Lecture_2.ipynb

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   "cell_type": "markdown",
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   "source": [
    "![rmotr](https://user-images.githubusercontent.com/7065401/52071918-bda15380-2562-11e9-828c-7f95297e4a82.png)\n",
    "<hr style=\"margin-bottom: 40px;\">\n",
    "\n",
    "# The Sakila Database\n",
    "\n",
    "One of the best example databases out there is the <a href=\"https://dev.mysql.com/doc/sakila/en/\">Sakila Database</a>, which was originally created by MySQL and has been open sourced under the terms of the BSD License.\n",
    "\n",
    "The Sakila database is a nicely normalised schema modelling a DVD rental store, featuring things like films, actors, film-actor relationships, and a central inventory table that connects films, stores, and rentals.\n",
    "\n",
    "<img width=\"1200px\" src=\"https://user-images.githubusercontent.com/7065401/58504872-fa243b00-8161-11e9-85ed-4b7d8d7ce9f7.png\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![purple-divider](https://user-images.githubusercontent.com/7065401/52071927-c1cd7100-2562-11e9-908a-dde91ba14e59.png)\n",
    "\n",
    "## Hands on! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sqlite3\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "## Loading our data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn = sqlite3.connect('data/sakila.db')\n",
    "\n",
    "df = pd.read_sql('''\n",
    "    SELECT\n",
    "        rental.rental_id, rental.rental_date, rental.return_date,\n",
    "        customer.last_name AS customer_lastname,\n",
    "        store.store_id,\n",
    "        city.city AS rental_store_city,\n",
    "        film.title AS film_title, film.rental_duration AS film_rental_duration,\n",
    "        film.rental_rate AS film_rental_rate, film.replacement_cost AS film_replacement_cost,\n",
    "        film.rating AS film_rating\n",
    "    FROM rental\n",
    "    INNER JOIN customer ON rental.customer_id == customer.customer_id\n",
    "    INNER JOIN inventory ON rental.inventory_id == inventory.inventory_id\n",
    "    INNER JOIN store ON inventory.store_id == store.store_id\n",
    "    INNER JOIN address ON store.address_id == address.address_id\n",
    "    INNER JOIN city ON address.city_id == city.city_id\n",
    "    INNER JOIN film ON inventory.film_id == film.film_id\n",
    "    ;\n",
    "''', conn, index_col='rental_id', parse_dates=['rental_date', 'return_date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "## The data at a glance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>rental_date</th>\n",
       "      <th>return_date</th>\n",
       "      <th>customer_lastname</th>\n",
       "      <th>store_id</th>\n",
       "      <th>rental_store_city</th>\n",
       "      <th>film_title</th>\n",
       "      <th>film_rental_duration</th>\n",
       "      <th>film_rental_rate</th>\n",
       "      <th>film_replacement_cost</th>\n",
       "      <th>film_rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rental_id</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>1</th>\n",
       "      <td>2005-05-24 22:53:30</td>\n",
       "      <td>2005-05-26 22:04:30</td>\n",
       "      <td>HUNTER</td>\n",
       "      <td>1</td>\n",
       "      <td>Lethbridge</td>\n",
       "      <td>BLANKET BEVERLY</td>\n",
       "      <td>7</td>\n",
       "      <td>2.99</td>\n",
       "      <td>21.99</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2005-05-24 22:54:33</td>\n",
       "      <td>2005-05-28 19:40:33</td>\n",
       "      <td>COLLAZO</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>FREAKY POCUS</td>\n",
       "      <td>7</td>\n",
       "      <td>2.99</td>\n",
       "      <td>16.99</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005-05-24 23:03:39</td>\n",
       "      <td>2005-06-01 22:12:39</td>\n",
       "      <td>MURRELL</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>GRADUATE LORD</td>\n",
       "      <td>7</td>\n",
       "      <td>2.99</td>\n",
       "      <td>14.99</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005-05-24 23:04:41</td>\n",
       "      <td>2005-06-03 01:43:41</td>\n",
       "      <td>PURDY</td>\n",
       "      <td>1</td>\n",
       "      <td>Lethbridge</td>\n",
       "      <td>LOVE SUICIDES</td>\n",
       "      <td>6</td>\n",
       "      <td>0.99</td>\n",
       "      <td>21.99</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2005-05-24 23:05:21</td>\n",
       "      <td>2005-06-02 04:33:21</td>\n",
       "      <td>HANSEN</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>IDOLS SNATCHERS</td>\n",
       "      <td>5</td>\n",
       "      <td>2.99</td>\n",
       "      <td>29.99</td>\n",
       "      <td>NC-17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  rental_date         return_date customer_lastname  store_id  \\\n",
       "rental_id                                                                       \n",
       "1         2005-05-24 22:53:30 2005-05-26 22:04:30            HUNTER         1   \n",
       "2         2005-05-24 22:54:33 2005-05-28 19:40:33           COLLAZO         2   \n",
       "3         2005-05-24 23:03:39 2005-06-01 22:12:39           MURRELL         2   \n",
       "4         2005-05-24 23:04:41 2005-06-03 01:43:41             PURDY         1   \n",
       "5         2005-05-24 23:05:21 2005-06-02 04:33:21            HANSEN         2   \n",
       "\n",
       "          rental_store_city       film_title  film_rental_duration  \\\n",
       "rental_id                                                            \n",
       "1                Lethbridge  BLANKET BEVERLY                     7   \n",
       "2                 Woodridge     FREAKY POCUS                     7   \n",
       "3                 Woodridge    GRADUATE LORD                     7   \n",
       "4                Lethbridge    LOVE SUICIDES                     6   \n",
       "5                 Woodridge  IDOLS SNATCHERS                     5   \n",
       "\n",
       "           film_rental_rate  film_replacement_cost film_rating  \n",
       "rental_id                                                       \n",
       "1                      2.99                  21.99           G  \n",
       "2                      2.99                  16.99           R  \n",
       "3                      2.99                  14.99           G  \n",
       "4                      0.99                  21.99           R  \n",
       "5                      2.99                  29.99       NC-17  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16044, 10)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16044 entries, 1 to 16049\n",
      "Data columns (total 10 columns):\n",
      " #   Column                 Non-Null Count  Dtype         \n",
      "---  ------                 --------------  -----         \n",
      " 0   rental_date            16044 non-null  datetime64[ns]\n",
      " 1   return_date            15861 non-null  datetime64[ns]\n",
      " 2   customer_lastname      16044 non-null  object        \n",
      " 3   store_id               16044 non-null  int64         \n",
      " 4   rental_store_city      16044 non-null  object        \n",
      " 5   film_title             16044 non-null  object        \n",
      " 6   film_rental_duration   16044 non-null  int64         \n",
      " 7   film_rental_rate       16044 non-null  float64       \n",
      " 8   film_replacement_cost  16044 non-null  float64       \n",
      " 9   film_rating            16044 non-null  object        \n",
      "dtypes: datetime64[ns](2), float64(2), int64(2), object(4)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>store_id</th>\n",
       "      <th>film_rental_duration</th>\n",
       "      <th>film_rental_rate</th>\n",
       "      <th>film_replacement_cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16044.000000</td>\n",
       "      <td>16044.00000</td>\n",
       "      <td>16044.000000</td>\n",
       "      <td>16044.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.506171</td>\n",
       "      <td>4.93549</td>\n",
       "      <td>2.942630</td>\n",
       "      <td>20.215443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.499978</td>\n",
       "      <td>1.40169</td>\n",
       "      <td>1.649678</td>\n",
       "      <td>6.081771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>9.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>14.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>2.990000</td>\n",
       "      <td>20.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>4.990000</td>\n",
       "      <td>25.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.00000</td>\n",
       "      <td>4.990000</td>\n",
       "      <td>29.990000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           store_id  film_rental_duration  film_rental_rate  \\\n",
       "count  16044.000000           16044.00000      16044.000000   \n",
       "mean       1.506171               4.93549          2.942630   \n",
       "std        0.499978               1.40169          1.649678   \n",
       "min        1.000000               3.00000          0.990000   \n",
       "25%        1.000000               4.00000          0.990000   \n",
       "50%        2.000000               5.00000          2.990000   \n",
       "75%        2.000000               6.00000          4.990000   \n",
       "max        2.000000               7.00000          4.990000   \n",
       "\n",
       "       film_replacement_cost  \n",
       "count           16044.000000  \n",
       "mean               20.215443  \n",
       "std                 6.081771  \n",
       "min                 9.990000  \n",
       "25%                14.990000  \n",
       "50%                20.990000  \n",
       "75%                25.990000  \n",
       "max                29.990000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "## Numerical analysis and visualization\n",
    "\n",
    "We'll analyze the `film_rental_rate` column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    16044.000000\n",
       "mean         2.942630\n",
       "std          1.649678\n",
       "min          0.990000\n",
       "25%          0.990000\n",
       "50%          2.990000\n",
       "75%          4.990000\n",
       "max          4.990000\n",
       "Name: film_rental_rate, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['film_rental_rate'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.9426302667663933"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['film_rental_rate'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.99"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['film_rental_rate'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f25cb837df0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['film_rental_rate'].plot(kind='box', vert=False, figsize=(14,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f25cb858e80>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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teH2VjRCITkvNALQKYP+KzkjjY01Sym0ppbIv4Q8APKliPUSmpoQSNYagKqYYgKjDXlquHyB/8wlXgFwOGy4NdzcPohN10s3G3J4Lg6c7/wPUG9HMDAVxnY0QiE5NzQB0GcC0EGJSCOEC8DEAz+9/gRBicN8fPwTghor1EJna0nY9lExE1AtAvQE3/C47AxB1zEvLKUS7XBgJeU/8HpdGunFtbRcVHh6nDru5Xg8r50/ZAEFxrj+A25tpSMlzmESnoVoAklJWAHwKwNdQDzZfklJeE0J8VgjxocbLflUIcU0I8QqAXwXwCbXqITK7pcawx7GIT7XvIYTAZK+fs4CoY16KJfGm0dCpGns8NtKNfLmKuxziSx12814aY2Ef/O7jN/A4zPn+LqRyZTb1IDql9vyNfAAp5VcBfPXAxz6z73//NoDfVrMGIqtY2s4h5HMi6HGq+n0mIn68wu1E1AHpQhnzW1n8gzcd1j+ndZeG6+eHXllJ4fxAe57EE7Xi5r1dXGjjNae00r69kUFf0NO29yWyGq2bIBBRm8QSOYypuP1NMR7xYS1V4CwKUt2txvmJR07ZQWsq6keX24HXVnh4nDqnUK5iIZ7FhcHTd4BTnGuEqVsb6ba9J5EVMQARmcRSIovxsHrb3xTjYT+qNYn1VEH170XWdr1xfuK0HbRsNoFHh4N4ld2zqIPubGRQk8DFNq4ARbvcCPtduMMARHQqDEBEJlCu1rCWKmBcxfM/itFGyFpunDkiUsv1tV2EfE70B92nfq/HRnpwY22XK5fUMTfvNRogtHnb5XRfF1eAiE6JAYjIBFaTeVRrshlO1KQ0WVhK8EA5qev6+i5mhoKnaoCgmBkMolStsYMhdczcVgYuuw1jbf65fH4ggLmNDDvBEZ0CAxCRCSgd4DqxBW4g6IHLbuMKEKmqUq3h5r00Ztp0fkJ5Cn9jnTNUqDPubmYwGfXDYW/vrdZ0fwDpYgXrO9yGTHRSDEBEJqCEkfEONEGw2wRGQl7EGIBIRQvxLEqVGmaG2hOAzvR2wWETzcYKRGq7u5XFmb72/0xWZgpxGxzRyTEAEZnA8nYWbocNfYHTn5VoxWjYxxUgUpXSAOFim1aAXA4bzvR2MQBRRxQrVSxtZ3G2t6vt732uv/6ebIRAdHIMQEQmsLSdw1jYB5vt9GclWjEW9mF5mwGI1HN9bRcuez20tMv5gQBuMgBRByzGc6hJ4Exf+wNQj8+FvoAbt+5l2v7eRFbBAERkAsuJXNsP2j7MeMSH3UIFqVypY9+TrOX6+i7ODXTB2cbzExcGA1hN5bFbKLftPYkOM7dZDyftDPD7nesP4DZXgIhOjAGIyOCklPUA1IEW2Aq2wiY1SSlxfW23bQ0QFBeUIZJcBSKV3d3KQAj1AtDZvi7Mb7ETHNFJMQARGVw8U0KuVO1IBzjFGAMQqWgrU8R2ttS28z+K8wP19+M2OFLb3GYGwz1eeF12Vd7/TK8f2VIVm+miKu9PZHYMQEQGt9yYx9OJDnAKrgCRmuY26tuHzvW3d4DkULcHAY8Dt+6xFTapa24zo9rqDwBMRuvvfXeL54CIToIBiMjglBDSiSGoii63A9EuFxshkCruNM5PnG3zAXIhBC4MBHBznStApJ5aTWI+nmn79bvfVG/9gRcH+xKdDAMQkcGtJPIAgJGQt6Pfl62wSS1zmxkEPA5V2rqfHwjg1kaaZydINaupPArlmqoBaCDogcdpw/wWAxDRSTAAERncaiqPaJcLHqc6e80fZIwBiFRyZzONs31dEKL9bd3PDwSRLlSwtlNo+3sTAcDclrod4ADAZhOYjHZxBYjohBiAiAxuNZXHcE9nV3+AegBaS+VRrtY6/r3J3OY2s5hW6en5ucb7Km2KidrtrkpbOA+aivoxzzNARCfCAERkcKupPIY7vP0NqG+5q0ngHp+kUxulciXEM0XVbh6VwZR3GYBIJfPxLHp8ToT9LlW/z1SvH7FkHqUKH0IRHRcDEJGBSSmxlspjqFuLAFRvurCSzHf8e5N5KSsz033t7QCniPhd6PY62T2LVLO0ncVEB7pyTkb9qNYktyITnQADEJGBbWdLKJRrmqwAKdvuVpL85Uvto1YHOIUQAmd6/Tw8TqpZjOcw0YHB1FONM0Y8B0R0fAxARAa22lh90eIM0GCPB0JwBYjaa24zA4/Tpuo1faa3iytApIpCuYq1nTwmop1ZAQLAc0BEJ8AARGRga6lGANJgBcjtsKM/4MFqigGI2udOY4Ckzdb+DnCKM31d2EwXsVsoq/Y9yJpiiRyk3Asnaur2OhHtcnEFiOgEGICIDEwJH1qsAAH1RgjcAkftdHdT3QGSwF57Ym6Do3ZTwsh4B84AAfWgxeuY6PgYgIgMbCWZh99lR7fXqcn3Hw55uQWO2iZbrGA1lVetBbbiTG/95pSd4KjdFrfrYWSyQwFoKtqFea4AER0bAxCRgSktsNUYGNmKkZAX93YKqHAWELWBci5H7RWg0bAPTrvgOSBqu8XtHEI+J7p9nXkoNRH1I54pIs3tnETHwgBEZGCrSW2GoCpGQj5UahIb6aJmNZB5KNuHlO5WanHabRiP+BmAqO0W49mONEBQjDe6zS1tcysy0XEwABEZ2NqONkNQFc1W2JxDQW2wEM9CCGAsrH4L4TO9ftzl2Qlqs8V4Z2YAKZS/K5wFRHQ8DEBEBpUtVpDKlTGk6QpQ/XuzExy1w2I8i6FuLzxOu+rf60xvF5a2syhz+ya1Sb0FdqGjAYgrQEQnwwBEZFBad4AD0AxfbIRA7bCwncNEVP3VH6AegMpViRifnFObKKswnbqGASDgcSLsd3EFiOiYGICIDEoZgjqi4RY4j9OOvoCbrbCpLTq5fWhK6QTHbXDUJsoZtk7MANpvLOzDcoLXMdFxMAARGdRKcwWoc08bDzMc8nILHJ1aMlvCTr7csZtH5fssbfPGkdpjscMzgBTjER+3wBEdEwMQkUGtpfJw2gX6Am5N6xgJ+bgFjk5toRFEOrUC1ONzodvr5I0jtc3idg5hv6vjc9nGwz6spfIoVXiejahVDEBEBrWazGOg2wObTZsZQIqRkBdrqTyqNalpHWRsytPzTrYQnoj4moMriU4rlsh1pIPhQWMRP2qSzWiIjoMBiMigVlPazgBSDPd4Ua5KbKYLWpdCBrYYz8LWoRbYivGInwGI2iaWzGlyJnOvExyvZaJWMQARGVR9CKq253+Afa2wuQ2OTmE+nsVIyAeXo3O/liaifqwmuXWITq9ak1hL5TGqwQrQOGcBER0bAxCRAZUqNWykC5oOQVWMhOq/fHkOiE5jcTvb0e1vQH0LXE2CXQzp1O7tFlCuSoyGOh+AegNueJ12nmcjOgYGICID2tgtQEpguMejdSnNbXi8iaSTklJiMZ7DZKSzN49Kty5ug6PTUuZJjYY7/1BKCIGxMDvBER0HAxCRASmrLXrYAud12RHtcvEALp1YPFNCpljRZAUIABbjvHGk02kGIA1WgABglLOAiI6FAYjIgJSwoYctcAAwzFbYdArKCkynA1DY70LA7eDhcTq1WDIPIYAhjRrTjEd8WE7kICW7cRK1ggGIyICUhgOD3dpvgQOAkR4vAxCd2EKjBfZkhwdICiEwHvVhkVuH6JRWEjkMBj0dbeKx33jEh0K5hs10UZPvT2Q0DEBEBrSayqE34IbHade6FAD1lajVVB41zgKiE1iMZ+GwCU1aCE+wFTa1QSyZw4gGHeAUSvt4ngMiag0DEJEBraUKmm21OMxwjxelSg3xLJ8+0vEtbmcxGvbBYe/8r6SJiB8ryTzKVbbCppOLJfKanf8B9hp6cDsnUWtU/W0jhHhOCHFLCDEnhPj0Q173ESGEFELMqlkPkVmspvIY0VkAAjgLiE5mIZ5rNiTotPGID9Wa5LVLJ1asVLGRLmjSAU4x3OOFTXAWEFGrVAtAQgg7gM8D+CCAGQAfF0LMHPK6AIBfA/AjtWohMpNaTWI1lddNAwRgrxkDO8HRcUkpsaTBDCCF8n25DY5Oai1VH0ug5QqQy2HDUI+XW+CIWqTmCtDTAOaklPNSyhKALwL48CGv+1cA/i2Agoq1EJlGPFtEqVJrrrroQTMA8Sk6HdNWuohcqYpJjQLQeIRnJ+h09mYAaTuWYDziwxJXgIhaomYAGgYQ2/fnlcbHmoQQbwYwKqX8ysPeSAjxSSHEFSHEla2trfZXSmQgq80ZQPoJQEGPEwGPgytAdGzKlp0xjW4ee7vc8LvszU50RMcVS2o3BHW/sbAPKwxARC3RrAmCEMIG4HcB/OZRr5VSfkFKOSulnO3t7VW/OCIdW0vVF0v11AQBqAcyrgDRcWkdgIQQGI/4eXicTiyWyMNpF+gLaDuWYCTkw3a2hGyxomkdREagZgBaBTC6788jjY8pAgAeBfAtIcQigLcCeJ6NEIgebjVVv2HU0xkgABhptMImOo7lRA5CaHs9T0R93AJHJxZL5jDc44XdJjStQ9mCx5lsREdTMwBdBjAthJgUQrgAfAzA88onpZQ7UsqolHJCSjkB4IcAPiSlvKJiTUSGt5rMI+B2oNvr1LqUNxjuYQCi41tuDJB0O7SbaTUe8SOWzKHCVth0AiuJnObnfwA052itJBnmiY6iWgCSUlYAfArA1wDcAPAlKeU1IcRnhRAfUuv7Epmd3jrAKYZDXqQLFewWylqXQgYS08HN40TEh3JVNreXEh1HLJnHiIYd4BRKF7oYzwERHcmh5ptLKb8K4KsHPvaZB7z23WrWQmQWK8m87s7/AMBwT/2X72oyj+CgvlanSL+WEzm8c1rbs51KAIslcxjTaB4RGVO2WEEiW9K8AQIARLtc8DhtiHELHNGRNGuCQEQns5bK66oDnIKtsOm4CuUqNnaLmjVAUCjfn0Mk6biaHeB0sAIkhMBIyMctcEQtYAAiMpB0oYzdQkWfW+B6OAyVjke5UdN61WWw2wuHTTAA0bHFEvWfd1pv41SMhrzNmojowRiAiAxECRd6XAGK+F1wOWwMQNSyZZ0MkLTbBIZDXp6doGNrDkHVyUOp0bCvuSpFRA/GAERkIM0hqDr5ZbufzSY4C4iOZXlb2xlA+42FfQxAdGyxZA4+lx1hv0vrUgDUO8GlCxXs5NmMhuhhGICIDETPK0BAva4VrgBRi5YTefhcdkR0cPM4GvZxCxwdWyyRx2jIByG0nQGkYCc4otYwABEZyGoqD5fdht4ut9alHIorQHQcy4kcxsL6uHkcDfmQzJWRZht3OoaVZE4XHeAUe8NQGYCIHoYBiMhAVpN5DPZ4YNN44viDDIe8iGeKKJSrWpdCBqCHGUAKZRseD5BTq6SUWNHJDCDF3jBUXsdED8MARGQgqzptga1QalvjNjg6gpSyuQKkB2yFTceVypWRKVaaoUMPur1OBNwOboEjOgIDEJGBrCZ1HoBCbIVNrYlnSsiXq7oLQLxxpFY1ZwDp5BoGGrOAwj4OQyU6AgMQkUEUK1VsposY0nMA6uEwVGqNstKilwDU7XMi4HGwhTC1rDkDSEdb4ID6NjieASJ6OAYgIoO4t1MAoM8W2IqBbg9sgitAdLSYTmYA7TfGTnB0DHsrQPr6mTwa8iGWyENKqXUpRLrFAERkEMqqyoiOV4CcdhsGgh6uANGRlKChp/MTDEB0HLFEDj0+JwIep9alvMFo2It8uYrtbEnrUoh0iwGIyCCU+Tp6XgEC6vVxFhAdZTmRw0DQA4/TrnUpTaNhH1aSedRqfHJOR4sl87rb/gZwFhBRKxiAiAxiNZmHEPVtZnrGWUDUCj11gFOMhn0oVWrYTBe1LoUMYCWhrxlAipEwW2ETHYUBiMgg1lJ59Ha54Xbo54n5YYZDXtzbLaBSrWldCumYnmYAKdgKm1pVq9VnAOl6BYiNEIgeiAGIyCBWU3ndb38DgOEeH6o1iQ0+RacHKJSruLdb0N0KEAMQtWozXUSpWsOIzq5hAPC7HQj7XRzqS/QQDEBEBqH3IaiK5iwgbr+gB1hN5SElMBbR1/U81OOBEDw7QUdrdoDT6UMptsImejgGICIDqNUk1lMFg6wA1WtcYyMEegC9zQBSuB12DAY9DEB0JD22cd9vNOTjGSCih2AAIjKArUx9u9LWp0AAACAASURBVIUhVoCUYagMQPQAer55HGUrbGqBsr1Mrz+TR8L1ZjTsaEh0OAYgIgNQwoRef9nu53XZEfG7+PSRHmh5OweP04beLrfWpdxnNOzj4XE60koyh/6gW1dt3PcbCflQqrKjIdGDMAARGYBynsYIW+CAep1cAaIHUVpgCyG0LuU+Y2EfNnaLKJSrWpdCOhZL5jCiww5wCuVsEsM80eEYgIgMwEgrQAAw1O3FKn/x0gPUA5Bf6zIOpZxL4gFyephYIq/bBgjA3vZSnmcjOhwDEJEBrCbzCHocCHicWpfSEmUFSEruP6c3klIipsMhqIpRtsKmI5SrNazv5HV5hk2hPCzjVmSiwzEAERnAaiqPIYOs/gD1X76Fcg2JbEnrUkhnEtkSsqUqxsL6vJ5HG3Vxhgo9yHqqgJqELoegKjxOO/oCbq4AET0AAxCRAayl8hjR8XaLg5qzgHgOiA5otsCO6PPmsbfLDY/TxhUgeiDlXM2ITkO8gg09iB6MAYjIAFaTxhiCqmi2wub2CzpACRZ6fXouhMAYW2HTQ8R0fg0r6sNQ+TOY6DAMQEQ6t5MvI12sGKYDHIDmahVXgOgg5eZR3x20fNw6RA8US+ZgtwkMdnu0LuWhRkM+rO8UUKnWtC6FSHcYgIh0rtkCu0e/N4wHdXud8LvsfPpI94kl8ugNuOF16XN+CtDYOpTIsYkHHSqWyGOoxwOHXd+3UKNhL6o1ifWdgtalEOmOvv/2ElFzFWWoR99PG/cTQnAWEB1qWccd4BRjYR+ypSqbeNChYsmc7re/AXurrFzNJLofAxCRzq2ljDUEVTHc4+UZILqPUQIQAMR4/dIh6jOA9H0NA3tnlLgST3S/lgKQEOI/CSF+SgjBwETUYaupPFwOG6J+t9alHAtXgOig5vwUnYd5Zb7L0nZW40pIb/KlKuKZYrNdup4N9nhgE2AnOKJDtBpo/i8A/wjAHSHEvxFCnFexJiLaR+kAZ7MJrUs5luEeH3byZWSKFa1LIZ1YS+Xr81N0vgKk3NzyyTkdtNIIE3q/hgHAabdhsNvLLXBEh2gpAEkpvyGl/McA3gxgEcA3hBDfF0L8nBDCGKPpiQxqJZU31PkfRXMWEG8iqUEZLqr3m0efy4FolxvL27xxpDdqzgDS+SqmYjTs5VZOokO0vKVNCBEB8AkA/xTASwD+D9QD0ddVqYyIABhvBpCiOQsoxZtIqmsOQdV5AAKUG0deu/RGyqqgEc4AAfVGCFwBIrqfo5UXCSH+EsB5AH8M4GeklOuNT/2pEOKKWsURWV2hXN9vbqQW2IoRrgDRAbFkDk67QH9Q/yuaY2Efri4ltS6DdCaWyMHtsKE3YIwzmaMhHzbTRRTKVXic+m09T9Rpra4A/b6UckZK+b8r4UcI4QYAKeWsatURWZwyv8FoHeAAoLfLDZfdhhU2QqCG5UQOIyEf7AY4zzYW9mEtlUeZQyRpn1gij5GQF0Lo/xoG9s6zsSEN0Ru1GoD+t0M+9oN2FkJE99sbgmq8AGSzCQz2eLgCRE0riZyBzk74UJN7beiJgMYMIANs4VQotXIbHNEbPTQACSEGhBBPAvAKIZ4QQry58c+7ARjnJwCRQSnnZ4wYgIB63byBJIURZgApRptDJHn90p5YwhhDUBXN65gPooje4KgzQB9AvfHBCIDf3ffxNIB/oVJNRNSwmsxDCGCgW/9nJg4z3OPFt+9saV0G6UC6UEYyVzbM0/OxSL3OZT45p4adfBm7hYohZgAp+gKNrci8jone4KEBSEr5RwD+SAjxESnlX3SoJiJqWEnl0R/wwOUw5gzi4ZAXm+kiSpWaYf8dqD2UlRSjrAANBD1w2gUDEDUp28iMtAJkswkMh7ycaUV0wEMDkBDiZ6WU/x+ACSHEbxz8vJTydw/5MiJqk9Vk3jBnJg4z1OOFlMD6Th7jEb/W5ZCGlJbSRrl5tNsEhnvYCpv2GGkI6n4jIV7HRAcd9UhWuWPpAhA45J+HEkI8J4S4JYSYE0J8+pDP/5IQ4jUhxMtCiO8KIWaOWT+Rqa2m8obsAKcY6WErbKqLGWgGkGI0zBkqtKc5yNcgIV7B65jofkdtgfu9xv/9X4/7xkIIO4DPA3g/gBUAl4UQz0spr+972Z9IKf/vxus/hPo5o+eO+72IzKhSrWF9p2DoFSAlvLEVNsUSOQQ8DnT7nFqX0rKxsA9feW396BeSJcSSxruGgXpgS+bKyBQr6HK3NP6RyPRa2pQvhPicECIohHAKIb4phNgSQvzsEV/2NIA5KeW8lLIE4IsAPrz/BVLK3X1/9AOQxymeyMw20kVUa9KQQ1AVg91eCMEVIDJWBzjFWNiHVK6M3UJZ61JIB4zWAU6hPETjKhDRnlZPJf9kI6z8NIBFAGcB/NYRXzMMILbvzyuNj72BEOKXhRB3AXwOwK8e9kZCiE8KIa4IIa5sbbGjFFmD0rXHyCtALocNfQE3h/ARYsm84W4eOUOF9osl84bqAKdQrmM2QiDa02oAUtZMfwrAn0kpd9pVgJTy81LKMwD+OYB/+YDXfEFKOSulnO3t7W3XtybSNSU0GPkMEFBvhc0VIGuTUiKWyDVbSxvFGAMQNUgpsZLMYcRgIR4ARrkCRHSfVgPQfxFC3ATwJIBvCiF6ARSO+JpVAKP7/jzS+NiDfBHAf91iPUSmpzytM+oQVMVwyMcVIIvbShdRrNSaN2JGoTw5Zyts2soUUSgb7xoGgLDfBZ/Lzk5wRPu0FICklJ8G8AyAWSllGUAWB87zHOIygGkhxKQQwgXgYwCe3/8CIcT0vj/+FIA7rRZOZHaryTyiXW54nHatSzmV4R4v1nfyqNV4xM+qlABhtPbB3V4ngh5Hs/sXWVezA5zBrmEAEEJgNOTjdUy0z3HagVxAfR7Q/q/5Dw96sZSyIoT4FICvAbAD+H+klNeEEJ8FcEVK+TyATwkh3gegDCAJ4J8c+9+AyKRWUjlDn/9RDIe8KFclNtNFDHR7tC6HNBAz6PwUABiL+LgCRM0ZQEZr5KEYCXmb/w5E1GIAEkL8MYAzAF4GUG18WOIhAQgApJRfBfDVAx/7zL7//WvHKZbISlaTeTwy3K11GafWnAWUyjEAWdTydh5CGHM751jYh5vraa3LII0tbytNaYwZgEbDPvxoIQEpJYQQWpdDpLlWV4BmAcxIKbmHhagDajWJtVQBH3h0QOtSTq05CyiZx5PjGhdDmlhO5NAf8BhyO+do2IdvXN9ErSZhs/HG0apiyRx6A254Xca7hoH6ClCmWEEqV0bI79K6HCLNtdoE4XUAxr8TIzKIrUwRpWqtuXpiZMPNFSDuP7eqWNJ4M4AUoyEfStUaNtJH9f0hMzPiHKv9mi3duQ2OCEDrASgK4LoQ4mtCiOeVf9QsjMjKlL3aRt1usZ/f7UCPz8lW2BYWS+QwYsD5KcDemQ9lCxRZUyyRN2QHOIUyg4uNEIjqWt0C9ztqFkFEb9RsgW3gX7j7Dfd4uQJkUcVKFfd2C4Z9ej62rxX2W6YiGldDWihValjfyWMsfN8sd8NQHkBwBYiorqUAJKV8QQgxDmBaSvkNIYQP9c5uRKQCs8wAUgz3eLEQz2pdBmlgNZmHlHtPoI1mqMcLIYAYVzAtay2VR00as4uhIuhxotvrZCc4ooaWtsAJIX4BwJ8D+L3Gh4YB/JVaRRFZ3Woqj5DPCb/7OJ3q9Ws4VF8BYh8V61GCw1jEmDePLocNQ91exNgK27KM3MZ9v9Gwl1vgiBpaPQP0ywCeBbALAFLKOwD61CqKyOpWknlTnP9RDPd4kStVkcqVtS6FOkyZoWPULXBA/caRs4CsywzXMFBfheUWOKK6VgNQUUpZUv7QGIbKR7lEKllN5kyz/Q1Ac6ArzwFZz0oiB5fDht4ut9alnNhYmMNQrSyWyMNpF+gPGnuO2WjYh5VkHrUab9+IWg1ALwgh/gUArxDi/QD+DMCX1SuLyLqklFhN5ZuhwQyGe+pPThmArGc5kcNoyGvoGTqjIR+20kXkS9WjX0ymE0vkMBLywW7gaxgARkNelCo1bGWKWpdCpLlWA9CnAWwBeA3ALwL4KoB/qVZRRFa2nS2hUK6ZpgMcAAz11J+cshW29cSSOcOfnVDOL/EAuTXFkjlTPJBStlXzOiZqMQBJKWuoNz34Z1LKj0opf1/yNDORKpQOcGY6AxT2u+Bx2rgCZEHL28YeIAnsHX7nNjhrMvoQVMWo0gqbjRCIHh6ARN3vCCHiAG4BuCWE2BJCfKYz5RFZz6rJWmADgBCiPguIK0CWspMrY7dQMWwLbIVSPwOQ9ewWykjlyqYIQCPNYai8jomOWgH6ddS7vz0lpQxLKcMA3gLgWSHEr6teHZEFrabqv5zMtAUOAIZDPq4AWYxZ2gdHu1zwOu18cm5BSlgw+jUMAB6nHb0BNzvBEeHoAPTfAfi4lHJB+YCUch7AzwL479UsjMiqVpJ5BDwOdHudWpfSVsM9XgYgi1lu3jwaO8wLIdgJzqJiJmmBrRgNcRYQEXB0AHJKKeMHPyil3AJgrrszIp1YNdkMIMVIyItEtoRcqaJ1KdQhS9v1m8fxiF/jSk5vNOzj1iELUsKC0bdxKkY4C4gIwNEBqHTCzxHRCa0k86Y6/6NQ/p3WuApkGcuJLKJdLnS5HVqXcmqjYS9iyRzY/8dalhM5BD0OdPvM8cx3NOzF+k4BlWpN61KINHVUAHpcCLF7yD9pAJc6USCRlZhxBpBCOdO0wkYIlrEYN0f3LKC+BSpXqmI7y2d/VrKcMH4b9/1GQz5UaxLrOwWtSyHS1EMDkJTSLqUMHvJPQEppjschRDqyky8jU6yYMwA1VoB4Dsg6lhM5TJhg+xuwdwaE54CsJZY0T4gH9mZaKdtTiayq1UGoRNQBezOAzBeA+oMeOGyCrbAtolipYm0n37zhMjplFYDngKyjVpNYSeRNtQKknMdbSmQ1roRIWwxARDqybKKWqwfZbQID3R6uAFlELJGHlMC4SQKQsgrAJ+fWsZkuolStmern8UDQA5fdhmVex2RxDEBEOrJssparB3EYqnUsbdefMJuhAxxQn6Ey2O3B4jafnFuFGX8e220Co2EvgzxZHgMQkY4sJ3II+10IeMx5xG44xFlAVtFsgW2im8fxiI83jhbSHIJqsi3J4xE/gzxZHgMQkY7ETNZx6KCRHi82dgsoswWr6S0ncgi4HQj7XVqX0jYTEX9zZYvMbzmRgxB7HSzNQhnqy5buZGUMQEQ6spwwV8ehg4ZDXtQkcI8tWE1vcTuLsYgPQgitS2mb8Ygf8UwJ6UJZ61KoA2KJHAaCHrgddq1LaavxSL2lezzDlu5kXQxARDpRqdawmsxjLGyup437DffUwx1nAZnf8nbONA0QFBNsIWwpZpsBpFD+Xi6zExxZGAMQkU6s7xRQqUnTrwABnAVkdtWaRCyZM00DBEWzhTADkCUsbucwabJrGADGwryOiRiAiHTCzC2wFYPdHgDASpK/eM1sLZVHuSpN1QAB2HtyzgPk5pcpVhDPFDEeNdc1DACjYS+EYAAia2MAItIJM7ZcPcjjtGMg6Gn+u5I5Na9lk22B87sd6A242QjBApT/xhMmXAFyO+wY5M9hsjgGICKdWE7k4LAJDHab9wwQUL8p5hA+c1s08c3jRMSHRV6/prcYb7RxN1mIV4xFfAzyZGkMQEQ6sZzIYSTkhd1mnq5Zh5mI+LDEJ4+mtrydg8thw0DQo3UpbTfOVtiWsGiyQb4H1Vu68+cwWRcDEJFOxBI5jJn0l+1+4xE/ttJFZIsVrUshlSxuZzEW9sFmwjA/EfFhY7eIXInXr5ktbWfRG3Cjy+3QuhRVjEV82M6WkOHPYbIoBiAinajPADL39jdg74wT95+b19J2znQNEBTKigCvX3Nb3M41256b0XizExxXM8maGICIdGAnX0YqVzZ1AwTFOGepmJqUsh7mTXrzqJxrUs6IkDktbWdNu/0N2DcLiD+HyaIYgIh0IGaBDnAK5ckjh/CZ01amiFypasoGCMBeZzs+OTevXKmCjd2iqVeAmtcxVzLJohiAiHQgZoEZQIpunxM9Pic7aZmU2btndXudCPtdvH5NTFmdNvMKUNDjRMjn5Eo8WRYDEJEOWGEI6n7jYbbCNquFeAYAcKa3S+NK1DPOFsKmpvy3nYyaNwABwFjEz5V4siwGICIdWErkEPI5EfQ4tS6lI8YjfizxF68pzcezcNltGOoxb0MPthA2N2V1z6zn2BTjYR/PspFlMQAR6UAskbPE+R/FeMSHtVQB5WpN61Kozea3shiP+Ew9z2o84sPaTh6FclXrUkgFS9tZRPwu0z+Qmoj6sbaTR7HC65ishwGISAeWLTIDSDEW9qFak1hN5rUuhdpsIZ41/dahiYgfUu6d3SNzWYznTHuGbb+paP065nZksiIGICKNVao1rCbzGA2Zd8vQQcrh4kWeozCVak1iaTuLKROf/wHqT86Betgj81nazpq2i+F+yoOKu1u8jsl6GICINLaayqNSk5b4hatQ2stymKS5rCbzKFclpky+AjTVW//3m2cAMp1CuYq1nYKpO8ApJnsZ5Mm6VA1AQojnhBC3hBBzQohPH/L53xBCXBdCvCqE+KYQYlzNeoj0SPnlo/wysoLegBtep50HyU1mvtEBzuzXctDjRLTLjfmtjNalUJspD2UmoubfAqdcx0rnRiIrUS0ACSHsAD4P4IMAZgB8XAgxc+BlLwGYlVI+BuDPAXxOrXqI9GqxEYCstAIkhMBY2McAZDLzW9ZoHwzUV4G4dch8rPbzeCrq5woQWZKaK0BPA5iTUs5LKUsAvgjgw/tfIKX8eymlcgf0QwAjKtZDpEuL2zl0uR2Idrm0LqWjxjhLxXQW4lkEPQ5E/Oa/ls/0dnEFyISUhzKWCUC9/uaDCyIrUTMADQOI7fvzSuNjD/LzAP5axXqIdGk+nsVE1AchzNs2+DATER+WEznUalLrUqhNFuJZTPZ2WeJaPtPrRzJXRjJb0roUaqOF7Sx6fE50+8zdAlsxGfVjO1vCTq6sdSlEHaWLJghCiJ8FMAvg3z3g858UQlwRQlzZ2trqbHFEKluMW6Pj0EFjET+KlRo200WtS6E2WYhnTd8AQbHXCIGrQGZydzNjmWsY2NuuusDVeLIYNQPQKoDRfX8eaXzsDYQQ7wPwPwH4kJTy0DshKeUXpJSzUsrZ3t5eVYol0kKpUsNKMmeJMxMHjTcGv3IbnDnkS1WspvKWuZanovVW3zwHZC7zcfO3cd9P+XdlIwSyGjUD0GUA00KISSGEC8DHADy//wVCiCcA/B7q4WdTxVqIdCmWzKEmrbPffL8JzgIyFeW/45TJO8ApRkJeOO0Cd3kOyDTShTK20kWcsVAAGgv7YBPgOSCyHNUCkJSyAuBTAL4G4AaAL0kprwkhPiuE+FDjZf8OQBeAPxNCvCyEeP4Bb0dkSs2OQxZ5ar7fcMgLl8PGX7wm0WznbpFr2WG3YSLCA+Rmovy3tEqIBwCXw4bRsI8zrchyHGq+uZTyqwC+euBjn9n3v9+n5vcn0jur3TTuZ7cJTER83EJkEgsWax8M1G+U5za5AmQWynmuMxYKQECjFTZ/DpPF6KIJApFVLcSz6PY6EbJIx6GDJqN+7j03ifmtLAaCHvjdqj5X05Wp3i4sJ3KoVGtal0JtcHczC7tNYCxsrQA0Ge3CQjwLKdmRk6yDAYhIQ4vbWUxE/ZZoG3wY3kCax3w8Y6mtQ0D9yXm5KhFL5rUuhdpgPp7BWNgHl8Nat0aTvX7ky1Xc2y1oXQpRx1jrbzmRzizGc5iM+LQuQzO8gTSPhXjWcls5lQ5ad7kNzhTmt6zTxn2/M0orbG6DIwthACLSSKFcxdpO3pINEBTKDeQ8O2kZ2namiFSubKn2wcDeWRHOAjK+ak3W51hZbBUTqK8AAWAjBLIUBiAijSxt5yClNRsgKJo3kHzyaGh3Gisg033WCkA9PhcifhevXxNYS+VRrNQsF+IBoD/ggddpbzYyIbICBiAijSjzQ6w0c+KgHp8LIZ+TT9ANrhmA+q13LU/1shW2GVj557HNJjAR9XMlniyFAYhII0r7XCtuudhvqreLN5AGN7eRRpfbgYGgR+tSOm4q2sUAbwJWnAG035leP0cSkKUwABFp5O5WBsM9Xvhc1mkbfJipqJ97zw3uzmYGZ/u6LNnN8EyfH/FMCalcSetS6BTm4xkEPQ5E/C6tS9HEdF8AsWQO+VJV61KIOoIBiEgjc5sZnLHYmYnDTPV2YStdRLpQ1roUOiElAFnRdF8AwN42QDKmuc0MpnqtGeKB+vZVKfe2AhKZHQMQkQZqNYm7WxmcteB+84Om2AjB0HZyZWyli5ZrgKBQzj3d3khrXAmdxp2NDM5Z8AybQvl3v7PJ65isgQGISANrO3kUyjXLPjXfT5m7wXMUxjS3Vb9hsmIDBAAY7vHC77LjzgavX6PazhSxnS3hXH9A61I0Mx7xw2ETvI7JMhiAiDSgNEA4Y9EDt/uNR/yw2wTubnIFyIiUGyZlK5jVCCFwtj/AFSAD2+tiaM1rGACcdhsmo35u5STLYAAi0oDSbYcrQIDLYcN4xMetFwZ1ZzMDj9OG4R6v1qVo5lxfF27zyblh3WmEVytvgQPqq7h3GOTJIhiAiDQwt5lBj8+JsEU7Dh10ri/ArRcGpTRAsNmseXgcAM71BxDPFJHIshOcEd3eyCBg0Tbu+53tC2A5kUOhzE5wZH4MQEQauLtZb4Bg1Y5DB53r78Lidpa/eA1obiNt+WYebIRgbLc30pju58/j6b4u1CQb0pA1MAARaeDulnXbBh/mbH8ANQkscB6QoWSKFaztFCx9dgIAzg80WmEzABnSnc2MZc+w7ac0geB2ZLICBiCiDktmS9jOlnDG4k/N9zvHJ+iGpPz3snqYHwh6EHA7eA7IgJSti1btYrjfRNQHu000m/QQmRkDEFGHKYPmzvSxA5xiMlrvBMdzQMZyc70egGYGgxpXoi0hBKb7uxjgDeh2swECV4DcDjvGIz5ex2QJDEBEHXar8cuFWy72KL94ufXCWG7e20WX22HpDnCKc/0BthA2IOWhCwNQ3XRfFx9EkSUwABF12M31NLrcDoyEeNO4HzvBGc/N9TTODwQs3QFOMd0fQCJbQjxT1LoUOobbG2kEPA70B91al6IL5/sDbEhDlsAARNRht+7Vbxqt3nHoIHaCMxYpJW7c28WFAT45B+o3jsDetkAyhjsbGUz3sQOc4sJgEDXJ85hkfgxARB0kpcTNe7vNrlG0h53gjGVtp4B0oYILFj//o7g4WP87fWN9V+NKqFV7P495DSsuNv4+M8iT2TEAEXXQvd0CdgsVPjU/BDvBGcvNxo3+RV7LAIBIlxv9QTcDkIGs7dR/Hs8M8hpWjId98DrtuM7rmEyOAYiog27eq9/cn+eB2/uwE5yxKNfyOQagppnBIG8cDeT6Wv2/1cwQV4AUNpvA+YEAbt7jdUzmxgBE1EG3GjeNF7jl4j5uhx2TUX/zxpr07ea9NEZCXgQ9Tq1L0Y2Lg0HMbWZQrPAcmxEoq3XcAvdGFweDuLGehpRS61KIVMMARNRBt+6lMRD0oNvHm8bDzAwGuYXIIG6u7zLIHzAzFESlJrmKaRDX13YxEfGhy+3QuhRduTgYwE6+jPWdgtalEKmGAYiog242OsDR4WaGglhN5bGTK2tdCj1EoVzFfDzbPPhPdcpAWG6DM4Yb93abh/5pT7MRArfBkYkxABF1SLlaw93NDC7wpvGBLvIG0hDmNjOo1iRXgA4Yj/jhddq5imkAmWIFS9u5ZmilPcpDuhvsBEcmxgBE1CGL8SxK1Ro7wD0En6Abw7W1HQA8PH6Q3SZwYTDQPFxP+tXsYsgAdJ+gx4mRkJdBnkyNAYioQ/Y6wPEX7oP0BtzoDbh5A6lzr63uIOB2YDzs07oU3bnY6ATHA+T6ptzcM8Qf7iLPY5LJMQARdci1tV047QJn+vxal6JrbISgf6+t7uKR4SBsNqF1KbozMxhEulDBaiqvdSn0ENfXd9HtdWKw26N1Kbp0cTCIhXgW+RI7GpI5MQARdcjrqzs4PxCA22HXuhRdmxkK4s5mGqVKTetS6BDlag031ndxabhb61J0SVlRuMZVTF27vp7GzGAQQjDEH+bRoSBqktuRybwYgIg6QEqJ19d2eNPYgouDQZSrEnObbCWsR7c36uH00kiP1qXo0sWBIOw2gddWdrQuhR6gUq3hFjvAPdRjjb/fr62kNK6ESB0MQEQdsJLMI5Ur45EhBqCjKI0QuA1On15frd/YM8wfzuuy41x/AK/wxlG3bm9kUCjX8NgIr+EH6Q/Wz2O+yiBPJsUARNQBvGls3WTUD4/Txq0XOsUGCEd7fKQbr67ssBGCTr3aCKePj3IV80GEEPXreJUBiMyJAYioA15b3YHDJjgEtQV2m8CFgWAzNJK+sAHC0R4f7cFOvoyl7ZzWpdAhXlnZQdDjwESEIf5hLg334O5WBpliRetSiNqOAYioA15b3cF0fwAeJxsgtOLxkW68vrqDao1P0PWEDRBao2yt4jY4fXp1JYXHRnrYAOEIj410Q0rgGh9GkQkxABGpTEqJ11d3cGmYB25b9fhoD7KlKu5usRGCnrABQmvO9Qfgdth4fkKHCuUqbt5L8/xPCy41/n/0GgMQmRADEJHK1nYKSObKfGp+DMre/JdjfIKuJzzL1hqn3YZHhoLNsyakH9fWdlGtSZ7/aUG0y43hHi+DPJkSAxCRypR2uI/yprFlkxE/Ah4HXmEA0pUXl1Lo8Tl5dqIFj4304LXVHVSqnGelJ80GCFzFbMml4W4GeTIlBiAilb2+ugO7TXDmxDHYbAKPj/TwDIXOXF1O4smxEM9OtOBNoz0olGu4w3lWuvLqyg76Am4MdHu0LsUQLo10Y3E7h51cWetSl6ImwQAAHydJREFUiNpK1QAkhHhOCHFLCDEnhPj0IZ9/pxDiRSFERQjxUTVrIdLKi8tJXBxkA4Tjeny0GzfX0yiUq1qXQgBSuRLmNjN483hI61IModkIgauYuvJKLMXtb8fwJmU7Mh9GkcmoFoCEEHYAnwfwQQAzAD4uhJg58LJlAJ8A8Cdq1UGkpUq1hpdjKbx5jDeNx/X4SA8qNYlra5wHpAcvLddvgHgtt2Yi4kfI58SLy0mtS6GGnXwZ8/EsHmcDhJY9PtoDmwCuLia0LoWordRcAXoawJyUcl5KWQLwRQAf3v8CKeWilPJVANwkTaZ0ayONXKmKJ/nU/NiUJ498gq4PLy4nYbcJPD7Km8dW2GwCT46HcWWRAUgvlDDKEN+6LrcDM0NBXFnidUzmomYAGgYQ2/fnlcbHiCzjxSX+wj2pvqAHg90engPSiatLScwMBuFzObQuxTCemghhPp7FVrqodSkE4PJCAg6bwJvGuAXuOGbHw3hpOYUyG3qQiRiiCYIQ4pNCiCtCiCtbW1tal0PUsqtLSfQF3BgJebUuxZAeH+lpbr0i7ShbObmSeTyzE2EAwNUlbh/SgyuLSTwy3M0Qf0xPjoeQL1dxY53bkck81AxAqwBG9/15pPGxY5NSfkFKOSulnO3t7W1LcUSdcHU5iSfH2TXrpGYnQlhO5LCxW9C6FEu7ea++lfMJPjk/lkvD3XA7bLjMbXCaK1aqeHklhacnGOKPa7bx/zNu5yQzUTMAXQYwLYSYFEK4AHwMwPMqfj8iXdlMFxBL5Ln97RTeMhkBAPxogU/QtaScneAK0PG4HDa8abQHV3iAXHOvreygVKk1V+WodYPdXgz3eHGV54DIRFQLQFLKCoBPAfgagBsAviSlvCaE+KwQ4kMAIIR4SgixAuAfAvg9IcQ1teoh6rQXlxpds3jTeGIXBwPocjvw44VtrUuxtB8tJDDY7cFwD7dyHtdTE2G8vraLbLGidSmW9uNGCH2KAehEZidCuLyYgJRS61KI2kLVM0BSyq9KKc9JKc9IKf9142OfkVI+3/jfl6WUI1JKv5QyIqV8RM16iDrpxeUkXHYbHh3mANSTcthteHI8hB9zBUgztZrED+5u421nItzKeQKzEyFUaxIvs5uhpq4sJnG2rwthv0vrUgxpdjyEzXQRK8m81qUQtYUhmiAQGdGPFhJ4bKQbbgcHoJ7G05Nh3N7IIJEtaV2KJd3aSCORLeGZM1GtSzGkJ8dDsAkwxGuoVpO4spjAUzz/c2LK1sEfznM1nsyBAYhIBTv5Ml5bSeGZs7xpPK23TNZ/8V7mOQpNfP9u/YbnmTMRjSsxpoDHiUeGuvGDu7xx1MqtjTR2CxXMjnP720md7w8g4nc1fx4QGR0DEJEKfjS/jZrkTWM7XBqpd9LiE3Rt/OBuHJNRP4Z4/ufE3j4dxYvLSWR4DkgT35uLAwDexp/HJ2azCTxzNorvzsV5DohMgQGISAXfv7sNj9PGtsFt4HbY8cRYDwOQBirVGn40n+CN4ym9YzqKSk3ih3x6rolv34njbF8XQ/wpvf1sBFvpIu5sZrQuhejUGICIVPC9uTiemgjz/E+bPD0ZwbW1HezkylqXYimvr+0iXaxwJfOUnhwPweu047uNlQjqnEK5ih/Nb+Md09yOfFpvn67PYfzuHV7HZHwMQERttrlbwJ3NDJ7l+Z+2ede5KGoSvIHsMGXr0FunGIBOw+2w4+nJML5zZ0vrUiznymISxUoN75zmEPXTGu7xYjLqb/5cIDIyBiCiNlMOiT7Lrllt8/hID4IeB751a1PrUizlO3e2cGEggGiXW+tSDO8d01Hc3cpiLcU2wp30nTtbcNoF3jLFBgjt8OzZCH44v41ytaZ1KUSnwgBE1Gbfm4uj2+vEzBDn/7SLw27DO6Z78cLtLR7A7ZCdfBmXF5N474U+rUsxhXdw+5Amvn0njtnxMHwuh9almMLbz0aRLVXxCudakcExABG1kZQS352L421TEdhtHBrZTu8614vNdBE376W1LsUSXri9hWpN4icuMgC1w7n+LvQF3Pg2t8F1zGa6gBvru3g7z/+0zdumorDbBL51i9cxGRsDEFEb3VhPY32ngPfyprHt3nmu/gT9hdv8xdsJf3djA2G/C28a5fDIdhBC4D3n+/DCrS2UKtw+1AnfuV1fbeP5n/bp9jkxOx7C169vaF0K0akwABG10d/drP9SePd5/sJtt4FuDy4MBPACnzyqrlKt4Vu3t/Ce831cyWyjn3ykH+liBT+YZzvsTvjGjQ30Bdx4hNuR2+r9M/24tZHG8nZO61KITowBiKiNvnlzE4+PdKMv4NG6FFN617leXFlKcKCkyl5cTiGVK3P7W5s9ezYKv8uOr127p3UpplcoV/HC7S28f6YfNob4tvrJmQEAwN9e53VMxsUARNQm8UwRL8dSeO+Ffq1LMa2fuNiPclXi726yG5yavnlzA0674OyUNvM47Xj3+T58/foGajU281DT9+/GkStV8f4Z/jxut7GIDxcGAvhbboMjA2MAImqTr1/fgJTgU3MVPTn+/7d35+FR1vfex9/fmUky2RNCICEJJICACogSRMSqpVVR64649LQutXb16WLr1XPa5zznnLbantPn2F49vU5dW/fWpfaxCopWj6Kissm+QwKBEAgJWUkmM/N7/sioiBBZMtyZmc/ruubKzNwzySc/bib39/4tdyHFuRnMWV7vdZSk5Zzj5VUNTK0qIjeY5nWcpHP+yUPZ3dbN0m3NXkdJavNWNZCTEWCaLuIbF+edNJRFNU00dYS8jiJyVFQAifSTOSvqGVGUpfHmceT3GReOL+G1dbvo0DC4uFhd38rmxg4unljqdZSk9NlxQ0jzGy+t0tnzeOmJRJm3uoFzxxaTEfB7HScpnX9SCVGHeuMlYakAEukHTR0h3t60h4smlGKm8ebxdNGEUrrDUf3hjZO/Lasn4DNmnlzidZSklBdM44yRRcxdWa9rWsXJ25v20NQR4pJThnkdJWmNL8ujrCCT55fv8DqKyFFRASTSD+at2kkk6rh4gs6ax9uUykEMzslgzgoNg+tvzjmeX76D6aMHU5id7nWcpHXZpDK2Ne1jUa2GwcXDc+/vIDcY0GqccWRmXDZpGPM3NLKrrcvrOCJHTAWQSD94btkOhg/S8LfjYf9hcJ0hDYPrT0u27qWueR9f0PC3uLpwfAlZ6X6eWVzndZSk09UTYd6qnVxwcomGv8XZlaeVEYk6nntfvUCSeFQAiRyj7Xv3sWDzHq48rUzD346TiyeW0tUTZZ7mUfSrpxdvIzPNz4XqyYyr7IwAM8eX8MLyerp6Il7HSSqvrGmgrTvMpRr+Fnejh+QysTyfZ5du9zqKyBFTASRyjJ5dUodzcNVp5V5HSRmnVw6ivDCTpxZv8zpK0ugMhfnbsnounlhKTkbA6zhJb9bkctq6w7omUD/788JtDMsPMn20lnA/Hq48tYxVO1pZu7PV6ygiR0QFkMgxcM7xzJLtTK0aRMWgLK/jpAyfz7h6cgVvbdzDtiZdjbw/vLhyJ+3dYWZXV3gdJSWcUVVEWUEmzyzR2fP+UtfcyZsbG7m6ugK/Ln56XFxyyjACPuMv2o8lwagAEjkG72xuYktjB7Mmq/fneJtVXY4ZPKV5FP3isXe3UjU4mymVhV5HSQk+n3HVaWXM37BbRXw/eWpR72fB1dX6PD5einIymDFuCE8vrtNwTkkoKoBEjsHDC2ooyErTcqseKCvI5OwTinniva2EwlGv4yS05XV7WVzbzJfOGKF5bMfRdVOH4zPj4QU1XkdJeKFwlMff28o5Y4opL1Rv/PF0w5mVNHWEeEEXqJYEogJI5Cjt2LuPeasbuKa6gmCaVhvywo3TK9nd1q0lsY/RH9+uITvdzyydOT+uSvMzmTm+hD8t3KYL+x6jF1bsYHdbNzdNr/I6Sso5c1QRo4fk8NCCGl3bShKGCiCRo/TIO7VEneMfzhjhdZSUdc4JxYwcnM0f3tqiP7xHqaG1i+eX1TNrcjl5wTSv46Scm86spK0rzNMaynnUnHM8+GYNo4fkcPYJWvzgeDMzbpg2guV1LSys0bWtJDGoABI5Ci37enh0QS0XjS/V4gce8vmMm6ZXsqyuhXc2N3kdJyHd+8ZmIs7xlbNGeh0lJU0eUcjkEYXc8/omDeU8Sm9ubGTF9hZunl6lIZwemTW5gqLsdP7rtY1eRxE5LCqARI7CIwtqaOsO841zR3kdJeVdXV1BcW4Gv311g9dREk5jezePvVvLZZOGMbxIhbwXzIzbZoxmR0sXzy5VL9CRcs7x61c2UJof5KrJZV7HSVmZ6X5uPquKN9bvZkVdi9dxRD6VCiCRI9Ta1cMDb27h3LHFjC/L9zpOygum+fna2SN5e9MeFtaoF+hI/P5/NtEdjvKtz472OkpKO2dMMRPL8/ntqxvpDmslrSPx1sY9LK5t5pvnjiIjoLmYXvrytBHkBQP858vrvI4i8qlUAIkcoXte30RzZw+3nzfW6ygS88WpIxick8Ev567VXKDDtK2pk4cX1DLrtHJGFed4HSelmRk/vGAsdc37eGRBrddxEkYk6rhr7hrKCjKZPUXXr/JabjCNb352NK+t283bmxq9jiPSJxVAIkdgZ0sXD7y5hcsmDWNCuXp/BorMdD+3nz+GRbXNzF250+s4CeGXL67F54Pbz1chPxB85oRizhlTzG9f3UhzR8jrOAnhmSV1rNrRyh0zx6r3Z4C48cxKhuUHuWvOWiJRnYySgUsFkMgR+Onzq4k6+IEOGgec2dUVjCvJ5c45a9gX0jCivszfsJvnl9dz69mjKMkPeh1HYv7pohNp7w7zi7lrvY4y4LV09vAfL61jUkUBl+o6bANGMM3PHTPHsWJ7C4+/t9XrOCKHpAJI5DC9uraBF1bU879mjNbKbwOQ32f8y6UnU9e8T2PQ+7AvFOHHz66kanA239QiHgPK2JJcvvqZkfx50TYWbNrjdZwB7edzVtPUEeJnl4/Xym8DzGWThjF9dBH/PnctDa1dXscROSgVQCKHYW9niB8/u5LRQ3K49WwdNA5UZ4ws4vqpw3ngzS0s2arrURzMXXPXsLWpk59fMV4X8B2AvvO5Exg+KIsfPr2M1q4er+MMSP+zbhdPLqrjls9UaSGaAcjM+PnlEwhFovzgqWVENRROBiAVQCKfwjnHj55ZQWN7N3fPnkR6QP9tBrIfXTiO0vxMbnt8KS2dOoDc30urdvLwglpuOauKM0fpgpEDUWa6n7uvmUR9Sxf/9JcVWtTjAA2tXdz+5DLGDs3le58f43UcOYTKwdn87y+cxPwNjTz41hav44h8go7kRD7FffM38+KqnfzwgrFa+CAB5AXT+K/rT+09UHrqfU3EjdnQ0MYPnlzG+LI8fjhTc9gGsskjCvn+eWN4fnk998/XweMHunoifOuxJXSGIvzui6eqB3OA++LU4Zx/0lDumruWtzdqVTgZWFQAifRh3qqd3DV3LRdPLOWrnxnpdRw5TKcOL+SfLzmJV9bs4mcvrPY6jud2tXZx80MLCab7ufdL1VoxKwF845xRXDyhlDvnruFFrWxINOq44+nlLKpt5t9nTWT0kFyvI8mnMDP+7+xTGDk4m288toSNu9q8jiTyIRVAIofw+vrdfPvxpUwsL+BXs07RRNsE8+Vpldw8vYo/vFXD3S+vT9mhRI3t3Vx//7vsaQ9x35erGVaQ6XUkOQw+n/Grq09hUkUBtz2xhFfXNngdyTPRqOPHf13Bc8t2cMfMsVyiVd8SRm4wjQdumEJ6wMf1973LlsYOryOJACqARA5qzop6vvrwIkYPyeGhm6aQma4z5onoJxefyOzqcn7z9w3cOWdNyg2Hq93TwdW/X0BdcycP3jiFSRUFXkeSI5CZ7uePN53OuJI8vvbIYp5ctM3rSMddV0+E7z35Pk+8t43bZozmG+doEZpEM7woi8dumUo46pj132+zuFYL1Ij3VACJ7CcciXL3y+v51uNLmFCWz2O3TKUgK93rWHKUfD7jF1dO5IZpI7hv/hZu/uPClFkY4e9rGrj8d2/R3Bni0a9M5YyRRV5HkqOQn5nGo7f0/vvd8fRy7pyzhp5I1OtYx8XOli5m37OA//f+Dn54wVi+f94Y9cQnqDFDc3nq69PICQa47r53eHZpXcr2ysvAYIm2A1ZXV7tFixZ5HUOSUE1jB7c/tYzFtc1ceWoZd145QZNsk8jj727l/zy3kiG5QX56+cnMGDfU60hx0dbVw69eWsdDC2o5sTSP311/KiOLc7yOJceoJxLlX/+2ikff2cpJpXn8x9UTOXlYci7K4pzjqUV1/OyF1USijruvmcT5J5d4HUv6QVNHiK8/spj3apq4cHwJP718PINzMryOJUnKzBY756oPuk0FkKS65o4Qv311I4+8U0Mw4OdnV4znskllXseSOFi6tZk7nl7Ohl3tXHDyUL77+TGcWJrndax+0R2O8OSiOn798nr2dIS4eXoVd8wcqyI+yby4sp6f/HUlzZ09zK4u59szTqAsSeZ1OedYsHkPd7+8noU1zZxeOYi7rprAKBXwSSUciXLPG5v5zSsbCKb5+No5o7jxzEqyMwJeR5MkowJI5ADOOZbXtfDEe1v56/vbCYWjzK6u4HvnjWFoXtDreBJHoXCUe9/YxO9f30x7d5gZ44Ywu7qCGeOGJOQ1nmr3dPDMku08/m4tje0hTq8axE8uPpGJ5Zrvk6yaO0L8+pX1PPFe75ygmeNLuHZKBWeMLMLnS7whYu3dYeYsr+dPC7eyZOtehuRm8L3zxnBNdUVC/j5yeDY0tPHLF9fyyppdFGalcdVp5Vx7eoVW+JN+41kBZGYzgd8AfuB+59wvDtieATwMTAb2ANc452r6+p4qgORo1bfsY3FtMwu3NPHKml1s37uPYJqPK04t4+bpVZwwVB+6qaSls4c/vl3DI+/U0tjeTX5mGtNHFzF99GCmjSxiRFE2/gF48NXS2cPirU0srGnm9XW7WV3fihnMGDuEG6dXctbowZonkSK2793HfW9s5i9L6mjtCjM4J51zxw7hzFFFTKoooLIoe0AWEN3hCGvr21iytZnX1+9mwaY9dIejjByczQ1nVnLNlAr1XKaQpVubuW/+ZuataiAcdYwqzmbGuCFMqRzEpOEFDMnVSUk5Op4UQGbmB9YD5wF1wELgOufc6v1e801gonPu62Z2LXCFc+6avr6vCiDZXyTq6OqJ0NUTob07TFNHiKaOEHs6Quxu66amsYOaPR1saeyksb0bgGCaj7NGD+aCk0s4/6QS8rPSPP4txEvhSJT5Gxt5YXk9b21spL6lC4DMND9jhuYwqjiH0oIgJfmZlOYFKcxOIycjjdxggJxggJz0wDEfZEaijn09ETpDYbpCUVq7etjd3k1jWze727tpaOlic2MHm3a1syOWL+AzJlUUMHN8CTPHl1BemHXMbSGJqasnwrzVDby8uoHX1+2itSsMQF4wwKghOQwflMWIQVmUFWZSkJXOoOx0CrPSyM9MJzPdT0bAR8Bnx1Q4R6OOUCRKKBKlvSvM3s4e9u4L0dLZw662brY1dbKtuZOtTfvYuKuNnkjvsUfV4GzOGVPMJacM47ThBSreU1hjezd/W7aDv6/Zxbtb9ny4j5TmB6ksymZEURbDi7IozQ9SkJVOYVbvfpwXTCOY1rsfD8SCX7zjVQE0DfgX59wFscf/COCcu2u/17wUe80CMwsAO4Fi10eogVIAbd3TyXf+vPTDxwcm/tjDAza6Q2/CccBrXV+v3X9b3/+OH/s+ff2MT7zv0NkPfHFf7/3ktsPLc7DH3eEo3T0RusKRDz8gD6U4N4OqomwqB2cxriSP6spCTizNI82feEOdJP6cc2xu7GBxTTNrd7axdmcrNY0dNLR197mEdprfCPh6DyIDfiPg/+g+QDQKUedit96fE3UfFT6hcN+reuVmBKgqzmZUcQ6jh+QweUQhp5QXaHl2+YRI1LFhVxvLtu1lWV0LNY0dbG3qZMfeffS1CrzPICPgJ5jmI+D3YYAZGMYHNUnvc70PwtEooXDsFol+6mdxMM1HeWEWFYWZjCvNY2JZPhPK81W4y0HtC0VYtaOF97ftZeX2FmqbOtnW1Elje6jP96X7fWQEfGTECiK/z/AZ+Kx3Pzbb//EB2wA8LsC9/On99avPmlzOF6eO6J9vdoz6KoDiOeOsDNj/ogV1wNRDvcY5FzazFqAIaNz/RWZ2K3ArwPDhw+OV94j4fJBzwIS9A89c2ce20ce2Q7/vk++1Q27r+329f8gOua2P1x74jY82+ye32SG3ffJnfvREesBHMM1HMM1PMOAnI81HMOAjOyNAUU46g7IzGJSVTlFOuiZVyhExM0YV53xi0nUk6tjd1s3O1i5a9vXQ3hWmrauHtq4wbd1heiJRwpEo4agjHHGxr72PPzho9Bn4fR//o+szCKb7yUoLkJnuIzM9QFaan+yMAMW5GQzJzWBwToYKHTlsfp8xriSPcSV5XDPlo+d7IlEaWrvY29lDc2eI5s4eWjpDdPVE6Q5H6A5H6erp/doTieJc78mnD05Q9d7/6Lk0n4/0wH43/0dfc4MBCmI9TAVZaRTlpFOck6HeHTlsmel+qisHUV056GPPt3eH2d3WTVNHiL2x/bh1Xw+hyEf7b3dsn+7qicZONvWecIo6h3MHPxn1wTYvefnj+7MzJD1BTjAnxNGhc+5e4F7o7QHyOA4A5YVZPPKVA+s5EUlGfp9Rkh+kJF9j0SUxpfl7e2DKC71OInL0cjIC5GQEqBqc7XUUSXDxLNO2AxX7PS6PPXfQ18SGwOXTuxiCiIiIiIhIv4tnAbQQOMHMqswsHbgWeO6A1zwH3BC7Pwt4ta/5PyIiIiIiIscibkPgYnN6vg28RO8y2A8651aZ2b8Bi5xzzwEPAI+Y2Uagid4iSUREREREJC7iOgfIOTcHmHPAc/+83/0u4Op4ZhAREREREflAYizVICIiIiIi0g9UAImIiIiISMpQASQiIiIiIilDBZCIiIiIiKQMFUAiIiIiIpIyVACJiIiIiEjKUAEkIiIiIiIpQwWQiIiIiIikDBVAIiIiIiKSMsw553WGI2Jmu4Far3PsZzDQ6HWIJKc2jj+1cfypjeNPbRxfat/4UxvHn9o4/gZKG49wzhUfbEPCFUADjZktcs5Ve50jmamN409tHH9q4/hTG8eX2jf+1MbxpzaOv0RoYw2BExERERGRlKECSEREREREUoYKoGN3r9cBUoDaOP7UxvGnNo4/tXF8qX3jT20cf2rj+Bvwbaw5QCIiIiIikjLUAyQiIiIiIilDBVA/MLOrzWyVmUXNbECvepFozGymma0zs41m9iOv8yQbM3vQzHaZ2UqvsyQjM6sws9fMbHXsM+I7XmdKNmYWNLP3zGxZrI3/1etMycrM/Ga21Mye9zpLMjKzGjNbYWbvm9kir/MkGzMrMLOnzWytma0xs2leZ0omZjY2tu9+cGs1s+96netQNASuH5jZiUAUuAf4gXNOH1z9wMz8wHrgPKAOWAhc55xb7WmwJGJmZwPtwMPOufFe50k2ZlYKlDrnlphZLrAYuFz7cP8xMwOynXPtZpYGvAl8xzn3jsfRko6ZfR+oBvKcc1/wOk+yMbMaoNo5NxCun5J0zOwhYL5z7n4zSweynHN7vc6VjGLHb9uBqc65gXTtzg+pB6gfOOfWOOfWeZ0jCZ0ObHTObXbOhYA/AZd5nCmpOOfeAJq8zpGsnHP1zrklsfttwBqgzNtUycX1ao89TIvddGavn5lZOXAxcL/XWUSOlJnlA2cDDwA450IqfuLqc8CmgVr8gAogGdjKgG37Pa5DB4+SoMysEjgVeNfbJMknNjTrfWAX8LJzTm3c/34N3EHvaAeJDwfMM7PFZnar12GSTBWwG/hDbBjn/WaW7XWoJHYt8ITXIfqiAugwmdkrZrbyIDf1SIhIn8wsB3gG+K5zrtXrPMnGORdxzk0CyoHTzUzDOfuRmX0B2OWcW+x1liR3lnPuNOBC4FuxIcrSPwLAacB/O+dOBToAzSuOg9jwwkuBp7zO0peA1wEShXPu815nSEHbgYr9HpfHnhNJGLF5Kc8Ajznn/uJ1nmTmnNtrZq8BMwEt7NF/pgOXmtlFQBDIM7NHnXP/4HGupOKc2x77usvMnqV3GPgb3qZKGnVA3X69w0+jAiheLgSWOOcavA7SF/UAyUC2EDjBzKpiZxSuBZ7zOJPIYYtN0H8AWOOc+0+v8yQjMys2s4LY/Ux6F01Z622q5OKc+0fnXLlzrpLez+FXVfz0LzPLji2UQmxo1vmoiO83zrmdwDYzGxt76nOAFqOJj+sY4MPfQAVQvzCzK8ysDpgGvGBmL3mdKRk458LAt4GX6J08/qRzbpW3qZKLmT0BLADGmlmdmX3F60xJZjrwJWDGfkuDXuR1qCRTCrxmZsvpPWnysnNOyzRLohkKvGlmy4D3gBeccy96nCnZ3AY8FvusmATc6XGepBMr3s8DBvxoBy2DLSIiIiIiKUM9QCIiIiIikjJUAImIiIiISMpQASQiIiIiIilDBZCIiIiIiKQMFUAiIiIiIpIyVACJiIiIiEjKUAEkIiIiIiIpQwWQiIiIiIikjP8PJr1Jfjvn6AoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['film_rental_rate'].plot(kind='density', figsize=(14,6)) # kde"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Rentals')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df['film_rental_rate'].value_counts().plot(kind='bar', figsize=(14,6))\n",
    "ax.set_ylabel('Number of Rentals')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "## Categorical analysis and visualization\n",
    "\n",
    "We'll analyze the `rental_store_city` column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Woodridge     8121\n",
       "Lethbridge    7923\n",
       "Name: rental_store_city, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rental_store_city'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f25bf853910>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['rental_store_city'].value_counts().plot(kind='pie', figsize=(6,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Rentals')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df['rental_store_city'].value_counts().plot(kind='bar', figsize=(14,6))\n",
    "ax.set_ylabel('Number of Rentals')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "## Column wrangling\n",
    "\n",
    "We can also create new columns or modify existing ones.\n",
    "\n",
    "### Add and calculate a new `rental_rate_return` column\n",
    "\n",
    "We want to know the rental rate of return of each film. To do that we'll use this formula:\n",
    "\n",
    "$$ rental\\_gain\\_return = \\frac{film\\_rental\\_rate}{film\\_replacement\\_cost} * 100 $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rental_id\n",
       "1    13.597090\n",
       "2    17.598587\n",
       "3    19.946631\n",
       "4     4.502046\n",
       "5     9.969990\n",
       "Name: rental_gain_return, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rental_gain_return'] = df['film_rental_rate'] / df['film_replacement_cost'] * 100\n",
    "\n",
    "df['rental_gain_return'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f25bf7c7e20>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['rental_gain_return'].plot(kind='density', figsize=(14,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.34"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rental_gain_return'].mean().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.6"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rental_gain_return'].median().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f25bf7c20d0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df['rental_gain_return'].plot(kind='density', figsize=(14,6)) # kde\n",
    "ax.axvline(df['rental_gain_return'].mean(), color='red')\n",
    "ax.axvline(df['rental_gain_return'].median(), color='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Each rental represents <b>13.6%</b> of film cost."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So <b>7.35</b> rentals are needed to recover film market price (`film_replacement_cost`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.352941176470589"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100 / 13.6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While in average each film is rented <b>16.74</b> times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.747390396659707"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['film_title'].value_counts().mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![green-divider](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "\n",
    "## Selection & Indexing:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the rental records of the customer with lastname `HANSEN`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>rental_date</th>\n",
       "      <th>return_date</th>\n",
       "      <th>customer_lastname</th>\n",
       "      <th>store_id</th>\n",
       "      <th>rental_store_city</th>\n",
       "      <th>film_title</th>\n",
       "      <th>film_rental_duration</th>\n",
       "      <th>film_rental_rate</th>\n",
       "      <th>film_replacement_cost</th>\n",
       "      <th>film_rating</th>\n",
       "      <th>rental_gain_return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rental_id</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>5</th>\n",
       "      <td>2005-05-24 23:05:21</td>\n",
       "      <td>2005-06-02 04:33:21</td>\n",
       "      <td>HANSEN</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>IDOLS SNATCHERS</td>\n",
       "      <td>5</td>\n",
       "      <td>2.99</td>\n",
       "      <td>29.99</td>\n",
       "      <td>NC-17</td>\n",
       "      <td>9.969990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>2005-05-25 21:48:41</td>\n",
       "      <td>2005-06-02 18:28:41</td>\n",
       "      <td>HANSEN</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>JUMPING WRATH</td>\n",
       "      <td>4</td>\n",
       "      <td>0.99</td>\n",
       "      <td>18.99</td>\n",
       "      <td>NC-17</td>\n",
       "      <td>5.213270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>2005-05-27 15:02:10</td>\n",
       "      <td>2005-05-29 10:34:10</td>\n",
       "      <td>HANSEN</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>LESSON CLEOPATRA</td>\n",
       "      <td>3</td>\n",
       "      <td>0.99</td>\n",
       "      <td>28.99</td>\n",
       "      <td>NC-17</td>\n",
       "      <td>3.414971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>809</th>\n",
       "      <td>2005-05-29 19:10:20</td>\n",
       "      <td>2005-06-05 19:05:20</td>\n",
       "      <td>HANSEN</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>INDIAN LOVE</td>\n",
       "      <td>4</td>\n",
       "      <td>0.99</td>\n",
       "      <td>26.99</td>\n",
       "      <td>NC-17</td>\n",
       "      <td>3.668025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006</th>\n",
       "      <td>2005-05-31 00:57:08</td>\n",
       "      <td>2005-06-02 22:35:08</td>\n",
       "      <td>HANSEN</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>SALUTE APOLLO</td>\n",
       "      <td>4</td>\n",
       "      <td>2.99</td>\n",
       "      <td>29.99</td>\n",
       "      <td>R</td>\n",
       "      <td>9.969990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1368</th>\n",
       "      <td>2005-06-15 14:27:47</td>\n",
       "      <td>2005-06-23 18:07:47</td>\n",
       "      <td>HANSEN</td>\n",
       "      <td>1</td>\n",
       "      <td>Lethbridge</td>\n",
       "      <td>HUNCHBACK IMPOSSIBLE</td>\n",
       "      <td>4</td>\n",
       "      <td>4.99</td>\n",
       "      <td>28.99</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>17.212832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2603</th>\n",
       "      <td>2005-06-19 06:21:25</td>\n",
       "      <td>2005-06-26 03:19:25</td>\n",
       "      <td>HANSEN</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>CAT CONEHEADS</td>\n",
       "      <td>5</td>\n",
       "      <td>4.99</td>\n",
       "      <td>14.99</td>\n",
       "      <td>G</td>\n",
       "      <td>33.288859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5209</th>\n",
       "      <td>2005-07-09 11:22:39</td>\n",
       "      <td>2005-07-17 09:31:39</td>\n",
       "      <td>HANSEN</td>\n",
       "      <td>1</td>\n",
       "      <td>Lethbridge</td>\n",
       "      <td>WHALE BIKINI</td>\n",
       "      <td>4</td>\n",
       "      <td>4.99</td>\n",
       "      <td>11.99</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>41.618015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5266</th>\n",
       "      <td>2005-07-09 14:17:40</td>\n",
       "      <td>2005-07-16 10:42:40</td>\n",
       "      <td>HANSEN</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>LOATHING LEGALLY</td>\n",
       "      <td>4</td>\n",
       "      <td>0.99</td>\n",
       "      <td>29.99</td>\n",
       "      <td>R</td>\n",
       "      <td>3.301100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5592</th>\n",
       "      <td>2005-07-10 04:26:13</td>\n",
       "      <td>2005-07-19 02:32:13</td>\n",
       "      <td>HANSEN</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>LUKE MUMMY</td>\n",
       "      <td>5</td>\n",
       "      <td>2.99</td>\n",
       "      <td>21.99</td>\n",
       "      <td>NC-17</td>\n",
       "      <td>13.597090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5635</th>\n",
       "      <td>2005-07-10 06:28:39</td>\n",
       "      <td>2005-07-17 08:35:39</td>\n",
       "      <td>HANSEN</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>FISH OPUS</td>\n",
       "      <td>4</td>\n",
       "      <td>2.99</td>\n",
       "      <td>22.99</td>\n",
       "      <td>R</td>\n",
       "      <td>13.005655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6129</th>\n",
       "      <td>2005-07-11 08:15:09</td>\n",
       "      <td>2005-07-18 13:00:09</td>\n",
       "      <td>HANSEN</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>STOCK GLASS</td>\n",
       "      <td>7</td>\n",
       "      <td>2.99</td>\n",
       "      <td>10.99</td>\n",
       "      <td>PG</td>\n",
       "      <td>27.206551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6497</th>\n",
       "      <td>2005-07-12 03:04:29</td>\n",
       "      <td>2005-07-17 21:36:29</td>\n",
       "      <td>HANSEN</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>DANCING FEVER</td>\n",
       "      <td>6</td>\n",
       "      <td>0.99</td>\n",
       "      <td>25.99</td>\n",
       "      <td>G</td>\n",
       "      <td>3.809157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7786</th>\n",
       "      <td>2005-07-28 07:18:26</td>\n",
       "      <td>2005-07-29 03:00:26</td>\n",
       "      <td>HANSEN</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>KARATE MOON</td>\n",
       "      <td>4</td>\n",
       "      <td>0.99</td>\n",
       "      <td>21.99</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>4.502046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8300</th>\n",
       "      <td>2005-07-29 02:57:59</td>\n",
       "      <td>2005-08-05 01:12:59</td>\n",
       "      <td>HANSEN</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>VOYAGE LEGALLY</td>\n",
       "      <td>6</td>\n",
       "      <td>0.99</td>\n",
       "      <td>28.99</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>3.414971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8597</th>\n",
       "      <td>2005-07-29 12:55:55</td>\n",
       "      <td>2005-08-05 18:54:55</td>\n",
       "      <td>HANSEN</td>\n",
       "      <td>1</td>\n",
       "      <td>Lethbridge</td>\n",
       "      <td>TUXEDO MILE</td>\n",
       "      <td>3</td>\n",
       "      <td>2.99</td>\n",
       "      <td>24.99</td>\n",
       "      <td>R</td>\n",
       "      <td>11.964786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8787</th>\n",
       "      <td>2005-07-29 20:43:49</td>\n",
       "      <td>2005-07-31 15:15:49</td>\n",
       "      <td>HANSEN</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>LEGALLY SECRETARY</td>\n",
       "      <td>7</td>\n",
       "      <td>4.99</td>\n",
       "      <td>14.99</td>\n",
       "      <td>PG</td>\n",
       "      <td>33.288859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10043</th>\n",
       "      <td>2005-07-31 19:02:07</td>\n",
       "      <td>2005-08-07 17:58:07</td>\n",
       "      <td>HANSEN</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>MARS ROMAN</td>\n",
       "      <td>6</td>\n",
       "      <td>0.99</td>\n",
       "      <td>21.99</td>\n",
       "      <td>NC-17</td>\n",
       "      <td>4.502046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12179</th>\n",
       "      <td>2005-08-18 01:21:21</td>\n",
       "      <td>2005-08-19 00:59:21</td>\n",
       "      <td>HANSEN</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>FOREVER CANDIDATE</td>\n",
       "      <td>7</td>\n",
       "      <td>2.99</td>\n",
       "      <td>28.99</td>\n",
       "      <td>NC-17</td>\n",
       "      <td>10.313901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13477</th>\n",
       "      <td>2005-08-20 01:07:00</td>\n",
       "      <td>2005-08-26 02:47:00</td>\n",
       "      <td>HANSEN</td>\n",
       "      <td>2</td>\n",
       "      <td>Woodridge</td>\n",
       "      <td>FINDING ANACONDA</td>\n",
       "      <td>4</td>\n",
       "      <td>0.99</td>\n",
       "      <td>10.99</td>\n",
       "      <td>R</td>\n",
       "      <td>9.008189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14350</th>\n",
       "      <td>2005-08-21 08:58:38</td>\n",
       "      <td>2005-08-30 03:29:38</td>\n",
       "      <td>HANSEN</td>\n",
       "      <td>1</td>\n",
       "      <td>Lethbridge</td>\n",
       "      <td>PRIMARY GLASS</td>\n",
       "      <td>7</td>\n",
       "      <td>0.99</td>\n",
       "      <td>16.99</td>\n",
       "      <td>G</td>\n",
       "      <td>5.826957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  rental_date         return_date customer_lastname  store_id  \\\n",
       "rental_id                                                                       \n",
       "5         2005-05-24 23:05:21 2005-06-02 04:33:21            HANSEN         2   \n",
       "134       2005-05-25 21:48:41 2005-06-02 18:28:41            HANSEN         2   \n",
       "416       2005-05-27 15:02:10 2005-05-29 10:34:10            HANSEN         2   \n",
       "809       2005-05-29 19:10:20 2005-06-05 19:05:20            HANSEN         2   \n",
       "1006      2005-05-31 00:57:08 2005-06-02 22:35:08            HANSEN         2   \n",
       "1368      2005-06-15 14:27:47 2005-06-23 18:07:47            HANSEN         1   \n",
       "2603      2005-06-19 06:21:25 2005-06-26 03:19:25            HANSEN         2   \n",
       "5209      2005-07-09 11:22:39 2005-07-17 09:31:39            HANSEN         1   \n",
       "5266      2005-07-09 14:17:40 2005-07-16 10:42:40            HANSEN         2   \n",
       "5592      2005-07-10 04:26:13 2005-07-19 02:32:13            HANSEN         2   \n",
       "5635      2005-07-10 06:28:39 2005-07-17 08:35:39            HANSEN         2   \n",
       "6129      2005-07-11 08:15:09 2005-07-18 13:00:09            HANSEN         2   \n",
       "6497      2005-07-12 03:04:29 2005-07-17 21:36:29            HANSEN         2   \n",
       "7786      2005-07-28 07:18:26 2005-07-29 03:00:26            HANSEN         2   \n",
       "8300      2005-07-29 02:57:59 2005-08-05 01:12:59            HANSEN         2   \n",
       "8597      2005-07-29 12:55:55 2005-08-05 18:54:55            HANSEN         1   \n",
       "8787      2005-07-29 20:43:49 2005-07-31 15:15:49            HANSEN         2   \n",
       "10043     2005-07-31 19:02:07 2005-08-07 17:58:07            HANSEN         2   \n",
       "12179     2005-08-18 01:21:21 2005-08-19 00:59:21            HANSEN         2   \n",
       "13477     2005-08-20 01:07:00 2005-08-26 02:47:00            HANSEN         2   \n",
       "14350     2005-08-21 08:58:38 2005-08-30 03:29:38            HANSEN         1   \n",
       "\n",
       "          rental_store_city            film_title  film_rental_duration  \\\n",
       "rental_id                                                                 \n",
       "5                 Woodridge       IDOLS SNATCHERS                     5   \n",
       "134               Woodridge         JUMPING WRATH                     4   \n",
       "416               Woodridge      LESSON CLEOPATRA                     3   \n",
       "809               Woodridge           INDIAN LOVE                     4   \n",
       "1006              Woodridge         SALUTE APOLLO                     4   \n",
       "1368             Lethbridge  HUNCHBACK IMPOSSIBLE                     4   \n",
       "2603              Woodridge         CAT CONEHEADS                     5   \n",
       "5209             Lethbridge          WHALE BIKINI                     4   \n",
       "5266              Woodridge      LOATHING LEGALLY                     4   \n",
       "5592              Woodridge            LUKE MUMMY                     5   \n",
       "5635              Woodridge             FISH OPUS                     4   \n",
       "6129              Woodridge           STOCK GLASS                     7   \n",
       "6497              Woodridge         DANCING FEVER                     6   \n",
       "7786              Woodridge           KARATE MOON                     4   \n",
       "8300              Woodridge        VOYAGE LEGALLY                     6   \n",
       "8597             Lethbridge           TUXEDO MILE                     3   \n",
       "8787              Woodridge     LEGALLY SECRETARY                     7   \n",
       "10043             Woodridge            MARS ROMAN                     6   \n",
       "12179             Woodridge     FOREVER CANDIDATE                     7   \n",
       "13477             Woodridge      FINDING ANACONDA                     4   \n",
       "14350            Lethbridge         PRIMARY GLASS                     7   \n",
       "\n",
       "           film_rental_rate  film_replacement_cost film_rating  \\\n",
       "rental_id                                                        \n",
       "5                      2.99                  29.99       NC-17   \n",
       "134                    0.99                  18.99       NC-17   \n",
       "416                    0.99                  28.99       NC-17   \n",
       "809                    0.99                  26.99       NC-17   \n",
       "1006                   2.99                  29.99           R   \n",
       "1368                   4.99                  28.99       PG-13   \n",
       "2603                   4.99                  14.99           G   \n",
       "5209                   4.99                  11.99       PG-13   \n",
       "5266                   0.99                  29.99           R   \n",
       "5592                   2.99                  21.99       NC-17   \n",
       "5635                   2.99                  22.99           R   \n",
       "6129                   2.99                  10.99          PG   \n",
       "6497                   0.99                  25.99           G   \n",
       "7786                   0.99                  21.99       PG-13   \n",
       "8300                   0.99                  28.99       PG-13   \n",
       "8597                   2.99                  24.99           R   \n",
       "8787                   4.99                  14.99          PG   \n",
       "10043                  0.99                  21.99       NC-17   \n",
       "12179                  2.99                  28.99       NC-17   \n",
       "13477                  0.99                  10.99           R   \n",
       "14350                  0.99                  16.99           G   \n",
       "\n",
       "           rental_gain_return  \n",
       "rental_id                      \n",
       "5                    9.969990  \n",
       "134                  5.213270  \n",
       "416                  3.414971  \n",
       "809                  3.668025  \n",
       "1006                 9.969990  \n",
       "1368                17.212832  \n",
       "2603                33.288859  \n",
       "5209                41.618015  \n",
       "5266                 3.301100  \n",
       "5592                13.597090  \n",
       "5635                13.005655  \n",
       "6129                27.206551  \n",
       "6497                 3.809157  \n",
       "7786                 4.502046  \n",
       "8300                 3.414971  \n",
       "8597                11.964786  \n",
       "8787                33.288859  \n",
       "10043                4.502046  \n",
       "12179               10.313901  \n",
       "13477                9.008189  \n",
       "14350                5.826957  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['customer_lastname'] == 'HANSEN']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a list of all the films with the highest replacement cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.99"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['film_replacement_cost'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['IDOLS SNATCHERS', 'LAWLESS VISION', 'SONG HEDWIG',\n",
       "       'LOATHING LEGALLY', 'PATIENT SISTER', 'RESERVOIR ADAPTATION',\n",
       "       'JEEPERS WEDDING', 'GOLDFINGER SENSIBILITY', 'CHARIOTS CONSPIRACY',\n",
       "       'HONEY TIES', 'GRAFFITI LOVE', 'SLACKER LIAISONS', 'DIRTY ACE',\n",
       "       'BLINDNESS GUN', 'WYOMING STORM', 'FEUD FROGMEN', 'SALUTE APOLLO',\n",
       "       'JINGLE SAGEBRUSH', 'HILLS NEIGHBORS', 'UNCUT SUICIDES',\n",
       "       'EVERYONE CRAFT', 'FLATLINERS KILLER', 'BALLROOM MOCKINGBIRD',\n",
       "       'RIVER OUTLAW', 'ARABIA DOGMA', 'VIRGIN DAISY', 'JERICHO MULAN',\n",
       "       'SASSY PACKER', 'TRACY CIDER', 'LOVER TRUMAN', 'DOCTOR GRAIL',\n",
       "       'GILMORE BOILED', 'PRINCESS GIANT', 'CRUELTY UNFORGIVEN',\n",
       "       'REIGN GENTLEMEN', 'WEST LION', 'BONNIE HOLOCAUST', 'EARTH VISION',\n",
       "       'RANDOM GO', 'CLOCKWORK PARADISE', 'FANTASIA PARK', 'RIGHT CRANES',\n",
       "       'CUPBOARD SINNERS', 'OSCAR GOLD', 'SMILE EARRING',\n",
       "       'HOLLYWOOD ANONYMOUS', 'POSEIDON FOREVER',\n",
       "       'EXTRAORDINARY CONQUERER', 'QUEST MUSSOLINI', 'JAPANESE RUN',\n",
       "       'CLYDE THEORY', 'DESPERATE TRAINSPOTTING'], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['film_replacement_cost'] == df['film_replacement_cost'].max(), 'film_title'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many `PG` or `PG-13` rating films were rented?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6797"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df['film_rating'] == 'PG') | (df['film_rating'] == 'PG-13')].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![purple-divider](https://user-images.githubusercontent.com/7065401/52071927-c1cd7100-2562-11e9-908a-dde91ba14e59.png)"
   ]
  }
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