Linear Regression (depth).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set()\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rng = np.random.RandomState(1)\n",
    "x = 10 * rng.rand(50)\n",
    "y = 2 * x - 5 + rng.randn(50)\n",
    "plt.scatter(x, y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "model = LinearRegression(fit_intercept=True)\n",
    "\n",
    "model.fit(x[:, np.newaxis], y)\n",
    "\n",
    "xfit = np.linspace(0, 10, 1000)\n",
    "yfit = model.predict(xfit[:,np.newaxis])\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.plot(xfit, yfit);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "slope: 2.027208810360695\n",
      "Intercept: -4.998577085553202\n"
     ]
    }
   ],
   "source": [
    "print(\"slope:\", model.coef_[0])\n",
    "print(\"Intercept:\", model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5000000000000127\n",
      "[ 1.5 -2.   1. ]\n"
     ]
    }
   ],
   "source": [
    "rng = np.random.RandomState(1)\n",
    "X = 10 * rng.rand(100,3)\n",
    "y = 0.5 + np.dot(X, [1.5, -2., 1.])\n",
    "\n",
    "model.fit(X, y)\n",
    "print(model.intercept_)\n",
    "print(model.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.17022005e+00, 1.73907352e+01, 7.25231927e+01],\n",
       "       [7.20324493e+00, 5.18867376e+01, 3.73752880e+02],\n",
       "       [1.14374817e-03, 1.30815988e-06, 1.49620548e-09],\n",
       "       [3.02332573e+00, 9.14049845e+00, 2.76347041e+01],\n",
       "       [1.46755891e+00, 2.15372915e+00, 3.16072440e+00],\n",
       "       [9.23385948e-01, 8.52641608e-01, 7.87317280e-01],\n",
       "       [1.86260211e+00, 3.46928663e+00, 6.46190062e+00],\n",
       "       [3.45560727e+00, 1.19412216e+01, 4.12641722e+01],\n",
       "       [3.96767474e+00, 1.57424429e+01, 6.24608929e+01],\n",
       "       [5.38816734e+00, 2.90323473e+01, 1.56431145e+02],\n",
       "       [4.19194514e+00, 1.75724041e+01, 7.36625540e+01],\n",
       "       [6.85219500e+00, 4.69525764e+01, 3.21728209e+02],\n",
       "       [2.04452250e+00, 4.18007224e+00, 8.54625174e+00],\n",
       "       [8.78117436e+00, 7.71090232e+01, 6.77107778e+02],\n",
       "       [2.73875932e-01, 7.50080261e-02, 2.05428931e-02],\n",
       "       [6.70467510e+00, 4.49526682e+01, 3.01393035e+02],\n",
       "       [4.17304802e+00, 1.74143298e+01, 7.26708346e+01],\n",
       "       [5.58689828e+00, 3.12134324e+01, 1.74386272e+02],\n",
       "       [1.40386939e+00, 1.97084925e+00, 2.76681493e+00],\n",
       "       [1.98101489e+00, 3.92442000e+00, 7.77433445e+00],\n",
       "       [8.00744569e+00, 6.41191864e+01, 5.13430903e+02],\n",
       "       [9.68261576e+00, 9.37530479e+01, 9.07774739e+02],\n",
       "       [3.13424178e+00, 9.82347155e+00, 3.07891350e+01],\n",
       "       [6.92322616e+00, 4.79310604e+01, 3.31837571e+02],\n",
       "       [8.76389152e+00, 7.68057946e+01, 6.73117652e+02],\n",
       "       [8.94606664e+00, 8.00321082e+01, 7.15972573e+02],\n",
       "       [8.50442114e-01, 7.23251789e-01, 6.15083780e-01],\n",
       "       [3.90547832e-01, 1.52527609e-01, 5.95693272e-02],\n",
       "       [1.69830420e+00, 2.88423714e+00, 4.89831204e+00],\n",
       "       [8.78142503e+00, 7.71134256e+01, 6.77165766e+02],\n",
       "       [9.83468338e-01, 9.67209972e-01, 9.51220384e-01],\n",
       "       [4.21107625e+00, 1.77331632e+01, 7.46757023e+01],\n",
       "       [9.57889530e+00, 9.17552352e+01, 8.78913791e+02],\n",
       "       [5.33165285e+00, 2.84265221e+01, 1.51560348e+02],\n",
       "       [6.91877114e+00, 4.78693941e+01, 3.31197382e+02],\n",
       "       [3.15515631e+00, 9.95501134e+00, 3.14096168e+01],\n",
       "       [6.86500928e+00, 4.71283524e+01, 3.23536576e+02],\n",
       "       [8.34625672e+00, 6.96600012e+01, 5.81400253e+02],\n",
       "       [1.82882773e-01, 3.34461088e-02, 6.11671714e-03],\n",
       "       [7.50144315e+00, 5.62716493e+01, 4.22118578e+02],\n",
       "       [9.88861089e+00, 9.77846253e+01, 9.66954111e+02],\n",
       "       [7.48165654e+00, 5.59751846e+01, 4.18787106e+02],\n",
       "       [2.80443992e+00, 7.86488327e+00, 2.20565926e+01],\n",
       "       [7.89279328e+00, 6.22961858e+01, 4.91690917e+02],\n",
       "       [1.03226007e+00, 1.06556084e+00, 1.09993591e+00],\n",
       "       [4.47893526e+00, 2.00608611e+01, 8.98512981e+01],\n",
       "       [9.08595503e+00, 8.25545788e+01, 7.50087191e+02],\n",
       "       [2.93614148e+00, 8.62092681e+00, 2.53122608e+01],\n",
       "       [2.87775339e+00, 8.28146455e+00, 2.38320126e+01],\n",
       "       [1.30028572e+00, 1.69074296e+00, 2.19844892e+00]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "X = np.array([2, 3, 4])\n",
    "\n",
    "poly = PolynomialFeatures(3, include_bias=False)\n",
    "poly.fit_transform(x[:, None])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "poly_model = make_pipeline(PolynomialFeatures(7),\n",
    "                          LinearRegression())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d25c3a34a8>]"
      ]
     },
     "execution_count": 23,
     "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": [
    "rng = np.random.RandomState(1)\n",
    "x = 10 * rng.rand(50)\n",
    "y = np.sin(x) + 0.1 * rng.randn(50)\n",
    "\n",
    "poly_model.fit(x[:, np.newaxis], y)\n",
    "yfit = poly_model.predict(xfit[:, np.newaxis])\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.plot(xfit, yfit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predicting bicycle traffic\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "counts = pd.read_csv('E:/Python/Data/Fremont_Bridge.csv',\n",
    "                    index_col = 'Date', parse_dates=True)\n",
    "\n",
    "daily = counts.resample('d').sum()\n",
    "daily['Total'] = daily.sum(axis=1)\n",
    "daily = daily[['Total']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-10-03</th>\n",
       "      <td>3521.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-04</th>\n",
       "      <td>3475.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-05</th>\n",
       "      <td>3148.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-06</th>\n",
       "      <td>2006.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-07</th>\n",
       "      <td>2142.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Total\n",
       "Date              \n",
       "2012-10-03  3521.0\n",
       "2012-10-04  3475.0\n",
       "2012-10-05  3148.0\n",
       "2012-10-06  2006.0\n",
       "2012-10-07  2142.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']\n",
    "\n",
    "for i in range(7):\n",
    "    daily[days[i]] = (daily.index.dayofweek == i).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Mon</th>\n",
       "      <th>Tue</th>\n",
       "      <th>Wed</th>\n",
       "      <th>Thu</th>\n",
       "      <th>Fri</th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-10-03</th>\n",
       "      <td>3521.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2012-10-04</th>\n",
       "      <td>3475.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2012-10-05</th>\n",
       "      <td>3148.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2012-10-06</th>\n",
       "      <td>2006.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-07</th>\n",
       "      <td>2142.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "             Total  Mon  Tue  Wed  Thu  Fri  Sat  Sun\n",
       "Date                                                 \n",
       "2012-10-03  3521.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0\n",
       "2012-10-04  3475.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0\n",
       "2012-10-05  3148.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0\n",
       "2012-10-06  2006.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0\n",
       "2012-10-07  2142.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "columns overlap but no suffix specified: Index(['holiday'], dtype='object')",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-46-65c16f1efc51>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mcal\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mUSFederalHolidayCalendar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mholidays\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcal\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mholidays\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'2012'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'2016'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mdaily\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdaily\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mholidays\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'holiday'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[0mdaily\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'holiday'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minplace\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mjoin\u001b[1;34m(self, other, on, how, lsuffix, rsuffix, sort)\u001b[0m\n\u001b[0;32m   6334\u001b[0m         \u001b[1;31m# For SparseDataFrame's benefit\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6335\u001b[0m         return self._join_compat(other, on=on, how=how, lsuffix=lsuffix,\n\u001b[1;32m-> 6336\u001b[1;33m                                  rsuffix=rsuffix, sort=sort)\n\u001b[0m\u001b[0;32m   6337\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6338\u001b[0m     def _join_compat(self, other, on=None, how='left', lsuffix='', rsuffix='',\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_join_compat\u001b[1;34m(self, other, on, how, lsuffix, rsuffix, sort)\u001b[0m\n\u001b[0;32m   6349\u001b[0m             return merge(self, other, left_on=on, how=how,\n\u001b[0;32m   6350\u001b[0m                          \u001b[0mleft_index\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mon\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mright_index\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6351\u001b[1;33m                          suffixes=(lsuffix, rsuffix), sort=sort)\n\u001b[0m\u001b[0;32m   6352\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6353\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mon\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\merge.py\u001b[0m in \u001b[0;36mmerge\u001b[1;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[0;32m     60\u001b[0m                          \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindicator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mindicator\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     61\u001b[0m                          validate=validate)\n\u001b[1;32m---> 62\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     63\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\merge.py\u001b[0m in \u001b[0;36mget_result\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    572\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    573\u001b[0m         llabels, rlabels = items_overlap_with_suffix(ldata.items, lsuf,\n\u001b[1;32m--> 574\u001b[1;33m                                                      rdata.items, rsuf)\n\u001b[0m\u001b[0;32m    575\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    576\u001b[0m         \u001b[0mlindexers\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mleft_indexer\u001b[0m\u001b[1;33m}\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mleft_indexer\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36mitems_overlap_with_suffix\u001b[1;34m(left, lsuffix, right, rsuffix)\u001b[0m\n\u001b[0;32m   5242\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mlsuffix\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mrsuffix\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5243\u001b[0m             raise ValueError('columns overlap but no suffix specified: '\n\u001b[1;32m-> 5244\u001b[1;33m                              '{rename}'.format(rename=to_rename))\n\u001b[0m\u001b[0;32m   5245\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5246\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mlrenamer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: columns overlap but no suffix specified: Index(['holiday'], dtype='object')"
     ]
    }
   ],
   "source": [
    "from pandas.tseries.holiday import USFederalHolidayCalendar\n",
    "\n",
    "cal = USFederalHolidayCalendar()\n",
    "holidays = cal.holidays('2012','2016')\n",
    "daily = daily.join(pd.Series(1, index = holidays, name = 'holiday'))\n",
    "daily['holiday'].fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>2012-10-03</th>\n",
       "      <td>3521.0</td>\n",
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       "      <th>2012-10-05</th>\n",
       "      <td>3148.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2012-10-06</th>\n",
       "      <td>2006.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2012-10-07</th>\n",
       "      <td>2142.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Total  Mon  Tue  Wed  Thu  Fri  Sat  Sun  holiday\n",
       "Date                                                          \n",
       "2012-10-03  3521.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0      0.0\n",
       "2012-10-04  3475.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0      0.0\n",
       "2012-10-05  3148.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0      0.0\n",
       "2012-10-06  2006.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0      0.0\n",
       "2012-10-07  2142.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0      0.0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 17)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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LcW+jXHw73aTieqjBC/OmUm9TqrW3dRJgZN44WCYvjfcuLK5XCQf9zKeuDzOA3ox9XzbGSr4m/IkTw1M9sE2YAWB/1vmH0CxLG1UC/b//Vvh8PvbPxljN14U/cbKbkwBDQ7jkAd0srlXw+bYPaYGeY1krNIQPAy0Pdng7yeK80zOW8VYU5U5FUT7f//p5iqJcVRTl8/3/ftTWEbpIT9NY3qhxYG5mxxsz9mdj1JtdyjWxi1sGBmIMD2IlL7aB0DSNpfUa+7LbhxlAX4xanR6FstgtDIxE5HaJcYB9fQOxKrhuQDdiC+nYjhdiHMjO0NM0x4tbJmVxjHljFO44OW92vUlHUZQ3Aj8FGPub5wHvUlX1/7VzYCKQLzVpdXoc2CKBNMr+EcWl4mEnhmaKpTE8iH2ZGD4QPtRQqrWpNTvceDy74+tGPaLZbXZPImAcmdvpWZtLRQj4fawKfryu3uxQqrU5usWZ6FH2jcybrZK0ojCO551JRggH/cKFTc4C94x8fwfwQ4qi/IOiKL+vKMrOGvIwRgvO7bblBgdc2DKZYSWnZ8z37yBPOBRgLh0VXpa1/Li60f9d9PPEg2cts708Ab+f+XR0T8gCo/NGbHmMubDTAmOcElp2MHy6q+etquonFUU5MfKjrwEfUlX164qivAW4D/h/dnufna6wtwMrPu/Bc3oXsVNHsju+37NO6m0hK82ubXJa8b4b5SZz6SiHDmZ2fN3R/UkefHKNeDLKTDQ09eduxgpZHrmkN9A6dSSz4/vdeEoPZZXqHbF1U2qSnAlx/Ojsjq87sj/J159YJZaIkoiJqZsnjXYSu+mmpce6S/W20LrJV5rEIkFOHZsdtFDeimMHU1xereAPh5jfZeGyAjMXEP+5qqpG67k/B943zi+trTl3gH1hIWnJ5527rLfgjAV9O75fpK/Pc1cKtshphTydbo+NQp0bjmZ2fa/ZhN6w6rGn1rasKJsGq3Rz9pKhG/+O7xfu6+b8VXF109M0VnJ6Fe9u75Xph+Uef3r1urLzabFMN/2Ww/HQzroJoRvvC4tFYXWj9fNe+7Ix1td3PnKajeuL6WNPrfLsEzsvwuOy0+Jj5rTJXyuK8u39r78P+LqZQXkBY/u3sMsqmkmE+0fSxN3+bRQbaMDCFsU5m1nI6K9ZL4orz1A3O8uTiIWIR4NCx4kL5SadrrZrCAiGYSKRn7VVQze7yBMNB8kkwkLLUq61aba7zI8zb9K6vE7VSJjxvO8F3q8oSgtYBl5r7ZDEYTVfJxjwkxlpnboVPp+PhUyU9WIdTdN23Fq5xVpxvIUIYK7/EK4VxD0FsFao4/MxVhJyPhNjcb0qrG6MhWUc3Rj5CpFPnEwiz0ImxtNXi3S6vR1PDbnFJPPGMPBrIhlvVVUvAHf1v/4G8J02jkkY1gp1FjLRbasRR5lPx7iyVqXa6NgSi5wWwxDP7+Kpgjc879VCnblUdKwJv5COcnG5TLHaIpPYeSF2g3F3eAD7ssZxQXF1s1aok+7vRndjPh3jqStF8uXmWPI7zSS6MeLcTs0b8ZY6Qag12lQbnbEfKGPVFfXM6kQPYdow3mLK0mx3KVZaE+jG2e3spKyOeToDdN34QNjLiDvdHhulxliygPjPmuH07BaeA715mN/nc0wWaby3Yai0SR9CMSfV+gTGeyYaYiYSFHZCGeMaVzdzgutm3COpwCCMJ+rdnOvFBpo23kIEI/NG0MVoknkT8PvJJiOOySKN9zZM4g3BME4sqsFbKzQIBf2kxywimh+J4YvGuMlKg0EYSNAY/lqhTjDgGzukM5eKki+36PbEKyufZIcHXvC8dXnGSViC/qwVKi3aHft1I433NgyUNqaBEP0hXC/W9S33mAm7hXSMVrtHScCS/7UJEmLgjYV1LhXdsQXDKPPpKD1NIy9gyb+xI5gb09jNZcTWzXqxQSYR3rHMfxQjROfEzkga720w/viGMnZjPiPu9q86YfweRkINAspjTPSxdSNw2KTZ7lKpt8c2djDUjYj5lZxhvMdsRTCbjODzwYaAujHi95PMGyfDQNJ4b0O+pHs1s6nxtrLxaIhYJMi6gLFII1ywMKaxg6FXK6JHlCsbBmI83URCAVIzITFl6T8vk/RdMQyjiHHvjWJ/3oy5GAUDfmaTESHnTa6kx+/HdRJgxIlz4FmTxnsbNkoNIuEAM5Hxj8LPp6P9hI1YcWLjQZrEuxPZW82VGgQDPpITNAGbS8fYKDaEaw2b64c+xvVUQXzP2wdkJziSOZeOkS81hWsNuzHY4U0yb/o1Eg7MG2m8tyFX0uOQkxR1zKejNFtdqg3nrkIaB8NTHXcXASMFBwIm+XKlJtlkZKzz9wYLmSjdniZca9hc30DM7lIINorQnnepQSoRJhQc37TMp6NoDHchomAsrGbmjRPJcWm8t6DR6lBtdCZSGgw9ItHO4A5DQON7EMZrDcMvCu1Oj2K1NZGnCqPHBcWSxzDA44YZYMR4CyZLr6cnUSfVjdOVieNiJqSVSehte52YN9J4b0HOMHbJCR9Cw+CVBPPuypN7d7FIkFgkKNyJhnxFH092Qt0YuhRtMTITNomEAyRiIdYFe86K1RbdnjZx3/S5wbwRUzeTzBu/30cmEXZk3kjjvQXDjPlknndWUG81V2ri941/jthgNhkRbiHKD46iTShLX5eiLUbGs5adwECAvpPQE2rixPDNz5u+bgR71nImdqygOxaFcotez17dSOO9BRsmtkswXKGFMxDlBplkeOxzxAbZVIR6s0O9KU4Mf6Abs563YAZio9QkEQuN1QdklPlUlHZHrHP45ueN4fSIpZtcuUEsEiA2waEF0B2FnqZRqNgrjzTeW7BhcsWdFXD71+tpFMqtiY0diDmpTHtDfe9OJN1omjZIjE+KiCdODN1MKo+x6xBxxyryvJHGewvMbv/S8TB+n08oY1eoNOlp2sTJVxgJNQhk8IZJpMnkScZCBAN+oXZFlXqbdqdnUjfiOQobExboGIiYXzF2nFkTunHKUZDGewuGccjJHkK/30cmGRYqdjdMuojrQUyCmQQf6D3XZ5MRsWQxuYsAMUN0A6dngpMzBrMpsfIrlswbm+WRxnsLcqUmqfhkZ1UNssmI7u3anKwYl8FCNIXnLZp3ZyYOCbo8paozTYPGwaynCsNQg0jGe6PUIBzyE49OrptsUqz8St7kDm/0d+wOA41lnRRFuVNRlM9v+tlPKIryFVtG5SI9TSNXbkwcMjGYTerFIKVay+KRmcPssUcYjUWKYyBypaYpTxWG8tidSBoXsyEgGDHegsgCevx90sI2A+P5FGUxms7zdmZh3dV4K4ryRuBDQHTkZ88BXgOId6fUlJSrLTrdyc+qGgwMniBbQDPVlQbG30CUmLfhmZmZUCBenHiasEkqHsbnE0c3RmWx2XkjWhhomoU1GQ/rhToChE3OAvcY3yiKMgf8FvAf7BqUm5iNqRoMDJ4gmXMz1ZUGkVCAeDQojOdtNpFsMCvYTmKasEkwoPdmF0WWQfGUyWvmRDsNNM3C6vf5yCYjtodNdg1Oqar6SUVRTgAoihIAfh94AzBRDfhOV9jbgdnPe2qpDMDRgylT73HicAaAVs9amc2+V6neJhjwc+rY7MTnvAH2zc6wtF5lfj5h2eW9ZmW5tKFfunvErG6OZAGxdBPw+zh9Yo6ACd0sZGc4v1gSQjfL/W6Ch/cnTb3Hyb5umoLoptLvT3TDyTmi4clj+Pvn4jx2foNMNm4qdzYOk47qDuAG4APoYZSbFEV5t6qqu3rha2tlE8Mzx8JC0vTnXVosAhDymRtzAD0ZdmmpaJnM08izmquRTYbZ2KiY+v1ULMT5VpeLV/LEo9NfrDyNLOcu5wGIBnzmdKPpurkskG4yiTA5k7pJxkJ0uj3OXcqRmhm/w+J2TCPLhSu6bsJ+k/Omr5sry2LoZnmjSiIWolysY+YdkrEgmgZPn18fXExshp0Wn4mWBFVVv6aq6s2qqt4N/Bjw2DiG20sYySyzt4yLlHjpdHuUquYKdAyyAvVrMUJAkx7hNJgVSJaeplGstMhMWBY/yiBpKYA8hYqeoDc7b0RKjmv9QwuT9DTZjBPyyKOCmzBahmYS5jyZQaGOABMqX26iYS7pYjCIEwsQiywMmlKZkyceDRIKilGoU6616WmaaWMHYiX5Bk6PSd1Ew0FmIkEhFqJqo0Or3TOdfIXRs972zZuxwiaqql4A7trtZ3sB4yFMm5xUg0IdARKWxqSe6iFMieMRGd7duJcob2ZYqOO+boZOgnnjnRHouOC0O1bQk5aiOD1grjbCwIl5Iz3vTRQqLWYiwYkbBY0ym9Rv93a7UMeSCdX3IES4xKBQaRINmyvQMZhNRSnX2rQ7XQtHNjlD3ZiPVQ89bzEWI58PUnHzeZHZZFSIQp3ClCdnwJnwqTTemyhUmlPFIUHf1vc0jWLV3UIdS7y7vnERobClUGlOJQsM/xaGF+8W1iysYsW8U/EwAb95kyJK1aiVuyI754003iO02nqhwTTeEIhTyTdIIiXNy2M8wG5vzTvdHuVae2rdGH8L1w3EQDfTLKxi6EbTNPIWLKzizBsjfm/+WUvOhAj4fbbuWKXxHqFQnS5jbjD07sR4CKfZ/sUiQaLhAIWyu55qySLdZAXTzTTyhEP6jTpuL0S1Zod2pzfVcwbi7PKmPTkDeqFOOhG2dWGVxnsEK7ZL+u+Hr3k/tzAmQcpkgs8gk4i4PqHyFhi70d93PWxiJMUs2OXlSk1Xb9SZ9oSWwWAnIci8scJRKFZa9GzSjTTeI1iRRILRpkHuGoh8pUVqRu9jPQ3ZZGTQe9otDM9/+rCJKJ53i3DQP1XyFXTdNNtd6k33ErBWeKowGjZxPx8RDJjrjjhKJhmh29Mo25T7ksZ7BKseQhHCJpqmUShPH4eEocEsuijPtOeIDUTaFWUSkanL2kXoLmidbtyfN6B7/plEeHrd2LzLk8Z7BKsewrQAsbtGq0uz3Z1aFhAjMWbVVlYEA9Ht9SjVWlPvImD0hIZ7xwWt2rEmjCSfi7rp9fRTYmYLwUbJ2Hx6RhrvEax6CKPhILGIu0k+q4wdjIYaRJBnOt0EA36SMyFXQ1qlahtNm95JgKF35+ZxwWFIazp5jCSfm/OmVGvpurFg3mRtdnqk8R7B2Eqn49Z4q256EFYlkWBk++diqGFQXWlJGMhl3Vi4sA7ixC7WFFgqT183diX5dsMOp0d63g5QqLRIxEKWtHDMJNxN8llxjthAhDLsQqU5deWrQSYRodnqulbJZ6WBSAsQBipUmgT8PhIz03edzCT0JF+l3rZgZJMz2EVMccbbwO5z69J4j2BFBZ+B8T5uJfks9SAEiOEXytNXvhpkk+7KM0yMT28gREjAFipN0gm9Idu0ZFze5dkyb6TnbS/1ZodGq2vJigvDldutOPG0N5uM4vaEanesqXw1cFseq+oJABIxI8nnznPW0zQKlZZ1To/L88ZK423kvmTM22aKFlXwGbh9qsFK787tJJ9VRzgN3C7UsepUE+idEt2M4Vfqbbq96VrbjuL+vLEuVwRwZCFh6pakcZjuFPoewkpvCEYyzS5u//w+H0kLblgB/e+yWpjo5jvLsNIbGn0ft2L407a23UwmEebCcpmeplkSupgEKxPjMHKyybV5Y62j8IZX3W5bd1HpefcZ9gGx+CF0y0CU+3FIi1Z9N5N8Vu4iYCSR5OLCOm1r21EGSb6a80k++3ZF7s2biIW6iYaDzFhwfeBWjDVCRVHuBO5XVfVuRVFuAn4X8AHfBF6vqqq7zZEtwPqH0L2kmNaPQx7dF7fsPUeTfFY92ONi9a7I7QSsnuCzRha41uBN28dmUqzeFWUT7se8rZLFbnb1vBVFeSPwIfQLhwF+E3izqqrfBcwAL7NveM5hZRwShmfF3XgIq40OnW7P0ofQzSSf1bpJzugnI9zQjdHa1qodHowm+dzUjTXyxCJBwkG/KyGtTrdHyWLd2Mk4YZOzwD0j3/+wqqr/oChKGDgArNgyMoex2oMIBf0kYiF3J5QNxtuNSWV1Esnv77frdGEhKlq8wwN3HQWrd6xuJmCtajvsFLvuf1VV/aSiKCdGvu8qinIc+FugCKjjfNBOV9jbwaTQ7Wj7AAAgAElEQVSfV2128fng9PFZAlN24RuMIRtjeaNmieyTvMflnJ5YPLw/adnf/fiRDABtzTf1e5rRDcCZE3OEgtMX6YCum3NXi8zPJ6ZuQDSJPBv9uPTBfVbqRtd3W5t+nk36+/XWUDdWJccXZmd4/PwGs7PxqefiJPLkbNCNnZgKXqqqehG4QVGUnwHeBfyfu/3O2lrZzEeZYmEhOfHnreVqpGbC5HJVy8aRiIaoNztcupKfKk48qTwXrxQACPms+7v7u3ql6NXl0lTvaUo3+Zq+i8nXTH/uZuKRIJ2uxvlLuamMzqTyXLicByDit043vq5uQK+ulB3XzcpGlWDAT73SoFG1xluORwL0NDh7MTdVg6hJ5Tl/WZ83VupmWnZaRCZe1hRF+UtFUW7of1sG3GvybBF6gs/6RMWglarDfSesjhGPvpdbYRPLdeNSsy0r2xYYuJ2PsKJ96ihunTixY97YiZk9yW8Bf6AoyueAVwNvtnZIzlNvdmh1epbFVA3cmlR2xLyTM6F+ks9ZWRqtDvWmdZWvBm5dh2aHbuLRIMGAn6JFnu+4GO1TrTZ2e2ne2MlYe3lVVS8Ad/W//kfgu2wck+PkbfCGwL0LVa0+Fw3uteu0I8E3+n5OJy2tLmoBI8kXdnwXYWX71FHcmzfW68ZOZJEO9q24bp3QGHR5i1lbHJBNOt+u0zbduHS8zvg8K895g/73KVZatlXzbYVdxs54P+fnjXVth51AGm/s8YZgxEA47K1adcXWZtyo5Bs22LIppOVCzNuq1rajZBJheppGueacPMZzbUXzs1GGJfLOzxs7dGMX0nhjv+ftpHfX0zSKlZblMWJwpzLRqltaNuNWiXyhYl1r21HcWIxsmzdxl8ImFrYddgJpvLG+0MAgNajkc+4hrNSs7fI2ipsGwuqt7EwkSMjhSr5W29rWtqO4cW+qXWETo7eIk7JY3XbYCaTxxr4jQm5U8tmZMXdjJ2GXgRgk+ZzUjY0VfK7qxgZvNZuMODxvvFVdCdJ4A6PtU63v/mWcAtAcSvLZmTF3I8lXqLTwgS0NlzKJCKVai27PmVKFop0Lqwvn1u00eJlEmGqjQ7vjTM87rx0TBGm8AT2uatU1TpvJJCJ0uj1qDrVStXNCZV0KmyTjYYIWtSwYJZOIoGn6be5OYMcRTgM3rt0rlJtEQgGiYesTfEN5nHnW7NSNXTzjjbdd1ZUGThccDE7O2JEUczjJN9SNPRPK6VCD1a1tRxkmk51dWK2urjRwOr9ip27s4hlvvIfXONllIJydVHZu/4wkn1PGrt7s0mpb29p2FKfDQHbGiJ1OwBrtU+1zevaObuziGW+87eg1MYrT3p2R5LGjJ/Gwks/hCWX3rsjxhdX7uhm0T7V53ji1GOVt1I1dSONtt4FwuNS3UGkRDvptu+0mk4hQrDpTyWd3ubLjIa3B3ZX2GbySQ7qx29g5Pm/6z4BdurEDabxtqq40GBoI57w7O6orDQZJPgcq+ezeyrqxNU/EQoSC9ky7tJO6sal4ymCgG8fmTctW3diBd0ZqE8OLh71vILq9HqVqy9atn5NhILvP3roRNrEzIebks2b3jjXtcJWll+6uNJDG22YDkYiFCPidqbIsVdto2Jt0cbJfS6Fs78IaiwSJhAOO6Mau1rajZB3c5dkd0nLyGsF6s0OjZa9u7EAab5u35k4mkpwoNHDU867af/bWqfsS7WptO8pANw709R7sWO10FBIRR3ZFRY/dXWkgjXelSTDgIx61J8EHw4fQ7laqTpxVdTZsYlS+2umthinX2nS69lZZOrGwDvqbOJCAdaJ9aiYZpt7s0GzZW2XpxTPeMOZlDIqi3Ancr6rq3YqiPAd4H9AFmsCrVVX17A3yhUrL1gQfGK1US1TqbVI2GiInmsk7GlctN/XKV7+9ugHdM55LR237HLta247iZAzfifapozuJ/eEZ2z6n4IBu7GBXz1tRlDcCHwKMJ/s9wOtVVb0b+DPgl20bnc30ev32qTavuE4dScs7uDXP2xxX1asr7U2+gnM7CbtPZ4y+t1MLq90FLU7NGy82pYLxwiZngXtGvv8xVVUf6n8dBBqWj8ohyjU9lGG7gUg6U2U5bJ9qnzxOJfmqjQ6drn3VlQZO7SScqOCLRQKEQ37b+4E41T4161B1sl1th+1m17CJqqqfVBTlxMj3SwCKonwn8PPAC8b5oJ2usLeDcT6v2NRjaQf3JW0d39GDaQC6Pp/pzxnn92r92OCZE3PMRK3vkGgwn45SqrXslWWpBMCBhYStujl2KANAB3t102jrMfXTx2eZS8dMfc44zKVjFG3WzfJGFYAD8zbr5rChG/P2Y5zfq/d1c+aEvbqxGlNZOkVRfhR4C/BDqqqujfM7a2tlMx9lioWF5Fifd/5yHoBIwGfr+AL9ROXlpaKpzxlXnrVcjWg4QLXcoFq2b0OUjIW4ulZlabk4cbe/cWU5dzkHQNRm3fg1feJeWS7ZqpvljSo+oN1osdayr8NkMhZieb3K8kqRgN8m3VwpABAN+u3VTc8Z3aw4pBsz7LT4THzaRFGUn0T3uO9WVfXcFONyHadui3aqOVWh0nRk6+dEu04nYsT6+ztzQqNQaZKKhyc2qJOSSYTRsLfNrVPtU9Nx5+ZN0gHdWM1Eo1UUJQC8F0gCf6YoyucVRXm7LSNzAKeOCDnRSrXT7VGutR3JmDuRGHOqy1vaAVnsbjs8iiO6cWjepOJhfNg7b+xuO2wnY4VNVFW9ANzV/3bWttE4jFNZZidaqTpRBGLgRJLPqZtNIqEAM5HgoCDIDoatbR1cWMtNOGjPZzi1sAYDfpJxewvc7G47bCfe2idYjFMGwokqS6OqzhHj7cCVW07ebJJJRmz17pzsFe1Ej3Knwo3GZ9h5jWDRwXljNc944x0O+YlF7Cs0MLC7laoRI7bzmKCBU2GTgN9HImbfqRkDu+9LdPJ+RKO/Sd6BhdWJ9qmZRIRmu0vDpipLu7uK2skz3HjbX11pYHcrVScNhBNJPrtb245id2Wis56qMwurU+1T7ZbHqwU68Aw23p1uj3LV/upKA7sfwuH2z34DYXeSr6cZla/OeEN7yUCkHchH5MvOtU+121FwMtxoNc9Y412qtvT2qU4ZCJtbqQ6O1jkQVx0k+WzyVCs1415Rhw2E7Z63/fJEw0FikYBtz5nT7VPtzq8M540Mm3gGp7dL9nt3fQPh0DVOmaR9rVSdNHajn2Obd2fzPambsbPNrdPtU52aN166/szgGWy8XTIQNj6EsUiASNj+5CsMk3yttvWJJCd6tIxi932Jw9a29idfQX/WKvU27Y71bW6dbp86TMDapxufD1JxZ3RjJdJ4O7X9szkWWXCgO+Iow3ad1m9nDU/Vzkb/o9iuG6O1rQPJVxjKU7ThUgan26faHdIqVlqOVL7agfdGbBF23125GTtPNLQ7PSr1tjvG24ZQw8C7c8h4D+9LtF43w9a2LujGBnmcDjcmZ/RFz46F1cnKVzt45hrvwbloZxQ3aKVqg7EzPCynwgxgr7fqdEjLzvsSh61tndSNjQurgwVHAH6/j3QibIss9WaHVqdHJu69ZCU8k423w5432JdIcuOsqhPenZM3m9imG4d3EaOftRcWVv2z7KmyzDucSLYa4Y33Sr5my/2C+Upz4A07RTYRpmTDfYlFNyaUjQYiX24SDvqJRey7V3Qz+n2JXRoWtwR1y9jpn23Dwlpu4sPZBF8mEaHT7VFtWKsbN+aNlQhtvNcLdf7Tf/sn/uqrlyx/70LZ+U5ixkNSsjjJ52QfEAO7wyZOVVca2NXmNj8wEC6ETWza5TndPtUueZysfLUDoY23wWL/5g6rGF7j5OyKm7Hp2JMb3t0gyWdxLLLb61GqOlddaWCfgTBCQG543tbKYiT4nJQF7JNn0KNFet7Wk7Yp8eJWP4Nhqa/VnrfzHsQwyWetLKVqW698dTgOacTXLV9YXYh5h4IB4lHrK2Br/QSfU0c4DYYJWHvmjdOLkVUIbbwNA2F1h7R8f0I5/hDaFCd2y4OwI8nnxi5i9PPsW1idf9Ysd3pc6sBn97zZ02ETRVHuVBTl85t+9l8VRfm/bBnVCFkbyrDdinXZGbubiQSJhJxLvoKe5Gu0utSb1iWSnK7gM7DPQDQJBvzEo84lX0H/+9WaHZoWVsC6t2O1Tzd65eseNd6KorwR+BAQ7X+/oCjKZ4GX2Tw2oN/P12oD4dJDaFfHN6OCz2mM7WbRwgSs05WvBnbGvDOJsKPJVxipsrRQnrwLISCw7/RModwkFQ/h9zurG6sYx/M+C9wz8n0CeBvwR3YMaDNZG24GcbrQwCBjQyVfu9NzJfkK9hSD5F1I8IF+9M2Htbrp9fqtbV04R2zHOXy3QkCJWIiA39oqS03TKDrYEtoOdt3Lqar6SUVRTox8fx44ryjKD0zyQTtdYb8Th/engCU0f2Ci99jptfWWfs769PFZFrIzpsZllng0SKXenvjvsd3rV3I1AA7Mx03/jc1y9GAKgK7fb5lumv1mSqeOzbKwkJhugBOSSUYoW6ibXKlBT9M4MOeCbg7ouun5rNNNY6CbrOPyzKWjlGrW6aZSa9Hu9Njvgm6swrFA3Npa2dTvhfp7gwtX8xzKRsf6nYWF5I6ft7JRAaDbbJsel1lS8TDrhfpEn7uTPOeuFAGIhvyOyxJAr3i7vFhk7Wh6rN/ZTTdL633dtFzQzUyYpVyV1dXS2GGOneS5sFwCIBYKuKAbnUuLBdaOpMb6nV11s6brRmt3HJcnORPiwlKZldXS2A2+dpLnal+WWNh53UzCTguL0KdNYKQlpMVb8+RMiGDAefEziYil9yU63cd7FDvixIVyi1gkQDTsbIIP9Nhqq92j3rRINy42+h9eRGxl2KRFMODMvaKbySQidHsalVrbkvcb5L082tcEPGC87TjC5WYnMatjkW7F78Em4+2mbiw+ceJWjBiGTo8dunE6+QrWP2tuzhurGMu9UVX1AnDXpp+9zYbxXEfW4glVb3ZotrqOn/E2yIwkYBcysanfz82zqql4CJ/PuoSl0dr26D5nY90Gowbi0Hx86vcb1BO4YLxTcWsT/Uby9dSh8UIwVjNaZXls//Qxaq+XxoMHPO/EjJ5ptqryzW2l2eZ5u2AgAn4/qXjYMt0UXdeNtQbPTe8uGPCTmrGuwK1ca9HTtD00b7x7a7yB8Mbb7/P1W0JaNKFcKgIxyFp8vM59gxexrF2n03c9bsYuA5F2Ka5qZQVs3uUwwyCkZfG88WpfE/CA8QZdccWKvvJPi9srrvWxuxbxaJBQ0NnqSoNsIkK706NmQRGVm7uI0c+1ykAUKk2i4YCjrW1HySStK3Azck5u9QGxY944ea+oHXjDePczzWULMs151w2E9VtzN7d+w2Zb08tj6MY1A2FDwlII3Vggj9vzJmtxlWWh4uy9onbgCeNtZaih4FJTKoO0hVvzVttobete0sXKUIPbnndyJtS/L3F6WdqdHuVa27XnDCzWjUul8QaxSJBw0G9JfsXrd1caeMJ4Gw+MFYpzO2Fp5X2Jxs3tbsbtrPRWB+eiXdKN39e/L9ECWYx7RcVYWL0/b3w+n2UxfP1eUfeSr1bhCeNtqec9iHW5OamsMRBu7yLA2q15QYAkkmEgpk3Aup1bGf3svRA20T87TKnaotub7hpBt1pCW40njLelsbt+Bz43O4llEhFL7kvMlRuA28bbuiKqQqVJIhYiFHTvscwkwnS62tT3Jbp9qgmGNQVWVCcXyi1Xk6+g7/I0Tb+wYxryAswbK/CG8TbCJlM+hKLEuqy6L3FwAkAE422J5+389WebsepImttH68D6fIQo82baZ0163g5i1d2PlXqbbs/9WFfGoja3huc9mxyvYZcdGEVU08piHGnbKwZChCu2UjNhvQJ2SlmMyle3jZ31xtu9eWMFnjDesUiQaDgw9dbc7SIQA6sWI7ea449iVZKvUHVfFhiG6KbVjdvJVwC/30c6Hp56F+F2IZiBVZcy5KTn7SxWXIeWFyAOOfr50y5G+XKTgN/9QgOjynKaIiq37kfcTNaiUIMIyVewpgJWhGTl6OdPHdJyseeMlXjGeGcSESr1Nu2O+UyzCFtZsHb7l01GXC80GLTrrJtPJOVK+t/CzRAQWBs2cTv5Cro8nW5vqgSsMDtWi46lFspN4tEgkbA7VclW4SnjDdMpblhoIMr2z7ws3V6PYqUlxNbPiipLEU7OgIUJy7L7CT6wxuAVBPFU03HrwiZuP2dW4B3jbUGSz4h1ue3dpeLhqe9LLFXb9DRNiIfQilMNA92k3NVNPBokGJiuyrLW6NBodZlNiaCb6RdWUcImg9zXFDag3uzoiXEB5s20eMZ4W3GjjijeXTDgJxmfLskniixgza4oXxIjiWRFJd/gFJDLCxEM/565aeZNyZBHjGdtql1ExXDg3JdlWsY6ca8oyp3A/aqq3q0oyhngDwANeAR4naqq05U8jcHwUgbzHlG+1CQWCbpaaGCQSYRZztXQNM3UzSQFgY47WbErypebhEN+4lERdBPh3GKJnqaZyieIdI7YWEAMA2yGXLmJzwdpAcrJjXnT6fZMXWO4V44Jwhiet6IobwQ+BBjSvgt4q6qq3wP4gJfbN7whVmSac+WmMCtuJhGZ6r5EkY47WRM2aZBNRl25YmszmUSYnma+i+XAUxVAN7MWeN75kh6/D/jd36gb4Q6zBW4iLazTMo42zgL3jHx/B/CF/tefBV5s9aC2wvhjmz1/a8S6sgJs/WDkrHfZnEck0kM47cLa7nQp19pCGDuYXp58WZytueF550163r2eXpUsgiwwOm/M6UYkp2dadt2jqqr6SUVRToz8yKeqqnFotAykx/mgna6wH4fZ2Th+H5Tr7bHea/NrLi2XADi8Lzn1WKzg2MEUfHORrt9vSp5aS/fYzxyfYyE7/V2Y0zCvaYSCfirNjilZltarABxcSAihm6MH9Xsauz6fSd3oUcQzJ+ZYWHDnPs5RkjMhijVz82ajWKfb0zggiG6OH9LNTQdzumm0dd2cPjYrhDzTYCbAOBrfTgKFcX5pba1s4qOuJZOMsLJR3fW9FhaS173m7KUcALGg35KxTEskoIcHzl3Oc2xuZsfXbiXP8noVnw86zRZra9PflDIt6XiYtVzNpG7yAMyExdBNuN+07PyVAqf272x8t5Jnsf+91u4IIU8mEWE1X2d1tbRjWGpL3SwWAYiHA0LIEu7HCs5fzXPjkZ0vQ95KnqW1iv5FRwzd7MZOC4yZINaDiqLc3f/6B4AvmngPU8ymouTL5lpCGkUgooRNhokks1vzBql42FTSxg7mUlGK1ZapIqqcYEmkuSmTfPmyXqATDolRBDKbjNBsd01dVWecAhLh5AwMj/manTe5coNwyC/EoYVpMTPzfwl4u6IoXwHCwJ9aO6TtmUtF6WmaqWRFXpBzxAZz/UXEjIHQNI18uSVMHBJGYqsmchIiJfhgeCRuw6RuciVxYsQwnaMgmm7m0tMvrKIkxqdlrOVHVdULwF39r58EXmjjmLZldFJNaoRFewizSfMPYaXeptPtCeOpAsyl+4tRscG+zGQxeJGSr6CHGfw+nyljV2t2aLa7wsgCw3mTKzU4um+yGLwoxVMG8WiQcMhvamE1rqY7IkAewgrE2HOPyTRbJlGqKw1CQT/peNjUQyhiYx1jcpuRJyfY1tzv95FNhgfFNpMgWpgBRuaNiRMaop3O8Pl8zCajpmyA23ekWo2njPc0sci8gM1oZlMR8uXmxN34DAMpSvweYH5K3YSDYhToGOj5lebE+RWRKl8NZqcI0eVLDQL91rKiMJfSm9Q125PVSIhUKWoFnjLe08Qic6WGUBMKdAPR6WqUq5PF8DeKuvxzInl3A897co8oX9Z1I1Icci4VRdMmb9s7DDOI86xlp4l59xtsuXlt4GbMVo2uCzhvpsFTxnuYrJjsIRw2ChJLaXMmDZ6xeBl/DxEwu7C2Oz1KtbZwujEbBhKlte0oWZMFYd1eTy/QEWghAvMJWBHnzTR4ynjPRPSwx6QTKl8WK1lpYNaDENHzjoaDxKPBiWUxTqeItysyysonlEfAkFYo6CcVD09s7ArlFpomVvwezIeBjNeLNG+mwVPG2+fzMZeKTq40wZIuBoO+E5Ma71KDYMAnRKOgUeZSUTZKjYlubckVxYxDmvXucgKVxo8ym4yQKzcn042gTs+cyV2RiE7PNHjKeIP+IFUbHRqt8QsODKXNp90tI9+MsX2bOGxSbDCbjLp+g85mZlNRWu3Jbm1ZF1U3Jg3EerFOOh4mFBQnMQ5GfqU3UbMt0U4BGZhdWNdLevGUSIcWpsF7xtuE4taKdUC8WJeZsEmr3aVUawsnC4wYvOL48qz3dTMvmDxGEVV+gues19MLdESTBUa7C46vmw1BwwyGLJMsrHrxVEPIeWMWzxlvM5WJQ89bLMUlZ0IEA/49MaEAZtOT62ZdUN3ETORXCpUm3Z4mpIGYNbOwFsRcWMOhAMmZ0ETn1ks1/f7beQHnjVk8Z7zNnAJYL+pnVUW7+sjv8zGbjEwUNhE5Y24m1LBebOBDvK25mfyKsRAtTFhh6gTGmNYK48uzZiysGbF0A/QLdcbPrwzi3QLOG7N4znjPmQibrBfqzKXFixGDnqgrVVu0O+MVHIicdDGjm41inUwyIkyDrVFmU3p+pT5mQ6d1QcNzAAt9A2yEEMdhvdggEQsRDYtTPGUwm4ro5e718WL4Iu9YzSLejNmF2f7EWB9z+9fsx4hF2/oZTJq0FNnzNrzn9TG91U63R64sZowYYKGfRB33WVsviBkCgmFCeK0wnvHuaRobxfrA6IvGYN6MqRvpeQvAbFJvGjSuByHqSRMDYzu7mp9MHhEfwnQiTDDgG8RKdyNfbqJpe0c3op6cAZiJBknEQoMFZjeKlRadriakLDDFvJGet3sEA35mUxHWJp5QYiptX2Yyj2jDiBELFr8HPYY/n46NLYvoulmYUDeDsIlgZ9YN5tNR1ov1sXrpiHoKyGDieSPwjtUsnjPeAPuyMYrVFs3W7nFi0R9C4wqzcR/CtWKDbErMGDHouqk2OlQbu8cihdeNESeeYDFKJ8Q7422wkInR6Y7XD38QAhIw+Qr6cwawOu68KdSJRQJCNT+bFjEtwC5MsuoOvDtRH8IJtn/Ndpd8ucn+7M7XprnJJPKIHCOGyTzvbq9HXuD4PQxPjYwjjxGWXBBUnvl0FB+MtQPvaRqrhTr7sjNCNT+bFlPLkKIoEeAjwCmgBLxOVdWnrBzYTiyMrLpHdmkuL/rWXM/mB8aK4RuTbp/LFw7vxEA3+TonD+58x+CgeErQhTUWCZKcCY3l3entY8WNEcO1i9GzjmZ2fK3onncoGCCTjIw1bwrlJu1Oj/0CzxszmPW8fxaoqKp6F/B64P3WDWl3JvG8V/M1wiG9MY+I+Hw+9mX0OPFuZ1YNb1Zk4z3wvMfQzUquTsDvEzZGDLo8G8UGvd7Oulnp60ZkA7EwwYmTYfxeTKcHdN3kS81d7031wrwxg1njfRPwWQBVVVXg2ZaNaAwWxjQQmqaxkquzPzsj5Blvg4VsjFa7R3GXvt6DhzAjcNjEiOGPsZ1dzddYyMQI+MWN3i1kYnR72q7FOqu5GoDQIa1hDH/3Eycr+TqzqQihoMC6ycbQGC4022HYCZHnjRnMauYh4F8piuJTFOUu4LCiKI5laQbbv10MRKHSotnuCu0Nwfhx4tW8YSDElWc+HcPH7gtrpd6m2uhwYFbsCTVu3Hs51/e8BZZnNhXF59u9UKfZ0nMroutm3Hmz0p83e83zNpt6/TC6t/054MvA11VV3fHox8JC0uRHbU06EWaj3Nz2fRcWkiz3C19OHslY/vlWcvJoFr56iWZP21GefN8zf/aZBaIRcbPm89kYG6XGjrJsXMwBcOJwWmjdnDqaBS5Q746nm5tvWCAxI2aIDvTFZb2ws27OXS0CcOKQ2Lo5fSwLQL2zs26K/U6KN51ZGNwqtBcwawG+DfiSqqpvUBTl+cDp3X5hba1s8qO2Zj4V5cJymeWV4nXb7oWFJGtrZdRz6wAkI0HLP99KZoJ6SOfpi3luPZ697t8Nea6sVMgkwpRLdcSVBuaSEZ64VODqYoFw6NoNmSHLE2f7uokKrpvQUDfPOz133b8b8lxeLpGIhahXm9Srk1835hT7MjG+dXaD85dyJGKha/7NkOXxs2sApGMhoXUTDei6OXc5v+U4DXkuLZWJhAK0Gy3WmuO3xBWBnRZPs2GTp4B7FUX5CvDrwP9t8n1Mc2B2hm5P23HLtNLfyoq+/Ts4FwdgaaO67WvanR65UoN9AsdUDYzQwcpOuun/2wHBt7KGbhZ30E2n22O92GD/rNiyAByc03WzvFHb9jXL/fj9gTmxnzVjXu80bzRNY7VQY182tqeOCYJJz1tV1XXgxRaPZSIOLfQn1XptMME2M4h1CT6pMokwsUiAq+vbP4QruRoacEBwWQAOGQZvvcrRbY5yDuL3gi+siViIVDzM4g662Sg26PY0DnhgYR11FM4cSW/5mpWcN3QTiwSZTUV2nDe5UpNWuye8LGYQN5W8C0MDUdn2Ncu5GjORIMlN20PR8Pl8HJqLs5qv0+lufezJeEAPz+98rl0EjIV1p0m1uK4f4RStTe9WHJqbYaPYoNneOq2ztOENYwcj3mpuZ887GPB5ovf1obk4hUqL2jYVvcYzeGR+awfPy3jWeB+eN7azWz+E7U6X5VyNwwtxT2yXDs7H6fa0bUMNxkNoGEaRGehmG+Pd6fZY2qhyeD4h9BFOg4PzcTS2DzVcWdMdiCMLHlhY+7rZTpaeprG0UWNfdga/X3zdHNrFDhjP4CFpvMVhNh0lHPJzdW1rA7G4XkPTvDGhYGjwlrYxeIsDz1v8hzAdDxOPBrf1vFdyNbo9jSMeWIhguMvbLm7W0H8AAAxzSURBVLY6NN7iy5OIhUjEQtvG8NeLDRqtridkgRHjvc2zdrW/Mz/sEXkmwbPG298PNSznanR714caLq/2J9Qu5fOisOtDuFYhHg2SFrRSdBSfz8eh+Tir+dqWl0xc6S+4XllYD/UTd9sZvKtrVaLhgGc61h2ej7OWr295ifflFX3ebJerEI3d502VYMC35854g4eNN+iK63R7W544Mbyhox4xEIZHfXnt+hh+s91ltVDn8Lw3QkCgy6Npw3jwKF7yVAEO9w3ZpZXrdeO18BzA8QNJNIYOziiXV/Ujd8f2i3u+exRjV3Rli3nT6+khoAOzcaGreM3iaYmOH9AfsAtL15/xNJTple1SNhkhNRPiwlLpun87f7WIpnlnQsHQc7u4vIVuVg3deGNhTc2EmUtFuLBUuq7/zJXVSj8E5A1ZYGTebKEbw6B7xfOeiQbZn41xYal8XZ/yK6tlmu0ux/d7Q5ZJ8bTxPnVI71p3bvFag9fraVxYKrOQiRITuBJxFJ/Px8mDKTZKTYqVa4s8nryUB+DkoZ279InEqUP6MbSzm3SjaRrnl0rMpiLCNgvbihMHU5Rq7evu5zR0c9xDC6sx1kvbGO/kTMgT4TmDk4dS1Jqd63bgT14qDP59L+Jp431sX4KA38e5Td7qldUytWaHM4d3bnspGsZDdn7TTsJ4CE/t0mJVJI7sixMO+q9bWFdyNUq1NqcPbX3GWFSM9rbnNz1rT1zQjfeZw96R58DsDJFQgAsr1z5nuVKD9WKDkwdTngkBwYhuNj1rT17OX/Pvew1PG+9QMMDRfQkur5avaQv5xMX+hNqmCEFUDOO8eTF68lKemUjQU0mXgN/PiQNJrq5XrkmMPXFB72ly2kPGDuBkP9SwWTePX8gRiwQ8dRTN7/dx4kCSxbUqlZHb1x89twGwa69v0dhp3gQDPs+EgCbF08YbdCPQ6Wqc7TfTAXjsvP4QeskbAt3z9vngif5WHPR2l0sbVW44kvaUNwRw6nAaTYOzV4eT6tHzfePtsa3syUMpAn4fj18Y6qZUa3F1rcKpgylPnIke5dnHs2iAOvKsPWYY7yPeMt7H9icIBf3XzJtKvc25q0VOHUwJe2XgtHheqltP6c2CvnVWf/B6msbXn1glORPyxJnoUeLREKcPpzl7tTjwiB7rG4ubT866OTRT3HxCH7OhG03T+JfHV4hHg5w46J0YMUA0HORZRzNcXCkP+q4/3JfrphPe082zT+gN0IznS9M0HnxyjXDQP0hoeoVQMMCzj2e5ulYd3BL/+MU8mubNeTMunjfeNx7LEA76+Wa/S935pRKFcpPbz8x7zhsCuO3UHJoGj/S9oAef1Du83XLq+o52ovOsoxki4QAPPb2GpmlcXq2wXqhzy6k5Tx7dGjoK+rP20NP6/28/M+/amMxy8mCKWCTIQ0+v0+tpLK5XubpW4dZTc0JfwLAdm3Xz4FPenTfj4j0tbSIcCnDrqTmWNmqcvVrki99cAuCOZy24PDJzPK8/7i88tEi+3OThczlOH0kL3xlxK0JBP8+9YZ61QoPHL+b54rc8rhtlAR/wDw8tUqq2+ObT6xzZlxh06vMSwYCfb3/2PvLlJo+cz/Hlh5cBuEPxpm6ee8M8Pp8+byr1Nt9Q1zg4H+eEx3YRk+B54w3wvXccAeCDn3qULz+8xIG5mcFK7DUOzce5+eQs6uUC93/8G/Q0jR/8zpNuD8s0L77jKAAf+czjfOGhRebSUZ5zg/c8VdB7Yd92eo6ziyXe+bFv0Olq/NB3nfRcLsLghc85BMBH/+oJ/v4bV5hNRTxrvGdTUe5Q9nFptcI7/vBfaHV6/OB3nvCsbsZhTxjvG49luOvm/WyUGmga/NwrbvNkyMTgR190hkgowGq+zpnDab7v+UfdHpJpTh1K8T23HWSj1KTb6/Fzr7jN0wmkV33vGaLhACu5GicOJHnJXSfcHpJpThxI8aLnHiZfbtLq9PiZl91KKOjYbYaW88oXniIeDbKSr3N0X8LTTs84+Ha7sdwiNLtv5Oj2ejx6PsdsKspzbzoo9A0g47BWqHN5tcItJ2c5fCjjaXl6PY1HL+RIx8PcccshT8sC+gmgi8u6bo4c9rhuNI1Hz+dIzYR5/q3e102u1OD8UombTsxy7EjW8/IsLCS39UK9UX44BgG/n9tOe3M7vhULmdjg8luv4/f7PBvG2or5dIz59B7RjW9v6WY2FWXWA33IrcCU8VYUJQR8FDgBdIGfVVX1CQvHJZFIJJIdMBt8/EEgqKrqdwK/BrzDuiFJJBKJZDfMGu8ngaCiKH4gBXjrSmaJRCLxOKYSloqiHAU+BSSAeeBfqar6jxaPTSKRSCTbYNbzfgPw16qqPgu4HfiooijPjCyBRCKRCIDZ0yZ5hqGSHBACvHtAVCKRSDyG2bBJAvgwcBAIA+9RVfXjFo9NIpFIJNvgVJGORCKRSCzEu3XKEolE8gxGGm+JRCLxIJ4pj+9XdX4YvaozAvwG8BjwB4AGPAK8TlXVXv/1Z4C/UFX1lv73B4CPocfol4CfVlW15qwUQ6aVZ+R9XgB8TFVV17pXWaCbWfTagUf6b/nnqqq+x0ERrsECeeLAB4CT6M/b61VV/ZqzUgyxQJ53A8/pv90BoKCq6l0OijDAAlmOAX8E+NAPW/yEm3ZgGrzkef8ksKGq6vcAPwC8H3gX8Nb+z3zAywEURfkp4BPoZ9AN3gR8tP/ax4Cfc3DsWzGtPMZ5+19CP+3jJtPK8jzg/1dV9e7+f64Z7j7TyvMfgUf6r/1ZQHFw7FsxlTyqqv4HVVXvBr4fKKLL5BbT6uYNwP9QVfUFwKPAaxwcu6V4yXj/CfArI993gDuAL/S//yzw4v7XeeCFm37/DcB/71eFHgVW7BvqWEwlT/9c/QeBf2/vMMdiWt3cATxPUZQvKIryJ4qiHLRzsGMwrTwvAVqKovx1/33+2r6hjsW08hi8HnhAVdWH7RjkmEwry0NAtv+1p6vDPWO8VVWtqKpaVhQlCfwp8FbAp6qqcVymDKT7r/2fqqpWN/2+hn4W/RHgRcCXHRv8FkwrD7rH8V9UVb3q2KC3wQJZngDuU1X1hcBfAO9zaOhbYoE880BWVdWXAJ8G/otDQ98SC+RBUZQw+m7V67JcAX5eUZRH0T33P3Fo6JbjGeMNgzDB54A/6p8r7438cxIo7PT7qqq2VVW9CXgt8Ie2DXRMzMqjKMoh4HuA+xRF+TwwqyjKJ2we7o5MqZu/7/8uwJ8Dz7VlkBMwpTwbwF/2v/408HxbBjkB084ddG/2H1RVLdo0xLGZUpbfRs933Qz8IgLYAbN4xngrirIfeAD4ZVVVP9z/8YOKotzd//oHgC/u8Pv/n6IoL+p/W+ZahTvONPKoqrqoqqpixIiBnKqqP2b3mLdjWt0AHwJ+uP/19wFft2Oc42KBPF9C77wJYMRWXcMCeUA33p+1Z4TjY4EsefS4PcAiwxCK5/DMaRPgzeh/6F9RFMWIef0i8N7+lu5x9G3UdrwX+KCiKL+KbrjdjhVPK49ITCvLm4APK4ry74Eq8DN2DnYMppXnN4EPKYryFfSY6qvtHOwYWPGsKYjhpU4ry+uB9yuKEkBPbr7OzsHaiaywlEgkEg/imbCJRCKRSIZI4y2RSCQeRBpviUQi8SDSeEskEokHkcZbIpFIPIiXjgpKJGOjKMoJ9GZXj/V/FAP+EXiTqqrbtkZQFOVzqqq+aLt/l0hEQXrekr3Moqqqz1FV9TnAjcAyu59nvtv2UUkkFiA9b8kzAlVVNUVR7gNWFEW5Db1Y4xZgP/At4MeB+wEURfmqqqp3KoryfwC/ht618Tzws6qqbrgigESyCel5S54xqKraAp4C/jXQUlX1O4AzQAb4QVVVf6H/ujsVRVkAfgt4iaqqz0XvDHi/OyOXSK5Het6SZxoa8CBwTlGU16GHU24AEptedydwDPicoiigd6TMOThOiWRHpPGWPGPo975QgFPArwPvAT6C3sLVt+nlAeBLqqq+rP+7Ua438BKJa8iwieQZQf8SjrcD/wScBv5YVdWPoLcPfRG6sQboKooSBL4KfIeiKM/q//xXcLmXtUQyivS8JXuZQ4qiPNT/OoAeLvlx4AjwcUVRfhxooV/McbL/uk8B30S/neXfAX/c70B3Bf0KLolECGRXQYlEIvEgMmwikUgkHkQab4lEIvEg0nhLJBKJB5HGWyKRSDyINN4SiUTiQaTxlkgkEg8ijbdEIpF4EGm8JRKJxIP8b7CKP+XBHsywAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def hours_of_daylight(date, axis=23.44, latitude=47.61):\n",
    "    \"\"\"Compute the hours of daylight for the given date\"\"\"\n",
    "    days = (date - pd.datetime(2000, 12, 21)).days\n",
    "    m = (1. - np.tan(np.radians(latitude))\n",
    "         * np.tan(np.radians(axis) * np.cos(days * 2 * np.pi / 365.25)))\n",
    "    return 24. * np.degrees(np.arccos(1 - np.clip(m, 0, 2))) / 180.\n",
    "\n",
    "daily['daylight_hrs'] = list(map(hours_of_daylight, daily.index))\n",
    "daily[['daylight_hrs']].plot()\n",
    "plt.ylim(8, 17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Total</th>\n",
       "      <th>Mon</th>\n",
       "      <th>Tue</th>\n",
       "      <th>Wed</th>\n",
       "      <th>Thu</th>\n",
       "      <th>Fri</th>\n",
       "      <th>Sat</th>\n",
       "      <th>Sun</th>\n",
       "      <th>holiday</th>\n",
       "      <th>daylight_hrs</th>\n",
       "      <th>annual</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-10-03</th>\n",
       "      <td>3521.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.277359</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-04</th>\n",
       "      <td>3475.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.219142</td>\n",
       "      <td>0.002740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-05</th>\n",
       "      <td>3148.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.161038</td>\n",
       "      <td>0.005479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-06</th>\n",
       "      <td>2006.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.103056</td>\n",
       "      <td>0.008219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-07</th>\n",
       "      <td>2142.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.045208</td>\n",
       "      <td>0.010959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Total  Mon  Tue  Wed  Thu  Fri  Sat  Sun  holiday  daylight_hrs  \\\n",
       "Date                                                                           \n",
       "2012-10-03  3521.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0      0.0     11.277359   \n",
       "2012-10-04  3475.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0      0.0     11.219142   \n",
       "2012-10-05  3148.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0      0.0     11.161038   \n",
       "2012-10-06  2006.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0      0.0     11.103056   \n",
       "2012-10-07  2142.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0      0.0     11.045208   \n",
       "\n",
       "              annual  \n",
       "Date                  \n",
       "2012-10-03  0.000000  \n",
       "2012-10-04  0.002740  \n",
       "2012-10-05  0.005479  \n",
       "2012-10-06  0.008219  \n",
       "2012-10-07  0.010959  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily['annual'] = (daily.index - daily.index[0]).days / 365.\n",
    "daily.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily.dropna(axis=0, how='any', inplace=True)\n",
    "\n",
    "column_names = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun', 'holiday',\n",
    "                'daylight_hrs', 'annual']\n",
    "\n",
    "X = daily[column_names]\n",
    "y = daily['Total']\n",
    "\n",
    "model = LinearRegression(fit_intercept = False)\n",
    "model.fit(X, y)\n",
    "daily['predicted'] = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "daily[['Total', 'predicted']].plot(alpha=0.5);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}