EDA_DataVisualization.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "    <img src=\"https://gitlab.com/ibm/skills-network/courses/placeholder101/-/raw/master/labs/module%201/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **SpaceX  Falcon 9 First Stage Landing Prediction**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assignment: Exploring and Preparing Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimated time needed: **70** minutes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this assignment, we will predict if the Falcon 9 first stage will land successfully. SpaceX advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is due to the fact that SpaceX can reuse the first stage.\n",
    "\n",
    "In this lab, you will perform Exploratory Data Analysis and Feature Engineering.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Falcon 9 first stage will land successfully\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing\\_1.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several examples of an unsuccessful landing are shown here:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n",
    "\n",
    "Perform exploratory Data Analysis and Feature Engineering using `Pandas` and `Matplotlib`\n",
    "\n",
    "*   Exploratory Data Analysis\n",
    "*   Preparing Data  Feature Engineering\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries and Define Auxiliary Functions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will import the following libraries the lab\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# andas is a software library written for the Python programming language for data manipulation and analysis.\n",
    "import pandas as pd\n",
    "#NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays\n",
    "import numpy as np\n",
    "# Matplotlib is a plotting library for python and pyplot gives us a MatLab like plotting framework. We will use this in our plotter function to plot data.\n",
    "import matplotlib.pyplot as plt\n",
    "#Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's read the SpaceX dataset into a Pandas dataframe and print its summary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>Date</th>\n",
       "      <th>BoosterVersion</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Orbit</th>\n",
       "      <th>LaunchSite</th>\n",
       "      <th>Outcome</th>\n",
       "      <th>Flights</th>\n",
       "      <th>GridFins</th>\n",
       "      <th>Reused</th>\n",
       "      <th>Legs</th>\n",
       "      <th>LandingPad</th>\n",
       "      <th>Block</th>\n",
       "      <th>ReusedCount</th>\n",
       "      <th>Serial</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-06-04</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0003</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2012-05-22</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2013-03-01</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>ISS</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0007</td>\n",
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       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2013-09-29</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>PO</td>\n",
       "      <td>VAFB SLC 4E</td>\n",
       "      <td>False Ocean</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1003</td>\n",
       "      <td>-120.610829</td>\n",
       "      <td>34.632093</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2013-12-03</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>3170.000000</td>\n",
       "      <td>GTO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>28.561857</td>\n",
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      "text/plain": [
       "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n",
       "0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n",
       "1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n",
       "2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n",
       "3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n",
       "4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n",
       "\n",
       "       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n",
       "0    None None        1     False   False  False        NaN    1.0   \n",
       "1    None None        1     False   False  False        NaN    1.0   \n",
       "2    None None        1     False   False  False        NaN    1.0   \n",
       "3  False Ocean        1     False   False  False        NaN    1.0   \n",
       "4    None None        1     False   False  False        NaN    1.0   \n",
       "\n",
       "   ReusedCount Serial   Longitude   Latitude  Class  \n",
       "0            0  B0003  -80.577366  28.561857      0  \n",
       "1            0  B0005  -80.577366  28.561857      0  \n",
       "2            0  B0007  -80.577366  28.561857      0  \n",
       "3            0  B1003 -120.610829  34.632093      0  \n",
       "4            0  B1004  -80.577366  28.561857      0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df=pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\")\n",
    "\n",
    "df=pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/dataset_part_2.csv')\n",
    "\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's try to see how the `FlightNumber` (indicating the continuous launch attempts.) and `Payload` variables would affect the launch outcome.\n",
    "\n",
    "We can plot out the <code>FlightNumber</code> vs. <code>PayloadMass</code>and overlay the outcome of the launch. We see that as the flight number increases, the first stage is more likely to land successfully. The payload mass is also important; it seems the more massive the payload, the less likely the first stage will return.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Kj1Ey818j4uHAEymKnvcDfgh8CbiuteiYmf8F3NwtXkRcCpwMPCAidgJvBM4DDgWuiQiAz2fmb2fmTRFxOfBViiF212TmXWWoVwCXAPemuCbp1HVJ3wd8MCJuoegpekaVxytJkiRJkiRJkiRpOES/O1BGxKGZ+ZO+Bp1nY2NjOT4+3nQakiRJkiRJkiRJ0lyImTcZPgtmu2FEvGYW29wTuKKXhCRJkiRJkiRJkiSp32ZdGAXeGhEv6rQyIg4BNgDP7jkrSZIkSZIkSZIkSeqjKoXRfwcujoinTV9RFkU/AjwX+Ks+5SZJkiRJkiRJkiRJfVGlMPq/gG8B/xARj51aGBEBfAD4NeBvMvNV/U1RkiRJkiRJkiRJknoz68JoZv4AOBX4L+DqiDiyXHUR8CLgg5l5Tt8zlCRJkiRJkiRJkqQeVekxSmZuB54D3Bf4ZET8LXAm8PfAWf1PT5IkSZIkSZIkSZJ6t7DqHTJza0S8APgn4FHARuA3MnNfv5OTJEmSJEmSJEmSpH7oWBiNiJfOcN9PAU8ArgJeXFxqtJCZH+hLdpIkSZIkSZIkSZLUB916jF4CZJvlUS6fqoT+9bRlCVgYlSRJkiRJkiRJkjQwuhVGvWaoJEmSJEmSJEmSpKHQsTCame+fz0QkSZIkSZIkSZIkaa4saDoBSZIkSZIkSZIkSZprFkYlSZIkSZIkSZIkDb2OhdGI2BoRq+oEjYgHRsRfRMRr6qcmSZIkSZIkSZIkSf3RrcfobuBjEfG1iHhNRKzoFigiDo2I50TEpcCtwJnATf1LVZIkSZIkSZIkSZLqWdhpRWaeHBGrgf8LvA14a0R8FxgHbqMonN4LWAI8EngccA/gZ8AlwBsy8/Y5zV6SJEmSJEmSJEmSZqFjYRQgMzcAGyLiWcDLgacB/6vNpncBXwY+CrwvM+/oc56SJEmSJEmSJEmSVFvXwuiUzLwGuAYgIh4BLKPoKfpfwO3ATZn5w7lKUpIkSZIkSZIkSZJ6MavCaKvMvBm4eQ5ykSRJkiRJkiRJkqQ5saDpBCRJkiRJkiRJkiRprlkYlSRJkiRJkiRJkjT0LIxKkiRJkiRJkiRJGnoWRiVJkiRJkiRJkiQNPQujkiRJkiRJkiRJkoaehVFJkiRJkiRJkiRJQ8/CqCRJkiRJkiRJkqSht7DXABHxSOA5wB7gssy8s+esJEmSJEmSJEmSJKmPZt1jNCLeEBG3RcTilmXPBL4ErAPeDVwfEUv6n6YkSZIkSZIkSZIk1VdlKN3nAF/PzMmWZW8DEngj8B5gBfCq/qUnSZIkSZIkSZIkSb2rUhg9Gvja1ExEHAk8Hnh3Zv7fzPwd4Frg+bMNGBEXRcTtEXFjy7LFEXFNRHyjnC5qWXdeRNwSETdHxCktyx8fETeU694VEVEuPzQiPlIu3xwRR1d4vJIkSZIkSZIkSZKGRJXC6CKgtbfoSRS9RT/esuyLwLIKMS8BTp227LXApzPzWODT5TwR8WjgDOAx5X3eHRGHlPd5D3A2cGz5NxXz5cDuzDwGeAfwpxVykyRJkiRJkiRJkjQkqhRG7wCObJl/GvAzYHPLsntWiZmZn2P/YivAKuD95e33c3cP1FXAZZn5k8ycAG4BVkbEEcB9M3NTZibwgWn3mYq1AXjGVG9SSZIkSZIkSZIkSaOjSmH0y8DzIuKxEXEM8OvAv2Xmf7VsczRwW485PSgzbwMopw8slx8J7GjZbme57Mjy9vTl+90nM/cCdwJL2v3TiDg7IsYjYvyOO+7o8SFIkiRJkiRJkiRJGiRVCqN/BtwP2ArcXN7+f1MrI+JewMnAeB/za9Wup2d2Wd7tPgcuzHxvZo5l5tjhhx9eM0VJkiRJkiRJkiRJg6jKsLf/CjwXuAL4GLA6M69u2eSJwK3lul58rxwel3J6e7l8J7C0ZbujgO+Uy49qs3y/+0TEQopi7vSheyVJkiRJkiRJkiQNuSo9RsnMT2TmCzJzdWZ+bNq6azPzlzJzQ485XQmcWd4+E9jYsvyMiDg0IlYAxwJbyuF2fxQRTyivH/rSafeZirUauLa8DqkkSZIkSZIkSZKkEbKwH0EiYhHw08z8ccX7XUox/O4DImIn8EbgAuDyiHg5sB14IUBm3hQRlwNfBfYCazLzrjLUK4BLgHsDV5d/AO8DPhgRt1D0FD2j7mOUJEmSJEmSJEmSdPCK2XagjIhnAKcAb8vM3eWyBwJ/DzyJoli5PjN/f45ynTdjY2M5Pj5Xl0qVJEmSJEmSJEmSGhVNJ9CEKkPp/i7wa1NF0dI64MnALcAu4FURcXof85MkSdrf5ARcfBqcv6SYTk40nZGGhW2reb4G9fnczcr2XXs4/cJNHPO6qzj9wk1s37Wn6ZQ0aNyXNFdsW5IkSQOhSmH0OODfpmYi4t4U1+28JjMfATwC2AH8dl8zlCRJarVxDWy7DvbtLaYb1zSdkYaFbat5vgb1+dzNytoNW9kyMcnefcmWiUnWbtjadEoaNO5Lmiu2LUlqzx+OSJpnVQqjDwS+0zJ/AnAvimt7kpk/Aj5OUSCVJEmaGzs2d5+X6rJtNc/XoD6fu1m5ftvurvOS+5LmjG1LktrzhyOS5lmVwuhPgHu3zD8ZSOBzLct+CCzuQ16SJEntLT2h+7xUl22reb4G9fnczcrxyxd1nZfclzRnbFuS1J4/HJE0z6oURieAp7fMvwD4RmZ+u2XZUuD7/UhMkiSprVXrYflJsGBhMV21vumMNCxsW83zNajP525W1q0+jpUrFrNwQbByxWLWrT6u6ZQ0aNyXNFdsW5LUnj8ckTTPIjNnt2HE7wHvBL4A/BR4IvDmzDy/ZZubgNsy85n9T3X+jI2N5fj4eNNpSJIkSZIkSZI0vCYniuFzd2wuiqKr1sPiFU1nJY2KaDqBJiyssO17gCcAv07xZP0j8KdTKyNiJfAo4NJ+JihJkiRJkiRJkobQ4hVw1lVNZyFphMy6MJqZPwN+IyJ+u5jNH03b5FvALwG39i89SZIkSZIkSZIkSepdlR6jAGTmDzss/z5eX1SSJEmSJEmSJEnSAFrQdAKSJEmSJEmSJEmSNNcq9RiNiPsArwROAY4EDm2zWWbmw/qQmyRJkiRJkiRJkiT1xawLoxFxf+DfgEcDPwTuC9wJ3BO4d7nZd4Cf9TdFSZIkSZIkSZIkSepNlaF0X09RFH05sKhc9g7gF4AnAtcD3wQe1c8EJUmSJEmSJEmSJKlXVQqjzwM+l5kXZ2ZOLczC54HTgEcCf9TnHCVJkiRJkiRJkiSpJ1UKo0speoVO2UfLNUYz83bgauCM/qQmSZIkSZIkSZIkSf1RpTC6B7irZf5O4MHTtvkecGSvSUmSJEmSJEmSJElSP1UpjO6g6DU65avAUyLikJZlTwK+24/EJEmSJEmSJEmSJKlfqhRG/wV4akREOf8R4GHAP0XEmoj4e+AJwFV9zlGSJEmSJEmSJEmSerKwwrbvB+4JHEXRe/SvgacDzweeXW5zHfD6PuYnSZIkSZIkSZIkST2bdWE0M68HXtEyvxf4tYh4PHAMcCvwhczc1+8kJUmSJEmSJEmSJKkXVXqMtpWZXwS+2IdcJEmSJEmSJEmSJGlOVLnGqCRJkiRJkiRJkiQdlLr2GI2Il9YJmpkfqJeOJEmSJEmSJEmSJPXfTEPpXgJkhXhRbm9hVJIkSZIkSZIkSdLAmM01RvcCHwe+Ose5SJIkSZIkSZIkSdKcmKkw+i/AU4DnAw8E/ga4PDP/e47zkiRJkiRJkiRJkqS+WdBtZWY+DXgEsA44BrgYuC0i/jIiHjcP+UmSJEmSJEmSJElSz7oWRgEy85bMfA2wFDgd2Ay8AvhSRGyJiJdHxH3mOE9JkiRJkiRJkiRJqm3GwuiUzNybmR/NzFOBhwFvBY4A3gt8JyJOnKMcJUmSJEmSJEmSJKknsy6MtsrMbZn5x8DZwLeBXwAO72diEfHqiLgpIm6MiEsj4l4RsTgiromIb5TTRS3bnxcRt0TEzRFxSsvyx0fEDeW6d0VE9DNPSZIkSZIkSZIkSYOvcmE0Ih4SEa+PiG8BHweWAB8Cru9XUhFxJPB7wFhmPhY4BDgDeC3w6cw8Fvh0OU9EPLpc/xjgVODdEXFIGe49FAXcY8u/U/uVpyRJkiRJkiRJkqSDw6wKoxGxICKeFxFXArcC5wM/Al4FPCQzz8zMnX3ObSFw74hYCBwGfAdYBby/XP9+4Pnl7VXAZZn5k8ycAG4BVkbEEcB9M3NTZibwgZb7SJIkSZIkSZIkSRoRC7utjIgVwMuBsyiuJ/pjioLk32TmlrlKKjO/HRHrgO3AfwGfysxPRcSDMvO2cpvbIuKB5V2OBD7fEmJnuexn5e3pyyVJkiRJkiRJkiSNkK6FUYqelwDjwBuBSzPzx3ObEpTXDl0FrAB+APx9RLyk213aLMsuy9v9z7Mphtxl2bJlVdKVJEmSJEmSJEmSNOBmGko3gL0UvUXfAHwtIrbP8LetD3k9E5jIzDsy82fAPwBPBL5XDo9LOb293H4nsLTl/kdRDL27s7w9ffkBMvO9mTmWmWOHH354Hx6CJEmSJEmSJEmSNJoi4sERcVlEfDMivhoRV0XEwyPixqZymqnHKMA92L+4OB+2A0+IiMMohtJ9BkWv1R8DZwIXlNON5fZXAh+OiD8HHgIcC2zJzLsi4kcR8QRgM/BS4C/n9ZFIkiRJkiRJkiRJIyQiAvgY8P7MPKNc9ovAg5rMq2thNDNn6lE6JzJzc0RsAK6n6LH6JeC9wC8Al0fEyymKpy8st78pIi4HvlpuvyYz7yrDvQK4BLg3cHX5J0mSJEmSJEmSJGluPA34WWb+9dSCzPxyRBw9NV/e/iBwn3LR72Tmv5ejxn4EuC9FLfMVwL8D7wPGKC6beVFmvqNqUpHZ9pKbI21sbCzHx8ebTkOSJEmSJEmSJEmaCzGnwSN+D1iRma+etvxo4OOZ+dhy5Nh9mfnfEXEscGlmjkXE/wHulZlviYhDgMOAhwMXZOazyjj3z8wfVM1rNkPpSpIkSZIkSZIkSVI/3QP4q3KI3bsoip8AXwAuioh7AFeUPU2/BTw0Iv4S+CfgU3X+YSND5UqSJEmSJEmSJEkaWjcBj59hm1cD3wOOoxgi954Amfk54CnAt4EPRsRLM3N3ud1ngTXA39ZJysKoJEmSJEmSJEmSpH66Fjg0Iv6/qQUR8cvA8pZt7gfclpn7gN8EDim3Ww7cnpl/Q3Fd0eMj4gHAgsz8KPDHwPF1knIoXUmSJEmSJEmSJEl9k5kZEb8KvDMiXgv8N3ArcG7LZu8GPhoRLwQ+A/y4XH4y8AcR8TPgP4GXAkcCF0fEVKfP8+rkFZlZ535DbWxsLMfHx5tOQ5IkSZIkSZIkSZoL0XQCTXAoXUmSJEmSJEmSJElDz8KoJEmSJEmSJEmSpKFnYVSSJEmSJEmSJEnS0FvYaUVEXFszZmbmM2reV5IkSZIkSZIkSZL6rmNhFDi5w/Kk/QVZp5ZnjzlJkiRJkiRJkiRJUl91HEo3Mxe0/gH3Aq4EJoCzgBXAvcvpy4BvARvL7SRJkiRJkubO5ARcfBqcv6SYTk40nZEkSZKkAReZs+vgGRF/QlEQfWxm/qDN+sXADcD7MvMN/Uxyvo2NjeX4+HjTaUiSJEmSpE4uPg22XXf3/PKT4KyrmstnLk1OwMY1sGMzLD0BVq2HxSuazkqSJEkHt3ajw/bd0a/9pwXAi4BzgaXADuCdwKW3XvAr+3qJHRGnAn8BHAL8bWZeMNN9OvYYbePFwEfbFUUBMnMS2AC8pEJMSZIkSZKk6nZs7j4/TDauKYrA+/YW041rms5IkiRJmlFZFP0ocCEwBjyonF4IbCjX1xIRhwDrgecAjwZeFBGPnul+Vf7hQ4CfzrDNz4AjKsSUJEmSJEmqbukJ3eeHySgVgSVJkjRMXgQ8C7jPtOX3AZ4NnNFD7JXALZn5rcz8KXAZsGqmO1UpjO4EVkXEPdutjIhDy3/47QoxJUmSJEmSqlu1vhg+d8HCYrpqfdMZzZ1RKgJLkiRpmJzLgUXRKfcBXt1D7CMphuWdsrNc1lWVwuj7gWOAayPiKWUXVSLikIh4KvBp4KHAJRViSpIkSZIkVbd4RXFN0TfsKqbDfM3NUSoCS5IkaZgs7XF9N+2ukZoz3WlhhX9wAfB44HnAZ4B9ETEJLKYosAZwZbmdJEmSNPK279rD2g1buX7bbo5fvoh1q49j2ZLDmk5LknSwmSoCS1ITJieKaxvv2Fz0WF+1frh/jCJJ6qcdFNcV7ba+rp3sX1g9CvjOTHeadY/RzPxZZj4feAlwLXAnRVH0Toreoi/OzOdn5t4KSUuSJElDa+2GrWyZmGTvvmTLxCRrN2xtOiVJkiSpmo1rYNt1sG9vMd24pumMJEkHj3cCP+6w7sfAO3qI/QXg2IhYUV4G9AyKDpxdVekxCkBmfhj4cPX8JEmSpNFy/bbdXeclSZKkgbdjc/d5SZI6uxRYDTyL/a81+mPgU8BldQNn5t6I+B3gk8AhwEWZedNM96tyjVFJkiRJFRy/fFHXeUmSJGngLT2h+7wkSR3cesGv7ANeAJwNjAPfK6dnA6vL9bVl5lWZ+fDMfFhmvmU294nMGa9DOnLGxsZyfHy86TQkSZJ0kPMao5IkSTroeY1RSRpW0XQCTahUGI2II4DXA6cARwL3bLNZZmblIXoHiYVRSZIkSZIkSe344zdJ0pAYycLorIfSjYgjKbq3nkMx9u+hwHbgG8BdFE/gVuBf+5+mJEmSJEmSJDVv7YatbJmYZO++ZMvEJGs3bG06JUmSNEtVrjH6BuDBwKmZeVy57OLMfCTwUIqLm94b+LX+pihJkiRJkiRJg+H6bbu7zkuSpMFVpTB6CvCJzPzn6SsycyfwQorC6Jv7lJskSZIkSZIkDZTjly/qOi9JkgZXlcLog4GbWubvoiiEApCZ/wlcA6zqT2qSJEmSJEmSNFjWrT6OlSsWs3BBsHLFYtatPm7mO0mSpIGwsMK2PwTu2TK/Gzhy2jZ3Aof3mpQkSZIkSZIkDaJlSw7j8nNObDoNSZIODm+63wLgRcC5wFJgB/BO4FLedOe+XkJHxEXAc4HbM/Oxs7lPlR6j2ygSnrIVeHpEHFb+8wXAs4GdFWJKkiRJkiRJkiRJGjZFUfSjwIXAGPCgcnohsKFc34tLgFOr3KHKP/w08LSIuEc5/37gIcC/R8TbgeuAxwAfqZKAJEmSJEmSJEmSpKHzIuBZwH2mLb8PRWfLM3oJnpmfAyar3KfKULrvoxg+9wHAbZn5oYh4PPC7wOPKbS4D3lIlgYPJ9l17WLthK9dv283xyxexbvVxLFtyWNNpSZIkdTY5ARvXwI7NsPQEWLUeFq9oOitJg2oA3zM8D5MkSZ14nKCRNYDH7VIH53JgUXTKfYBXAx+et2yo0GM0M7+RmX+ambe1LHs1cARwInBEZv5GZv53PxKLiPtHxIaI+HpEfC0iToyIxRFxTUR8o5wuatn+vIi4JSJujohTWpY/PiJuKNe9KyKibk5rN2xly8Qke/clWyYmWbtha68PU5IkaW5tXAPbroN9e4vpxjWVQ2zftYfTL9zEMa+7itMv3MT2XXvmIFFJA6EP7xn95nmYJEnqxOMEjawBPG6XOlja4/q+63XsXjLzjszcnJnf60dCLf4C+ERmPhI4Dvga8Frg05l5LMXQvq8FiIhHU3S3fQzFWMLvjohDyjjvAc4Gji3/Ko013Or6bbu7zkuSJA2cHZu7z8+CXzZII6QP7xn95nmYJEnqxOMEjawBPG6XOtjR4/q+q1UYjYijIuJ/RcRvRsTzIuKofiYVEfcFnkIxfC+Z+dPM/AGwiuLappTT55e3VwGXZeZPMnMCuAVYGRFHAPfNzE2ZmcAHWu5T2fHLF3WdlyRJGjhLT+g+Pwt+2SCNkD68Z/Sb52GSJKkTjxM0sgbwuF3q4J3Ajzus+zHwjvlLpVCpMBoRyyLiE8A24ArgEuBjwLaI+EREHN2nvB4K3AFcHBFfioi/jYj7AA+aGsq3nD6w3P5I9q8q7yyXHVnenr78ABFxdkSMR8T4HXfc0TapdauPY+WKxSxcEKxcsZh1q4/r4SFKkiTNg1XrYflJsGBhMV21vnIIv2yQRkgf3jP6zfMwSZLUiccJGlkDeNwudXApcA0HFkd/DHwKuKyX4BFxKbAJeERE7IyIl894n6Ij5ayCPxj4AkVh8Vbgc8BtFNcYfRJFMfM7wFhmfrfOA2j5X2PA54GTMnNzRPwF8EPgdzPz/i3b7c7MRRGxHtiUmR8ql78PuArYDrwtM59ZLn8y8IeZ+b+6/f+xsbEcHx/v5SFIkiQNje279rB2w1au37ab45cvYt3q41i25LCm05IkSZIkSVJ9MS//5U33W0BxOcxXU1xTdAdFT9HLeNOd++YlhxYLK2z7xxRF0dcAf56Zd02tKK/n+Wrgz4DXA7/TY147gZ2ZOTUw9gaK64l+LyKOyMzbymFyb2/ZvvUCrUdRFGl3lrenL5ckSdIsLVtyGJefc2LTaUiSdHCYnICNa4prfS09oejBsXhF01lJkiRJzSiKnx8u/xpXZSjdXwE+lZlvby2KAmTmXZm5jqLb63N7TarscbojIh5RLnoG8FXgSuDMctmZwMby9pXAGRFxaESsAI4FtpTD7f4oIp4QEQG8tOU+kiRJkiRJ/bVxDWy7DvbtLaYb1zSdkSRJkqRSlR6jDwb+boZtvgicXDub/f0u8HcRcU/gW8BZFIXcy8sxgrcDLwTIzJsi4nKK4uleYE1L8fYVFNdCvTdwdfknSZIkSZLUfzs2d5+XJEmS1JgqhdE7geUzbLOs3K5nmfllYKzNqmd02P4twFvaLB8HHtuPnCRJkiRJkrpaekLRU7R1XpIkSdJAqDKU7r8BqyPiie1WRsQJFD04/60fiUmSJB0Mtu/aw+kXbuKY113F6RduYvuuPU2nJEmSmrRqPSw/CRYsLKar1jedkSRJkqRSZObsNow4Hvh34BDgMuAzwG0UQ+yeDLwI2AeclJlfnItk58vY2FiOj483nYYkSToInH7hJrZMTP58fuWKxVx+zokNZiRJkiRJkiTNKJpOoAmzHko3M6+PiNUU1+t8MfAbLasDmARedrAXRSVJUn9s37WHtRu2cv223Ry/fBHrVh/HsiWHNZ1W312/bXfXeUmSJEmSJEmDocpQumTmxymuM/oS4B3AReX0N4HlmXll3zOUJEkHpbUbtrJlYpK9+5ItE5Os3bC16ZTmxPHLF3WdlyRJkiRJkjQYZt1jdEpm/hj4cPknSZLU1qj0pFy3+rgDesZKkjQsRmUECEmSJEmjoXJhVJIkaTaOX75ov2tvDmtPymVLDuv/NUUnJ2DjGtixGZaeAKvWw+IV/f0fkiTNwtQIEMDPR4DwWtqSJEmSDlYdC6MR8dK6QTPzA3XvK0kabfZKGB72pOzBxjWw7bri9rbrivmzrmo2J0nSSBqVESAkSZIkjYZuPUYvAbJivCjvY2FUklSLvRKGx5z0pBwVOzZ3n5c0O/a+lno2KiNASJIkSRoN3QqjZ81bFpIkleyVIFEUcKZ6jE7NS6rO3tdSzxwBQpIkSdIw6VgYzcz3z2cikiSBvRIkoOjVNr2Xm6Tq7H0t9cwRICRJkiQNk249RiVJmnf2SpAohvq0V5vUO3tfS5IkSZKkFpFZ9TKiw29sbCzHx8ebTkOSJEk6OAzqtTwHNS9JkqS54vGPJGn2oukEmmBhtA0Lo5IkSVIFF5+2f8/M5SfZ61mSJKkJHpdJkmZvJAujC5pOQJIkSdJBzmt5SpIkDQaPyyRJ6srCqCRJkqTeTL92p9fylCRJaobHZZIkdWVhVJIkSVJvVq0vhmlbsLCYrlrfdEaSJEmjyeMySZK68hqjbXiNUUmSJEmSJEmSJA0xrzHaTUT8RUQ8ei6TkaRhtn3XHk6/cBPHvO4qTr9wE9t37Wk6JdUxOQEXnwbnLymmkxNNZyRJkiRJkiRJmoUqQ+n+LnBDRHwuIl4cEfecq6QkaRit3bCVLROT7N2XbJmYZO2GrU2npDo2roFt18G+vcV045qmM5IkSZIkSZIkzUKVwujpwKeBk4APAN+JiHUR8Yg5yUyShsz123Z3nddBYsfm7vPDxN6xkiRJkiRJkobIrAujmbkhM58NPAz4M+CnwO8DX42IayPi9Ii4xxzlKY0Mh1sdXscvX9R1XgeJpSd0nx8m9o6VJEmSJEmSNESq9BgFIDNvzczzgGXc3Yv0qcClwM6IuCAiHtbfNKXR4XCrw2vd6uNYuWIxCxcEK1csZt3q45pOSXWsWg/LT4IFC4vpqvVNZzR3Rql3rCRJkiRJkqShF5nZe5CIlcBHgSPLRfuATwBvzMwv9vwP5tnY2FiOj483nYZG1DGvu4q9++7eLxcuCG5562k9x92+aw9rN2zl+m27OX75ItatPo5lSw7rOa6kIXbxaUVP0SnLT4KzrmouH0mSJEmSJEn9Ek0n0ITKPUZbRcRTI+LDwL9QFEXvAN4J/BtwGvD5iPj1XpOURslcDbdqT1RJlY1S71hJkiSpDS93I0mSNFwq9xiNiMXAmcDZwMMpKsrXAe8B/j4zf1ZutxL4B+BHmfmofiY91+wxqibNVc/OueqJKkmSJEnSsDr9wk1smZj8+fzKFYu5/JwTG8xIkiSpb0ayx+jC2W4YEU8CzgFeANwL+E/gQuA9mXnD9O0zc0tEXAy8pk+5SiNh2ZLD5uQk6/jli/Y7metXT1RJkiRJkobV9dt2d52XJEnSwaXKULqfA14MfBP4HeAhmfnKdkXRFt8u/yQ1bN3q41i5YjELFwQrVyxm3erjmk5JkiRJw2hyorhO9flLiunkRNMZSVJtc3W5G0mSJDVj1kPpRsSlwLsz81/nNqXmOZSuJEmSJNV08Wmw7bq755efBGdd1Vw+ktSDubrcjSRJ0gBwKN1uMvNFc5mIJEmSJGkI7NjcfV6SDiJzdbkbSZIkNaPKULrzLiIOiYgvRcTHy/nFEXFNRHyjnC5q2fa8iLglIm6OiFNalj8+Im4o170rIkayAi5JkiRJ82LpCd3nJUmSJElqyKx7jE6JiF8GTgGOBA5ts0lm5st7Taz0KuBrwH3L+dcCn87MCyLiteX8ayLi0cAZwGOAhwD/HBEPz8y7gPcAZwOfB64CTgWu7lN+kiRJkqRWq9bDxjVFT9GlJxTzktTN5MSB7xuLVzSdlSRJkoZQlWuMBnAJ8BKKcYeT/ccfnprPzDyk58QijgLeD7wF+P3MfG5E3AycnJm3RcQRwGcz8xERcR7FP35bed9PAm8CbgU+k5mPLJe/qLz/Od3+t9cYlSRJkiRJmidem1iSJKkJIznCapWhdH8H+E3gg8AYxRP2TuCJwOuAHwGXAQ/tU27vBP4Q2Ney7EGZeRtAOX1gufxIYEfLdjvLZUeWt6cvP0BEnB0R4xExfscdd/TlAUiSJEmSJGkGXptYkiRJ86RKYfRM4ObM/K3MvL5c9oPM/HxmXgA8DXgB8PRek4qI5wK3Z+YXZ3uXNsum92htXX7gwsz3ZuZYZo4dfvjhs/y3kiRJkiRJ6onXJpYkjYLJiWKUhPOXFNPJiaYzkkZSlcLoI4Brpy37+TVKM/NLwMeBV/Yhr5OA50XErRS9UJ8eER8CvlcOoUs5vb3cfiewtOX+RwHfKZcf1Wa5JEmSJEmSBsGq9cXwuQsWFlOvTSxJGkYb1xRDx+/bW0w3rmk6I2kkVSmMBnBny/yPgcXTtvkG8Mhek8rM8zLzqMw8GjgDuDYzXwJcSdFzlXK6sbx9JXBGRBwaESuAY4Et5XC7P4qIJ5TXSH1py30kSZIkSRo+9kbQwWbxiuKaom/YVUwXr2g6I0mS+s+h46WBUKUw+m32vz7nt4DHT9vmWIqC6Vy5AHhWRHwDeFY5T2beBFwOfBX4BLAmM+8q7/MK4G+BW4BvAlfPYX6SJEmShokFJh2M7I0gSZI0eBw6XvPF89iuIrPtJTcP3DDig8AvZeZjy/kLgD8A3gT8A3Ay8BfAxzPz+XOQ67wZGxvL8fHxptOQJElSN5MTxZf9OzYXJ5Sr1tvDRP138WlFYWnK8pOK3kzSIDt/SVEUnbJgYdETT5IkSc3xHFbzZfbnsTFfKQ2SKj1GPwocUg5VC/BnwDbgzcBXgL8EfgC8tp8JSpIkSW3ZI0rzweGudDCyN4IkSdLgceh4zRfPY7uadWE0M6/IzEdl5kQ5Pwn8EvCHwHuB84D/mZlfn5NMJUmSpFYe6Gs+WGDSwWjV+uJX4QsWFtNV65vOSJIkSdJ88Ty2q4Wz2SgilgG/DCTwhczcAZCZdwLr5i49SZIkqYOlJ+w/NIwH+poLq9YfONyVNOimeiNIkiRJGj2ex3Y14zVGI2IdcC53jzWcwDsy8w/mNrXmeI1RSZKkg4DXZ5EkSZIkSaprJK8x2rUwGhG/AXyIohj6dYon6RHl6pdk5qVznmEDLIxKkiRJkiRJkiRpiI1kYXSma4y+HNgLPDMzH5OZjwZOAfaV66RGbN+1h9Mv3MQxr7uK0y/cxPZde5pOSZIkSZIkSZIkSQNspsLo44ArMvMzUwsy85+BjcAvzmFeUldrN2xly8Qke/clWyYmWbtha9MpSZIkSZIkSZIkaYDNVBhdBNzcZvnXgfv3PRtplq7ftrvrvCRJkiRJkiRJktRqpsLoAuBnbZb/jBEde1jVzcWwt8cvX9R1XpIkSZIkSZIkSWo1U2EUIOc8Cw21uRj2dt3q41i5YjELFwQrVyxm3erj+pCppKEyOQEXnwbnLymmkxNNZyRJkiRJkiRJalBkdq57RsQ+qhdGMzMX9pRVw8bGxnJ8fLzpNIbGMa+7ir377m5GCxcEt7z1tAYzkjQSLj4Ntl139/zyk+Csq5rLp9XkBGxcAzs2w9ITYNV6WLyi6awkSZIkSZIkjY6RHBl2Nj1Go+LfbGJqhDjsraRG7Njcfb5JG9cURdt9e4vpxjVNZyRJkiTV40gtkiRJOoh0LWJm5oI6f/OVvA4ODnsrqRFLT+g+36RBLtpKkiRJVfijP0mSJB1EDuohb3VwWLbkMC4/58Sm05A0alatP3C42kGx9IT9h/kdpKKtpMHlMNySpEHkj/4kSZJ0ELF3pyRpOC1eUVxT9A27iukgFQ9WrS+uebpgYTEdpKKtpMFljxxJ0iAa5JFaJEmSpGnsMSpJ0nybKtpKUhX2yJEk9WouRh8Y5JFaJEmSpGksjEqSJEkHA4fhlgbS9l17WLthK9dv283xyxexbvVxLFtyWNNpSe1NjT4Ad48+0OsP9vzRnyRJkg4iDqUrSZKk+TU5ARefBucvKaaTE01ndHBwGG5pIK3dsJUtE5Ps3ZdsmZhk7YatTackdeboA5IkSRpx9hiVJEnS/JqL3iqjwB450kC6ftvurvPSQHH0AUmSJI04e4xKkiRpftlbRdIQOX75oq7z0kBx9AFJkiSNOHuMSpIkaX7ZW0XSEFm3+rgDrjEqDawRGH3A6/5KkiSpm8jMpnMYOGNjYzk+Pt50GpJ0AE/ypT6ZnCiGb92xuSjKrVpffFGo+eHzL0mS5sjpF25iy8Tkz+dXrljM5eec2GBGBxGP0SRJGjXRdAJNsDDahoVRSYPKk3ypTy4+bf8ei8tPGvreE5IkSaPgmNddxd59d3/XtXBBcMtbT2swo4OIx8iSJI2akSyMeo1RSY3ZvmsPp1+4iWNedxWnX7iJ7bv2NJ3SwLt+2+6u85JmyWtcSpI07zz+13zwur898BhZkiSNAAujkhqzdsNWtkxMsndfsmVikrUbtjad0sDzJF/qk+nXtPQal5IkzTmP/zUf1q0+jpUrFrNwQbByxWKv+1uFx8iSJGkEWBjVaJucKIaKOX9JMZ2caDqjkWLvx+o8yZf6ZNX6YmiwBQuL6ar1TWckSdLQ8/hf82HZksO4/JwTueWtp3H5OSeybMlhTad08PAYWZIkjYCFTScgNWrjmruvn7HtumLe62d0tH3XHtZu2Mr123Zz/PJFrFt9XE8nmccvX7Tf9TLt/TizqZN8aT+TE8X7147Nxa+6V62HxSuazmqwLV7h+70kSfPM439pwA37MbLnTZIkCXuMatR5/YxK+j30lb0fpT6Z+pHHvr13/8hDkjS4HLVEI8rjf0mN8rxJkiRhj1GNuqUn3N1jdGpeHfV76Ct7P0p94o88JOng4qglGlEe/0tqlOdNkiSJAe0xGhFLI+IzEfG1iLgpIl5VLl8cEddExDfK6aKW+5wXEbdExM0RcUrL8sdHxA3lundFRDTxmDSgvH5GJdOHunLoK2lATP9Rhz/ykKTB5hezkiTNP8+bJEkSA1oYBfYC/yczHwU8AVgTEY8GXgt8OjOPBT5dzlOuOwN4DHAq8O6IOKSM9R7gbODY8u/U+XwgGnBT1894w65i6rUlunLoqyHjMH7Dwx95SNLBxS9mJUmaf543SZIkIDKz6RxmFBEbgb8q/07OzNsi4gjgs5n5iIg4DyAz31Zu/0ngTcCtwGcy85Hl8heV9z+n2/8bGxvL8fHxuXo4kjQYLj5t/6Gkl5/kMH6SJM2HyYli+Nwdm4ui6Kr1/kBPkiRJnXn8KGlujOQIqwN/jdGIOBr4JWAz8KDMvA2gLI4+sNzsSODzLXfbWS77WXl7+nJJksP4SZLUjKlRSyRJkqTZ8Br1ktQ3gzqULgAR8QvAR4FzM/OH3TZtsyy7LG/3v86OiPGIGL/jjjuqJytpqGzftYfTL9zEMa+7itMv3MT2XXuaTqn/HMZPkiRJkiRp8Pnjdknqm4EtjEbEPSiKon+Xmf9QLv5eOYQu5fT2cvlOYGnL3Y8CvlMuP6rN8gNk5nszcywzxw4//PD+PRBJB6W1G7ayZWKSvfuSLROTrN2wtemU+s/rq0iSJEmSJA0+f9wuSX0zkIXRiAjgfcDXMvPPW1ZdCZxZ3j4T2Niy/IyIODQiVgDHAlvKYXd/FBFPKGO+tOU+ktTR9dt2d50fClPD+L1hVzH12hSSJEmSJEmDxx+3S1LfDOo1Rk8CfhO4ISK+XC57HXABcHlEvBzYDrwQIDNviojLga8Ce4E1mXlXeb9XAJcA9wauLv8kqavjly9iy8TkfvOSJEmSJEnSvPMa9ZLUN5HZ9pKbI21sbCzHx8ebTkNSg7bv2sPaDVu5fttujl++iHWrj2PZksOaTkuSJEmSJEmSpH6IphNowqD2GJWkRi1bchiXn3Ni02lIkiRJkiRJ0sFjcgI2roEdm4vr4a5a7yW8NFAG8hqjkiRJkiRJkiRJOshsXAPbroN9e4vpxjVNZyTtx8KoJEmSJEmSJEmSerdjc/d5qWEWRiVJkiRJkiRJktS7pSd0n5caZmFUkiRJkiRJ0uCanICLT4PzlxTTyYmmM5IkdbJqPSw/CRYsLKar1jedkbSfyMymcxg4Y2NjOT4+3nQakiRJkiRJki4+rbhO3ZTlJ8FZVzWXjyRJwyGaTqAJ9hiV+s1fMUqSJEmSJPWP16uTJEl9YmFU6reNa4pfMe7bW0w3rmk6I0mSJEmSpIOX16uTJEl9YmFU6jd/xShJkiRJktQ/Xq9OkiT1ycKmE5CGztIT9r/uhb9ilCRJkiRJqm/xCq8pKkmS+sIeo1K/+StGSZIkSZIkSZKkgWOPUanf/BWjJEmSJEmSJEnSwLHHqCRJkiRJkiRJkqShZ2FUkiRJkiRJkiRJ0tCzMCpJkiRJkiRJkiRp6FkYlSRJkiRJkiRJkjT0LIxKkiRJkiRJkiRJGnoWRiVJkiRJkiRJkiQNvYVNJ6D+2L5rD2s3bOX6bbs5fvki1q0+jmVLDms6LUmSJEmSJEmSJGkg2GN0SKzdsJUtE5Ps3ZdsmZhk7YatTackSZIkSZIkSZIkDQwLo0Pi+m27u85LQ29yAi4+Dc5fUkwnJ5rOaGRs37WH0y/cxDGvu4rTL9zE9l17mk5JkiRJkiRJkqQDWBhtUD+LCccvX9R1vsncpHmxcQ1suw727S2mG9c0ndHIsMe6JEmSJEmSJOlgYGG0Qf0sJqxbfRwrVyxm4YJg5YrFrFt93MDkJs2LHZu7z2vO2GNdkg5SjrYgSZIkSZJGzMKmExhl/SwmLFtyGJefc2KvKXXMxUKHBt7SE4qeoq3zmhfHL1/ElonJ/eYlSQeBqdEW4O7RFs66qtmcJEmSJEmS5pA9RhvU7+Fv+2mQc5PaWrUelp8ECxYW01Xrm85oZPS7x7okaZ442oI0OOzBLUmSJEnzIjKz6RwGztjYWI6Pj8/5/9m+aw9rN2zl+m27OX75ItatPo5lSw6b8/87G4OcmyRJkvrg4tP2H21h+Un2GJWa4v4oSZIkaf5F0wk0wcJoG/NVGJUkSZIaMzlRDJ+7Y3MxBP2q9bB4RdNZSaPp/CWwb+/d8wsWwht2NZePJEmSpFEwkoVRrzEqSZIkjaLFK+yRJg2KpSfs32N06QnN5SJJkiRJQ8xrjEqSJEmS1KRV64vhcxcsLKar1jedkSRJkiQNJXuMSpIkSZLUJHtwS5IkSdK8GIkeoxFxakTcHBG3RMRrm85HkiRJkiRJkiRJ0vwa+sJoRBwCrAeeAzwaeFFEPLrZrCRJkiRJkiRJkiTNp6EvjAIrgVsy81uZ+VPgMmBVwzlJkiRJkiRJkiRJmkejUBg9EtjRMr+zXLafiDg7IsYjYvyOO+6Yt+QkSZIkSZIkSZIkzb1RKIxGm2V5wILM92bmWGaOHX744fOQliRJkiRJkiRJkqT5MgqF0Z3A0pb5o4DvNJSLJEmSJEmSJEmSpAaMQmH0C8CxEbEiIu4JnAFc2XBOkiRJkiRJkiRJkubRwqYTmGuZuTcifgf4JHAIcFFm3tRwWpIkSZIkSZIkSZLm0dAXRgEy8yrgqqbzkCRJkiRJkiRJktSMURhKV5IkSZIkSZIkSdKIszAqSZIkSZIkSZIkaehFZjadw8CJiDuAbbPY9AHA9/v0b0chVr/jjUKsfsczVrPxRiFWv+ONQqx+xzNWs/FGIVa/441CrH7HM1az8UYhVr/jjUKsfsczVrPxRiFWv+ONQqx+xzNWs/FGIVa/441CrH7HM1az8UYhVr/jjUKsfsc72GN9PzNP7dP/PGhYGO1BRIxn5pixmok3CrH6Hc9YzcYbhVj9jjcKsfodz1jNxhuFWP2ONwqx+h3PWM3GG4VY/Y43CrH6Hc9YzcYbhVj9jjcKsfodz1jNxhuFWP2ONwqx+h3PWM3GG4VY/Y43CrH6HW8UYg0jh9KVJEmSJEmSJEmSNPQsjEqSJEmSJEmSJEkaehZGe/NeYzUabxRi9TuesZqNNwqx+h1vFGL1O56xmo03CrH6HW8UYvU7nrGajTcKsfodbxRi9TuesZqNNwqx+h1vFGL1O56xmo03CrH6HW8UYvU7nrGajTcKsfodbxRi9TveKMQaOl5jVJIkSZIkSZIkSdLQs8eoJEmSJEmSJEmSpKFnYVSSJEmSJEmSJEnS0LMwWkNEXBQRt0fEjT3GWRoRn4mIr0XETRHxqh7j3SsitkTE1jLem3uJV8Y8JCK+FBEf7zHOrRFxQ0R8OSLGe4x1/4jYEBFfL5+7E3uI9Ygyp6m/H0bEuT3Ee3X53N8YEZdGxL16iPWqMs5NdXJq104jYnFEXBMR3yini3qI9cIyt30RMdZjXm8vX8+vRMTHIuL+PcT6kzLOlyPiUxHxkLqxWtatjYiMiAfMJlaX3N4UEd9uaW+n9ZJbRPxuRNxcvg5/1kNeH2nJ6daI+HIPsX4xIj4/ta9HxMrZxOoS77iI2FS+f/xjRNx3FnHavrf20P47xau8D3SJVXkf6BKr8j7QKVbL+kr7QJfcKu8D3XKrug90yavyPtAlVq19oEu8OvtA2+OBOvtAl1h12n+nWHXaf6dYdT8Duh5DVdkHuuRWp/13zKtG+++UV93PgE7xKu8DXWJVbv8tMfc7hq3T/rvEqnUM1CFWrWOgDrFqtf9O8VqW1zkOmp5brWOgTnlVbf9d8qrV/rvE6+U4aHqsWu0/2px39dj+28Wrex7QLlbd84B2sep+BhwQq2VdnfbfLre65wFtc6uzD3TIq+5nQLtYdY+B2sXq5f3//jHt+4K6+0CHWHXbf7tYddt/u1i1PwPaxWtZV/U8oF1uddt/27xqtv92edVt/+1i9fL+3y5enXOAtt9v1Wn/XWLVOQfoFKtu++8Ur855cNfvBKu0/y55VW7/3fKq2f475VbnPLhTrMr7QJdYdY+BXh3TvpOt0/67xOrlHKBdvLr7QLtYdY+BDojVsq7q+3+7vGq9/3fLreY+0C63up8B7WLVPQZqF6tu+z+gjlC3/Y+MzPSv4h/wFOB44MYe4xwBHF/e/h/AfwCP7iFeAL9Q3r4HsBl4Qo85/j7wYeDjPca5FXhAn57/9wP/u7x9T+D+fYp7CPBdYHnN+x8JTAD3LucvB36rZqzHAjcChwELgX8Gjq0Y44B2CvwZ8Nry9muBP+0h1qOARwCfBcZ6zOvZwMLy9p/2mNd9W27/HvDXdWOVy5cCnwS2VWnDHXJ7E7C2RntoF+tpZbs4tJx/YC+Ps2X9/wPe0ENenwKeU94+Dfhsj4/zC8BTy9svA/5kFnHavrf20P47xau8D3SJVXkf6BKr8j7QKVbdfaBLbpX3gS6xKu8D3R5n1X2gS1619oEu8ersA22PB+rsA11i1Wn/nWLVaf+dYtX9DOh4DFV1H+iSW5323ylWnfY/43HibNv/DLlV3ge6xKrc/lti7ncMW6f9d4lV6xioQ6xax0AdYtVq/53i1Wn/XXKr3P67xKp1DNTpMdZp/11y6+U4aHqsWu2fNuddPbb/dvHqnge0i1X3PKBdrLqfAQfEKpfXbf/tcqu1D3SIVfc8oO3jbFlf5TOgXV51j4Haxerl/f+A7wvq7gMdYtVt/+1i1W3/7WLV/gxoFy9r7gMdcqvb/tvFqtv+u36PVLH9t8url/f/dvFq7wPlfX7+/Vbd9t8hVu1joDaxah8DdYjX63HQft8J1mn/HfKq1f47xKp9DNTpcbYsr3Mc1Jpb7X2gTaw658Btv5Ot0/67xKr7/t8pXp3z4E6x6nwP1PF77Krtv0tetdp/l3h1zoNn/L5+tu2/S151zoE7xarT/tvWEeq0/1H6s8doDZn5OWCyD3Fuy8zry9s/Ar5GsVPUjZeZ+Z/l7D3Kv6wbLyKOAn4F+Nu6Mfqt/JXEU4D3AWTmTzPzB30K/wzgm5m5rYcYC4F7R8RCijej79SM8yjg85m5JzP3Av8C/GqVAB3a6SqKg27K6fPrxsrMr2XmzVVy6hLrU+XjBPg8cFQPsX7YMnsfZrkPdNmv3wH84WzjzCJeZR1ivQK4IDN/Um5ze695RUQApwOX9hArgalfM92PCvtAh3iPAD5X3r4GeMEs4nR6b63b/tvGq7MPdIlVeR/oEqvyPjDD51HlfaCfn29dYlXeB2bKq8o+0CVWrX2gS7w6+0Cn44HK+0CnWDXbf6dYddp/p1h1PwO6HUNV2gf6eTzWJVad9t81rxqfAZ3iVd4HusSq3P6h4zFsrc+AdrHqHgN1iFXrGKhDrFrtv1O8UuXPgH6eQ3SIVesYqFteVdt/l3i1PgM6xKrV/juo1f47qbsPdIhVax/oEKv2PtBBrfOAeVBrH+imzj7QRu3zgDbqvv93+r6g8j7QKVad9t8lVuX23yVWrfY/w3cslfaBfn5f0yVW5fY/U15V2n+XWHXf/zvF6/UzoPX7rV4/A34eqw/v/62x+vH+3xqv18+A6d8J9vIZ0I/vF9vF6sf7/wG59fAZ0Bqr18+A1lh123+772Trtv8DYvXY/tvFq7sPtItVt/13+h67Tvvv13fi3eLV3Qc65laj/beLVbf9t4tVp/13qiP09Rxg2FgYHRARcTTwSxS/kO8lziFl1+/bgWsys5d476R4E9zXS06lBD4VEV+MiLN7iPNQ4A7g4iiGmvrbiLhPH/IDOIMeTgQz89vAOmA7cBtwZ2Z+qma4G4GnRMSSiDiM4tcmS+vm1uJBmXlbme9twAP7ELPfXgZc3UuAiHhLROwAXgy8oYc4zwO+nZlbe8lnmt+JYniLi3ocwuDhwJMjYnNE/EtE/HIfcnsy8L3M/EYPMc4F3l4+/+uA83rM6UbgeeXtF1JxP5j23tpz++/Xe/UMsSrvA9Nj9bIPtMbqxz7Q5nHW3gemxeppH+jw/NfaB6bFOpce94Fp8WrtAx2OB2rtA/08tphFrFm3/06x6rb/dvHq7gNdHmfl9t8hVq32P8PzX7n9d4h3LjX2gQ6x6n4GvJMDj2Hrfga0i1XXTLGqvP+3jdXD+/8B8Xr4DGibG/Xe/9vFqvv+3ykvqPf+3y7eudT7DGgXq277b3fe1csxUL/O42YTq8o+0DZWzX3ggFg9HgN1epx19oF2seruA92e/6r7QLtY51Kv/beLVbf9d/q+oM4+0M/vHmYTa7btv2Osmu2/bbya+0C3x1m1/XeKVaf9z/T8V2n/nWKdS7323yleT+fB7P/9Vq/nwT19VzbLWHW/B9ovXg/HQfvF6vEz4IC86O17oNZY/fgeqN1rUPe7oNZY59LbeXBrrMrtv8t3spXbf5+/351tvFntA91iVW3/nWLVaf8zPMbK7b9LvMr7wCye/1m3/y6xzqVi++8Sq877f6c6wsFQB2hODkC31YPxDziaHofSbYn1C8AXgV/rY373Bz4DPLbm/Z8LvLu8fTK9D6X7kHL6QGAr8JSaccaAvcAJ5fxfUHFIkQ5x7wl8n+INo26MRcC1wOEUPR2uAF7SQ7yXA9dT/Erkr4F31IixXzsFfjBt/e66sVqWf5bqw8h1ivVHwMeA6DVWue484M11YlH8UmczcL9y/laqD58y/fl/EMXwIAuAtwAX9RDrRuBdFMMPrqQYfmFWz1uX5/89wP/p8TG+C3hBeft04J97jPdIiiEpvgi8EdhVIdZ+7629tP928VqW19kHOsWqsw90/AypsQ/8PFaf9oHpr0Ev+8D0WL3sA52e/zr7wPS8et0HpservQ+U978/5fFAH/aBn8fqpf13iVW5/XeKVaf9t4n3uD7sA63Pf+323yZW7fbf5fmv3P475NbrPtAaq3L7p8MxbJ323ylWnfY/i1izbv8zxara/tvFo+ZnQJfnv3L77xKrcvufxfNfqf13ya1y++8Sq9b7P23Ou+q0/27x6uwDs4hV6TOgW6wa+0C756z2+3+HeLU+AzrEqvUZMMPzX3UfaJdXrff/DrHqtv+23xfU2Qc6xarT/mcRq8pnwIzfiVRs/+3ivb3OPtDl+a/zGdApVp3PgJme/1m3/y551W3/neL1ch683/dbddp/p1h12v8sYtU9B+j4PV6VfWB6LHo8D27z/PdyDjw9Vq/nAJ1egzrnwdNzq30O0CZWnXOAtt/J1mn/nWLVbf+ziFflM2DG755n2/47xHppnfbf5fmve/zTKV6dz4CZnv8qnwGd8qpzDtApVt1joAPqCHXa/yj9NZ7AwfpHnwqjZcP/JPD7c5DjG6l/LZ+3ATvLN8DvAnuAD/Uprzf1kNeDgVtb5p8M/FMfcloFfKrHGC8E3tcy/1LKLzn6kN9bgVfWuN9+7RS4GTiivH0EcHPdWC3LP0sfCqPAmcAm4LBeY7WsW15lP2X/wuj/pOi1cmv5t5fiVzwP7lNuld5D2ryWnwBObpn/JnB4D8//QuB7wFE9trE7KQ9KKA5UftjH1/PhwJZZxjngvbXH9t/xvbrqPtApVp19oFte5fpZ7wPTY/W6D8wit1nvAx1ez1r7QJfnv/I+0CGv2vvALJ6zWe8D0+73RmBtL/vA9Fgt85Xaf6dYddp/t7zKZZU+A9rE++Ne9oEZcpt1++/wWtb+DOjw/Nf6DOiQW0+fA12es1m1fzocw9Zp/51itayfdfvvFqtq+58pr6rtv0O8j9Zp/7PMbVbtv8trWbn9z/D813n/75Rb5fY/y+es7vv/m+jT+39rvDr7QLdYVfeBmfKqug+0idWX9/8uuc1qH+jyevbjM6D1+e/pM6Alr368/7d7vqqcA7T9vqDOPtApVsv8rNt/t1hV2/9MeZXLqnwGtIv36Tr7wCxzm1X77/Ja1vkM6Pb8V2r/XfKq1f5n+ZxV+gxg2vdbddp/p1h12n+3WFXb/2xyK9dV+gxojUXv58Hd8ppV++/yWvb0/t/hNaj7XdD03Ho5D+72nM32HKDtd7J12n+nWC3zldp/t3hV94GZciuXzar9d4j1mTrtf5Z5zbr9d3k963wGdHv+q34GdMqrzjnAbJ6zuucAbwVeWaf9j9KfQ+k2KCKC4joCX8vMP+9DvMMj4v7l7XsDzwS+XidWZp6XmUdl5tEUwxlcm5kvqZnXfSLif0zdpri49I018/ousCMiHlEuegbw1TqxpnkRvQ8Nsh14QkQcVr62z6C4RlwtEfHAcrqMovdWP4YuuZLiQ5dyurEPMXsWEacCrwGel5l7eox1bMvs86i/D9yQmQ/MzKPL/WAncHzZBuvmdkTL7K9Scz8oXQE8vYz7cO7+hV1dzwS+npk7e4gBxXj4Ty1vPx3oZVje1v1gAfB6il89zXSfTu+ttdp/P9+rO8Wqsw90iVV5H2gXq5d9oEtulfeBLs//FVTcB2Z4LSvtA11i1doHujxndfaBTscDlfeBfh5bdIpVs/13ilXrM6BDvC/V2Qe65Fan/Xd6/q+gevvv9lpW/gzoEq/yPtDlOavc/rscw1Zu//08Hu4Uq0777xKrVvvvEO8Fddp/l9wqt/8uz/8VVGz/M7yWldt/l3iV23+X56zO+3+n8666x0B9O4/rFKvmZ0CnWHWOgdrF+kIPx0CdcqvzGdDp+b+C6p8B3V7LqsdAnWLVef/v9HxVbv/Q9fuCOp8BffvuoVOsmp8BnWLV/QxoF+/6mp8BnXKr8xnQ6fm/guqfAd1ey0rtv0usWucAXZ6zWvtAafr3W718D9SP78raxurD90DT4/XyXdDPY/Xhu6DpefXyPdD05/8KevseqN3rWfe7oOmxevkuaPpzVqf9d/pOtk777+v3u53i1dwHOsWq0/7bxfqHmu2/U15123+n1+AKqu8D3V7Pqu2/U6w67b/Tc1br/T/a1xEGsg4wMGZbQfXv7j+KhnUb8DOKN4iX14zzJIrraXwF+HL5d1oPeT0O+FIZ70bgDX16vCfTw1C6FNdM2Fr+3QT8UY/5/CIwXj7OK4BFPcY7DNhFOUxAj7HeTPHhcyPwQeDQHmL9K8UB9lbgGTXuf0A7BZZQ/PrzG+V0cQ+xfrW8/ROKX9d8sodYtwA7WvaDv+4h1kfL5/8rwD8CR9aNNW39rVQbPqVdbh8Ebihzu5LyVzs1Y92TomfCjRRDJTy9l8cJXAL8dh/a2JMohnvYSjH8xuN7jPcq4D/KvwuY3dAibd9be2j/neJV3ge6xKq8D3SJVXkf6BSr7j7QJbfK+0CXWJX3gW6Pk4r7QJe8au0DXeLV2QfaHg9QYx/oEqtO++8Uq0777xSr7mfAjMdQzH4YoU651Wn/nWLVaf8dH2PV9j9DbpX3gS6xKrf/aXFP5u5hSWt9BnSIVesYqEOsWsdAHWLVav+d4tVp/11yq3UM1CFWrWOgTo+xTvvvklvt46A2seq8/7c976rb/rvEq/MZ0ClWnc+ATrHqHAPNeK5apf13ya3OZ0CnWHU+Azo+zqr7QJe86rz/d4pV+/2fNt8X9LAPtItV9zy4Xay658HtYtX+DGgXr4d9oF1udc+D28Wqex7c9jFWbf9d8urlPLhdvFr7AG2+3+qh/beLVbf9t4tV+xioQ7y65wFdvxOs2P7b5VW3/beLVfsYqNPjrLkPtMut7nlwu1h12/8B38n20P7bxap9DtAhXt3PgHax6rb/rt9jV2z/7fKqfQ7QIV7dz4C2j7Nm+2+XV9323y5W3fZ/QB2hbvsflb+pLr6SJEmSJEmSJEmSNLQcSleSJEmSJEmSJEnS0LMwKkmSJEmSJEmSJGnoWRiVJEmSJEmSJEmSNPQsjEqSJEmSJEmSJEkaehZGJUmSJEmSJEmSJA09C6OSJEmSNKAi4pKIyIg4umXZ0eWyS/oQ/9aIuLXXOMMuIt5UPucnN52LJEmSJKk+C6OSJEmSNI/KAlu3v99qOsdOeinKRsRny/veFRH/s8M2U4XgZ/acrCRJkiRJ0yxsOgFJkiRJGlFv7rD8yzPc79vAo4A7+5rN/FkAvB04telEJEmSJEmjxcKoJEmSJDUgM99U834/A77e32zm1S3AKRHxrMy8pulkJEmSJEmjw6F0JUmSJOkg0m0424h4eER8NCJ2R8SPI+LfI+JXIuK3ug3TGxGHRcTbI2J7RPwkIm6JiNdERLRs8yZgopw9s4fhf18HJPD2iJjVOWn5Pz7bYV3X67BGxMMiYkNE7IqIH0XEpyLiseV2h0fEeyPitoj474j4QkQ8bYZczoyIL0XEf0XE7RFxUUQ8uMO2iyPibRHxtXL7OyPi0xHx7Dbb/vw1iohTy6GH74yInM1zJEmSJEmamT1GJUmSJGkIRMQjgeuAxcA/AV8BHgp8DLiqy13vAXwKeAhwNbAXeD5wAXAv7h7y97PA/YFXAVuBK1pifLlCql8CPgT8JnAmcHGF+1Z1NLAZ+BpwSTn/q8BnI+JE4BPAD4GPUDxvZwBXR8TDM3N7m3ivBp5dbv8J4EnAWcDJEXFCZt4xtWFELKd4zo4G/rXc/j7Ac4FPRMQ5mfk3bf7Haophhq8G/rq8vyRJkiSpDyyMSpIkSVIDyh6Y092amZfUDLmeorj3ysx8T8v/eQ7dC6MPoSh0Pisz/6u8z5uB/wBeHRFvzcyfZeZnI+JWisLol+sOBVz6I+CFwJ9ExEcyc08Psbp5KvD6zHzL1IKI+GPgfIqC6eUUz9e+ct01wAcoCqCvbhPvOcAJmfmllnjvAM6lKCS/vGXb9wPLgRdl5mUt29+fomD6roi4MjO/N+1/nAaclpmfqPF4JUmSJEldOJSuJEmSJDXjjW3+fqtOoIhYCjyd4vqdF7auy8yrgX+eIcTvTRVFy/vcDmwE7gc8ok5O3WTmDuCdwJHA7/c7fotbKQqWrd5fTg8F/mCqKFr6MEWP2V/sEO+DrUXR0puAO4HfiIhDASLiOIqi7Edbi6IAmfkDitf6XsAL2vyPjRZFJUmSJGlu2GNUkiRJkhqQmTHzVrP2i+V007RC35R/A57Z4b53ZuYtbZbvKKeLesytk7dR9LD8w4j4mzY9J/vhy5l517Rl3ymn/5GZP2pdkZl3RcT3gKM6xPuX6Qsy886I+DJFIfRRFMMKn1iuvl+HnsGHl9NHtVm3pcP/liRJkiT1yMKoJEmSJB387ldOOxUXuxUdf9Bh+d5yekidhGaSmT8sh+z9K4pel6+Yg39zZ5v/uzci2q4r7aW47mo7nZ7H75bTqddhSTl9VvnXyS90iSVJkiRJ6jOH0pUkSZKkg98Py+mDOqzvtLxpF1Jcy/T/i4h2vSenJJ1/2Hv/fifVRafn8cHl9M5p01dlZnT5O6tNrOxrxpIkSZKkn7MwKkmSJEkHv6nrXp4YEe3O857Up/8zNSxtX3qRZuZe4DVlvD/rsuluYOn0hRFxCJ2vBzoXntomh/uVOfw38LVy8efL6ZPnJy1JkiRJ0mxYGJUkSZKkg1xm7gA+CxwDnNO6LiJOpfP1RavaTdGjcVmf4pGZVwD/CjwXOKnDZluAZRHx7GnLXw8s71cus/CbEfFL05a9iWII3Usz8ycAmTlO8Zh+LSJe1i5QRPzPiHjgXCYrSZIkSdqf1xiVJEmSpOGwBrgOeHdEnAZ8BXgo8AJgI7AK2NfLP8jM/4yIzcCTI+LvKIbBvQu4MjO/0kPotRS9LI/psH4dcAqwMSI+AkwCTwRWUBSET+7hf1dxNXBdRFwO3EbRE/dJwK3Aa6dt+xvAtcD7IuL3gM0U13M9Cngc8FjgROD2+UhckiRJkmSPUUmSJEkaCpn5VYpC28cohnA9Fzga+FXg38rNftjuvhX9JvBPwKnAG4E/AY7vJWBmbgE+0mX9p4HnAzcBZwBnUhQjVwLbevnfFb0DeCXF0LnnAo8ELgGemJn7FTgzcyfweOCPKIrHLwZ+j6Kgu52iZ+8N85O2JEmSJAkgMrPpHCRJkiRJc6js3fkbwCMz8+am85EkSZIkqQn2GJUkSZKkIRARCyLiwW2WPwP4deCrFkUlSZIkSaPMa4xKkiRJ0nC4J7AjIj4DfB3YCzwGeBbwU4prkEqSJEmSNLIcSleSJEmShkBEHAK8E3g6cBRwGPB94HPABZn5peaykyRJkiSpeRZGJUmSJEmSJEmSJA09rzEqSZIkSZIkSZIkaehZGJUkSZIkSZIkSZI09CyMSpIkSZIkSZIkSRp6FkYlSZIkSZIkSZIkDT0Lo5IkSZIkSZIkSZKG3v8PKId6PSxJ8+MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1842.38x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(y=\"PayloadMass\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Flight Number\",fontsize=20)\n",
    "plt.ylabel(\"Pay load Mass (kg)\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that different launch sites have different success rates.  <code>CCAFS LC-40</code>, has a success rate of 60 %, while  <code>KSC LC-39A</code> and <code>VAFB SLC 4E</code> has a success rate of 77%.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's drill down to each site visualize its detailed launch records.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK 1: Visualize the relationship between Flight Number and Launch Site\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the function <code>catplot</code> to plot <code>FlightNumber</code> vs <code>LaunchSite</code>, set the  parameter <code>x</code>  parameter to <code>FlightNumber</code>,set the  <code>y</code> to <code>Launch Site</code> and set the parameter <code>hue</code> to <code>'class'</code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1842.38x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be Flight Number and y axis to be the launch site, and hue to be the class value\n",
    "sns.catplot(y=\"LaunchSite\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Flight Number\",fontsize=20)\n",
    "plt.ylabel(\"Launch Site\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now try to explain the patterns you found in the Flight Number vs. Launch Site scatter point plots.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that, for each site, the success rate is increasing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK 2: Visualize the relationship between Payload and Launch Site\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also want to observe if there is any relationship between launch sites and their payload mass.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1842.38x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be Pay Load Mass (kg) and y axis to be the launch site, and hue to be the class value\n",
    "sns.catplot(y=\"LaunchSite\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Pay load Mass (kg)\",fontsize=20)\n",
    "plt.ylabel(\"Launch Site\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now if you observe Payload Vs. Launch Site scatter point chart you will find for the VAFB-SLC  launchsite there are no  rockets  launched for  heavypayload mass(greater than 10000).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  3: Visualize the relationship between success rate of each orbit type\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we want to visually check if there are any relationship between success rate and orbit type.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a `bar chart` for the sucess rate of each orbit\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sucess Rate of each Orbit')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# HINT use groupby method on Orbit column and get the mean of Class column\n",
    "pl = df.groupby('Orbit')['Class'].mean()\n",
    "ax = pl.plot(kind='bar')\n",
    "ax.set_xlabel(\"Orbit\")\n",
    "ax.set_ylabel(\"Sucess Rate of each Orbit\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analyze the ploted bar chart try to find which orbits have high sucess rate.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We note that ES-L1, GEO, HEO, SSO have the best success rate.​"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  4: Visualize the relationship between FlightNumber and Orbit type\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each orbit, we want to see if there is any relationship between FlightNumber and Orbit type.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1842.38x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be FlightNumber and y axis to be the Orbit, and hue to be the class value\n",
    "sns.catplot(y=\"Orbit\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Flight Number\",fontsize=20)\n",
    "plt.ylabel(\"Orbit\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should see that in the LEO orbit the Success appears related to the number of flights; on the other hand, there seems to be no relationship between flight number when in GTO orbit.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  5: Visualize the relationship between Payload and Orbit type\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, we can plot the Payload vs. Orbit scatter point charts to reveal the relationship between Payload and Orbit type\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1842.38x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be Payload and y axis to be the Orbit, and hue to be the class value\n",
    "sns.catplot(y=\"Orbit\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"PayloadMass\",fontsize=20)\n",
    "plt.ylabel(\"Orbit\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With heavy payloads the successful landing or positive landing rate are more for Polar,LEO and ISS.\n",
    "\n",
    "However for GTO we cannot distinguish this well as both positive landing rate and negative landing(unsuccessful mission) are both there here.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  6: Visualize the launch success yearly trend\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can plot a line chart with x axis to be <code>Year</code> and y axis to be average success rate, to get the average launch success trend.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function will help you get the year from the date:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A function to Extract years from the date \n",
    "year=[]\n",
    "def Extract_year(date):\n",
    "    for i in df[\"Date\"]:\n",
    "        year.append(i.split(\"-\")[0])\n",
    "    return year\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vL6/ChJXP3fnDO0tp1agu1wzvGnWcpJHIQlDSNzOK73EaBcwFOgD9gUfNrMm3buQ+0d0Hu/vg1q2T55t/ItXVlt37uen5OWS2bMhd5x9T5vKj+rbjokEZPD5lBbPWbK2ChIkxZdkmvli1lf88ozsN6+qgyYMSWQhygU4x1zMIPvnHuhp41QMrgFVA7wRmEkl5RUXOLS/NY/veAzxy2YC43xB/c24f2jetz89fnEfe/uo3gltU5Nz7ThadWzRgzJDOUcdJKoksBDOAHmbWNdwBPAZ4o9gya4EzAMysLdALyE5gJpGU9+TH2UzJ2sSvzj6avh2axn27xvVq88Al/Vi7dQ93T1qSwISJ8a/561myYSe3nNmTOuk6cj5Wwp4Ndy8AbgAmA0uAF919kZmNM7Nx4WJ3Ad8xswXA+8Dt7r45UZlEUt2ctdu4950sRvdtV6EzcA3r1pJrT+rGPz9fywdLv0xAwsTILyji/neXcXT7Jpx7XIeo4ySdhA6SufskYFKxaeNjfl8PnJnIDCIS2LH3ADc+N4e2Terxh4uOq3CDtVvO7Mm0ZZv4xcsLePfm5tWiMd3zM9ayduse/nr1kEjbTCcrbR+JpAB3545X5rNxxz4euWzAEbUzrpuexgOX9GfH3nz+69Xkb0yXt7+Ah99fwdCuLTg1idpMJxMVApEU8Ozna3l74UZuHdWrUrps9unQhJ+P7MU7izby6uzkbkz3109WsXn3fm4fnZxtppOBCoFIDbd4/U7uenMxp/RszdiTulXaesee3I0hmc357RuLyN22p9LWW5m25eUzYWo2I/u0ZVCX1G0zXRYVApEaLG9/ATc8N5tm9YMjfipzfDytlvHAJf0pcufWl5KzMd3jU1aQpzbTZVIhEKnBfv36IlZtzuPBMf1p2ahupa+/U4sG/ObcvkzP3spfPllV6es/Euu27+Vvn63hwoEZ9Gyb2m2my6JCIFJDvTIrl1dm53Lj6T34zlGtEnY/Fw/OYMTRbbl3chZZG5OnMd1D/14GDjePVJvpsqgQiNRAKzft5s7XFzKsawtuOqNHQu/LzLjn+8fSuG46P0uSxnTLv9zFy7NyufwEtZmOhwqBSA2z70Ah1/9jNvVqp/HQmAGkVcFx860aBY3plmzYyYP/Xpbw+yvLfe9m0UBtpuOmQiBSw9z91hKWbtzF/Rf3o13TelV2v2f2bcclgzMYP3UlM1dH15huztptTF4UtJmuDl92SwYqBCI1yNsLNvD36Wu49qSunNa7TZXf/53n9KFDs6Ax3e4IGtN902a6Dj9Wm+m4qRCI1BA5W/fwi1fm069TM24bFU0T36AxXX9ytu3h7rcWV/n9T1u+menZW7nx9B5qM10OKgQiNcCBwiJufG4OAI9eOiDS7ppDu7Zg7MndeO6LHN5fUnWN6YqKnD+8vZROLepz6VC1mS4PFQKRGuC+yVnMzdnOH75/HJ1aNIg6Dj8f2ZPe7Rpz+yvz2bJ7f5Xc55sLNrB4w05uGdlLbabLSc+WSDX3YdZXTJiWzQ+Hdea7x7aPOg4QNKZ7cEx/du4t4JdV0JguaDOdRe92jfleP7WZLi8VApFq7Mud+7jlxXn0bteYO8/pE3WcQ/Ru14RbzuzJu4u/5OVZuQm9rxdm5rBmyx5+MbqX2kxXgAqBSDVVWOTc9Pwc9uYX8uhlA6lXOy3qSN/yk5O6MbRrC/7nX4vJ2ZqYxnR78gt4+P3lDM1swWm9qv5IqZpAhUCkmnrkg+VMz97KXecfQ/c2jaKOU6K0Wsb9F/cD4JaX5lGYgMZ0f/1kNZt27ef2s3qpzXQFqRCIVEPTs7fw8PvLuXBARy4alBF1nMPq1KIBvz63D1+s2spTH1fuKcm35eUzfspKRhzdlkFdWlTqulOJCoFINbNl935uen4OmS0bctf5x0QdJy4XD8rgzD5tuW/yMpZu3Flp631i6kp2q830EVMhEKlGioqcW16ax7Y9B3jksgHV5ktTZsbvLzyWJvXTufmFeewvKDzidW7YsZenP13NhQMy6NVObaaPhAqBSDXy5MfZTMnaxJ1nH03fDk2jjlMuLRvV5Z4Lj2PJhp386b3lR7y+B99bHraZTmx31VSgQiBSTcxZu41738nirGPa8aPju0Qdp0JG9GnLmCGdmDBtJTOOoDHdiq9289KsHH50fBcymkf/BbrqToVApBrYsfcANz43h7ZN6nHP94+r1kfH/OqcPmQ0r8/PX5xb4cZ0903Oon7tNK4/7ahKTpeaVAhEkpy788tX57Nxxz4euWwATevXjjrSEWlUN50/XdKfddv2cte/yt+Ybm7Odt5ZtJFrT+6WkNNvpiIVApEk94/P1zJpwUZuG9WLgZ2bRx2nUgzObMF1pxzFCzNzeG9x/I3p3IPGci0b1uEnJ3VLYMLUokIgksSWbNjJ/3tzMaf2as21NeyN7+YRPTm6fRPueGU+m+NsTPfR8s18lr2FG07vTqNqcsRUdaBCIJKk8vYXcP0/Z9Osfm3uv7hfjeuhUye9Fg/+oD+79sXXmK6oyLl38lIymtfnsmFqM12ZVAhEktSvX1/Eqs15PDimf40dC+/VrjG3jerFe4u/5KUyGtO9tWADC9ft5Ocje1I3Pfn6KlVnKgQiSejV2bm8MjuXG0/vwXeOahV1nIT68fCuDOvagv95Y1GpjekOFH7TZvq8/h2rOGHNp0IgkmRWbtrNr15byLCuLbjpjJr/ZalatYz7L+mHmXHLiyU3pnthRg6rt+zhtlG9SKthQ2TJQIVAJInsO1DIDf+cQ73aaTw0ZkDKvOllNG/Ab7/Xly9Wb+XJjw5tTLc3v5CH3l/O4C7NOb232kwnggqBSBK5+60lLNmwk/sv7ke7pvWijlOlvj+wI6P7tuP+d5exZMM3jen++umqsM1072r9RbpkpkIgkiQ+WbGZv09fw7UndeW0FPzka2b874XH0qR+bW5+YS77CwrZviefJ6as5IzebRiSqTbTiaIDcUWSgLtz7ztL6disPremcEvlFg3rcO9Fx3LN0zN54L1lAOzeX8Bto1P3OakKKgQiSWDyoo3My93BHy86LuUPjTy9d1suHdqZidOyqV2rFhf070jvdk2ijlWjJXRoyMxGm1mWma0wsztKWeZUM5trZovMbGoi84gko4LCIu57dxnd2zTiwoHJfbaxqvKrs4+mc4sGOM7NI3tGHafGS9gWgZmlAY8BI4FcYIaZveHui2OWaQY8Dox297VmlnoDo5LyXp2zjhVf7Wb8jwamzFFCZWlYN51/Xns8G3fspVMLtZlOtEQODQ0FVrh7NoCZPQ+cB8S2G7wMeNXd1wK4+1cJzCOSdPYXFPLQv5fTL6Mpo/q2izpOUunYrD4dm9WPOkZKSOTQUEcgJ+Z6bjgtVk+guZlNMbNZZnZFSSsys7FmNtPMZm7atClBcUWq3j+mr2Xd9r3cNkqHRkp0ElkISnpVF//KYDowCDgbGAXcaWbfGhB094nuPtjdB7du3bryk4pEYPf+Ah77cAUndm/J8B41u42EJLcyC4EFfmRmvw6vdzazoXGsOxfoFHM9A1hfwjLvuHueu28GpgH94osuUr395eNVbMnL57ZRvaOOIikuni2Cx4ETgEvD67sIdgKXZQbQw8y6mlkdYAzwRrFlXgdOMrN0M2sADAOWxJVcpBrblpfPn6dlM6pvW/p3ahZ1HElx8ewsHubuA81sDoC7bwvf2A/L3QvM7AZgMpAG/MXdF5nZuHD+eHdfYmbvAPOBIuBJd19Y4UcjUk08MXUlefkF3Hqmvigl0YunEBwIDwV1ADNrTfCmXSZ3nwRMKjZtfLHrfwT+GFdakRpgw469PP3pai4YkEGPto2jjiMS19DQw8D/AW3M7G7gY+D3CU0lUoM9/P5y3J2fjaj5Laaleihzi8Dd/2Fms4AzCI4EOt/dNY4vUgHZm3bz4sxcLj++i74oJUmjzEJgZn9398uBpSVME5FyeOC9ZdRNr8X1p3WPOorI1+IZGuobeyXcXzAoMXFEaq6F63bw5vwN/Hh4V1o3rpnnIJbqqdRCYGa/NLNdwHFmttPMdoXXvyI47FNEyuG+d7No1qA2157cLeooIocotRC4++/dvTHwR3dv4u6Nw0tLd/9lFWYUqfY+z97ClKxN/McpR9GkXu2o44gcIp6dxb80s+ZAD6BezPRpiQwmUlO4O/dOzqJtk7pc+Z3MqOOIfEs8O4t/AtxE0CJiLnA88BlwekKTidQQHyz9illrtnH3BcdQr3Zqn3RGklM8O4tvAoYAa9z9NGAAoBagInEoKnL+ODmLzJYNuGRwp7JvIBKBeArBPnffB2Bmdd19KaDvxYvE4V/z17N04y5uHtmT2mkJPSGgSIXF02IiNzyT2GvAe2a2jW93ERWRYg4UFvHAe8s4un0Tzj2uQ9RxREoVz87iC8Jff2tmHwJNgbcTmkqkBnhhRg5rtuzhr1cNoZZOQSlJrFzbqu4+FdhHsUZyInKovfmFPPz+coZkNufUXjqZkiS3w32h7HQzW2Zmu83sWTPrY2YzCRrOPVF1EUWqn6c/Xc1Xu/bzi9E6BaUkv8NtEdwPjAVaAi8D04G/u/sgd3+1KsKJVEc79h5g/NSVnNarNUMyW0QdR6RMh9tH4O4+Jfz9NTPb5O4PVUEmkWpt4rSV7Nh7gFtH6eA6qR4OVwiamdmFMdct9rq2CkS+7atd+/jLx6s5t18H+nZoGnUckbgcrhBMBc4t5boDKgQixTz2wQoOFBZxy8ieUUcRiVuphcDdr67KICLVXc7WPfzzi7VcMqQTma0aRh1HJG76qqNIJfnTe8uoZcZ/nq5TUEr1okIgUgmyNu7i/+au46rvZNKuab2ybyCSRFQIRCrBfe9m0ahOOuNOOSrqKCLlVmYhMLOLzaxx+PuvzOxVMxuY+Ggi1cPstdt4b/GXjD25G80b1ok6jki5xbNFcKe77zKz4cAo4G/om8UiQHDSmT++k0WrRnW4ZnjXqOOIVEg8haAw/Hk28IS7vw7oY48I8PGKzXyWvYXrT+tOw7rxNPMVST7xFIJ1ZjYBuASYZGZ147ydSI3m7tz7ThYdm9XnsmGdo44jUmHxvKFfAkwGRrv7dqAFcFsiQ4lUB28v3MiCdTu4eWRP6qbrFJRSfcWzLdseeMvd95vZqcBxwDOJDCWS7AoKi7jv3Sx6tGnEBQM6Rh1H5IjEs0XwClBoZt2Bp4CuwD8Tmkokyb06ex3Zm/K45cxepOmkM1LNxVMIity9ALgQeNDdbybYShBJSfsOFPLgv5fRr1MzRvVtG3UckSMWTyE4YGaXAlcAb4bTaicukkhy+8fna1m/Yx+/GNVLJ52RGiGeQnA1cAJwt7uvMrOuwLOJjSWSnHbvL+CxD1dwYveWnNi9VdRxRCpFPCevX2xmtwOdw+urgHsSHUwkGT35UTZb8/K5bVTvqKOIVJp4WkycC8wF3gmv9zezNxKcSyTpbM3L58mPVjG6bzv6d2oWdRyRShPP0NBvgaHAdgB3n0tw5JBISnn8wxXsyS/g1lE66YzULPEUggJ331FsmsezcjMbbWZZZrbCzO44zHJDzKzQzC6KZ70iVW399r08M30NFw7MoHubxlHHEalU8RSChWZ2GZBmZj3M7BHg07JuZGZpwGPAWUAf4FIz61PKcn8g+PaySFJ6+P3l4PCzETrpjNQ88RSCG4G+wH6CL5LtAH4Wx+2GAivcPdvd84HngfNKWf8rwFfxBBapais37ebFmTlcNqwzGc0bRB1HpNLFc9TQHuC/w0t5dARyYq7nAsNiFzCzjsAFwOnAkNJWZGZjgbEAnTuruZdUrQfeXUa92mnccHr3qKOIJEQ8Rw29Z2bNYq43N7N4hnFK+qZN8X0LDwK3u3thCct+cyP3ie4+2N0Ht27dOo67FqkcC9ft4K0FG/jx8K60alQ36jgiCRFP07lWYddRANx9m5m1ieN2uUCnmOsZwPpiywwGng+/ndkK+K6ZFbj7a3GsXyTh7p2cRbMGtbn25G5RRxFJmLh6DZnZ1+MxZtaF+I4amgH0MLOuZlYHGAMc8v0Dd+/q7pnungm8DPxURUCSxfTsLUxbtomfnnoUTeqpq4rUXPFsEfw38LGZTQ2vn0w4Xn847l5gZjcQHA2UBvzF3ReZ2bhw/vgKZhZJuOCkM0tp16QeV5yQGXUckYSKZ2fxO+HJ6o8nGPe/2d03x7Nyd58ETCo2rcQC4O5XxbNOkarw/pKvmL12O/97wbHUq62TzkjNFs/O4guAA+7+prv/Cygws/MTnkwkIoVFzh8nZ5HZsgEXD86IOo5IwsWzj+A3sd8sDncc/yZhiUQi9sa8dWR9uYufn9mL2mk6PbfUfPG8yktaJp59CyLVTn5BEQ+8t4w+7ZtwzrE6/5KkhngKwUwze8DMjjKzbmb2J2BWooOJROGFGWvJ2bqX20b1opZOQSkpIt4WE/nAC8BLwD7g+kSGEonCnvwCHv5gBUMzW3BqL31xUVJHPEcN5QGldg4VqSme/nQ1m3bt54kfDtQpKCWllFkIzOxDSvgCmbufnpBEIhHYsecA46es5PTebRic2SLqOCJVKp6dvrfG/F4P+D5QkJg4kmoKi5x9Bw7baqpKPD51BTv3FXDrmb2ijiJS5eIZGiq+Y/iTmG8Zi1RYYZFz9sMfsXTjrqijAPC9fh3o06FJ1DFEqlw8Q0Ox28m1gEFAu4QlkpTx7qKNLN24iytO6EJG8/qRZqllxvkDOkaaQSQq8QwNzSLYR2AEQ0KrgB8nMpTUfO7O+Kkr6dKyAb85ty9pOlRTJDLxDA3pRPVS6T5ftZV5uTv43fnHqAiIRKzU7xGEJ5RvF3P9CjN73cweLjZcJFJu46eupFWjOlw0SL18RKJ2uC+UTSD4IhlmdjJwD/AMwTmLJyY+mtRUSzbsZErWJq76TqY6e4okgcMNDaW5+9bw9x8AE939FeAVM5ub8GRSY/15WjYN6qTxo+O7RB1FRDj8FkGamR0sFGcAH8TMU9M5qZB12/fyxrz1jBnSmWYN6kQdR0Q4/Bv6c8BUM9sM7AU+AjCz7gTDQyLl9tRHqwD48Uk6BkEkWZRaCNz9bjN7H2gPvOvuB9tM1CJoRCdSLtv35PP8jLV8r18HOjaL9nsDIvKNww7xuPv0EqYtS1wcqcmenb6GPfmFjD2lW9RRRCSGTr8kVWLfgUKe/nQ1p/ZqTe92auMgkkxUCKRKvDwrl8278xl3ylFRRxGRYlQIJOEKi5w/f5RNv07NGNZV30UUSTYqBJJwkxdtZM2WPYw7uZtO+CKShFQIJKHcnQlTV9K1VUPO7KumtSLJSIVAEuqz7C3My93BtSd1U3M5kSSlQiAJNWFqNq0a1eHCger1L5KsVAgkYZZs2MnUZZu4+sSuai4nksRUCCRhJk7LpmGdNH40TM3lRJKZCoEkRO62Pbwxbz2XDu1M0wa1o44jIoehQiAJ8dTHqzDgmuFqLieS7FQIpNJty8vn+S9y+F7/DnRQczmRpKdCIJXu2elr2HugkOtOVjsJkepAhUAq1cHmcqf3bkOvdo2jjiMicVAhkEr10qxctuTlc93JajUtUl2oEEilKSxy/jwtm/6dmjFUzeVEqo2EFgIzG21mWWa2wszuKGH+D81sfnj51Mz6JTKPJNY7Czeydusexp2i5nIi1UnCCoGZpQGPAWcBfYBLzaxPscVWAae4+3HAXcDEROWRxHJ3xk9dSbdWDRnZR83lRKqTRG4RDAVWuHu2u+cDzwPnxS7g7p+6+7bw6nQgI4F5JIE+W7mFBet2cO3Jai4nUt0kshB0BHJirueG00rzY+DtkmaY2Vgzm2lmMzdt2lSJEaWyjJ+WTatGdblggJrLiVQ3iSwEJX0s9BIXNDuNoBDcXtJ8d5/o7oPdfXDr1q0rMaJUhsXrdzJt2SauPjFTzeVEqqH0BK47F+gUcz0DWF98ITM7DngSOMvdtyQwjyTIhGkrg+Zyx6u5nEh1lMgtghlADzPramZ1gDHAG7ELmFln4FXgcndflsAskiA5W/fw5vwNXDasM03rq7mcSHWUsC0Cdy8wsxuAyUAa8Bd3X2Rm48L544FfAy2Bx8PDDQvcfXCiMknlU3M5keovkUNDuPskYFKxaeNjfv8J8JNEZpDE2ZaXzwszcjivf0faN1VzOZHqSt8slgp75rOwudwpaichUp2pEEiF7M0v5G+freaM3m3o2VbN5USqMxUCqZCXZ+WwNS+f605Rq2mR6k6FQMqtoLCIP3+0igGdmzEks3nUcUTkCKkQSLm9/XVzuaPUXE6kBlAhkHJxdyZMC5vLHd026jgiUglUCKRcPl25hYXrdjL25G7UUnM5kRpBhUDKZfzUlbRuXJfz1VxOpMZQIZC4LVy3g4+Wb+aaE7uquZxIDaJCIHGbOC2bRnXTuWxY56ijiEglUiGQuORs3cNbC9RcTqQmUiGQuDz18SpqGVxzoprLidQ0KgRSpq15+Tw/Yy3n9+9Iu6b1oo4jIpVMhUDK9Mxnq9l3oIixJ6u5nEhNpEIgh7U3v5C/fbqaEUe3oYeay4nUSCoEclgvzcph254Dai4nUoOpEEipCgqLmDgtm0FdmjMks0XUcUQkQVQIpFSTFm4kd9tertO+AZEaTYVASuTuTJi6km6tGzJCzeVEajQVAinRJyu2sGj9Tq5TczmRGk+FQEo0fupK2qi5nEhKUCGQb1m4bgcfr9jMNcO7UjddzeVEajoVAvmWCWouJ5JSVAjkEDlb9/DW/PX8cFhnmtRTczmRVKBCIIf480fZpNUyrhmu5nIiqUKFQL62Zfd+XpyZwwUDOtK2iZrLiaQKFQL52jOfrVFzOZEUpEIgAOzJL+CZz1Yz4ui2dG+j5nIiqUSFQAB4cUbQXO4/TtXWgEiqUSEQCgqL+PNHqxjcpTmDuqi5nEiqUSEQ3lqwgXXb96rVtEiKUiFIcUFzuWy6t2nEGb3bRB1HRCKgQpDiPlq+mcUbdjJWzeVEUpYKQYqbMG0lbZvU5bz+HaKOIiIRUSFIYQtyd/DJii1cc6Kay4mksoQWAjMbbWZZZrbCzO4oYb6Z2cPh/PlmNjCReeRQE6atpHHddC5VczmRlJawQmBmacBjwFlAH+BSM+tTbLGzgB7hZSzwRKLyyKHWbMlj0oIN/PD4LmouJ5Li0hO47qHACnfPBjCz54HzgMUxy5wHPOPuDkw3s2Zm1t7dN1R2mKnLNvG7NxeXvWCK2LH3AOm1anH1iZlRRxGRiCWyEHQEcmKu5wLD4limI3BIITCzsQRbDHTuXLFhjEZ10+nRtlGFbltTndi9lZrLiUhCC0FJxyJ6BZbB3ScCEwEGDx78rfnxGNSlOYO6DKrITUVEarRE7izOBTrFXM8A1ldgGRERSaBEFoIZQA8z62pmdYAxwBvFlnkDuCI8euh4YEci9g+IiEjpEjY05O4FZnYDMBlIA/7i7ovMbFw4fzwwCfgusALYA1ydqDwiIlKyRO4jwN0nEbzZx04bH/O7A9cnMoOIiByevlksIpLiVAhERFKcCoGISIpTIRARSXEW7K+tPsxsE7CmgjdvBWyuxDgVpRyHUo5DJUOOZMgAylHckeTo4u6tS5pR7QrBkTCzme4+WDmUQzmSP4NyVF0ODQ2JiKQ4FQIRkRSXaoVgYtQBQspxKOU4VDLkSIYMoBzFJSRHSu0jEBGRb0u1LQIRESlGhUBEJMVV60JgZp3M7EMzW2Jmi8zspnB6CzN7z8yWhz+bh9NbhsvvNrNHi61rkJktMLMVZvawmZV00pyE5jCzBmb2lpktDddzT4TPxztmNi9cz/jwHNRVniNmnW+Y2cIIn48pZpZlZnPDS5uIctQxs4lmtix8nXy/KjOYWeOY52CumW02swcjei4uteB/dn74em0VUY4fhBkWmdm98WaoYI6RZjYrfNyzzOz0mHVV+D0Md6+2F6A9MDD8vTGwDOgD3AvcEU6/A/hD+HtDYDgwDni02Lq+AE4gOGva28BZVZ0DaACcFv5eB/goihzhvCbhTwNeAcZEkSOcfyHwT2BhhK+PKcDgJHid/g/wu/D3WkCrKP4mMeudBZwcwf9KOvDVwccf3v63EeRoCawFWofX/wackcAcA4AO4e/HAOti1lXx97CKvLCT9QK8DowEsoD2MU90VrHlrir2x2wPLI25fikwoapzlLCeh4Bro8wB1Ab+BfwgihxAI+Dj8J+jXIWgknNMoYKFoJJz5AANo8wQM69HmMeqOkf4utwEdCF44xsPjI0gxxDg3zHXLwceT3SOcLoBW4C6HOF7WLUeGoplZpkE1fJzoK2HZzoLf5a1Gd+R4LSZB+WG06o6R+x6mgHnAu9HlcPMJhN86toFvBxRjruA+wlOXFRhlfR3+Ws4HHJnuTa7KylH+JoAuMvMZpvZS2bWtiozFHMp8IKH7zxVmcPdDwD/ASwgOL1tH+Cpqs5BcFKt3maWaWbpwPkcevrdROb4PjDH3fdzhO9hNaIQmFkjguGLn7n7zoqsooRp5X5xV0KOg+tJB54DHnb37KhyuPsogk8adYHTy1i80nOYWX+gu7v/X3lvW5k5Qj9092OBk8LL5RHkSCc4r/cn7j4Q+Ay4r4ozxBpD8Dott0p4bdQmKAQDgA7AfOCXVZ3D3beFOV4gGMpdDRQkOoeZ9QX+AFx3cFJJ8eK9/2pfCMIXxCvAP9z91XDyl2bWPpzfnuBT7eHkEvyDHZRB8CmjqnMcNBFY7u4PlidDAnLg7vsIzi19XgQ5TgAGmdlqguGhnmY2JYIcuPu68Ocugv0VQyPIsYVgy+hgYXwJGFjFGQ6uqx+Q7u6z4r3/Ss7RH8DdV4ZbJC8C34kgB+7+L3cf5u4nEAzpLE9kDjPLIHgNXOHuK8PJR/QeVq0LQbh5/hSwxN0fiJn1BnBl+PuVBONupQo3vXaZ2fHhOq8o6zaJyBGu63dAU+Bn8d5/Zecws0YxL8J0gvNKL63qHO7+hLt3cPdMgh11y9z91KrOYWbpB49ICf9pzwHiPoKpEp8PJ9hfc2o46QxgcVVmiHEpFdgaqMQc64A+Znawm+ZIYEkEObDwCLLwyJ6fAk8mKkc4PPgW8Et3/+Tgwkf6HnZEO5yivhC8OTjBZuHc8PJdgj357xNU5veBFjG3WQ1sBXYTVNE+4fTBBP/cK4FHKccOsMrKQVDFneAFfXA9P4kgR1tgRrieRcAjBJ/+qvzvEjM/k/IfNVRZz0dDgqNjDj4fDwFpEb1OuwDTwnW9D3SO4m8CZAO9I/6fHUfwvzKfoEC2jCjHcwQFeTHlOLquIjmAXwF5McvOBdoc6XuYWkyIiKS4aj00JCIiR06FQEQkxakQiIikOBUCEZEUp0IgIpLiVAhEymCBj83srJhpl5jZO1HmEqksOnxUJA5mdgzBN3kHAGkEx2+P9m++2VmedaW5e2HlJhSpOBUCkThZ0Gs+j+ALZnkEX+46lqAH0G/d/fWwcdjfw2UAbnD3T83sVOA3wAagv7v3qdr0IqVTIRCJk5k1BGYD+cCbwCJ3fzb82v8XBFsLDhS5+z4z6wE85+6Dw0LwFnCMu6+KIr9IadKjDiBSXbh7npm9QNBi4BLgXDO7NZxdD+hM0Ojr0bBzaiHQM2YVX6gISDJSIRApn6LwYsD33T0rdqaZ/Rb4EuhHcDDGvpjZeVWUUaRcdNSQSMVMBm48eIIaMxsQTm8KbHD3IoJzFsR9rmeRqKgQiFTMXQSnS5xvZgvD6wCPA1ea2XSCYSFtBUjS085iEZEUpy0CEZEUp0IgIpLiVAhERFKcCoGISIpTIRARSXEqBCIiKU6FQEQkxf1/KZx3a3Hg7g4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\n",
    "df['Year'] = Extract_year(df[\"Date\"])\n",
    "df_groupby_year = df.groupby(\"Year\",as_index=False)[\"Class\"].mean()\n",
    "sns.lineplot(data = df_groupby_year, x=\"Year\", y=\"Class\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.title('Space X Rocket Success Rate')\n",
    "plt.ylabel(\"Success Rate\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you can observe that the sucess rate since 2013 kept increasing till 2020\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features Engineering\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By now, you should obtain some preliminary insights about how each important variable would affect the success rate, we will select the features that will be used in success prediction in the future module.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>FlightNumber</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Orbit</th>\n",
       "      <th>LaunchSite</th>\n",
       "      <th>Flights</th>\n",
       "      <th>GridFins</th>\n",
       "      <th>Reused</th>\n",
       "      <th>Legs</th>\n",
       "      <th>LandingPad</th>\n",
       "      <th>Block</th>\n",
       "      <th>ReusedCount</th>\n",
       "      <th>Serial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>ISS</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>PO</td>\n",
       "      <td>VAFB SLC 4E</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3170.000000</td>\n",
       "      <td>GTO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FlightNumber  PayloadMass Orbit    LaunchSite  Flights  GridFins  Reused  \\\n",
       "0             1  6104.959412   LEO  CCAFS SLC 40        1     False   False   \n",
       "1             2   525.000000   LEO  CCAFS SLC 40        1     False   False   \n",
       "2             3   677.000000   ISS  CCAFS SLC 40        1     False   False   \n",
       "3             4   500.000000    PO   VAFB SLC 4E        1     False   False   \n",
       "4             5  3170.000000   GTO  CCAFS SLC 40        1     False   False   \n",
       "\n",
       "    Legs LandingPad  Block  ReusedCount Serial  \n",
       "0  False        NaN    1.0            0  B0003  \n",
       "1  False        NaN    1.0            0  B0005  \n",
       "2  False        NaN    1.0            0  B0007  \n",
       "3  False        NaN    1.0            0  B1003  \n",
       "4  False        NaN    1.0            0  B1004  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = df[['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights', 'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount', 'Serial']]\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  7: Create dummy variables to categorical columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the function <code>get_dummies</code> and <code>features</code> dataframe to apply OneHotEncoder to the column <code>Orbits</code>, <code>LaunchSite</code>, <code>LandingPad</code>, and <code>Serial</code>. Assign the value to the variable <code>features_one_hot</code>, display the results using the method head. Your result dataframe must include all features including the encoded ones.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Flights</th>\n",
       "      <th>GridFins</th>\n",
       "      <th>Reused</th>\n",
       "      <th>Legs</th>\n",
       "      <th>Block</th>\n",
       "      <th>ReusedCount</th>\n",
       "      <th>ES-L1</th>\n",
       "      <th>GEO</th>\n",
       "      <th>...</th>\n",
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       "      <th>B1054</th>\n",
       "      <th>B1056</th>\n",
       "      <th>B1058</th>\n",
       "      <th>B1059</th>\n",
       "      <th>B1060</th>\n",
       "      <th>B1062</th>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   FlightNumber  PayloadMass  Flights  GridFins  Reused   Legs  Block  \\\n",
       "0             1  6104.959412        1     False   False  False    1.0   \n",
       "1             2   525.000000        1     False   False  False    1.0   \n",
       "2             3   677.000000        1     False   False  False    1.0   \n",
       "3             4   500.000000        1     False   False  False    1.0   \n",
       "4             5  3170.000000        1     False   False  False    1.0   \n",
       "\n",
       "   ReusedCount  ES-L1  GEO  ...  B1048  B1049  B1050  B1051  B1054  B1056  \\\n",
       "0            0      0    0  ...      0      0      0      0      0      0   \n",
       "1            0      0    0  ...      0      0      0      0      0      0   \n",
       "2            0      0    0  ...      0      0      0      0      0      0   \n",
       "3            0      0    0  ...      0      0      0      0      0      0   \n",
       "4            0      0    0  ...      0      0      0      0      0      0   \n",
       "\n",
       "   B1058  B1059  B1060  B1062  \n",
       "0      0      0      0      0  \n",
       "1      0      0      0      0  \n",
       "2      0      0      0      0  \n",
       "3      0      0      0      0  \n",
       "4      0      0      0      0  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HINT: Use get_dummies() function on the categorical columns\n",
    "features_one_hot = features\n",
    "\n",
    "features_one_hot = pd.concat([features_one_hot, \n",
    "                              pd.get_dummies(df['Orbit']),\n",
    "                             pd.get_dummies(df['LaunchSite']),\n",
    "                             pd.get_dummies(df['LandingPad']),\n",
    "                             pd.get_dummies(df['Serial'])], axis=1)\n",
    "\n",
    "features_one_hot.drop(['Orbit', 'LaunchSite', 'LandingPad', 'Serial'], axis = 1, inplace=True)\n",
    "\n",
    "features_one_hot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  8: Cast all numeric columns to `float64`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that our <code>features_one_hot</code> dataframe only contains numbers cast the entire dataframe to variable type <code>float64</code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "    FlightNumber   PayloadMass  Flights  GridFins  Reused  Legs  Block  \\\n",
       "0            1.0   6104.959412      1.0       0.0     0.0   0.0    1.0   \n",
       "1            2.0    525.000000      1.0       0.0     0.0   0.0    1.0   \n",
       "2            3.0    677.000000      1.0       0.0     0.0   0.0    1.0   \n",
       "3            4.0    500.000000      1.0       0.0     0.0   0.0    1.0   \n",
       "4            5.0   3170.000000      1.0       0.0     0.0   0.0    1.0   \n",
       "..           ...           ...      ...       ...     ...   ...    ...   \n",
       "85          86.0  15400.000000      2.0       1.0     1.0   1.0    5.0   \n",
       "86          87.0  15400.000000      3.0       1.0     1.0   1.0    5.0   \n",
       "87          88.0  15400.000000      6.0       1.0     1.0   1.0    5.0   \n",
       "88          89.0  15400.000000      3.0       1.0     1.0   1.0    5.0   \n",
       "89          90.0   3681.000000      1.0       1.0     0.0   1.0    5.0   \n",
       "\n",
       "    ReusedCount  ES-L1  GEO  ...  B1048  B1049  B1050  B1051  B1054  B1056  \\\n",
       "0           0.0    0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "1           0.0    0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "2           0.0    0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "3           0.0    0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "4           0.0    0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "..          ...    ...  ...  ...    ...    ...    ...    ...    ...    ...   \n",
       "85          2.0    0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "86          2.0    0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "87          5.0    0.0  0.0  ...    0.0    0.0    0.0    1.0    0.0    0.0   \n",
       "88          2.0    0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "89          0.0    0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "    B1058  B1059  B1060  B1062  \n",
       "0     0.0    0.0    0.0    0.0  \n",
       "1     0.0    0.0    0.0    0.0  \n",
       "2     0.0    0.0    0.0    0.0  \n",
       "3     0.0    0.0    0.0    0.0  \n",
       "4     0.0    0.0    0.0    0.0  \n",
       "..    ...    ...    ...    ...  \n",
       "85    0.0    0.0    1.0    0.0  \n",
       "86    1.0    0.0    0.0    0.0  \n",
       "87    0.0    0.0    0.0    0.0  \n",
       "88    0.0    0.0    1.0    0.0  \n",
       "89    0.0    0.0    0.0    1.0  \n",
       "\n",
       "[90 rows x 80 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_one_hot = features_one_hot.astype(float)\n",
    "features_one_hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_one_hot.to_csv('dataset_part_3.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now export it to a <b>CSV</b> for the next section,but to make the answers consistent, in the next lab we will provide data in a pre-selected date range.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<code>features_one_hot.to_csv('dataset_part\\_3.csv', index=False)</code>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.linkedin.com/in/nayefaboutayoun/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Nayef Abou Tayoun</a> is a Data Scientist at IBM and pursuing a Master of Management in Artificial intelligence degree at Queen's University.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Log\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Date (YYYY-MM-DD) | Version | Changed By    | Change Description      |\n",
    "| ----------------- | ------- | ------------- | ----------------------- |\n",
    "| 2021-10-12        | 1.1     | Lakshmi Holla | Modified markdown       |\n",
    "| 2020-09-20        | 1.0     | Joseph        | Modified Multiple Areas |\n",
    "| 2020-11-10        | 1.1     | Nayef         | updating the input data |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright © 2020 IBM Corporation. All rights reserved.\n"
   ]
  }
 ],
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