Uber Drive Analysis.ipynb

{
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   "cell_type": "markdown",
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    "id": "98yskoA9197_"
   },
   "source": [
    "#### Instructions:\n",
    "\n",
    "For all questions after 10th, Please only use the data specified in the note given just below the question\n",
    "You need to add answers in the same file i.e. PDS_UberDriveProject_Questions.ipynb' and rename that file as 'Name_Date.ipynb'.You can mention the date on which you will be uploading/submitting the file.For e.g. if you plan to submit your assignment on 1st January 2021, you can rename the file as 'STUDENTNAME_01-Jan-2021'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WiVXvuYj198C"
   },
   "source": [
    "# Load the necessary libraries. Import and load the dataset with a name uber_drives ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "1t_S2Qw_198D"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns \n",
    "from warnings import filterwarnings\n",
    "filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "rOMfio427XfR"
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>START_DATE*</th>\n",
       "      <th>END_DATE*</th>\n",
       "      <th>CATEGORY*</th>\n",
       "      <th>START*</th>\n",
       "      <th>STOP*</th>\n",
       "      <th>MILES*</th>\n",
       "      <th>PURPOSE*</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-01-2016 21:11</td>\n",
       "      <td>01-01-2016 21:17</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.1</td>\n",
       "      <td>Meal/Entertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01-02-2016 01:25</td>\n",
       "      <td>01-02-2016 01:37</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01-02-2016 20:25</td>\n",
       "      <td>01-02-2016 20:38</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Errand/Supplies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01-05-2016 17:31</td>\n",
       "      <td>01-05-2016 17:45</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01-06-2016 14:42</td>\n",
       "      <td>01-06-2016 15:49</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>63.7</td>\n",
       "      <td>Customer Visit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1150</th>\n",
       "      <td>12/31/2016 1:07</td>\n",
       "      <td>12/31/2016 1:14</td>\n",
       "      <td>Business</td>\n",
       "      <td>Karachi</td>\n",
       "      <td>Karachi</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1151</th>\n",
       "      <td>12/31/2016 13:24</td>\n",
       "      <td>12/31/2016 13:42</td>\n",
       "      <td>Business</td>\n",
       "      <td>Karachi</td>\n",
       "      <td>Unknown Location</td>\n",
       "      <td>3.9</td>\n",
       "      <td>Temporary Site</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1152</th>\n",
       "      <td>12/31/2016 15:03</td>\n",
       "      <td>12/31/2016 15:38</td>\n",
       "      <td>Business</td>\n",
       "      <td>Unknown Location</td>\n",
       "      <td>Unknown Location</td>\n",
       "      <td>16.2</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1153</th>\n",
       "      <td>12/31/2016 21:32</td>\n",
       "      <td>12/31/2016 21:50</td>\n",
       "      <td>Business</td>\n",
       "      <td>Katunayake</td>\n",
       "      <td>Gampaha</td>\n",
       "      <td>6.4</td>\n",
       "      <td>Temporary Site</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1154</th>\n",
       "      <td>12/31/2016 22:08</td>\n",
       "      <td>12/31/2016 23:51</td>\n",
       "      <td>Business</td>\n",
       "      <td>Gampaha</td>\n",
       "      <td>Ilukwatta</td>\n",
       "      <td>48.2</td>\n",
       "      <td>Temporary Site</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1155 rows × 7 columns</p>\n",
       "</div>"
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       "           START_DATE*         END_DATE* CATEGORY*            START*  \\\n",
       "0     01-01-2016 21:11  01-01-2016 21:17  Business       Fort Pierce   \n",
       "1     01-02-2016 01:25  01-02-2016 01:37  Business       Fort Pierce   \n",
       "2     01-02-2016 20:25  01-02-2016 20:38  Business       Fort Pierce   \n",
       "3     01-05-2016 17:31  01-05-2016 17:45  Business       Fort Pierce   \n",
       "4     01-06-2016 14:42  01-06-2016 15:49  Business       Fort Pierce   \n",
       "...                ...               ...       ...               ...   \n",
       "1150   12/31/2016 1:07   12/31/2016 1:14  Business           Karachi   \n",
       "1151  12/31/2016 13:24  12/31/2016 13:42  Business           Karachi   \n",
       "1152  12/31/2016 15:03  12/31/2016 15:38  Business  Unknown Location   \n",
       "1153  12/31/2016 21:32  12/31/2016 21:50  Business        Katunayake   \n",
       "1154  12/31/2016 22:08  12/31/2016 23:51  Business           Gampaha   \n",
       "\n",
       "                 STOP*  MILES*         PURPOSE*  \n",
       "0          Fort Pierce     5.1   Meal/Entertain  \n",
       "1          Fort Pierce     5.0              NaN  \n",
       "2          Fort Pierce     4.8  Errand/Supplies  \n",
       "3          Fort Pierce     4.7          Meeting  \n",
       "4      West Palm Beach    63.7   Customer Visit  \n",
       "...                ...     ...              ...  \n",
       "1150           Karachi     0.7          Meeting  \n",
       "1151  Unknown Location     3.9   Temporary Site  \n",
       "1152  Unknown Location    16.2          Meeting  \n",
       "1153           Gampaha     6.4   Temporary Site  \n",
       "1154         Ilukwatta    48.2   Temporary Site  \n",
       "\n",
       "[1155 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_drives=pd.read_csv('uberdrives.csv')\n",
    "uber_drives"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DZktgL3E198I"
   },
   "source": [
    "## Q1. Show the last 10 records of the dataset. (2 point)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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    "colab": {
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       "      <th>1145</th>\n",
       "      <td>12/30/2016 10:15</td>\n",
       "      <td>12/30/2016 10:33</td>\n",
       "      <td>Business</td>\n",
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       "      <td>Karachi</td>\n",
       "      <td>2.8</td>\n",
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       "      <td>12/30/2016 11:31</td>\n",
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       "      <td>Business</td>\n",
       "      <td>Karachi</td>\n",
       "      <td>Karachi</td>\n",
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       "      <th>1148</th>\n",
       "      <td>12/30/2016 16:45</td>\n",
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       "      <td>Karachi</td>\n",
       "      <td>Karachi</td>\n",
       "      <td>4.6</td>\n",
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       "      <th>1149</th>\n",
       "      <td>12/30/2016 23:06</td>\n",
       "      <td>12/30/2016 23:10</td>\n",
       "      <td>Business</td>\n",
       "      <td>Karachi</td>\n",
       "      <td>Karachi</td>\n",
       "      <td>0.8</td>\n",
       "      <td>Customer Visit</td>\n",
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       "    <tr>\n",
       "      <th>1150</th>\n",
       "      <td>12/31/2016 1:07</td>\n",
       "      <td>12/31/2016 1:14</td>\n",
       "      <td>Business</td>\n",
       "      <td>Karachi</td>\n",
       "      <td>Karachi</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Meeting</td>\n",
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       "      <th>1151</th>\n",
       "      <td>12/31/2016 13:24</td>\n",
       "      <td>12/31/2016 13:42</td>\n",
       "      <td>Business</td>\n",
       "      <td>Karachi</td>\n",
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       "      <td>3.9</td>\n",
       "      <td>Temporary Site</td>\n",
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       "    <tr>\n",
       "      <th>1152</th>\n",
       "      <td>12/31/2016 15:03</td>\n",
       "      <td>12/31/2016 15:38</td>\n",
       "      <td>Business</td>\n",
       "      <td>Unknown Location</td>\n",
       "      <td>Unknown Location</td>\n",
       "      <td>16.2</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1153</th>\n",
       "      <td>12/31/2016 21:32</td>\n",
       "      <td>12/31/2016 21:50</td>\n",
       "      <td>Business</td>\n",
       "      <td>Katunayake</td>\n",
       "      <td>Gampaha</td>\n",
       "      <td>6.4</td>\n",
       "      <td>Temporary Site</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1154</th>\n",
       "      <td>12/31/2016 22:08</td>\n",
       "      <td>12/31/2016 23:51</td>\n",
       "      <td>Business</td>\n",
       "      <td>Gampaha</td>\n",
       "      <td>Ilukwatta</td>\n",
       "      <td>48.2</td>\n",
       "      <td>Temporary Site</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "           START_DATE*         END_DATE* CATEGORY*            START*  \\\n",
       "1145  12/30/2016 10:15  12/30/2016 10:33  Business           Karachi   \n",
       "1146  12/30/2016 11:31  12/30/2016 11:56  Business           Karachi   \n",
       "1147  12/30/2016 15:41  12/30/2016 16:03  Business           Karachi   \n",
       "1148  12/30/2016 16:45  12/30/2016 17:08  Business           Karachi   \n",
       "1149  12/30/2016 23:06  12/30/2016 23:10  Business           Karachi   \n",
       "1150   12/31/2016 1:07   12/31/2016 1:14  Business           Karachi   \n",
       "1151  12/31/2016 13:24  12/31/2016 13:42  Business           Karachi   \n",
       "1152  12/31/2016 15:03  12/31/2016 15:38  Business  Unknown Location   \n",
       "1153  12/31/2016 21:32  12/31/2016 21:50  Business        Katunayake   \n",
       "1154  12/31/2016 22:08  12/31/2016 23:51  Business           Gampaha   \n",
       "\n",
       "                 STOP*  MILES*         PURPOSE*  \n",
       "1145           Karachi     2.8  Errand/Supplies  \n",
       "1146           Karachi     2.9  Errand/Supplies  \n",
       "1147           Karachi     4.6  Errand/Supplies  \n",
       "1148           Karachi     4.6          Meeting  \n",
       "1149           Karachi     0.8   Customer Visit  \n",
       "1150           Karachi     0.7          Meeting  \n",
       "1151  Unknown Location     3.9   Temporary Site  \n",
       "1152  Unknown Location    16.2          Meeting  \n",
       "1153           Gampaha     6.4   Temporary Site  \n",
       "1154         Ilukwatta    48.2   Temporary Site  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_drives.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ipD0ZQ9O198O"
   },
   "source": [
    "## Q2. Show the first 10 records of the dataset. (2 points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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     "base_uri": "https://localhost:8080/",
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    "id": "XzYRsxR1198O",
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       "      <td>01-02-2016 01:25</td>\n",
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       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
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       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
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       "      <th>3</th>\n",
       "      <td>01-05-2016 17:31</td>\n",
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       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
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       "      <td>Meeting</td>\n",
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       "      <th>4</th>\n",
       "      <td>01-06-2016 14:42</td>\n",
       "      <td>01-06-2016 15:49</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>63.7</td>\n",
       "      <td>Customer Visit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>01-06-2016 17:15</td>\n",
       "      <td>01-06-2016 17:19</td>\n",
       "      <td>Business</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>4.3</td>\n",
       "      <td>Meal/Entertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>01-06-2016 17:30</td>\n",
       "      <td>01-06-2016 17:35</td>\n",
       "      <td>Business</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>Palm Beach</td>\n",
       "      <td>7.1</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>01-07-2016 13:27</td>\n",
       "      <td>01-07-2016 13:33</td>\n",
       "      <td>Business</td>\n",
       "      <td>Cary</td>\n",
       "      <td>Cary</td>\n",
       "      <td>0.8</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>01-10-2016 08:05</td>\n",
       "      <td>01-10-2016 08:25</td>\n",
       "      <td>Business</td>\n",
       "      <td>Cary</td>\n",
       "      <td>Morrisville</td>\n",
       "      <td>8.3</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>01-10-2016 12:17</td>\n",
       "      <td>01-10-2016 12:44</td>\n",
       "      <td>Business</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>New York</td>\n",
       "      <td>16.5</td>\n",
       "      <td>Customer Visit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        START_DATE*         END_DATE* CATEGORY*           START*  \\\n",
       "0  01-01-2016 21:11  01-01-2016 21:17  Business      Fort Pierce   \n",
       "1  01-02-2016 01:25  01-02-2016 01:37  Business      Fort Pierce   \n",
       "2  01-02-2016 20:25  01-02-2016 20:38  Business      Fort Pierce   \n",
       "3  01-05-2016 17:31  01-05-2016 17:45  Business      Fort Pierce   \n",
       "4  01-06-2016 14:42  01-06-2016 15:49  Business      Fort Pierce   \n",
       "5  01-06-2016 17:15  01-06-2016 17:19  Business  West Palm Beach   \n",
       "6  01-06-2016 17:30  01-06-2016 17:35  Business  West Palm Beach   \n",
       "7  01-07-2016 13:27  01-07-2016 13:33  Business             Cary   \n",
       "8  01-10-2016 08:05  01-10-2016 08:25  Business             Cary   \n",
       "9  01-10-2016 12:17  01-10-2016 12:44  Business          Jamaica   \n",
       "\n",
       "             STOP*  MILES*         PURPOSE*  \n",
       "0      Fort Pierce     5.1   Meal/Entertain  \n",
       "1      Fort Pierce     5.0              NaN  \n",
       "2      Fort Pierce     4.8  Errand/Supplies  \n",
       "3      Fort Pierce     4.7          Meeting  \n",
       "4  West Palm Beach    63.7   Customer Visit  \n",
       "5  West Palm Beach     4.3   Meal/Entertain  \n",
       "6       Palm Beach     7.1          Meeting  \n",
       "7             Cary     0.8          Meeting  \n",
       "8      Morrisville     8.3          Meeting  \n",
       "9         New York    16.5   Customer Visit  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_drives.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R2F4GX15198S"
   },
   "source": [
    "## Q3. Show the dimension(number of rows and columns) of the dataset. (2 points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5YAWUNwZ198T",
    "outputId": "36e5d587-b2e3-406b-8678-b8d9ea8aa79a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1155, 7)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_drives.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of rows in the uberdrives data set is 1155 and the number of columns is 7."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H6x0S6vs198W"
   },
   "source": [
    "## Q4. Show the size (Total number of elements) of the dataset. (2 points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2IQc1x7D198X",
    "outputId": "1d740ac9-5d96-489e-fd2a-8863d2b94655"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8085"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_drives.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The total number of elements in the dataset is 8085"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c2ATRpjm198a"
   },
   "source": [
    "## Q5. Display the information about all the variables of the data set. What can you infer from the output?(1 +2 points)\n",
    "\n",
    "#### Hint: Information includes - Total number of columns,variable data-types, number of non-null values in a variable, and usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "D7Pfnrb6198b",
    "outputId": "33673c70-4ae3-410c-8495-a91a44c5a601"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1155 entries, 0 to 1154\n",
      "Data columns (total 7 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   START_DATE*  1155 non-null   object \n",
      " 1   END_DATE*    1155 non-null   object \n",
      " 2   CATEGORY*    1155 non-null   object \n",
      " 3   START*       1155 non-null   object \n",
      " 4   STOP*        1155 non-null   object \n",
      " 5   MILES*       1155 non-null   float64\n",
      " 6   PURPOSE*     653 non-null    object \n",
      "dtypes: float64(1), object(6)\n",
      "memory usage: 63.3+ KB\n"
     ]
    }
   ],
   "source": [
    "uber_drives.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above information we can infer that :-\n",
    "\n",
    "1) Identifying null values in the the column 'PURPOSE' of the dataset, implies that it was not a mandatory data for users to fill out, whereas the other fields were automatically generated considering user inputs during the booking of the ride.\n",
    "\n",
    "2) Wherever the data is being recorded it is made sure that the column 'MILES' accepts only numerical values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "emZkbUV_198g"
   },
   "source": [
    "## Q6. Check for missing values. (2 points) \n",
    "\n",
    "####  Note: Output should contain only one boolean value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XWU-AunT198h",
    "outputId": "cf6a6413-8b96-44a1-a3a6-cd3640761e2d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_drives.isnull().sum().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above output indicates there are null values present in the dataset provided."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UCvC2OPe198l"
   },
   "source": [
    "## Q7. How many missing values are present in the entire dataset? (2 points)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "o2-vHyGA198m",
    "outputId": "60923ca7-1704-41c0-9620-6be35e10ed3b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "START_DATE*      0\n",
       "END_DATE*        0\n",
       "CATEGORY*        0\n",
       "START*           0\n",
       "STOP*            0\n",
       "MILES*           0\n",
       "PURPOSE*       502\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_drives.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above information indicates there are 502 null values in the column 'PURPOSE' whereas all the other columns have none."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "617aSeL_198q"
   },
   "source": [
    "## Q8. Get the summary of the original data. (2 points).\n",
    "\n",
    "#### Hint: Summary includes- Count,Mean, Std, Min, 25%,50%,75% and max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "hMkibRQg198q",
    "outputId": "e4e9580a-3465-4bb5-d564-6b14bf8d4adb"
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MILES*</th>\n",
       "      <td>1155.0</td>\n",
       "      <td>10.56684</td>\n",
       "      <td>21.579106</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>310.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count      mean        std  min  25%  50%   75%    max\n",
       "MILES*  1155.0  10.56684  21.579106  0.5  2.9  6.0  10.4  310.3"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_drives.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above output displays the summary of the uber drives dataset showing count, mean, standard deviation, minimum, (25th, 50th, and 75th percentiles) and maximum of all the values of the column 'MILES'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8PmU-86n198v"
   },
   "source": [
    "\n",
    "\n",
    "## Q9. Drop the missing values and store the data in a new dataframe (name it\"df\") (2-points)\n",
    "\n",
    "#### Note: Dataframe \"df\" will not contain any missing value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3xZC0dvq198w",
    "outputId": "1a2eb61f-65f4-4344-c151-e7f623a35fe6"
   },
   "outputs": [],
   "source": [
    "df=uber_drives.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>START_DATE*</th>\n",
       "      <th>END_DATE*</th>\n",
       "      <th>CATEGORY*</th>\n",
       "      <th>START*</th>\n",
       "      <th>STOP*</th>\n",
       "      <th>MILES*</th>\n",
       "      <th>PURPOSE*</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-01-2016 21:11</td>\n",
       "      <td>01-01-2016 21:17</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.1</td>\n",
       "      <td>Meal/Entertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01-02-2016 20:25</td>\n",
       "      <td>01-02-2016 20:38</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Errand/Supplies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01-05-2016 17:31</td>\n",
       "      <td>01-05-2016 17:45</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01-06-2016 14:42</td>\n",
       "      <td>01-06-2016 15:49</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>63.7</td>\n",
       "      <td>Customer Visit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>01-06-2016 17:15</td>\n",
       "      <td>01-06-2016 17:19</td>\n",
       "      <td>Business</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>4.3</td>\n",
       "      <td>Meal/Entertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1150</th>\n",
       "      <td>12/31/2016 1:07</td>\n",
       "      <td>12/31/2016 1:14</td>\n",
       "      <td>Business</td>\n",
       "      <td>Karachi</td>\n",
       "      <td>Karachi</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1151</th>\n",
       "      <td>12/31/2016 13:24</td>\n",
       "      <td>12/31/2016 13:42</td>\n",
       "      <td>Business</td>\n",
       "      <td>Karachi</td>\n",
       "      <td>Unknown Location</td>\n",
       "      <td>3.9</td>\n",
       "      <td>Temporary Site</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1152</th>\n",
       "      <td>12/31/2016 15:03</td>\n",
       "      <td>12/31/2016 15:38</td>\n",
       "      <td>Business</td>\n",
       "      <td>Unknown Location</td>\n",
       "      <td>Unknown Location</td>\n",
       "      <td>16.2</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1153</th>\n",
       "      <td>12/31/2016 21:32</td>\n",
       "      <td>12/31/2016 21:50</td>\n",
       "      <td>Business</td>\n",
       "      <td>Katunayake</td>\n",
       "      <td>Gampaha</td>\n",
       "      <td>6.4</td>\n",
       "      <td>Temporary Site</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1154</th>\n",
       "      <td>12/31/2016 22:08</td>\n",
       "      <td>12/31/2016 23:51</td>\n",
       "      <td>Business</td>\n",
       "      <td>Gampaha</td>\n",
       "      <td>Ilukwatta</td>\n",
       "      <td>48.2</td>\n",
       "      <td>Temporary Site</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>653 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           START_DATE*         END_DATE* CATEGORY*            START*  \\\n",
       "0     01-01-2016 21:11  01-01-2016 21:17  Business       Fort Pierce   \n",
       "2     01-02-2016 20:25  01-02-2016 20:38  Business       Fort Pierce   \n",
       "3     01-05-2016 17:31  01-05-2016 17:45  Business       Fort Pierce   \n",
       "4     01-06-2016 14:42  01-06-2016 15:49  Business       Fort Pierce   \n",
       "5     01-06-2016 17:15  01-06-2016 17:19  Business   West Palm Beach   \n",
       "...                ...               ...       ...               ...   \n",
       "1150   12/31/2016 1:07   12/31/2016 1:14  Business           Karachi   \n",
       "1151  12/31/2016 13:24  12/31/2016 13:42  Business           Karachi   \n",
       "1152  12/31/2016 15:03  12/31/2016 15:38  Business  Unknown Location   \n",
       "1153  12/31/2016 21:32  12/31/2016 21:50  Business        Katunayake   \n",
       "1154  12/31/2016 22:08  12/31/2016 23:51  Business           Gampaha   \n",
       "\n",
       "                 STOP*  MILES*         PURPOSE*  \n",
       "0          Fort Pierce     5.1   Meal/Entertain  \n",
       "2          Fort Pierce     4.8  Errand/Supplies  \n",
       "3          Fort Pierce     4.7          Meeting  \n",
       "4      West Palm Beach    63.7   Customer Visit  \n",
       "5      West Palm Beach     4.3   Meal/Entertain  \n",
       "...                ...     ...              ...  \n",
       "1150           Karachi     0.7          Meeting  \n",
       "1151  Unknown Location     3.9   Temporary Site  \n",
       "1152  Unknown Location    16.2          Meeting  \n",
       "1153           Gampaha     6.4   Temporary Site  \n",
       "1154         Ilukwatta    48.2   Temporary Site  \n",
       "\n",
       "[653 rows x 7 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis=0,inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above mentioned is the new dataframe with no null values. Hence, it has less number of entries compared to the original dataset (i.e. lesser number of rows)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2vIFI9G61983"
   },
   "source": [
    "## Q10. Check the information of the dataframe(df). (1 points)\n",
    "\n",
    "#### Hint: Information includes - Total number of columns,variable data-types, number of non-null values in a variable, and usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DrmfQwDc1983",
    "outputId": "27394741-1b4e-4249-ee9b-728d388a6855"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 653 entries, 0 to 1154\n",
      "Data columns (total 7 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   START_DATE*  653 non-null    object \n",
      " 1   END_DATE*    653 non-null    object \n",
      " 2   CATEGORY*    653 non-null    object \n",
      " 3   START*       653 non-null    object \n",
      " 4   STOP*        653 non-null    object \n",
      " 5   MILES*       653 non-null    float64\n",
      " 6   PURPOSE*     653 non-null    object \n",
      "dtypes: float64(1), object(6)\n",
      "memory usage: 40.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Pqm4jhgT1986"
   },
   "source": [
    "## Q11. Get the unique start locations. (2 points)\n",
    "#### Note: This question is based on the dataframe with no 'NA' values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jsuoItZX1987",
    "outputId": "22a926cf-9774-4223-a2d5-cf91ab160401"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "131"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['START*'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 131 unique start locations in the dataframe with no NA values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s3s-pY1R198_"
   },
   "source": [
    "## Q12. What is the total number of unique start locations? (2 points)\n",
    "#### Note: Use the original dataframe without dropping 'NA' values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OOZy88AA199A",
    "outputId": "e0c77ca6-7379-4546-f980-5e3dc9db8074"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "176"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_drives['START*'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 176 unique start locations in the dataframe without dropping NA values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LuKFv1_P199D"
   },
   "source": [
    "## Q13. What is the total number of unique stop locations. (2 points)\n",
    "#### Note: Use the original dataframe without dropping 'NA' values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5YeWcpZT199E",
    "outputId": "6e7f87a0-29e8-4bd3-9ee2-fc32b5259767"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "187"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_drives['STOP*'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 187 unique stop locations in the dataframe without dropping NA values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VmkTnPlt199H"
   },
   "source": [
    "## Q14. Display all Uber trips that has the starting point as San Francisco. (2 points)\n",
    "#### Note: Use the original dataframe without dropping the 'NA' values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "fwhKZQfs199I",
    "outputId": "949bc9c3-ed15-4299-95e4-c42980a2dfd6"
   },
   "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>START_DATE*</th>\n",
       "      <th>END_DATE*</th>\n",
       "      <th>CATEGORY*</th>\n",
       "      <th>START*</th>\n",
       "      <th>STOP*</th>\n",
       "      <th>MILES*</th>\n",
       "      <th>PURPOSE*</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>05-09-2016 14:39</td>\n",
       "      <td>05-09-2016 15:06</td>\n",
       "      <td>Business</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>Palo Alto</td>\n",
       "      <td>20.5</td>\n",
       "      <td>Between Offices</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>6/14/2016 16:09</td>\n",
       "      <td>6/14/2016 16:39</td>\n",
       "      <td>Business</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>Emeryville</td>\n",
       "      <td>11.6</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836</th>\n",
       "      <td>10/19/2016 14:02</td>\n",
       "      <td>10/19/2016 14:31</td>\n",
       "      <td>Business</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>Berkeley</td>\n",
       "      <td>10.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>11-07-2016 19:17</td>\n",
       "      <td>11-07-2016 19:57</td>\n",
       "      <td>Business</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>Berkeley</td>\n",
       "      <td>13.2</td>\n",
       "      <td>Between Offices</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>11-08-2016 12:16</td>\n",
       "      <td>11-08-2016 12:49</td>\n",
       "      <td>Business</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>Berkeley</td>\n",
       "      <td>11.3</td>\n",
       "      <td>Meeting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>927</th>\n",
       "      <td>11-09-2016 18:40</td>\n",
       "      <td>11-09-2016 19:17</td>\n",
       "      <td>Business</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>Oakland</td>\n",
       "      <td>12.7</td>\n",
       "      <td>Customer Visit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>933</th>\n",
       "      <td>11-10-2016 15:17</td>\n",
       "      <td>11-10-2016 15:22</td>\n",
       "      <td>Business</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>Oakland</td>\n",
       "      <td>9.9</td>\n",
       "      <td>Temporary Site</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>966</th>\n",
       "      <td>11/15/2016 20:44</td>\n",
       "      <td>11/15/2016 21:00</td>\n",
       "      <td>Business</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>Berkeley</td>\n",
       "      <td>11.8</td>\n",
       "      <td>Temporary Site</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          START_DATE*         END_DATE* CATEGORY*         START*       STOP*  \\\n",
       "362  05-09-2016 14:39  05-09-2016 15:06  Business  San Francisco   Palo Alto   \n",
       "440   6/14/2016 16:09   6/14/2016 16:39  Business  San Francisco  Emeryville   \n",
       "836  10/19/2016 14:02  10/19/2016 14:31  Business  San Francisco    Berkeley   \n",
       "917  11-07-2016 19:17  11-07-2016 19:57  Business  San Francisco    Berkeley   \n",
       "919  11-08-2016 12:16  11-08-2016 12:49  Business  San Francisco    Berkeley   \n",
       "927  11-09-2016 18:40  11-09-2016 19:17  Business  San Francisco     Oakland   \n",
       "933  11-10-2016 15:17  11-10-2016 15:22  Business  San Francisco     Oakland   \n",
       "966  11/15/2016 20:44  11/15/2016 21:00  Business  San Francisco    Berkeley   \n",
       "\n",
       "     MILES*         PURPOSE*  \n",
       "362    20.5  Between Offices  \n",
       "440    11.6          Meeting  \n",
       "836    10.8              NaN  \n",
       "917    13.2  Between Offices  \n",
       "919    11.3          Meeting  \n",
       "927    12.7   Customer Visit  \n",
       "933     9.9   Temporary Site  \n",
       "966    11.8   Temporary Site  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=uber_drives[(uber_drives[\"START*\"]=='San Francisco')]\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above is the dataframe with all the Uber trips that have starting point as San Francisco."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZMsuzJcG199O"
   },
   "source": [
    "## Q15. What is the most popular starting point for the Uber drivers? (2 points)\n",
    "#### Note: Use the original dataframe without dropping the 'NA' values.\n",
    "\n",
    "#### Hint:Popular means the place that is visited the most"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a5QGyjza199P",
    "outputId": "e811a583-6780-4729-90ab-6f1e3a3e2716"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cary                201\n",
       "Unknown Location    148\n",
       "Morrisville          85\n",
       "Whitebridge          68\n",
       "Islamabad            57\n",
       "                   ... \n",
       "Gampaha               1\n",
       "Bellevue              1\n",
       "Fuquay-Varina         1\n",
       "Eagle Rock            1\n",
       "Ridgeland             1\n",
       "Name: START*, Length: 176, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_drives['START*'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can infer from the above information that the most popular starting point for Uber drivers is Cary (201 times)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S13-HiPG199T"
   },
   "source": [
    "## Q16. What is the most popular dropping point for the Uber drivers? (2 points)\n",
    "#### Note: Use the original dataframe without dropping the 'NA' values.\n",
    "\n",
    "#### Hint: Popular means the place that is visited the most"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YP-hwvNT199T",
    "outputId": "725bec1c-38bc-401f-f71d-dc23d22640cd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cary                   203\n",
       "Unknown Location       149\n",
       "Morrisville             84\n",
       "Whitebridge             65\n",
       "Islamabad               58\n",
       "                      ... \n",
       "Greater Greenspoint      1\n",
       "Tenderloin               1\n",
       "Bywater                  1\n",
       "Soho                     1\n",
       "Ridgeland                1\n",
       "Name: STOP*, Length: 187, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uber_drives['STOP*'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can infer from the above information that the most popular dropping or stoppage point for Uber drivers is Cary (203 times)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6Iqizk7B199W"
   },
   "source": [
    "## Q17. What is the most frequent route taken by Uber drivers. (3 points)\n",
    "#### Note: This question is based on the new dataframe with no 'na' values.\n",
    "#### Hint-Print the most frequent route taken by Uber drivers (Route= combination of START & END points present in the Data set)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f41v9gc4199X",
    "outputId": "80499077-8872-4f3f-a504-d648ad8ec910"
   },
   "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>START_DATE*</th>\n",
       "      <th>END_DATE*</th>\n",
       "      <th>CATEGORY*</th>\n",
       "      <th>START*</th>\n",
       "      <th>STOP*</th>\n",
       "      <th>MILES*</th>\n",
       "      <th>PURPOSE*</th>\n",
       "      <th>Route</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-01-2016 21:11</td>\n",
       "      <td>01-01-2016 21:17</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>5.1</td>\n",
       "      <td>Meal/Entertain</td>\n",
       "      <td>Fort Pierce to Fort Pierce</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01-02-2016 20:25</td>\n",
       "      <td>01-02-2016 20:38</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Errand/Supplies</td>\n",
       "      <td>Fort Pierce to Fort Pierce</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01-05-2016 17:31</td>\n",
       "      <td>01-05-2016 17:45</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Meeting</td>\n",
       "      <td>Fort Pierce to Fort Pierce</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01-06-2016 14:42</td>\n",
       "      <td>01-06-2016 15:49</td>\n",
       "      <td>Business</td>\n",
       "      <td>Fort Pierce</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>63.7</td>\n",
       "      <td>Customer Visit</td>\n",
       "      <td>Fort Pierce to West Palm Beach</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>01-06-2016 17:15</td>\n",
       "      <td>01-06-2016 17:19</td>\n",
       "      <td>Business</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>4.3</td>\n",
       "      <td>Meal/Entertain</td>\n",
       "      <td>West Palm Beach to West Palm Beach</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1150</th>\n",
       "      <td>12/31/2016 1:07</td>\n",
       "      <td>12/31/2016 1:14</td>\n",
       "      <td>Business</td>\n",
       "      <td>Karachi</td>\n",
       "      <td>Karachi</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Meeting</td>\n",
       "      <td>Karachi to Karachi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1151</th>\n",
       "      <td>12/31/2016 13:24</td>\n",
       "      <td>12/31/2016 13:42</td>\n",
       "      <td>Business</td>\n",
       "      <td>Karachi</td>\n",
       "      <td>Unknown Location</td>\n",
       "      <td>3.9</td>\n",
       "      <td>Temporary Site</td>\n",
       "      <td>Karachi to Unknown Location</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1152</th>\n",
       "      <td>12/31/2016 15:03</td>\n",
       "      <td>12/31/2016 15:38</td>\n",
       "      <td>Business</td>\n",
       "      <td>Unknown Location</td>\n",
       "      <td>Unknown Location</td>\n",
       "      <td>16.2</td>\n",
       "      <td>Meeting</td>\n",
       "      <td>Unknown Location to Unknown Location</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1153</th>\n",
       "      <td>12/31/2016 21:32</td>\n",
       "      <td>12/31/2016 21:50</td>\n",
       "      <td>Business</td>\n",
       "      <td>Katunayake</td>\n",
       "      <td>Gampaha</td>\n",
       "      <td>6.4</td>\n",
       "      <td>Temporary Site</td>\n",
       "      <td>Katunayake to Gampaha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1154</th>\n",
       "      <td>12/31/2016 22:08</td>\n",
       "      <td>12/31/2016 23:51</td>\n",
       "      <td>Business</td>\n",
       "      <td>Gampaha</td>\n",
       "      <td>Ilukwatta</td>\n",
       "      <td>48.2</td>\n",
       "      <td>Temporary Site</td>\n",
       "      <td>Gampaha to Ilukwatta</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>653 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           START_DATE*         END_DATE* CATEGORY*            START*  \\\n",
       "0     01-01-2016 21:11  01-01-2016 21:17  Business       Fort Pierce   \n",
       "2     01-02-2016 20:25  01-02-2016 20:38  Business       Fort Pierce   \n",
       "3     01-05-2016 17:31  01-05-2016 17:45  Business       Fort Pierce   \n",
       "4     01-06-2016 14:42  01-06-2016 15:49  Business       Fort Pierce   \n",
       "5     01-06-2016 17:15  01-06-2016 17:19  Business   West Palm Beach   \n",
       "...                ...               ...       ...               ...   \n",
       "1150   12/31/2016 1:07   12/31/2016 1:14  Business           Karachi   \n",
       "1151  12/31/2016 13:24  12/31/2016 13:42  Business           Karachi   \n",
       "1152  12/31/2016 15:03  12/31/2016 15:38  Business  Unknown Location   \n",
       "1153  12/31/2016 21:32  12/31/2016 21:50  Business        Katunayake   \n",
       "1154  12/31/2016 22:08  12/31/2016 23:51  Business           Gampaha   \n",
       "\n",
       "                 STOP*  MILES*         PURPOSE*  \\\n",
       "0          Fort Pierce     5.1   Meal/Entertain   \n",
       "2          Fort Pierce     4.8  Errand/Supplies   \n",
       "3          Fort Pierce     4.7          Meeting   \n",
       "4      West Palm Beach    63.7   Customer Visit   \n",
       "5      West Palm Beach     4.3   Meal/Entertain   \n",
       "...                ...     ...              ...   \n",
       "1150           Karachi     0.7          Meeting   \n",
       "1151  Unknown Location     3.9   Temporary Site   \n",
       "1152  Unknown Location    16.2          Meeting   \n",
       "1153           Gampaha     6.4   Temporary Site   \n",
       "1154         Ilukwatta    48.2   Temporary Site   \n",
       "\n",
       "                                     Route  \n",
       "0               Fort Pierce to Fort Pierce  \n",
       "2               Fort Pierce to Fort Pierce  \n",
       "3               Fort Pierce to Fort Pierce  \n",
       "4           Fort Pierce to West Palm Beach  \n",
       "5       West Palm Beach to West Palm Beach  \n",
       "...                                    ...  \n",
       "1150                    Karachi to Karachi  \n",
       "1151           Karachi to Unknown Location  \n",
       "1152  Unknown Location to Unknown Location  \n",
       "1153                 Katunayake to Gampaha  \n",
       "1154                  Gampaha to Ilukwatta  \n",
       "\n",
       "[653 rows x 8 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Route'] = df['START*']+' to '+df['STOP*']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cary to Morrisville                         52\n",
       "Morrisville to Cary                         51\n",
       "Cary to Cary                                44\n",
       "Cary to Durham                              30\n",
       "Unknown Location to Unknown Location        30\n",
       "                                            ..\n",
       "Hazelwood to Weston                          1\n",
       "Hudson Square to Lower Manhattan             1\n",
       "Lexington Park at Amberly to Whitebridge     1\n",
       "Gulfton to Downtown                          1\n",
       "Kissimmee to Kissimmee                       1\n",
       "Name: Route, Length: 239, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Route'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can infer from the above information that the most frequent route taken by Uber drivers is from Cary to Morrisville (52 trips) followed by Morrisville to Carry (51 trips)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oBrYyZ-L199Z"
   },
   "source": [
    "## Q18. Display all types of purposes for the trip in an array. (2 points)\n",
    "#### Note: This question is based on the new dataframe with no 'NA' values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "p3fe_lTe199a",
    "outputId": "42c0d950-2141-4e13-9684-27b3c4dda5b2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Meeting', 'Meal/Entertain', 'Errand/Supplies', 'Customer Visit',\n",
       "       'Temporary Site', 'Between Offices', 'Moving', 'Airport/Travel',\n",
       "       'Commute', 'Charity ($)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['PURPOSE*'].value_counts().index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above mentioned are all the types of purposes for the trip in the dataframe with no 'NA' values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WBN9Ufxc199d"
   },
   "source": [
    "## Q19. Plot a bar graph of Purpose vs Miles(Distance). What can you infer from the plot(2 +2 points)\n",
    "#### Note: Use the original dataframe without dropping the 'NA' values.\n",
    "#### Hint:You have to plot total/sum miles per purpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 351
    },
    "id": "JaNSQ_qSa-Rg",
    "outputId": "4314b7a0-0f56-441d-ee93-e9c723e75115"
   },
   "outputs": [
    {
     "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": [
    "plt.figure(figsize=(14,6))\n",
    "sns.barplot(x='PURPOSE*',y='MILES*',estimator=np.sum,data=uber_drives,ci=None)\n",
    "plt.xticks(rotation=25);\n",
    "plt.title('Plot of Purpose vs Distance (in miles)');\n",
    "plt.xlabel('Purpose');\n",
    "plt.ylabel('Distance (in miles)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can infer from the graph above that\n",
    "\n",
    "1) The most distance travelled by the customers were for the 'Meeting' and 'Customer Visit' purposes.\n",
    "\n",
    "2) Whereas the least distance travelled by the customers were for the 'Chairty', 'Airport/Travel' and 'Moving' purposes in no definite order.\n",
    "\n",
    "However, the above graph does not account for the null values in the dataset. As the null values constitute for more than 50% of the total values in the column 'PURPOSE' of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = uber_drives.copy()\n",
    "df2.fillna('Not Specified',inplace=True)\n",
    "df2;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MfB1wCrBLm20X4OT2/hRg+4iYKyJWpjpt+EOrnndHRGzUervbuW8ZSZIkSZrpZh/A/zwYOCEi3gj8C3gNQGZeGhEnAJcBDwJvz8yH2jJ7AEcB8wBntJckSZIkjYoxCZQy82zg7Pb+FuAFw8x3EHDQENPPA9YZvRRKkiRJ0lRjMY6SJEmSJE0oBkqSJEmS1GGgJEmSJEkdBkqSJEmS1GGgJEmSJEkdBkqSJEmS1GGgJEmSJEkdBkqSJEmS1GGgJEmSJEkdBkqSJEmS1GGgJEmSJEkdBkqSJEmS1GGgJEmSJEkdBkqSJEmS1GGgJEmSJEkdBkqSJEmS1GGgJEmSJEkdBkqSJEmS1GGgJEmSJEkdBkqSJEmS1GGgJEmSJEkdBkqSJEmS1GGgJEmSJEkdBkqSJEmS1GGgJEmSJEkdBkqSJEmS1GGgJEmSJEkdBkqSJEmS1GGgJEmSJEkdBkqSJEmS1GGgJEmSJEkdBkqSJEmS1GGgJEmSJEkdBkqSJEmS1GGgJEmSJEkdBkqSJEmS1GGgJEmSJEkdBkqSJEmS1GGgJEmSJEkdBkqSJEmS1PG4AqWImC8ipoxWYiRJkiRpPJhmoBQRs0XEjhFxWkTcCPwVuD4iLo2IT0fEqmOTTEmSJEkaO9MrUToLeDKwL7B0Zi6fmUsCzwF+BxwcEa8b5TRKkiRJ0piafTrfb5GZD3QnZuZ/gROBEyNijlFJmSRJkiQNyDRLlHpBUkQ8OSLmau83i4i9ImLh/nkkSZIkabIYaWcOJwIPRcRTgG8AKwPfGbVUSZIkSdIAjTRQejgzHwReAXwhM98NLDN6yZIkSZKkwRlpoPRAROwA7AKc2qbZNkmSJEnSpDTSQOkNwMbAQZl5VUSsDHxr9JIlSZIkSYMzvV7vAMjMyyJib2CF9vkq4ODRTJgkSZIkDcqISpQiYhvgIuDH7fO6EXHKKKZLkiRJkgZmpFXvDgA2AG4FyMyLqJ7vJEmSJGnSGWmg9GBm3taZljM7MZIkSZI0HoyojRLw54jYEZgSEasCewHnjF6yJEmSJGlwRlqi9A5gbeA+4DjgduBdo5QmSZIkSRqoEQVKmXl3Zn4gM5+Vmeu39/dOa5mImDsi/hARF0fEpRFxYJu+aET8LCIub38X6Vtm34i4IiL+FhFb9k1fLyIuad8dEhExoxssSZIkSdMzzap3EfEjptEWKTNfNo3F7wM2z8w7I2IO4DcRcQbwSuDMzDw4IvYB9gH2joi1gO2pkqsnAT+PiNUy8yHgcGB34HfA6cBWwBkj3UhJkiRJejym10bpMzO64sxM4M72cY72SmBbYLM2/WjgbGDvNv34zLwPuCoirgA2iIh/Agtm5rkAEXEM8HIMlCRJkiSNkmkGSpn5yyey8oiYApwPPAX4cmb+PiKWyszr2/qvj4gl2+zLUiVGPde2aQ+0993pkiRJkjQqplf17oTMfG1EXMKjq+AFVWj0tGkt36rNrRsRCwMnRcQ60/p3Q61iGtOHSu/uVBU9VlhhhWklTZIkSZKGNb2qd+9sf1/6RP5JZt4aEWdTbYtuiIhlWmnSMsCNbbZrgeX7FlsOuK5NX26I6UP9nyOAIwDWX399x3mSJEmSNEOm2etdXxW5qzPzauB/wB19r2FFxBKtJImImAfYAvgrcAqwS5ttF+Dk9v4UYPuImCsiVgZWBf7Q0nBHRGzUervbuW8ZSZIkSZrpRjTgbES8BfgIcA9Tq70lsMo0FlsGOLq1U5oNOCEzT42Ic4ETIuKNwL+A1wBk5qURcQJwGfAg8PZWdQ9gD+AoYB6qEwc7cpAkSZI0akYUKAHvA9bOzJtHuuLM/BPwjCGm3wK8YJhlDgIOGmL6ecC02jdJkiRJ0kwzogFngX8Ad49mQiRJkiRpvBhpidK+wDkR8XtqIFkAMnOvUUmVJEmSJA3QSAOlrwK/AC4BHh695EiSJEnS4I00UHowM98zqimRJEmSpHFipIHSWW0w1x/x6Kp3/x2VVEmaUD7/nS0HnYRR9+4dfzLoJEiSpDE00kBpx/Z3375p0+seXJIkSZImpBEFSpm58mgnRJIkSZLGi5F2Dy5JkiRJswwDJUmSJEnqMFCSJEmSpI6RduZARCwLrNi/TGb+ajQSJUmSJEmDNKJAKSI+CWwHXAY81CYnYKAkSZIkadIZaYnSy4HVM/O+6c0oSZIkSRPdSNsoXQnMMZoJkSRJkqTxYqQlSncDF0XEmcAjpUqZudeopEqSJEmSBmikgdIp7SVJkiRJk96IAqXMPHq0EyJJkiRJ48U0A6WIOCEzXxsRl1C93D1KZj5t1FImSZIkSQMyvRKld7a/Lx3thEiSJEnSeDG9QOk/AJl59XAzRERk5mNKmyRJkiRpoppe9+BnRcQ7ImKF/okRMWdEbB4RRwO7jF7yJEmSJGnsTa9EaStgN+C4iFgZuBWYG5gC/BT4fGZeNJoJlCRJkqSxNs1AKTPvBQ4DDouIOYDFgXsy89YxSJskSZIkDcRIx1EiMx8Arh/FtEiSJEnSuDC9NkqSJEmSNMsxUJIkSZKkjhEHShGxYkRs0d7PExELjF6yJEmSJGlwRhQoRcSbge8DX22TlgN+OEppkiRJkqSBGmmJ0tuBTYDbATLzcmDJ0UqUJEmSJA3SSAOl+zLz/t6HiJgdyNFJkiRJkiQN1kgDpV9GxH7APBHxQuB7wI9GL1mSJEmSNDgjDZT2AW4CLgHeApwOfHC0EiVJkiRJgzTSAWfnAY7MzK8BRMSUNu3u0UqYJEmSJA3KSEuUzqQCo555gJ/P/ORIkiRJ0uCNNFCaOzPv7H1o7+cdnSRJkiRJ0mCNNFC6KyKe2fsQEesB94xOkiRJkiRpsEbaRuldwPci4rr2eRlgu1FJkSRJkiQN2IgCpcz8Y0SsAawOBPDXzHxgVFMmSZIkSQMy0hIlgGcBK7VlnhERZOYxo5IqSZIkSRqgEQVKEXEs8GTgIuChNjkBAyVJkiRJk85IS5TWB9bKzBzNxEiSJEnSeDDSXu/+DCw9mgmRJEmSpPFipCVKiwOXRcQfgPt6EzPzZaOSKkmSJEkaoJEGSgeMZiIkSZIkaTwZaffgvxzthEiSJEnSeDGiNkoRsVFE/DEi7oyI+yPioYi4fbQTJ0mSJEmDMNLOHA4FdgAuB+YB3tSmSZIkSdKkM+IBZzPzioiYkpkPAd+MiHNGMV2SJEmSNDAjDZTujog5gYsi4lPA9cB8o5csSZIkSRqckVa9e32bd0/gLmB54JWjlShJkiRJGqSRBkovz8x7M/P2zDwwM98DvHQ0EyZJkiRJgzLSQGmXIabtOhPTIUmSJEnjxjTbKEXEDsCOwMoRcUrfVwsCt4xmwiRJkiRpUKbXmcM5VMcNiwOf7Zt+B/Cn0UqUJE0WLz75rYNOwqg7Y9uvDDoJkiTNdNOsepeZV2fm2cAWwK8z85dU4LQcENNaNiKWj4izIuIvEXFpRLyzTV80In4WEZe3v4v0LbNvRFwREX+LiC37pq8XEZe07w6JiGn+b0mSJEl6IkbaRulXwNwRsSxwJvAG4KjpLPMg8N7MXBPYCHh7RKwF7AOcmZmrtnXtA9C+2x5YG9gKOCwiprR1HQ7sDqzaXluNMN2SJEmS9LiNNFCKzLyb6hL8S5n5CmCtaS2Qmddn5gXt/R3AX4BlgW2Bo9tsRwMvb++3BY7PzPsy8yrgCmCDiFgGWDAzz83MBI7pW0aSJEmSZroRB0oRsTGwE3BamzbSwWqJiJWAZwC/B5bKzOuhgilgyTbbssA1fYtd26Yt2953p0uSJEnSqBhpoPQuYF/gpMy8NCJWAc4ayYIRMT9wIvCuzLx9WrMOMS2nMX2o/7V7RJwXEefddNNNI0meJEmSJD3GiEqFWicOv+z7fCWw1/SWi4g5qCDp25n5gzb5hohYJjOvb9XqbmzTrwWW71t8OeC6Nn25IaYPlc4jgCMA1l9//SGDKUmSJEmanmmWKEXEF9rfH0XEKd3XdJYN4BvAXzLzc31fncLUAWx3AU7um759RMwVEStTnTb8oVXPuyMiNmrr3LlvGUmSJEma6aZXonRs+/uZGVj3JsDrgUsi4qI2bT/gYOCEiHgj8C/gNQCtSt8JwGVUj3lvz8yH2nJ7UL3szQOc0V6SJEmSNCqmGShl5vnt7y8jYon2fkSNfzLzNww/1tILhlnmIOCgIaafB6wzkv8rSZIkSU/U9KreRUQcEBE3A38F/h4RN0XE/mOTPEmSJEkae9Pr9e5dVBW6Z2XmYpm5CLAhsElEvHu0EydJkiRJgzC9QGlnYIc2ACzwSI93r2vfSZIkSdKkM71AaY7MvLk7sbVTmmN0kiRJkiRJgzW9QOn+GfxOkiRJkias6XUP/vSIuH2I6QHMPQrpkSRJkqSBm1734FPGKiGSJEmSNF5Mr+qdJEmSJM1yDJQkSZIkqcNASZIkSZI6DJQkSZIkqcNASZIkSZI6DJQkSZIkqcNASZIkSZI6DJQkSZIkqcNASZIkSZI6DJQkSZIkqcNASZIkSZI6DJQkSZIkqcNASZIkSZI6DJQkSZIkqcNASZIkSZI6DJQkSZIkqcNASZIkSZI6DJQkSZIkqcNASZIkSZI6DJQkSZIkqcNASZIkSZI6DJQkSZIkqcNASZIkSZI6DJQkSZIkqcNASZIkSZI6DJQkSZIkqcNASZIkSZI6DJQkSZIkqcNASZIkSZI6DJQkSZIkqcNASZIkSZI6DJQkSZIkqcNASZIkSZI6DJQkSZIkqcNASZIkSZI6DJQkSZIkqcNASZIkSZI6DJQkSZIkqcNASZIkSZI6DJQkSZIkqcNASZIkSZI6DJQkSZIkqcNASZIkSZI6DJQkSZIkqcNASZIkSZI6DJQkSZIkqcNASZIkSZI6DJQkSZIkqWPUAqWIODIiboyIP/dNWzQifhYRl7e/i/R9t29EXBERf4uILfumrxcRl7TvDomIGK00S5IkSRKMbonSUcBWnWn7AGdm5qrAme0zEbEWsD2wdlvmsIiY0pY5HNgdWLW9uuuUJEmSpJlq1AKlzPwV8N/O5G2Bo9v7o4GX900/PjPvy8yrgCuADSJiGWDBzDw3MxM4pm8ZSZIkSRoVY91GaanMvB6g/V2yTV8WuKZvvmvbtGXb++50SZIkSRo146Uzh6HaHeU0pg+9kojdI+K8iDjvpptummmJkyRJkjRrGetA6YZWnY7298Y2/Vpg+b75lgOua9OXG2L6kDLziMxcPzPXX2KJJWZqwiVJkiTNOsY6UDoF2KW93wU4uW/69hExV0SsTHXa8IdWPe+OiNio9Xa3c98ykiRJkjQqZh+tFUfEccBmwOIRcS3wYeBg4ISIeCPwL+A1AJl5aUScAFwGPAi8PTMfaqvag+pBbx7gjPaSJEmSpFEzaoFSZu4wzFcvGGb+g4CDhph+HrDOTEyaJEmSJE3TqAVK49lNh39r0EkYdUvs8bpBJ0GSJEmasMZLr3eSJEmSNG4YKEmSJElSh4GSJEmSJHUYKEmSJElSh4GSJEmSJHUYKEmSJElSh4GSJEmSJHUYKEmSJElSh4GSJEmSJHUYKEmSJElSh4GSJEmSJHUYKEmSJElSh4GSJEmSJHUYKEmSJElSh4GSJEmSJHUYKEmSJElSh4GSJEmSJHUYKEmSJElSh4GSJEmSJHUYKEmSJElSh4GSJEmSJHUYKEmSJElSh4GSJEmSJHUYKEmSJElSx+yDToAkada09YlfHXQSRt1pr3rLoJMgSZpBBkrSCJ165IsHnYRR99Ldzhh0EiRJksYFq95JkiRJUoeBkiRJkiR1GChJkiRJUoeBkiRJkiR1GChJkiRJUoeBkiRJkiR1GChJkiRJUoeBkiRJkiR1GChJkiRJUoeBkiRJkiR1GChJkiRJUoeBkiRJkiR1GChJkiRJUoeBkiRJkiR1GChJkiRJUoeBkiRJkiR1GChJkiRJUoeBkiRJkiR1GChJkiRJUsfsg06AJEl6tJd9/+RBJ2HUnfLqbQedBEmaJgMlSZKkSeCEE28edBJG3Wtftfigk6BZiFXvJEmSJKnDQEmSJEmSOgyUJEmSJKnDQEmSJEmSOgyUJEmSJKnDQEmSJEmSOiZMoBQRW0XE3yLiiojYZ9DpkSRJkjR5TYhAKSKmAF8GXgysBewQEWsNNlWSJEmSJqsJESgBGwBXZOaVmXk/cDzgkN6SJEmSRsXsg07ACC0LXNP3+VpgwwGlRZIkDchrTvzzoJMw6r73qnUGnYRJ56+H3TDoJIy6Nd621Awtd8MX/jCTUzL+LPWuDWZoucjMmZyUmS8iXgNsmZlvap9fD2yQme/ozLc7sHv7uDrwtzFN6PAWB24edCLGKffN8Nw3w3PfDM99Mzz3zfDcN8Nz3wzPfTM8983wxtu+WTEzlxjqi4lSonQtsHzf5+WA67ozZeYRwBFjlaiRiojzMnP9QadjPHLfDM99Mzz3zfDcN8Nz3wzPfTM8983w3DfDc98MbyLtm4nSRumPwKoRsXJEzAlsD5wy4DRJkiRJmqQmRIlSZj4YEXsCPwGmAEdm5qUDTpYkSZKkSWpCBEoAmXk6cPqg0zGDxl11wHHEfTM8983w3DfDc98Mz30zPPfN8Nw3w3PfDM99M7wJs28mRGcOkiRJkjSWJkobJUmSJEkaMwZKkqQnJCLmGHQaJEma2QyUJqGImC0i/G2biJgzIl4bEesOOi0TSUTEoNMwM7XzYlJt03gQEcsDb+r7PGWAyRlXPOZGbla4b0XZOCJe2fs86DSNB5P9d9dgRMRCEfGsJ3p8eXBOIr2bcmY+nJkP+8DyiKWBjYCXDjohE0G7mU/JSdKAsfcw0s6LjIjFImLNQadrIouIuSLiyIhYIjOvAXaMiK9ExLeAGRv+fJLpuxanJW7T13ffWi4i1oBJGUjMBjwM7B0Rm1C9+M6yeg+wmfnwoNMyaAaLo+IlwKeApZ7ISvxhJrj2ULs1POpBcOmIOAw4NiI2H3ASBy4z/wX8FVgxIp486PSMd1keioh5IuJ9EbHsoNP0RPQCvoh4dkR8GjgS2M6H18evZcZMycz7gE9m5k0R8SRgYWBb4OOZee5AEzkgvQyG3ud2LV6lBY9fj4g3DzB5407vwbAXDLV9dTLwPWBdmHruTkR927V5RCwZEatQHWj9Hvgs8HLgNQNM4sD1AqSI2DAiPtoLkGdFffvimRGxzqDTMxENcQ0+DrgT2OSJZLoYKE18SwGrR8SU9mC7M/Ug+FfgN8CnI2LlgaZwjLSTJDrTXhUR5wObAc8Anj+ItI1nQ1UPioi3UwM9PwOYayLl7A5VkhoR2wCHAL8H7gCeTnsY0/T1qkW1zJiH2vFwU0RcDNwPbAf8k5ZDPisFoW3fzANsDSzfps0dEQsDnwHOAN4FfKkdh7O0bilCXzD0TuBnmblxZh7fN/+Eufb069uu9wMXUOfIYm3aD6hr0Taz0P159ohYMyKW6kw/FPgYcBVwYNSYmRP2dx+JobYtIraPiCOB44EPR8SEGb5nvOhl8gJExOJt8inAK4FFZ3S9BkoTUCdi/g9wNPC6zLwHeBqwYmYekpmHAX8CXtxu5JNWe4jLlovby8lbBNgeeHtm7gj8Ctgwqk2FeEz1oAXbtHmANYDtM3OnzLwSGPc3rd550XehXKO3TcCzgLMz8/vAPsDfgI2t7jC8iJgjWluKvmpRT4mII4BXZOZ/geuAN2fmZcCpwP5t/gcGlvAxFBGvB97Rrr0rUhlTJ1KlBc+kMqtmA74JnAP8Y0BJHTf6cs5fGxFHR8QLI2Ju4G7gFRGxZ0S8MyL2bvNPmFKliFghInZt76e0h/4bgO9l5icy8waAzHwQ+DVwDbNOqVIC2wAviIg3RMRLW2n0NZn5QuA+6pxZCibW7/549J5VOtOeD7wHOBR4N9VU4OkDSN6E038Pj2qPfmBEnAt8ICJeTBUcLAFsOKP/w4eECaQXAPQeBPs8Cfhki6C/A1wSEZu1734IbErL6ZxMoq+dSXuIWyYiPg58PCJWoG68awD3ttm+CdzDLFyq1C4k20XE6vBI9aBnRMT3ge9ExP7toW8R4PMR8fX23WcjYrFprXsQotobfTQiFu0LkLaJiIuArwHfaLNeA9wdEQtk5rXAbVQp46oDSPa41Qpl52wPqXMAa/V993bg+8AV1EM/wEHA6yNiLur8elpUFaqn9o6xySYiNoqI3dvH06iSSqjjaQvgz61EZE5gL+AFwBcyc/PMvGwWK22LYUp4DwZeRt2fXgHsC3wcOI+6Rj8E7BlVQ2IilS4EsEt7GH4I2IHK0T4tInaJiHX69sctVEnTkhEx34DSO2ba/piPui7vTmUazAvsExEXAJsDr8rMD/VKUybQ7z5N/ed8e1ZZICJ2iKnV2pcHfp2ZF2TmadR58epZ6Voxo3r7s318Sfv7AuBG4ECqFPdHwMv7Mk4fFwOlcS4i5u2972trsUtE/CYi9oqINTPzEuqGvWdmXgD8hbrokJknUwfKZpOhKLddYLaNiNcBW/VNXwf4NvVAfCNwMPAiKnB8LkDbT2sCW47Hh/7RFBFLRcSGwINUKcDlbfqcVLWgYzLzpcC7otpS7AV8mGoI+RVgNcZRUNEezKG25xuZ+d+oqqcHU1V43pCZzwGeERFbAP+hbtIvaMv9C5iHysFU064xD1APMusAh0bEHu3rBYAvUbme90V15PAb4N/Abpl5HZV7dyrwOWCu7vonsojYJCJOpQKjZSNi3laq9umI2B94C/Bp4Pa2yM+Am6jShLPbOr4MvH7MEz/GeveaTnvHHSNitagqicsAe1KB+POAGzLzjszcOzO/kZmHUve0XgnMRClduJ8K9nqZBEcCbwbeR92T9gMOj4hVWqnS/b3XZAkK4FHtz3p/N2nnyGnAL6m2jH/JzCuoKt4/zMw3Zuaf2r38tTChfvchtRLG99KOh4hYNKpN+c+AN1LHwlOpa26vFgxUO709ANtUT0dELAd8NyKWBp5DBd+fpKrbHZ6ZNwLfAlYB1p+R/2GgNE5F1XtfEdi5b9qC7UFwC2A3qoj6s+3rr1PVFuamivRXiOpVB+CjwGntwjxhtRPht1TbkhOAb7eHf6iHuh8Dp1PF+/NSOVYXATu1XOBnUQ8uV1IlJrOEloP5YuCgVu3lT1SvS2tQgcKNwHIRcRZ1E/t1Zv43M88Brqb21UJMfQAcmKjqLM8GtmyTHqrJcVL7fBkVAPV6Ufo89dD/W+Bi4IMR8T1gF6qR57JtvZPmIeXxioiVoqrA9PbDCsCZ1O+9NPDl9sDzN+Dt1LXmWOCYdo35CPUASGZ+Etg2M1+YmX8a840ZXa8DzsrMDTLzw5l5d5t+KlU6chtwIbB2RKzXctA/QeVknhYRf6buuT8dROLHQkSs3gLr5/dNeyP1MPx86ny9H3gqdW0GeF5mHtYeIheNiG9FtX27j7oeTSQLUjU87gXIzG8Au1IPbntT2/M/WuYddU16fmY+MAmCgkc652i5/Ivl1N7slqB+839QTQVe2AIEqMy47SLiXVFtdL4HTJnI1+S+tF8PfJUqhYcKlvcF3peZW1Al8x+iSh1Xp55doDIAr6VqA83S9yd4bLvjds/q1ZJammofezeVabcd8JvMfFZmfjMilm0ZWhcAz5mhAoPM9DWOXlTR/Wzt/TxUQHB6+/ss6oKzMHVx+SN1Qm3Y5v8ulXM1O1UlZstBb89M2B8LUzmw87f98Q3gbVTOwc7ApW2+Hancx58AW/ctP4W6UZ3Q9tcLB71NY3gcTen7/GTq4XZrYHGqlGg3YEmqc4MvAGv2zb8I1d7tLOBE4JkD3p7Z+t6vS91gvkM9iD6TuvnuSJWefgnYqW/+P/bOBWCT3vFBtafZa9C/1YD360rUjfzUzrFzPrBx+3wm8MH2frn2d2nqhr9r+/xZqrFsDHqbRmk/PaVdQ1Zqn9cC5gbmbp9PpkqU5mrX5vdRGVnPbtfjLYDFB70do7h/tqBKad/R9su8TL2PHUsFA715FwYOAD7fN23zdp2el+o9cb5Bb9MIt3uVIab9uHf94dH387moWg8/7x1Hbfp+wDKD3pZR2Df/AHZv71eiqla+iyqZ/jawVd+861FtR98LzDPotM+EbY/2d1Fgbapa8jP77l3rte/np2p3PJnKSDiKykD4FvBB4NxBb8uA9+Nsnc+zUzVDPgOc3qYtRAWiC1Ftu04ANmvf7UY9Jyzbu1bPUDoGvSN8DXuALN0uLBcCfweW7/vuE8BX2vsv9R0wL6NKAwBmH/Q2PMHt711o1gQ2aO83pEqErgFe1qb9mcp12Yyq+7xpmz431ShyjfZ5gc76ZxvtbRjUfqPvYRWYs3c8UEX5x7TP21FB5+xU/d19e/uJCqLe3da19IC35zG/U7v43do77tu011I9SQVV6nEQ7SGmnUeH9NZHNZ4+i3pgWXnQv9kgjpHO59mp3La9qI5goG7s+7f3zwZubO/npoKGA9q595xBb89o75++6ccAJ1GlAL2Ml+9SD0MvAs5p821IlRxcTpWuPep4nizXHiqT5UPUQ+4HqYyKRahquscAbwCWo0rclqLabM3Rln1K24+fozK3Lupd08fbq3tN7Zu+art29jIPprS/OwG/AObtm3cPqvfDPwEf6Ju+ILDaoLdxBvfLlM7nDdrv+Yr2+fXAJe397FQm3Xeo4ODNVMn05VRNh4Fvzyjsny2pDqTmoUoU39mmf5eqdtc7Fw6iavxAZSKsSmXw7gO8e9DbMYD99phnVypT/LR2vVm4TTuTqma/CPBl4JV98/6Iyuw7GXhBZ12POzPPqncDNkSR4qIR8TYql/cm6oJyLnXz6dXHXIw6AKCqx2wVEctl5ilUfW8y88GJWFwb5ZHBTjPzL8DNEfFBqmrLQdRJ8Oe2yHHUA9657fXViPhse78B1WCWzLyjrb/XM9qkHOAum9aW65PA79v+mI96IJk/IjalHuTupQbh3RNYKSJOoy7sdwDfbKv6T/tNBnKtyKk9ZL0iIg6LiC2pqhk7A3fF1Mau51ONwF9CXVCXo0odAT6UmXv1rW8O4L2ZuUVmXjV2WzNYfcd+9k2bLatK7juBlanGr1BVLq9t5+I5wJVRnTk8SN3AFwY2z8xfj+EmjIne/omI+dvfXlWNd1Jd9x5IBZIfoEoJXkGrIhYRe2SNk7NrZq6a1Ua0f90PT4ZrTzvvjqJKyc6nzrfPUQ/Ci1H7Y7OsjlOWpoKJ+zPzgaheNf8FvJDKsDgyM9dt969xo133ng0s0ndMbBARa7dZVgCuz8xrY2rnDWTmt6l71etialfYV1DHy1ZUsESb9/bM/PsYbdJMEVO7d+91nrNK2ycfoKo/fTAinpuZxwILRMSW7RpzO9VOZMfM/BoVTO+dmR8YyIY8Qb1nlfa+V+3wJX2zXEDtjzmo55FVI2IlKoNyayqjAarN539bs4nbqXY0f6Wq4h03+lsyPkTE1lHjf67dN23BiPgiVdr2DqqJxQHt+rMXFSQdSVW5uwMgM4+hMoJfm5nbZuaZ/f+n//43YoOOHn09EuUu0P5uRp1gvdzcKdSD7DF98x5M3aT+SF2celWJZuv/O5FfVBWM91I32Y2pqlXPb/vjY8CH23xzUBeVXqnTM6g2KU8Z9DaM0X56TG4nVYXlG+04WYzK/T6k7ct3Al/tm+8oYNH2eT2eQPH0TNqWbi7lFKpE6HtUtbnfM7WR69eAPfqW3Y2ppauvZWrpSO+8mNClrDNxPz+Jqhq2TGf6gtQD3cvbMfP+vu9eDVzQ+00GvQ0zeX8MVVqwM9XAfJrXU6qa3Ufa+5fQqnz0fT9p9hVV7btX0h9t279E5eZ+mOpE6Jnt+6dQJbzPpEoWfkhl+r2RKsl93qC3ZxrbuRo1NAJtu97X7kUHAP9HDTMAlVlwJX21FfquNRtTVXs/O8T6nzTUMTeeX8PcZ9anqhle2n7TLdr0D7ZjY0q7Vp9Hlaq8lQqO9pvo1+Kh7lPt78XteFmOqdWaF6Kqfn2KqVURf9POi6FqTKwILDHobRzDfbk99cx7erv3TKGq357fzqMVqFL7D7bz7edMrVE0O5W5+zAVgA913D7ha7AlSmMoquef3hg/vb9bR/X5/vmI2D2rd6TTaY3Ms3JtfgksGjXexAbUTedo4GOZeVBWd5Lk1AH8JkyOZQw92Oku1EnydOpE+AtVNePFbX/8EXhSRKyTNV7L2VR1KzLzwsw8IjOvaOue1Md4NhGxXl/j2NuooOevmXkLVY3uKVSg9HNgmYh4JtXpx9VUmyUy8/zMvLd1mDCmpZG9UsScmku5WEvDFOAuqk7ySrTzgurF7hfUGGGLUVWfLgV+HBHzZeYJmXl1267eeTGhOzN5vPpLq9u5MH9EfJt6eAlaj2K9eTPzdqr93/rU8bNp7/vM/H5mPrO97w5PMCH15Yz3Sgs26vv6EuCeiJir/3razo1F2t+3USX4v2nrOb1dvx8xGfZVRLw4Is6m2pUcFhFrUQ/7r6Kqe/8+Mw+kajm8ppUYXUOVFr05q2ThS1QJ//OoTMBx10lD371iAerhHuBmKsPpuVTg/Gng71HDUDyDevBfrbeOvmvNuW25J0V1JNTfPfR1vWNuIoiI6N1n+qZ9jOrE5RuZuTbV8UavI4KTqONj88w8nLo//5rKBP5QZn58Il2L+57VXh8RH4Y6r6MGld43Ii6lBpPemsrMnZcqdb66fZ4zM/9NVTN8ditlfCdwRvfa0tZ9dWbeNIabOBARsVlEnENlwq0NfDQzf0jd6z8D7JKZ52bmv6jnu4UycxUqY3yriHhSO472pEqbTu8+t/Q/Uzwhg4wkZ6UX1R3qa2kNeqkcllWpm8/a1IPsw9TNZCuqnUivMfWcVL3nf7T5l++se0LlTg2zf+Zqfxeiqres1vl+c6oEYUOqlOQQ4ID23dy0tjiTaZ8Ms5+6jRvXpertXkbVfe61xTmK6qVrwfb5JGCf9v6TwHsGuA29XOnX00oG+37Hvane1Q6jLo5LU7mWl1FVe57UN/9CVD33q6lcynmH+j+z+gtYve/9P4HjpzP/slTO3SFM7axg0u5LqgfEg9u+eTOVcfA84HPt+/6ORGZvx+UF7Vq8fmddk2o/tevuuVSu75xtP/2YCpI2pB5oem1SnkoFR09tn9ehcnuf3z7PNejtGWYbu+06F6RKr9/VtvEk+nKrqZKknakH37NoJfKddfZKlhanqu4NfDtnYJ/0H/fzUiVkvXYgm7dt36p9fhZVmtKrGfNx4IvAYu3zhNsHQ+yTK6mhNZ7dPr+Gan+3AtWr7DV923se9cz2Y6aWfjyZqnI35yDSP55eVHvG/Xr3JqoW0Lnt/fOoHmkX7Zv/dCpwgup46lzGsGOu3gOLRkFfjuXDUf3jv4+6oEyhOmRYmiqinYdq+HchdXFeBHgTdULt37e+pTPzP32fIyf4DxjVvfAeVJepR1AdV5wLvDUz/xA1VsndEbEkVW1j0cz8v4h4KXBbZv665YY/1OqJT5jStJEa6ndu7SeWodpqfSEz944an+QP1EVnIaqe7gmZ+cOI+BrVs9nJUYOu3tG3roHst4i4kgqOXp2Z50SNor1Z25aXU/Wz16WqvPwyM7/SltuKqlp5aEQsRGUc3d633gl/XjxevZy0/u1uOZwfotrp/ZpqV7M+VfVyuaF+975zaanMvIFJov9a3DdtS6qkYHkqR3JZKjNraaqazGmZuWbf/JGZGRGrAPdl5RJPahFxInBSZn6rb9o7qappG0e1W1uLyoS5IyK+QpVUHki139qQemi8a7ydk93rREQsSmXU3E+1FVmJuk+vAryf6iHzyr7530/1dLhHZv50slx3+rejXV+XoALDp1Ltpv+bmftExNepQPh7mXlPK62+PDMPiIj1qczf7+cEKj3qiRoAeAfgF5l5ZdR4lh+k2nHekZm7t1LFa7JKzYiI44ArM/MDEbExFQy8HXhRZv58MFsyPnSf0dq19y2Z+cq+eW6h9tX5EXEy1THZZ9q9bRfqXLuDelY8PDul96N5/k3qakmD0qu6lH0NdzPzf1RJ0SrAYe3EmUKdSPdSuQ57UDlb/6Gqmy0TbXyTto5ew/rHPBSNdzH06OwbUnW+v0oVpx5E1fP/Lq0hfguS5qMu0BdRDfgXyMxTszUkz6mNaCdVkNSCw+7D7xwR8VEqkLycytGav813K1PHaTiFar+1a0T8mLrZ9aoI9Tq26B1Ho77fImK+iHhTe8ik3XiOp6qVvqHN9nTg4Yj4LnVcvDsz/0aVGj0rIo6PiB9Qx8l9bZnbM/P26KsuOJHOi5mh3Xyyc5wsRXXZ/EqqasL2wNuyquk+HBEvaTesR52XfefSpAmSYGonChGxeEwdf2MK1aHApZl5W2ZeRo05tzz1kHNjVOc5vXVk+3tlZv47JnHV3r7t+g8VOPRfL75IDbi7LtXzFFTVV6hz+qlUNZl7MvPszLxzPJ2TMXSnJq+nSggzMz9E1V64i+q18FfU+E/PbPOu0Bb7GlXaNOGvO/1VllpmwHIR8Qkqg+UI4KHMfAVVSrRaRDydagKwMRU8wKMHdz8vM4+fiEFSsxq13fu14+UBajtPBhaMiNWoYPopfct8k3YeZFW9/DDwOoOkWI4KdB551sjMnwDzRMTqfbMeQV1/AQ6nquTRbm1HUUNQHJKZ23WDpN58o7UNk/IiPwgRMWdE9B7uH2oXm2Uj4usRsU9ELEPV5z6a6ioV6iH3RqqR9I1RA4CeFBFbUA+6e2aNdv+I7gPReNd3U+q1PVm3lXxAdf39J+pB/01UzyW/o0qUdouI7aLaAfyGGgfnjMw8oPugP9lExE4R8RtaD2QtNt4uIp6S1SbramDFFkQfCmwc1WMOVGcHt7dj5NdUdYEPZObLs9orPWKMj6Np3XgWaMf+zVSO/rGZuUlmfiUiVsjqtWavNu8JmbleVq9J/Q+vD02k8+KJasfEIz04RsS8EbFnRLy6zTIX1c5oXyrj4Uyq0T1UtZiD2/tJlbnQ0702tAe/Y6meH/eOiG0z83SqveOd0Xq4a9epd1DtVFalrx1XV06eHuwek4nVjqk5qSpX80TEnO2e1usB8CSqJOkqqge7l0TE7C0wemXLGByX+u5Fz4+IF7TJv6YajF/f5rmVavO4atRA50dQA7r/k6kPc/dTGXrXjFniZ7JhgsZnUEHQfZn5NGqfLBXVzuoq6h69K9Xd+RJUu5s5MvO0zNx8bLdgdGTmhdSzybJUScYSVI2fp1HPI7tSGXibRcQ27XqzPhVs99bx38z8zhgnfVxo96deKf61wLsj4uMRcVZU26Q5qEzSxfsWO5B6llk5M3/c1rNn78usdrKntuljGrsYKD1BUd15f4R6QN0iqoHfUlHVEz4A/JfK2f9uTm3Q98x2MNxCtRd5WUScReXInAGcmTVS930TMRiIiJdGxCciYuG+m9K27eH/S8BREfFk6mH5TdTD8faZuUdmXt9y8N5J1XF/LrBb78Rp63pUI+zJJKr6wo5UlakD2u+/CNUo9E1tth9RbSW2zMyzqPq8R0R19PEpqvpib76VgBVaicvAzvcR3nhOpIKl2aJKznYCToiIjTLzrsw8LjOPh6Ef7mYF7QbUa1z9UEQsHNV4/mgqx/vDbb/NSeV4zksNSP3elhnz5KwqjIu0IHTSnUPwqA4ant4ysLYBfpqZ6wEJvC0iVqbOkXWpkv7esldm5t5Upyirddc9WQyRiRX932Xm/VQp/rNppQZ9JQQbAH/PzPtoQXiO0yEp2kNZfxfOm0fEr4H3ADtHxJsz859UNbJF2/lE+5xU1eDTqJLs7TJzl/b9m6iaEP8ej9s9nIhYMyLeEBGL9/32L46IrVom5hXUtWP+tsih1PmxSmbeDfyMCgpWoYKFP7YMvMnmvdSwGn9l6phPl1NVSZ9OlUh/lGp3dB7Vru0nA0npONPuTw9HxOwRsRl1TO1OtSs/m3p+WYE6zmgZMfdSzwC9DL330tmfvfNsrDOoDJRmUFS1i12ok2hOqpHjh9uPPRfVe9S9mfn+zPxoLRKvoRr33Q1sE9XL0r8z8+3UuC7PzMxD+h9eJtKDTEQ8L6p3pPdQOU/ztsDxnVRd7/dl5nOoHKrtqdyEbwHHZebfIuIpEfHpiFgzq7h6/8zcPjMv7L8RTYZc3H4RsWJE7B8Rz6Pak+zcAqCb2+//P6q75rUjYtmsalF/AzZvN/VPUj0KPYdqYHsgQNYYQT+m6o0/NA7227RuPE+lzpv9qOoLZ1F1xPfOzN/1VtB3oZzwvYk9Hn3bnS1nf/GWQfMXKrf7kszcjfrtt6HaPf6S2qdzRcSGEXEGNW4WwEpZvQlNCvHY8UyeGxG7UqVnd1OZUBdFxC+okpArqSqfP6UeePpLZXtVF/9EDWw8KfU9JG8dEX+kxr9Zt33dezbotU16b0Q8ucXpe1Kl//9s6zkvMy9o78fF/aoFA7sB9B7i+65/S1HtjbahSg53iWorewT1sLtSm/8qqgOl29qD3GWZ+fu+DKdDMnOXrGqb42K7pyUinhoRp1JtP1cF1m333O9QGXO9QbvnpzpyeXJEzJ1VjewGpvZqdzl1/X4wM3+cmReP9baMkV8Aa1CZ4H+mmgjMT7UDvgzYL6uXtvcBr8nMrbPGfZzlRF/vxS1TdvaI2IsqLHgt1cbvv1TpM5l5D61qa1tF7/z5AHBFywz8bVbzgkcM6jwzUJoBLeA5gHpIuTkz98nMq9rF+U3tAeSENu+8bbHDqYvzrVQ97tdRN/HbAXo3mkHm+s8Em1KNNzfP6qL7uhY4XkbV416wzfd1Kqf7H1Qx/qci4njg+1Rd8KvgUbnCs02EG9HjEdVmZ772cWmmlgA8FXhBCy73iYh9qRzdC6ngaNe2zLlUcPTaltt5I9XW4jOZeX+0KjLtd+gNzjtoI7nxnEl1aLJdZr40O90IT7bjYHpi6KoxO1HV6e6jSuT+zdQ2fd+nzrNeVcd/Uw+7hwEnZ7UvmXSBZt/29DJU3kxVO9wva0DUO6ie2r6RmS+grj3PpUo4f051KtK70c9Fdal/O3VeTXgtwOkObv7sqPaAG1O91z1A9WxG1sCws2XVetibCjY/SZ2nz6eGprh5LLdhJCJi06hOGZ5MBUArRFV9PzMidgbIzOOogbcvBq6lMhRe2h7K/gXs1Bc0fyUzj22la7Tle+0sJtq16E3AOVmD++7XMiPnpzpIenbLaLmSGsvmSqp0f4e27PeBrSNiiaw2Z0fnJB+su/2+76eO9fdTAdGV7fc/FfhbO6fuzurw4THn2KygBTUPtwy8uVqm7INU73QLZ+bb2rn1Q6pL+Z4Tqe7zZ2vXm8jMGzJzr3F3buU46CpwIrx4dBe7ywM/au9/RzXaO5rKgXxbm/50qkrC2n3L/Yup3WsuOehtmsn7Zwr18L4x9dD/XOphban2+aPUGAq9+X9LjbMAVa1ja8Zp97GjsK/WAY6lDU5JZVj8gep29sVUVbQzqGp0P6NKhZ5L5XZeRT3EfZdq3Lh2W8eHqB56YBx3UUwFep9v73dm6mDJz6NKYacwtWvdmTJY3GR4UQ+nr2rvl2vHyxvb5zWpjJleV71vbMdPr6vaxQed/pm8L4YaIPa9VBeyB1FVOpahHvZW6pvnPOCd7f2hbZ+9fJj/MSm78KVKGnvDTjyJyuV9S/u8bNsnO7TPU/qWC2pYhhUHvQ1DbNNs1EPsBVTb3pdQGU4fp4LgD1ANw8+iAiKojMveoLJ7UxlRG1HVMHfrHmPj+Zo6wn30DCqDat7+35YqJfoqsGz7vC1V+jo/VeL6i751TKpnlhHut9mpzJJ5Bp2W8fQa4vxYjXqmOa7v+vEhKjDvzbMYVQq9EtUb5s5Uj8fzDrH+YQf5HsRrIpdejJmoXm7OjKm93cwOXNhKiz5JPbj+KjOflpmHAWQVR18JbNuXO7UbcF6LnG9s655Qv0FUpxWLdaZNycrRPYkqafs3VSrwdSp4egrVaHqbiHhVRDyLKjm6FaqKQ1ZD0PtiAIOdjpW+0oE/U7nVa0bVE3+YCohenZlnUCOcv5jKyXoh1eB+1cz8LZVLvgpwRFa7k0vbOj9KK23KdqUZp75F5djOk5nH5NTBkn+ZmYdlXxXBLJOq5GNaojpkWLOvpLHXBvJn1E3n1RFxDHAPFTyv1I6pq4BzqCoOUGNoXcrU3rjGXc7/jGg5to8pXY6I7agStY9QmTKfoqp1/JrW21KzDzVQ4dXU+feWrKozj6y/9z77ShAmou59JarN36eoTJi3RMR+VBuso6lSXqgHwtOY2tvUI+deOxdvyTaI8zizF/X775CZL6MCokupwPiZVC+z36cyDzZp9+07qBKntamg6ufUw9lFmXlk9xgb59fUkbiJ6nyid23pbc//qJLpXjf4Z1AZeVOo/fi1duzM1ntmmZVklYwcRj3jAY9pyzcpn1WGE0O0D48a+uZA6nz7DrBd1LABnwSeGhErtmVuoTIoDqfaIS1LPevc3f0/OfhmAo826EhtvL547MCehwKfbu9XpqLn+dvny6gG0911bEjVgZ80ObpUl8OvparOrU5dUHvjcU2hbjrrUzlYy1AD536Jqgv+deoB79u0krVZ7dX20U5UsfP3gHXa9F2oThyGyi0/geqmdtjjdKjlxuuLCqb36/scQ72fVV59589SVNWnLakOL9YBXsDUQYTnp9r6HdLOrV8Aa7Tv1qcavq4+iG0Y4/3VG7y7N0j1GVRVTahOQg6gOoNZnXpgnq9v2aWp6iCT8ngbbnuoGg4fbu93oqqNb9v20eW0AUGp3N5zqAeYgW/PCLZ38XaP7V1H+68lS1NVxt7Utw+OoEqcFmzvz6HahI5oP07UV7u2fLnvPJm977uPUYHim6ieRT/FOMvRH/C+s1bDY/fJ84BN2/vVgX/0fbcZ9ay3MPB5qhfb3ndzACv0fZ570NsykteEKs0YCy3XMvKxEe2RwJZRPbldRV1ot27ffZOqy0pErBcRx0bEMZn5e2p8pAmboztEvdtbqFKN31FVv+bPdsRnlQZcktW498LMvJ7qVWrRrC69f0r1fvLezPzBZM6N6eV+d6bNS+Xgbgl8g6pK9/z29aJUQJ1RvSa+NCK+3OrR38DU8Up663rUAJq932CC+Ghmfrz3oT/tE2w7npBuCUlWJx29LmZfRfVeuApt/BYqR/hwqtvvW6hqDDu18+hP1EPQ38Z0I0bREG1qVoiId1NtPHcHDo/qEOdEKqCEqkr2H2CZti8eoDqOAWosusy8NaaOdTcpjrfetbS3Pa390WeiOmoIqrrmayLie8Bbqd6nTm776BKqAx6o2gBvzokz9stt1APZI+P19N1XbqRqOWzVfuuLqRKUzalS+r2y2uYc05Z75Ho9WY6LPjdSJc+bRw3i/iDQG1j1CipD82nAj7M6oBpfOfoDlEP0CjkrGOYZZouIOJpqSvGJiHgHFfz8PCJ6XcNfS/XUuzj1jLhTe/Yhqzfnf7V1zZmZ906EWlXjPoFjLZuIeEZEHB0Rr46IpbM6W7iMqlIGdXPuDTb2OWosgXOpBmy/Y+oAWxPiQBhKTB3I8qG+i8S9VFH9H6n6/bd1lomIWC0iloyID1O5Uz9oX19CVY15OUzKmxHwSOPGzOoec8moMbSgzrdlqLZap1N159eJiGWp0oFXwiMPzItTvQO+IDPfkZl39v+PiXwjm1VvPF19x8iTImLviHgZVcp4DvB/WdW/vgMsGRFPa58XoHoxvJ8Kuv/Z1nV/Vkcxk0bfcdK7fu5DtUV5SWZuQV1n96GCxCdHxAZtmbWA3vhz22TmN4Za92S6/vRvS1RHMAdR+2VbqtrmHe3108x8TmZ+NyKWbtVmPkf1qBntQebSAWzCjJqbqt69KTyyH3rXldlpg5QztROco6lqy3dmdTTUXyV6wl5Tp6ftl+9Q99+josYz3JHKvFyP6uhlr8w8fJDpHM8m0/ViWqINAtt3f4qIeGNEPJvq9OXGzHwuVXL/JOA11PNgb/iSm6iChPuyxlBaJTPv7t7v2z1sQpx3E/IBfmYaItdytoj4GFVd7HQql+WQ9vXXqDGPZqPaCdzQouKHqBKlQ9tN6Mst2BpIn+8zopfWdgHt9Q70cETMExEfAv4YEQdQD/A7UjlyG3aDwHYxWZfKyVuGajx7Yvv679TI3l8b/S0aW/0XgfbbLx41svmFwGFRI78n9WC7bJv1RKq0YCuqF64LImKd9t3RmfmxzLy5HZOT7lydVW48w2k3oD2pIHk+4OLMPJaqFrVVRCyXmXdRJSifj4hXUDl598Ij7bq+MVn24xC5l9tEtcl6W5t0FBUo9noSPY0qIZhCVRn6UEScR2VgnQmQmRN2MNBp6T50RMTqEbFb24erUBl1twAvpDK2bqT2yWZRPW6+i9pnz8rM32QNEjvhjqOsmgpXUF1dL96m9e63vZKjc6jjhqwuvv/eWccs0Q4ya/D6d1M9jr6fqoK5f8uIm4zjIOlxioinUqX1vW6+t6LuP+tT15DzmDrm3B+oY2kOqvrzolG9aF5M3dNuaJnt/5zopfeT7uHr8erLtVwrIuZrF9nvUPUsoapJvSoiVs/quvhaKlC4hqqj2YuKj83Mb7d1TbgBUfvSujFVOrZW+7wjFfjsSvXadyT1sH87sEF/ENh38z6NGgz1rVndps/WTpSHMvPSyXRjiqG7b96aqv8+b2YuS1Vr+Hh76L2L2r/zZObtVLe7z6UaI89JDR7bC7ai7beHJ0KwraHF1LF9epkRK0XEJu2YeT7wuszcn6mlICdR7ZMWaZ8/RWXWvJIqEXjrWKZ/tHUzlKLGQFqe6pr4DGCviHh1Zv6ByrzqBU43U0HTHZl5CHWDf2tmvmSClYqMSO96AI+qYrdpuwatT7WdXRxYkSptexlV6v9JqmOhT1K5vSdS7Wf3zMyfjvmGzHzfo4Llz7dSsqUi4lBgT6oq5tfb8THLy8xbM/MjwO5Z4/6cOd2FNKlFxLsj4ukAmXkJ8IGo8dTmoJ7/VsrMPTLzCqqZydztefgOKoNv4cz8N1XF+dPAepn5iVbLYSI2DXiMWSpQimo/dFxEbNw+zxZVte4PVCPqoyJi0cy8jOoydFcqYPoG1eARqlvmd1PVyJ4aEUv0rX/ClCD1RMQ7IuLsqNGToXICbqONy0I9yB2RmX/OzCOpko+3UMHkOlFjVBwSEUv1TobMvCsz7+yVhLQH/Ql9ogynL9B+YUS8uk3+A5WTeXeb5yfArVHjb32a6mHo+KjezP4MfCart7ufUgF4b905WffbZBfVe90bYmqvhjC1StAuwPZR471cAHw/Io4AvhURv8waYPdq4H0RcQVVfezkzHx9Zn5lzDdmlMTUsb56D/2bRMTJVHfF36V6Ej2OChRfEVVN7KvU+GJbUr2drUiVnJCZ12fmeW1dk2Y8k2hVd3vXg77A+x1UUL0NVVq/BhUIXUaVSL8lMy9updQ7UAOgv4vqXXOHzPzjADZnpsvM/1ADVQdVNegsqprh67LGZbGqb0d2qnJr1tQypD4IfDKqKiZUDZeTs6qmfh/4b0Ss2r77G3WNOayVPr2KatfYC8LPy8xbYpL1XjxLBUrABsB2TO1GdwqVs7ZrZm5P9ei2V1T310tQ3YreQx0Ir4oabO3bwCta6cDrM/Ombi7fBPM/qkRjn4h4RVbj3r8Aa0XEPFQJxwv75j8aeG7WII7fp3Iy/5rVruZRJltJSMvQ7VYPelZE/BZ4O7BpVPXEO6ibNRHx5DbrAVSp0jVUTueJVKcGb2+5OGTmKZOptG1WFBFPjYhTqfEkVgWeHtVO7SPU7w418N6cwEaZeRDwUqpNyVuBKRHxYipj5kfUuGzHjfFmjJpWPewH8EjXu73pB1E9JB2TmWsCZ1OdnUDlYi5GXXfOojIU3k9Vf94pq9OYR5ks51HU0BJ7RsTK7fNOwP4tB/gk6vq9K1Ua+VRqjL+jqUy8r0XEUVTQuTA1JMOkfEjOzP9l5uuo4+JpmblvVqcdk7mDBumJSqrXy68AB7RMldOAi6OGXTibynjZss1/C9XZ0PxUz6LHtHvYo1c6ydqATupAKSLeEtU2pOffVLeFq0WN5bMW1W3mvhHxR6pHqYOz+nufHdgiIo6lHmr2A+5tJST/alWiLoOJfQHOzG9RgdGFwEsjYm8q1+BuavDTTwA7xtSqeBtQXXyTmWdk5quzjR01WfVVgcu+6kFviojXUV3Q7pGZL6caML4ceAXVpffyVNeZZLXTmi8iNs7Me7LGEPpVW9ekPg9nMW+iBtlbNzP3y8wzs8YfuYpWSpvV+9Y1wAYRsXzWuFqzAa+jemn7a2bem5nfnwxVo1ru4l4RcSnVucCBrYrUZyLi/yJiKaqk6GGqSi9UvfgFoqooPkS1r3ljRCxAdXM8b2Yempn/mGznT39pWMvVnQf4bUT8nDpO/k3Vapif2hc3UrnANwCbtMyu3alqihcBz8zML06mTKvhZOZ1mflgTO3VcNJvszSjsjpbmEJltBxCDVa+BTV21NuzmgdcQGW8LNuedf9CC6ByanOTSVN6NJRJdYMZwkHAoVFdyELVpwwqR3d7qrrChlQ1uo0zcz9g9nbjfh/wV6pu90GZeXBm3pGTpM5lxzeoTgXeTY1q/25qMNgNsgYY/BxVDehiqqrLb/sXnkzVXLoiYva+Ki9zR8SbI+KLVO7tL4BTqVKAC6mHvK9S43TcSl1Qtow24BqwWmae27uoTMSqmhpeRDwD2Ijq+bJ7XvwSuL/l0kGdQ9sAa0TEclRgsCbVNfNVY5boURYRb6Gql76Kup4eQAWJp7a/11G9Yt5MZdY8vZ0X/6CqsO7WVnU4NVjxndS+nCMiXgKT7/zpqyq2cJu0CNX99QezOvs4kmor+3nqOvNNav89B1iqZeZdl5knZeYXMvO+Md6EgZtsOdrSaIiIuagS+gcy81Cq05MvURl7t0XEi6igqDdGJtQ1+zxaV/Mw6Z6HHyMm8/ZFxCbUIIzvp3JqT6D1jtSm7db+zkm1uZmNqvJyfma+v7OuiVy9broi4r/AZpn5p4g4jAog5wXek5lnRPUotGh2egyabNrD7dtog6VltbVaiAqs16Bybd9DjQJ/ZlvmIOCqzPx6ROxAtXc7gOq2dmPgxJw6bsVsk+3BTqUFPMdRgynf1KZFC7Kn0NomZeaLImJtKjg6mgoCMocYoXyii2oPem1mXhMR+1EP/AtQnTTcQ7XZewh4NZVZ836qk4GrImJTYGdg78z8X2e9S7aSugktqv3RjdmGYGjHyouosepuAI6hStPeAayZmW9sy81PZfitRR1Tv4qIzwAnZeZvh/pfktQVEcdT1+DVqap1/6PGolsduCszXx0Ri/RfgyNiQeChrCYok95kL1E6h+pl7CLgRcAbqBv09dSI7btRDdn+Qo3H8XHgyP4gqT9AmqxBUvNVqlckqJv0x6jSpfnbPrglM//eaqFNuhKkqC5zPwb8imqz9YMWJK1D5YivCHwkMw+mev1bqG/xKcDrW3XOLam2W7dl5r8y87vZ1xbDIGlSe4Aau2ZzeCTo7l1jl6QebO+NatP2VeAjVDB+12QMkgAy89yc2kX3ldT+WJeqFrYv8MXM3LKV1v+yzfeq9vd3mbl7N0hq653QQVJEbBwRJ1D74bMRsWoLktalepp6M9Vz5mepKtA/AtaL1nlQVjuj/aiSpI3atPcZJEl6nP5Mddq1e7sWb0+V6P+MaodE7xrc9zx8+6wSJMEkL1ECiBoTaA2qetm3qOp361ID1H2C6hnnqqiOGm7qW25C9/v+eEU1Gr4TWCOrG0iiurC+Z7ApGxvtAeUCYP7eQ2vUCOZ3t7YBf83MPdv0PYBXZQ162eu96wAqwDoqq3fA/nXPUsfSrKrdRN5LdeLw7r7jaBGq3dqxVIC9+qz0QNsrRW1VmvekAsptqBLsu9p+eze1f5YH7s9qt/Wo5QeR9tHQAqRnU4OX/5YKGO/LzP+L6n309VRG3vZUFZf9s8ZTO4Lqsvd8Kqf3m1G9tP537LdC0mQQEa+kmlns0zftkWuuzy+Tv0QJqkrdW6g63TtQA57OQZUiHQnc1w6EXlWZCTcG0syQ1Wj4C9RDXm/aPZOx9GgomXkR1fvLgxGxREQcSbXNghog9xl98x4OLBsRW7TPDwIHZOZze0FS2NvSLKf9zt+hqvIeFTV484600gBg9sy8eVYKkmBqKWpWz5iXUwOg3gl8I2q8m4up9lkPZeYF/UFS//KTyB+BK7M667ie6jjoJ+27uYFnUYPmvjgz39aCpPmpEqS5qH31EwCDJElP0GxUPDRXb0LL2AqDpDLpS5QAIuI9wFKZuXdEzJGOQq0hRI3N8n0qx/ZXwGcz87b23UXABzLztPb5QIDM/HBnHVNyknRNrBnTGuHvRZVkLwR8LmfxgR372t+sRGVc3UUFkC8CTsjqNGaWERE3Uz2wrkcFRb+lGkl/HjgQuCgze2OV7E8NmvqTqM5lHhxuvZL0eEy2EvvRMKsESvNSOXhLDzot492sfNK0KkA3U+Nw/Lvz3Z7Abpn5zIEkThNORMyfk3DMmicqIt4A/CdrkOXetF5J/ixx7YmIfajqumtl5pURsTTV699GVEcyB1I1H5anqox/zgBJ0miZlZ/9pmeWCJQAImLOzLx/0OnQ+NbatG2SmW9pn1cEnp6Zp0TE/tQ4W/f3ze/FRRqB4apxzIrVO1qb0BuAVbLG7SMizgBOyczDW1W7J2eNuSVJGpBZJlACH2o1fa1jhmuBl1G9Im5MtVH68qz2MCeNllkxOOpqnTM8lJl7tFK2XYE3Zeblg02ZJKlnlgqUpJFo1WLeS1V/+Wp/mzaDbUkzQ+sF8HqqM4vLqWvNLN2WTZLGGwMlqaMbDLWe/x6e1XPAJc1cEbE7cEFmnjfotEiSHstASRqGPUxJkiTNugyUJEmSJKljVhhwVpIkSZIeFwMlSZIkSeowUJIkSZKkDgMlSZIkSeqYfdAJkCRpOBHxEHAJdb/6C7BLZt492FRJkmYFlihJksazezJz3cxcB7gfeOtIF2xjoEmSNEMMlCRJE8WvgadExGYRcWpvYkQcGhG7tvf/jIj9I+I3wGsi4uyI+EJEnBMRf46IDdp8i0bEDyPiTxHxu4h4Wpv+vIi4qL0ujIgF2vT/i4g/tvkPHPMtlySNOQMlSdK4FxGzAy+mquFNz72ZuWlmHt8+z5eZzwbeBhzZph0IXJiZTwP2A45p098HvD0z1wWeA9wTES8CVgU2ANYF1ouI5z7xrZIkjWcGSpKk8WyeiLgIOA/4F/CNESzz3c7n4wAy81fAghGxMLApcGyb/gtgsYhYCPgt8LmI2AtYODMfBF7UXhcCFwBrUIGTJGkSszMHSdJ4dk8r3XlERDzIozP65u4sc1fncw7xOYb4X5mZB0fEacBLgN9FxBZt3k9k5lcfb+IlSROXJUqSpInmamCtiJirlQK9YDrzbwcQEZsCt2XmbcCvgJ3a9M2AmzPz9oh4cmZekpmfpEqx1gB+AuwWEfO3+ZeNiCVHYbskSeOIJUqSpAklM6+JiBOAPwGXU1XipuV/EXEOsCCwW5t2APDNiPgTcDewS5v+roh4PvAQcBlwRmbeFxFrAudGBMCdwOuAG2feVkmSxpvI7NZIkCRpcoiIs4H3ZeZ5g06LJGliseqdJEmSJHVYoiRJkiRJHZYoSZIkSVKHgZIkSZIkdRgoSZIkSVKHgZIkSZIkdRgoSZIkSVKHgZIkSZIkdfw/5gaS8hXJMCEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,6))\n",
    "sns.barplot(x='PURPOSE*',y='MILES*',estimator=np.sum,data=df2,ci=None)\n",
    "plt.xticks(rotation=25);\n",
    "plt.title('Plot of Purpose vs Distance (in miles)');\n",
    "plt.xlabel('Purpose');\n",
    "plt.ylabel('Distance (in miles)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can infer from the graph above that\n",
    "\n",
    "1) If we include null values of the column 'PURPOSE', we can see that the distance travelled by 'Not Specified' purpose is the highest followed by 'Meeting' and 'Customer Visit' purposes.\n",
    "\n",
    "2) Whereas the distance travelled by the customers for the 'Chairty' , 'Airport/Travel' and 'Moving' purposes is significantly  low.\n",
    "\n",
    "If any decision regarding to the purpose of uber ride is to be taken by the company, we need to take all the null values into account as it represents the largest distance travelled by the rides (in miles)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b7-pD446199j"
   },
   "source": [
    "## Q20. Display a dataframe of Purpose and the total distance travelled for that particular Purpose. (3 points)\n",
    "#### Note: Use the original dataframe without dropping \"NA\" values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below is the dataframe that does not include null values of the column 'PURPOSE'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 390
    },
    "id": "Em4d7Ng9199k",
    "outputId": "abe48585-adf0-4848-bc0e-7023e883f12c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PURPOSE*\n",
       "Meeting            2851.3\n",
       "Customer Visit     2089.5\n",
       "Meal/Entertain      911.7\n",
       "Temporary Site      523.7\n",
       "Errand/Supplies     508.0\n",
       "Between Offices     197.0\n",
       "Commute             180.2\n",
       "Moving               18.2\n",
       "Airport/Travel       16.5\n",
       "Charity ($)          15.1\n",
       "Name: MILES*, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = uber_drives.groupby('PURPOSE*')['MILES*'].sum()\n",
    "df3.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can infer that the total distance travelled for the purpose of 'Meeting' is the highest (2851.3 miles), whereas, it is the least for 'Charity' (15.1 miles)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below is the dataframe that includes null values of the column 'PURPOSE'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PURPOSE*\n",
       "Not Specified      4893.5\n",
       "Meeting            2851.3\n",
       "Customer Visit     2089.5\n",
       "Meal/Entertain      911.7\n",
       "Temporary Site      523.7\n",
       "Errand/Supplies     508.0\n",
       "Between Offices     197.0\n",
       "Commute             180.2\n",
       "Moving               18.2\n",
       "Airport/Travel       16.5\n",
       "Charity ($)          15.1\n",
       "Name: MILES*, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df2.groupby('PURPOSE*')['MILES*'].sum()\n",
    "df4.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can infer that the total distance travelled for the purpose of 'Not Specified' is the highest (4893.5 miles), Whereas it is the least for Charity (15.1 miles)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CIZBaeSt199o"
   },
   "source": [
    "## Q21. Generate a plot showing count of trips vs category of trips. What can you infer from the plot (2 +1 points)\n",
    "#### Note: Use the original dataframe without dropping the 'NA' values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fRuxSI0W199p",
    "outputId": "adc11110-b792-40e9-e875-f5e0479037ee"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.countplot(uber_drives['CATEGORY*']);\n",
    "plt.title('Plot of count of trips vs category of trips');\n",
    "plt.xlabel('Category');\n",
    "plt.ylabel('Count of Trips');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can infer from the graph above that the number of business category trips are significantly larger than the personal category trips of customers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VgzSzOt9199t"
   },
   "source": [
    "## Q22. What percentage of Miles were clocked under Business Category and what percentage of Miles were clocked under Personal Category ? (3 points)\n",
    "\n",
    "### Note:Use the original dataframe without dropping the 'NA' values. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CATEGORY*\n",
       "Business    11487.0\n",
       "Personal      717.7\n",
       "Name: MILES*, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = uber_drives.groupby('CATEGORY*')['MILES*'].sum()\n",
    "df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(df5,labels=df5.index,autopct='%1.1f%%',radius=2)\n",
    "plt.pie([1],colors=['w'],radius=0.75)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above pie chart we can infer that the percentage of Miles clocked under Business Category is 94.1% whereas the percentage of Miles clocked under Personal Category is 5.9%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_kYn6PGTUntA"
   },
   "source": [
    "***THE END***"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "PDS_UberDriveProject_Solution1.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "toc": {
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   "nav_menu": {},
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   "toc_position": {},
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   "toc_window_display": false
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  "varInspector": {
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   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
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     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
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    "builtin_function_or_method",
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}