2009-08-07  2009     NaN  \n",
       "2009-08-10  2009   -3.38  \n",
       "2009-08-11  2009  -12.75  \n",
       "2009-08-12  2009   11.46  \n",
       "2009-08-13  2009    6.92  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, print a barplot to show the daily mean of the S&P 500 for each day of the week with a 95% confidence interval to signifiy the possible range of the mean values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Fred\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\", rc={'figure.figsize':(10, 6)})\n",
    "sns.barplot(x='weekday', y='change', ci=95, data=df5)\n",
    "plt.xlabel('Day of Week: 0 = Monday')\n",
    "plt.ylabel('Daily Price Change')\n",
    "plt.title('Average daily price change - SP500')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen above, the mean values overlap significantly between Monday and Tuesday (as well as every other day).  Next, look at snow data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Snow</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-07-31</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-08-01</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-08-02</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-08-03</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-08-04</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Snow\n",
       "dt              \n",
       "2009-07-31   0.0\n",
       "2009-08-01   0.0\n",
       "2009-08-02   0.0\n",
       "2009-08-03   0.0\n",
       "2009-08-04   0.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['Rain'], axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prepare the snow dataframe to merge with the S&P 500 data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reset the index of this dataframe in order to merge this data with another dataframe\n",
    "df.reset_index(level=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['DATE', 'SP500', 'price', 'date_time', 'weekday', 'day', 'month',\n",
      "       'year', 'change'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# print columns and reset index for S&P 500 data\n",
    "print(df5.columns)\n",
    "\n",
    "# Fill Nan values with 0\n",
    "df5['change'].fillna(0, inplace=True)\n",
    "\n",
    "# drop unnecessary columns \n",
    "df5.drop(['DATE'], axis=1, inplace=True)\n",
    "df5.drop(['SP500'], axis=1, inplace=True)\n",
    "df5.drop(['price'], axis=1, inplace=True)\n",
    "df5.drop(['date_time'], axis=1, inplace=True)\n",
    "df5.drop(['year'], axis=1, inplace=True)\n",
    "\n",
    "# reset index for merging data with weather data\n",
    "df5.reset_index(level=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>DATE</th>\n",
       "      <th>weekday</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-08-07</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-08-10</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>-3.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-08-11</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>-12.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-08-12</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>11.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-08-13</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>6.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE  weekday  day  month  change\n",
       "0 2009-08-07        4    7      8    0.00\n",
       "1 2009-08-10        0   10      8   -3.38\n",
       "2 2009-08-11        1   11      8  -12.75\n",
       "3 2009-08-12        2   12      8   11.46\n",
       "4 2009-08-13        3   13      8    6.92"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge the snow and S&P 500 data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dataframe for S&P 500 and Snow in NYC in minutes\n",
    "df_snow = (df.merge(df5, left_on='dt', right_on='DATE')\n",
    "          .reindex(columns=['DATE', 'weekday', 'day', 'month', 'change', 'Snow']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, see the merged data with the daily change of the S&P 500 and the daily snowfall:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>DATE</th>\n",
       "      <th>weekday</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>change</th>\n",
       "      <th>Snow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-08-07</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-08-10</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>-3.38</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-08-11</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>-12.75</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-08-12</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>11.46</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-08-13</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>6.92</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE  weekday  day  month  change  Snow\n",
       "0 2009-08-07        4    7      8    0.00   0.0\n",
       "1 2009-08-10        0   10      8   -3.38   0.0\n",
       "2 2009-08-11        1   11      8  -12.75   0.0\n",
       "3 2009-08-12        2   12      8   11.46   0.0\n",
       "4 2009-08-13        3   13      8    6.92   0.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_snow.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the average daily snowfall in NYC in centimeters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.23134554140127384\n"
     ]
    }
   ],
   "source": [
    "# average snowfall in centimeters:\n",
    "print(df_snow.Snow.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Execute a ttest to see if there is a significant difference in price change of the S&P 500 for days when there is no snow at all vs days when it snowed at least 0.1 cm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=1.2124311083958048, pvalue=0.2254620986994717)\n"
     ]
    }
   ],
   "source": [
    "print(ttest_ind(df_snow.change.where(df_snow.Snow == 0).dropna(), df_snow.change.where(df_snow.Snow > 0.1).dropna()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This ttest is showing that there is **not a significant difference.**  (It is greater than 0.05)  Next, sum up the total price change for days when it did not snow and days where it snowed at least 0.1 cm.  This shows the absolute cumulative change:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1927.120000000002\n",
      "-131.58000000000084\n"
     ]
    }
   ],
   "source": [
    "# For S&P 500\n",
    "print(df_snow.loc[df_snow['Snow'] == 0, 'change'].sum())\n",
    "print(df_snow.loc[df_snow['Snow'] > 0.1, 'change'].sum())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow, when it does not snow, the S&P 500 gained 1927 points over the past 10 years.  When it snowed at least 0.1 cm, it lost about 132 points.  Seems like snow is not good for stocks, but is this really true ?  See below for further investigation.  The following cell counts the number of days when there was no snow and the number of days where snow was at least 0.1 cm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2374\n",
      "125\n"
     ]
    }
   ],
   "source": [
    "print(df_snow.loc[df_snow['Snow'] == 0, 'change'].count())\n",
    "print(df_snow.loc[df_snow['Snow'] > 0.1, 'change'].count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As shown in the above cell, only 125 data points for when it snows at least 0.1 cm.  This small sample and the difference in sample size could be a problem.  The days whet it snowed at least 0.1 cm only account for about 5% of the days where it did not snow at all.\n",
    "\n",
    "Next, look at some graphs to back up the insignificance of the pvalue:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0xc6f44e0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_snow.plot(kind='scatter', x='Snow', y='change', title='Snowfall vs daily price change', \n",
    "                 figsize=(10, 6), grid=True)\n",
    "plt.axvline(x=15, color='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the graph above, it does look like when it snows, stocks perform worse.  The only problem is -- there are not very many data points for when it does snow.  Next, print a barplot to show the average daily return for when it does not snow at all vs when it snows at least 0.1cm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wEhQzAAAAJ0ExAwAAcBIUMwAAACdBMQMAAHASFDOUSrt379bw4cO1e/fuko4CAECheZR0AKA4xMfH69ixY8rMzFTjxo1LOg4AAIXCihlKpczMzHxfAQC4HVDMAAAAnATFDAAAwElQzAAAAJwExQwAAMBJUMwAAACcBMUMAADASVDMAAAAnATFDAAAwElQzAAAAJwExQwAAMBJUMwAAACcBMUMAADASVDMAAAAnATFDAAAwEl42HogLy9P8fHxWrt2rdLS0uTu7i4/Pz+1bNlSL730kry8vByZEwAAoNSzWcwmTJigCxcuKDY2Vn5+fjIMQ+np6Vq2bJneeOMNTZ482ZE5AQAASj2bxSwpKUmbNm3Kt6969epq0KCB2rdvX+zBAAAAXI3Na8w8PDx0/vz5a/afO3dOHh42+xwAAACKyGbD6t27tyIjI9WyZUv5+fnJZDIpPT1dmzdv1sCBA2964kuXLikmJkYff/yx7r333pseDwAA4HZns5h17txZ9erV0+bNm/Xrr7/KMAxVq1ZN//jHP1SnTp2bmvTAgQOKjY1VamrqTY0DAABQmhR4TjIgIEB5eXk6ffq03Nzc5Ofnd9OlTJISEhL05ptv6rXXXrvpsQAAAEoLm8Xs+PHjGjJkiDIzM+Xr62t9V6a7u7tmzpypunXrFnnSiRMnFvlYAACA0spmMRs1apRGjhypZs2a5du/fft2xcbGasWKFcUe7nqSk5NLZF7cXsxms/Xr3r17SzgNgNKCv1tQ3GwWs8uXL19TyiSpadOmmjJlSrGGKkhgYKC8vb1LbH7cHq7+O+Lt7a2goKASTgOgtODvFhSW2Wwu0mKSzdtl3HXXXdqwYcM1+zds2CAfH58bnggAAAAFs7liNnHiRI0YMUJvvPGGqlatar1dRvXq1TVt2jRHZgQAAHAJNotZ9erVtXz5cqWlpen06dOyWCz629/+Jl9f31s2+ebNm2/ZWAAAALc7m6cy//Of/0iSfH199ccffyghIUGzZ8/Wrl27HBYOAADAldgsZpMmTZIkLVmyRFOmTFFAQIAeeOABvf3221q0aJHDApak7Jy8ko4AuCT+2wPgqux+6OXy5cs1f/586ynMqKgoxcTE6Lnnniv2cCXNy9Nd3V5zjRJa2vzxx0VJ0uk/LvIzvA0tnlL6/34BgOuxuWJ2laenpypVqmTd9vHx4UPMAQAAioHNYvbTTz+pT58+ys3N1Zw5cyRJp06d0oQJE/TAAw84LCAAAICrsLn0lZSUpP3792vfvn1yc/urvyUmJurPP//U+PHjHRYQAADAVdgsZhUqVFBISIhCQkKs+7p3767u3bs7JBgAAICrsXuNGQAAAByDYgYAAOAkKGYAAABOosD7XixatEg///yzGjVqpNDQUEdlAgAAcEkF3vl//fr18vb21vTp0xUfH+/AWAAAAK6nwNtlrF69Wh4eHurRo4defvll9erVy4HRAAAAXIvNFTMPDw/rHf59fX2Vk5PjsFAAAACuqNAX/7u7uxdnDgAAAJdn81RmVlaWDh06JMMwrrv98MMPOyYhAACAi7BZzMxmswYNGpRv39Vtk8mkxMTE4k0GAADgYmwWs82bNzsyBwAAgMsr9DVmP/30kzIzM3X8+HENGzasODMBAAC4pAJvMPvfFixYoE2bNsnb25vbZgAAABQDmytmGzduVPPmzTV69GhZLBZ16dJFFotFV65cUa1atRyZEQAAwCXYLGazZs3SBx98oKpVq2rkyJHq1auXhg0bpvj4eE2bNs2RGQEAAFyCzVOZJpNJgYGBunDhguLi4jR58mRFRkZKkvLy8hwWEAAAwFXYLGZhYWEKDg6Wt7e3unTpov/85z+KjIzU4cOH5enp6ciMAAAALsFmMRswYIDCw8NVpUoVeXh4qH///mrWrJmysrI0Y8YMR2YEAABwCQW+K/Pee++1/vmTTz7RkSNH5Ovrq7vuuqvYgwEAALiaAu9jdvz4cf3555+SpOTkZK1YsUJbtmxxRC4AAACXY7OYJSYmqmvXrkpNTVVaWpp69uyprKwsrV69WvHx8Q6MCAAA4BpsFrM5c+Zo8eLFCgoK0hdffKEHH3xQEyZMUFxcnFatWuXIjAAAAC7BZjG7cuWKatasKUnau3evmjVrJkkqW7asDMNwTDoAAAAXYrOYXS1fhmFo3759atCggfWxzMzM4k8G3ASTu2e+rwAA3A5sviuzVq1amjdvnsxmszw8PPTYY4/JMAzNmzdPgYGBjswI3LAKf6uvy6e/V3m/R0o6CgAAhWazmI0ZM0axsbE6c+aMpk6dKjc3N40bN05JSUn67LPPHJkRuGHeFe+Td8X7SjoGAAA3xGYxq1KliuLi4vLte+WVVxQbGyt3d/diDwYAAOBqCrzB7P+qVKlSceUAAABweQXeYBYAAACOY7OY/f77747MAQAA4PJsFrNXXnlFkjRy5MhbPun69evVvn17tW7dWosWLbrl4wMAANyObF5jduHCBf3973/Xzp07NWHChGsej42NLdKEaWlpmj59ulatWiUvLy/FxMSoUaNGqlWrVpHGAwAAKC1srpjNmjVLfn5+cnNzk4+PzzX/FFVSUpIaN24sHx8flStXTm3atNGXX35Z5PEAAABKC5srZnXr1lXdunV1//33Kzw8XLm5uTIMQ56eN3cn9fT0dFWpUsW6XbVqVR08ePCmxgQAACgN7N4u48knn9SLL76o3bt3Ky8vT0888YTee+89+fr6FmlCi8Uik8lk3TYMI9+2PcnJyUWatygefPBhLZ7ynMPmA/CXzMwsHT78Q0nHKDaBDz0o77LlSjoG4HLMVzKVfOhwSccokN1i9vbbb+uxxx7TtGnTlJeXpwULFuitt97SRx99VKQJ/fz89O2331q3z5w5o6pVqxb6+MDAQHl7exdpbgC3h3LlyigoKKikYxSrvVNeLOkIKALzuTTrV36Gt5+g1+Y67O8Ws9lcpMUku/cxS01N1aBBg3TnnXfqrrvu0uDBg/XLL78UKaQkBQcHa9euXTp79qyuXLmir776Ss2aNSvyeAAAAKWF3RWz3Nxcmc1m6yrVlStXbujU4//y9fXVsGHD1KNHD+Xk5Khz586qV69ekccDAAAoLewWs/bt26tXr16KioqSyWTSypUr1aZNm5uaNDw8XOHh4Tc1BgAAQGljt5gNHDhQfn5+2r59uywWi6KiotS5c2dHZAMAAHAphfoQ82eeeUbPPPNMcWcBAABwaXyIOQAAgJOgmAEAADgJihkAAICTsFvMzpw5o759+6pNmzb6448/1KdPH6WnpzsiGwAAgEuxW8zGjRunVq1aydvbWxUrVlTdunUVGxvriGwAAAAuxW4x++2339SlSxe5ubnJ09NTI0eO1KlTpxyRDQAAwKXYLWYmk0kWi8W6fenSpXzbAAAAuDXs3sesdevWGjFihC5evKilS5dq+fLlateunSOyAQAAuBS7xax///5as2aNLBaLkpKS9Oyzzyo6OtoR2QAAAFxKoe78HxkZqcjIyOLOAgAA4NLsFrOHH374mmvKypQpo4CAAL3zzjuqWbNmsYUDAABwJXaLWY8ePVS+fHk9//zzcnNz0/Lly3X8+HE1b95cb731lhYsWOCInAAAAKWe3XdlfvPNNxo0aJAqVqyoO+64Qy+88IKOHDmi0NBQXbhwwREZAQAAXILdYnblypV8d/pPT0+X2WyWJOXl5RVfMgAAABdj91TmSy+9pE6dOikkJEQWi0W7du3S6NGjNXv2bNWvX98RGQEAAFyC3WIWFRWlRx99VNu3b5eHh4cGDhwof39//frrr/Lz83NERgAAAJdQqNtl/Pnnn7rnnntkGIZSUlKUkpKi1q1bF3c2AAAAl2K3mMXGxmrbtm2qXr26dZ/JZKKYAQAA3GJ2i9muXbu0YcMGVahQwRF5AAAAXJbdd2VWq1aNUgYAAOAAdlfM6tevr2HDhqlFixYqU6aMdT+nMgEAAG4tu8Vs3759kqTly5db93GNGQAAwK1nt5jxkUsAAACOYbeYpaamauHChcrMzJRhGLJYLDpx4oSWLl3qiHwAAAAuw+7F/6+++qpycnK0b98+3XPPPfrxxx8VEBDgiGwAAAAuxW4xu3z5ssaNG6eQkBA1a9ZMn3/+ufbv3++IbAAAAC7FbjHz8fGRJFWvXl3Hjh3TnXfeKZPJVOzBAAAAXI3da8yqV6+uiRMnqlOnTho7dqwyMzOVm5vriGwAAAAuxe6K2VtvvaUGDRrooYceUnR0tHbv3q3x48c7IhsAAIBLsbtiVrZsWbVq1UoZGRlq37692rdv74hcAAAALsduMVu0aJHeffdd5eTkSJIMw5DJZNLhw4eLPRwAAIArsVvMPvvsMy1btkwPPvigI/IAAAC4LLvXmFWsWJFSBgAA4AA2i9n58+d1/vx5PfbYY4qPj9cff/xh3Xf+/HlHZgQAAHAJNk9lNm7cWCaTSYZhSJImT55sfexWXGP2wQcfyN3dXa+88spNjQMAAFBa2CxmR44cKZYJL168qEmTJumf//ynXnzxxWKZAwAA4HZU4DVmu3bt0rFjx6zb8fHx2rVr101NmJiYKH9/f/Xu3fumxgEAAChtbBazxMREvfrqq8rIyLDuK1OmjEaOHKmtW7cWecLIyEj17dtX7u7uRR4DAACgNLJ5KnPOnDn67LPPVLduXeu+mJgYBQYGasKECWrevHmBA2/cuFGTJk3Kt69GjRqKj4+/qcDJyck3dTwAlLSgoKCSjgC4rL1795Z0hALZLGZmszlfKbsqMDBQmZmZdgdu166d2rVrd3PpriMwMFDe3t63fFwAAFD6Oep/jMxmc5EWk2wWM4vFYvOgq+/UBADcOEtujoJem1vSMVAE3sk9pMzf5H2XLz/D25AlN0duHp4lHaNANq8xe/jhh7V+/fpr9n/xxRfy9/cvzkwAUKo5+y8GoLS6Hf7bs7liNnToUHXt2lXbtm1T/fr1ZbFYtH//fu3Zs0cLFiy46Ym5fxkAAEB+NlfMfH19tWLFClWvXl1bt27Vzp07Vbt2ba1du1b33XefIzMCAAC4hAI/xLxSpUoaNGiQo7IAAAC4NLsfYg4AAADHoJgBAAA4CYoZAACAk7BbzCwWi+bOnavXX39dly5dUlxcnPLy8hyRDQAAwKXYLWZTpkxRSkqKDh48KEnavn37NR+1BAAAgJtnt5jt2rVLkydPlre3typUqKDPPvtMO3fudEQ2AAAAl2K3mHl4eMjN7f+f5uXlJQ+PAu+yAQAAgCKw27ACAgK0aNEi5eXl6fjx44qPj7/uh5sDAADg5thdMRs7dqx++OEH/fnnn+rWrZsyMzM1ZswYR2QDAABwKXZXzCpUqKABAwbonXfe0aVLl/TLL7/orrvuckQ2AAAAl2J3xWzBggV6+eWXJUnnzp3TK6+8ouXLlxd7MAAAAFdjt5gtW7ZMS5YskSTdd999WrNmjebPn1/swQAAAFyN3WKWl5enChUqWLfvuOMOmUymYg0FAADgiuwWsxo1amjq1Kk6efKkTp48qRkzZsjf398B0QAAAFyL3WI2btw4paamKjIyUp07d1ZqaqreeustB0QDAABwLXbflXn33Xdr9uzZjsgCAADg0mwWs4kTJ2rs2LHq37//dR//+OOPiy0UAACAK7JZzJ588klJUps2bRwWBgAAwJXZLGZPP/20JGnNmjWaN2+ewwIBAAC4KrsX/1+8eFGZmZmOyAIAAODS7F78X7ZsWbVo0UJ16tRRuXLlrPu5xgwAAODWslvMOnfu7IgcAAAALq/AYpaSkqLy5cvr0Ucfla+vr6MyAQAAuCSb15itXLlS3bt31yeffKKOHTtqx44djswFAADgcmyumC1YsEDr16+Xr6+v9u3bp+nTpyskJMSR2QAAAFxKge/KvHr68vHHH9e5c+ccEggAAMBV2SxmJpMp37a7u3uxhwEAAHBldu9jdtX/FjUAAADcWjavMTt69Kjq169v3c7KylL9+vVlGIZMJpO+++47hwQEAABwFTaL2b/+9S9H5gAAAHB5NovZPffc48gcAAAALq/Q15gBAACgeFHMAAAAnITdYrZ582YZhuGILAAAAC7NbjFbsGCBWrZsqQ8//FBnzpy56QlJD0FUAAAW4klEQVT37t2rzp07KyIiQj179tRvv/1202MCAACUBnaL2eeff674+HhlZmaqS5cuGjJkiHbt2lXkCUeOHKkJEyZo7dq1Cg8P14QJE4o8FgAAQGlSqGvM7r//fg0bNkyjRo1ScnKyhg8frvDwcB08ePCGJsvOztaQIUNUt25dSVKdOnV06tSpG08NAABQCtm8XcZVJ06cUEJCgtauXas6depozJgxatGihQ4cOKChQ4dq8+bNhZ7My8tLERERkiSLxaLZs2erVatWRU8PAABQitgtZtHR0erUqZMWLlwof39/6/7HH39cDRs2tHncxo0bNWnSpHz7atSoofj4eGVnZ2vUqFHKzc1Vv379bihwcnLyDT0fAIBbxWw2W7/u3bu3hNOgNDIZdt5yeeXKFZUtW/aWTXj58mUNGDBAPj4+mjp1qry8vAp1nNlsVnJysgIDA+Xt7X3L8gAAUFg9evTQb7/9pnvuuUfz588v6ThwYkXtLTZXzMLDwws8cP369YVP919Gjhyp6tWra9y4cXJz4zZqAAAAV9ksZn//+99v+WSHDh1SYmKiatWqpU6dOkmSqlatqk8++eSWzwUAAHC7sVnMKleurJo1a+qHH364ZZM99NBDOnr06C0bDwAAoDSxWcymTJmiuLg4vfLKK9c8ZjKZlJiYWKzBAAAAXI3NYhYXFydJN3Q7DAAAABSd3dtlnD17VuvWrdPly5dlGIYsFotOnDih999/3xH5AAAAXIbdYjZ06FCVKVNGP/74o4KDg5WUlKSgoCBHZAMAAHApdu9X8fvvv2vOnDlq1qyZunfvriVLluj48eOOyAYAAOBS7Bazu+++W5Lk7++vlJQU+fr6Kjc3t9iDAQAAuBq7pzIrV66suXPn6rHHHtOsWbNUoUIFZWVlOSIbAACAS7G7YjZ+/Hh5eXmpQYMGCgwM1MyZMzVixAhHZAMAAHAphVoxCw8P15UrVzRy5EiNHDnSEbkAAABcToErZvHx8QoJCVFwcLDq16+v0NBQLV261FHZAAAAXIrNFbPFixdr48aNmjVrlurWrSuTyaSDBw9a718WExPjsJAAAACuwOaK2cqVKxUXF6fHH39cZcuWVZkyZdSwYUPNnDlTK1ascGRGAAAAl2CzmOXm5srHx+ea/b6+vrJYLMUaCgAAwBXZLGZubrYvPzMMo1jCAAAAuDKb15jl5eUpIyPjuiUsLy+vWEMBAAC4IpvFLCUlRY0bN75uMTOZTMUaCgAAwBXZLGZHjhxxZA4AAACXZ/fO/wAAAHAMihkAAICToJgBAAA4iUIVs6ysLB09elSGYejKlSvFnQkAAMAl2S1m+/fvV6tWrdSvXz+lpaXpqaee0nfffeeIbAAAAC7FbjGbMmWK4uPj5ePjIz8/P02ZMkUTJ050RDYAAACXYreYZWVlqVatWtbt5s2bc4NZAACAYmC3mHl4eCgjI8N6U9njx48XeygAAABXZPMGs1f1799f3bt31x9//KHhw4dr586dGj9+vCOyAQAAuBS7xezpp59WzZo1tXPnTlksFg0cOFA1a9Z0RDYAAACXYreY7dmzR5JUu3ZtSdK5c+eUnJwsf39/VahQoXjTAQAAuBC7xWzSpEk6cuSIateuLTc3N6WkpKhKlSq6cuWKJk6cqFatWjkiJwAAQKln9+L/v/3tb/r888+1du1arV69WkuWLFH9+vW1du1a/eMf/3BERgAAAJdgt5idPHlSjRo1sm7Xq1dPqamp8vPzK9ZgAAAArqZQt8vYsWOHdXvHjh3y9PTU2bNnlZubW6zhAAAAXInda8zefPNNDR48WCaTSRaLRd7e3po5c6bmzp2rmJgYR2QEAABwCXaLWb169ZSYmKiUlBS5u7urZs2acnd3V926dR2RDwAAwGXYLWZnz57VunXrdPnyZRmGIYvFohMnTuj99993RD4AAACXYbeYDR06VGXKlNGPP/6o4OBgJSUlKSgoyBHZAAAAXIrdi/9///13zZkzR82aNVP37t21ZMkSPi8TAACgGNgtZnfffbckyd/fXykpKfL19b2pd2N+++23ioqKUnh4uPr376+MjIwijwUAAFCa2C1mlStX1ty5cxUYGKiVK1dq8+bNysrKKvKEo0eP1pQpU7R+/XrVqlVLn376aZHHAgAAKE3sFrPx48fLy8tLDRo0UGBgoGbOnKkRI0YUecINGzaoVq1aysnJUVpamu68884ijwUAAFCa2C1m7777rnr06CFJGjlypNasWaPQ0NAiT+jp6amjR4+qefPm+uabb9ShQ4cijwUAAFCa2H1X5uHDh2UYhkwm0w0NvHHjRk2aNCnfvho1aig+Pl516tRRUlKSli5dqmHDhmnp0qWFHjc5OfmGcgAAcKuYzWbr171795ZwGpRGdotZ1apV1aFDBz366KMqX768dX9sbGyBx7Vr107t2rXLt89sNuvrr79Wq1atJEkdO3bUu+++e0OBAwMD5e3tfUPHAABwK1z9/ePt7c2to1Ags9lcpMUku8Xs8ccf1+OPP16kUNdM5uGhcePGyc/PT4GBgdq4caPq169/S8YGAAC43dktZoMGDVJWVpZOnDih2rVry2w2q2zZskWazN3dXdOnT9cbb7yhvLw8+fr6auLEiUUaCwAAoLSxW8wOHDiggQMHysPDQ0uXLlVERIQ++uijIq90NWjQQKtWrSrSsQAAAKVZod6VGR8fLx8fH/n5+WnKlCmscgEAABQDu8UsKytLtWrVsm43b95ceXl5xRoKAADAFdktZh4eHsrIyLDeLoPPyQQAACgedq8x69+/v7p3764//vhDw4cP186dOzV+/HhHZAMAAHApdovZ008/rZo1a2rnzp2yWCwaOHCgatas6YhsAAAALsVuMRs+fLi6dOmibt26OSIPAACAy7J7jdkTTzyhadOmKTQ0VHFxcTpz5owjcgEAALgcu8Wsa9euSkhI0Mcff6yMjAzFxMRo4MCBjsgGAADgUuwWs6uysrKUnZ0twzDk5lbowwAAAFBIdq8x+/zzz7Vq1SplZ2erc+fOSkhI0N133+2IbAAAAC7F7tJXcnKyYmNjtWnTJr3wwgv65ptvFB0d7YhsAAAALsXuitn777+vjIwMzZkzR4sWLdLly5f1/PPPOyIbAACASymwmB0/flzz58/X2rVrdc899ygrK0v//ve/dccddzgqHwAAgMuweSqzb9++6t69uzw8PDR//nx98cUXKl++PKUMAACgmNgsZocOHdLDDz+s2rVrq3r16pJk/bxMAAAA3Ho2i9mWLVvUqVMnffHFFwoJCdHgwYNlNpsdmQ0AAMCl2CxmHh4eat++vRYsWKBVq1apatWqMpvNat26tZYsWeLIjAAAAC6hUHeKrVWrlmJjY7Vt2zb16dNHCQkJxZ0LAADA5dzQLfzLli2rZ599VqtXry6uPAAAAC6Lz1YCAABwEhQzAAAAJ0ExAwAAcBIUMwAAACdBMQMAAHASFDMAAAAnQTEDAABwEhQzAAAAJ0ExAwAAcBIUMwAAACdBMQMAAHASFDMAAAAnQTEDAABwEhQzAAAAJ0ExAwAAcBIUMwAAACdBMQMAAHASJVbMDh06pMDAwJKaHgAAwOmUSDG7cuWK3n77beXk5JTE9AAAAE6pRIrZ5MmT1bNnz5KYGgAAwGk5vJglJiYqKytLbdu2dfTUAAAATs2juAbeuHGjJk2alG9fjRo1dOnSJcXHxxd53OTk5JtMBgBA0ZjNZuvXvXv3lnAalEbFVszatWundu3a5du3fPlyxcXF6bnnnrPui4iI0KJFi1ShQoVCjRsYGChvb+9bmhUAgMK4+vvH29tbQUFBJZwGzsxsNhdpManYitn1REdHKzo62rpdp04drV271pERAAAAnBb3MQMAAHASJVrMjh49WpLTAwAAOBVWzAAAAJwExQwAAMBJUMwAAACcBMUMAADASVDMAAAAnATFDAAAwElQzAAAAJwExQwAAMBJUMwAAACcBMUMAADASVDMAAAAnATFDAAAwElQzAAAAJwExQwAgEIqV65cvq/ArUYxAwCgkHr16qVHH31UvXr1KukoKKU8SjoAAAC3i8aNG6tx48YlHQOlGCtmAAAAToJiBgAA4CQoZgAAAE6CYgYAAOAkKGYAAABOgmIGAADgJChmAAAAToJiBgAA4CQoZgAAAE6CYgYAAOAkKGYAAABO4rb5rEzDMCRJ2dnZJZwEAACgYFf7ytX+Uli3TTHLycmRJKWkpJRwEgAAgMLJyclRmTJlCv18k3GjVa6EWCwWXb58WZ6enjKZTCUdBwAAwCbDMJSTk6Py5cvLza3wV47dNsUMAACgtOPifwAAACdBMQMAAHASFDMAAAAnQTEDAABwEhQzAAAAJ0ExAwAAcBIUMwAAACdBMUOps379erVv316tW7fWokWLSjoOgFLk0qVLCgsL06+//lrSUVBKUcxQqqSlpWn69OlavHix1qxZo2XLlunHH38s6VgASoEDBw6oa9euSk1NLekoKMUoZihVkpKS1LhxY/n4+KhcuXJq06aNvvzyy5KOBaAUSEhI0JtvvqmqVauWdBSUYrfNh5gDhZGenq4qVapYt6tWraqDBw+WYCIApcXEiRNLOgJcACtmKFUsFku+D7k3DIMPvQcA3DYoZihV/Pz8dObMGev2mTNnOO0AALhtUMxQqgQHB2vXrl06e/asrly5oq+++krNmjUr6VgAABQK15ihVPH19dWwYcPUo0cP5eTkqHPnzqpXr15JxwIAoFBMhmEYJR0CAAAAnMoEAABwGhQzAAAAJ0ExAwAAcBIUMwAAACdBMQMAAHASFDOgFNmyZYvCw8PVpk0bDR48WJcuXSrpSE5hxowZGj9+/E2PExsbq+Tk5Gv2r1q1Sv369bvp8f/XxYsX1aNHj+s+lpeXp4kTJ6pt27YKDQ3VkiVLChzr1KlTatq0qc6ePXvLcwK4dShmQClx9uxZjR49WrNmzdKmTZt03333aerUqSUdq0SdPn1agwcP1ueff35LxktKSpIj7zCUkZGh77///rqPLV26VKmpqfriiy+0YsUKzZs3z+bnwq5Zs0bPPfec0tPTizMugFuAYgaUEjt27NAjjzwif39/SVLXrl21fv16GYah77//XhEREXbHmDVrlkaNGqU+ffqobdu26tmzp/WX+bFjx/T8888rPDxcHTt21Jo1a647xuLFi9WxY0c988wz6tatm3788UdJ0tNPP61Zs2apW7duatGihT744APrMcuWLVNYWJg6duyoF154QT///LMOHz6s5s2bW5/Tp08fvf7665Kk7OxsNWrUSBcvXizw+1mxYoUaNmyo3r1759uflpamiIgIpaWlXXPM/v379dxzzyk6OlpPPfWUxowZI0maPn260tPTNWLECB04cMDmnBcvXtSoUaMUFRWl8PBwvfPOO8rNzbXmiY6OVmRkpFq0aKHFixdL+uujw1544QV16tRJnTp1sr42o0ePVlZWliIiIpSXl5dvnq+//lpRUVHy8PBQxYoV1aFDB61bt+6aPGlpafr666/16aefFvhaXb58WaNHj1abNm3Uvn17TZs2TYZhaNSoUXrrrbfUrVs3tWnTRu+++67i4uIUExOjli1bateuXQWOC+AGGQBKhbi4OOPvf/+7dTsnJ8cICAgwLl68WOgxZs6cabRs2dJ6TL9+/YwZM2YYOTk5RsuWLY1NmzYZhmEYp0+fNpo2bWp89913+Y7Pzc01Hn74YSMtLc0wDMNYvXq1sXTpUsMwDKNFixbG5MmTrcc/8sgjxi+//GIkJSUZrVq1Mv7880/DMAxj5cqVRrt27QyLxWI8/fTTxtGjR40rV64YTz31lNGsWTPDMAxjy5YtxosvvnhD39e4ceMK9dxhw4YZu3fvNgzDMC5dumQ0atTI+P77763fw8GDB685ZuXKlUbfvn0NwzCMUaNGGfPnz7e+HiNGjDDmzJljXLp0yejSpYtx9uxZwzAMY9++fcZjjz1mGIZhzJ492/qzu3z5sjF06FDjwoULxsmTJ63P+V9t2rQx9u3bZ91OSEgwBg4cWOD3FhAQYH2d/9c777xjDBs2zMjNzTXMZrPx3HPPGbt37zZef/11Izo62sjOzjbS09ONgIAA6/cXHx9v9O7du8A5AdwYPpIJKCUsFotMJtM1+93cbmxhvGHDhqpQoYIk6aGHHlJGRoZSU1NlNpvVunVrSX999FXr1q21fft2Pf7449Zj3d3d1bZtW8XExOipp55SSEhIvlWvli1bWo+vXLmyMjIytH37drVv316VKlWSJEVFRWnixIn69ddfFRoaqm3btql27dpq3Lixjh49qmPHjikxMdGa5VabPHmytm3bpo8//ljHjx+X2WxWZmZmoY/fsmWLvv/+e61YsUKSlJWVJUkqX768Pv74Y23dulWpqak6cuSIddymTZuqb9++OnXqlIKDg/Xqq6/qjjvuUEZGhs15DMPI9/M2DOOGf9b/LSkpSaNHj5a7u7vc3d21cOFCSdLq1avVokULeXp6qkqVKipXrpyaNm0qSbr//vt1/vz5Is8J4FoUM6CUqFatWr5TbGlpaapYsaLKlSt3Q+OUKVPG+meTySTDMJSXl3dN6TMMw3qK7r9NnTpVKSkpSkpK0pw5c7R27VrNmDFDkuTt7X3N2BaL5Zoxro7dqlUrzZgxQ+np6WrSpIkqV66sHTt2aNu2bRo2bNgNfV+F1b17d9WpU0dNmzZVu3btdODAgRu6rsxisWjGjBmqWbOmJOnChQsymUw6ffq0nn32WXXp0kVBQUFq27at/v3vf0uS6tWrp8TERO3atUu7d+9WdHS0PvnkE/n4+Nicp1q1avmuGUtPT5efn18Rv2vJw8Mj38/41KlT1n8XvLy8rnkugOLBNWZAKRESEqIDBw4oNTVV0l8Xh19dobpZNWrUkIeHh7766itJf5W+TZs2KTg4ON/zzp49q+bNm8vHx0e9evXS0KFDbV68flXTpk21YcMG67sFV65cKR8fH1WvXl3169fXyZMntWXLFgUHB6tJkyaaN2+e/P39ddddd92S7+2/XbhwQd9//71GjBih1q1b6/Tp0/rll1+s5dHd3f26ZfS/hYSEKD4+XoZhKDs7WwMGDNDChQuVnJysSpUq6eWXX1ZISIi1lOXl5Wnq1Kn68MMP1apVK40dO1a1atXSsWPH5OHhoby8vOsWw5YtW2rlypXKzc3VhQsX9M9//lOtWrUq8vf+5JNPavXq1bJYLMrOztbgwYO1Z8+eIo8HoGgoZkApUblyZU2aNEmDBw9Wu3btlJKSYr1YvrAX/9vi6empDz/8UPPnz1d4eLh69+6tgQMHqnHjxvmeV6lSJQ0YMEC9evVSVFSU3n//fU2YMKHAsZs0aaJevXqpZ8+e6tChg9asWaO4uDi5ubnJzc1NzZo1U/ny5VWpUiUFBQUpIyMj32nMGTNmWFfkCsvWxf933nmn+vbtq06dOiksLExz5sxR/fr1deLECUlSaGioRo4cqR07dtgce+zYscrMzFR4eLjCw8MVEBCgF198UU2aNJGvr6/atm2rdu3a6dSpU6pUqZJOnDihnj176siRIwoLC9Mzzzyje++9Vx06dFCVKlVUr149dejQQefOncs3T9euXXXfffcpIiJCnTt3VufOndWwYcMivyaDBg2Sp6enIiIiFBkZqebNmxfb6WIAtpmMG1mjBwAnk5qaqhUrVmjEiBElHQUAbhorZgBuaz///LOef/75ko4BALcEK2YAAABOghUzAAAAJ0ExAwAAcBIUMwAAACdBMQMAAHASFDMAAAAnQTEDAABwEv8HAqlWa12vLa0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_snow['amount'] = df_snow['Snow'].apply(lambda x: 1 if x > 0.1 else 0)\n",
    "\n",
    "\n",
    "sns.set(style=\"whitegrid\", rc={'figure.figsize':(10, 6)})\n",
    "sns.barplot(x='amount', y='change', ci=95, data=df_snow)\n",
    "plt.xlabel('0:  no snow, 1: at least 0.1 cm')\n",
    "plt.ylabel('Average Daily Price Change of S&P 500')\n",
    "plt.title('No snow vs at least 0.1 cm - SP500')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even though there is a low enough pvalue; there are not enough samples as evidenced by the mean barplot above with 95% confidence.  There is just too much variance in the possible mean values for when it snows at least 0.1 cm.  The number of days where this occurs from the past 10 years is 125 which was calculated a few cells above.  \n",
    "\n",
    "The verticle black line above represents the range of the mean value for each dataset with 95% confidence.  There is way too much overlap in the possible mean values for each of the two distributions.  No real conclusion can be drawn for when it doesn't snow at all in NYC vs when it snows at least 0.1 cm.  \n",
    "\n",
    "As a final test, the next few cells randomly drops 95% of the days where it does not snow at all to obtain approximately the same number of days for when it snows at least 0.1 cm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_snow_drop = df_snow.where(df_snow.amount == 0).sample(frac=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "115\n",
      "125\n"
     ]
    }
   ],
   "source": [
    "# print days when it doesn't snow (after dropping 95%) and days where it snows at least 0.1 cm:\n",
    "print(df_snow_drop.loc[df_snow_drop['Snow'] == 0, 'change'].count())\n",
    "print(df_snow.loc[df_snow['Snow'] > 0.1, 'change'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "301.3299999999997\n",
      "-131.58000000000084\n"
     ]
    }
   ],
   "source": [
    "# For S&P 500 - print the cumulative return for 5% of days when it didn't snow and the days when it snowed at least 0.1cm:\n",
    "print(df_snow_drop.loc[df_snow_drop['Snow'] == 0, 'change'].sum())\n",
    "print(df_snow.loc[df_snow['Snow'] > 0.1, 'change'].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shown above, the return for days of 0 snow is positive 301 points vs negative 132 points on days where it snowed.  Run the ttest for approximately the same sample size for days when it didn't snow vs days where it snowed at least 0.1 cm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=0.6361253360706519, pvalue=0.5253059869498639)\n"
     ]
    }
   ],
   "source": [
    "print(ttest_ind(df_snow_drop.Snow.where(df_snow.Snow == 0).dropna(), df_snow.change.where(df_snow.Snow > 0.1).dropna()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion:  \n",
    "\n",
    "The pvalue here is 0.52 which is way too high to conclude any significant difference between zero snow days vs at least 0.1 cm days.  \n",
    "\n",
    "The barplot above further demonstrates the major overlap of the means of the two samples (with the full dataset).  Therefore, it is not possible to conclude that snow affects stock prices.  \n",
    "\n",
    "Using a different cutoff point for the amount of snowfall (as opposed to 0.1 cm) would probably yield similar results due to the small sample size for days when it does snow.  The difference in the price change summation for the two samples would have to be massive, given the small sample size.  Even though, with all of the data, the difference in price change is 2059 points, this is still not large enough to conclude a true difference in the two different populations."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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