brt-h/jupyter-labs-eda-dataviz.ipynb

## jupyter-labs-eda-dataviz.ipynb
{
 "cells": [
  {
   "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": 1,
   "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": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0005</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1004</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</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": 2,
     "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",
    "# If you were unable to complete the previous lab correctly you can uncomment and load this 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": 3,
   "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": [
    "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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rmas4vf0k3HJmVfu6U+Hsp6DPtk1aZr2sWgUT7s/eO247ErqXN9lgypzFTJy5qEbfYw1ZvnxFrb0hVy4tfwzVraJd9pW3Hv3LX0VKUquU57K0hwDLU0p/ruxIKb0IEBGfA56sDDZLxx6sde0rwHXAKdQMN2vbiOznnFkaZylQ160/mwP3Vnu+0eup/7vAISmleaXz5wJXreXc3wLfAm6r1vdh4NpSPW9FxHhgT+DJ9TyvmlHey1mpddtzSC+uOG0Prnl6Et07t+cLBw6lZ1fDkcbaZWBPHvv2obw1YyGDenX1ru9KsyfCiqXQpwGzJAbuDT0HwZxJVX07f7y8MXoOhMEHwMRHq/p2OaX8WgDmvAPvvgAD9mj43nITHsi+NhsGO51Y9pu0cw4ZymPjpzN1XvZm+ayDhrJFz7Y7I0dSTnY6EZ76E8wqLdO36bYw7KP51pSzymCz0ujJc+o8b0Pru3Fn7jn3QMa8O49Nu3eiX48cPjRvgdpVROsJNlPK9iPv3hc6N3CJXTW5STMX8Y0bX+L5t2ez25ab8KsTdmFQ77Z9Y0GLsWJZtuLA7Ldg+2MbtGffd4/ZgT2H9OaVKXPZb+tNG7alS48B2UoHlbr1hXY5vA9/5xm48hhYtTxrj74Oznoc2pXxUe0b99Zsp5VZwJhHuHnDp2Fc6WPcThvD5+6GzT5Q78v79ejMpt07MWNBVRi5U/8e5dex91nwSLXZmnt9sfwxtGGklH0eMPtt2OYI2Kjhy91LEuQbbg4DnmvAMcgCzWvIwsKfR0SHlFLprwFOiojKOekXp5SujIjbgbcj4n7gDuCalNKqWmNeAlwXEV8C/gdcmVKqc1pKRGwEbJRSmrCen5GI+BAwJaX0Uq19uPoDT1VrTy71qQWYu2g5X7v+RR54bRr9e3bhZ8fv5Iw6bRCHbNeXQ7Zz+eSm1qFdBdtu1gT7psx5J9u3rEOBQ6uU4LZz4MV/Ze0hB8Ep10LHMj7kad8x22Pz8Yuz/d12Pgl2+GD5tZz0T3jyjzDjddj2aBjegHDzpWvh1rOzN+4VHeDEK8uvZdSVcMe5Ve2Jj8GH/1jWEFv16c7D3zyEURNn03+TLgzZtNv6L5KkptalJ5z1GIy7A6Ii+7C4nNf3VmivrXrzyOvTV7f3HJzfXuYRwbCGfDCrlm/uZPjXx2HaGOjQFUb+IlsqT7n7xo0v8cxbswB45q1ZfOPGl7j+C/vkXJUAuOEz2SxJgEd/nb0n2ba8HZoigiN23Iwjdtxs/SevzVE/g2tOzrbNaN8Fjv4/qMjhpvbnrqoKNgGmv5oFP0MPqf8YfXeso2+HxtdWrvdfqQo2AZbOgycvyZYvracO7Sq4+OThfOvG0UyZs5i9hvTiO0dvX34th34fNt8FJj8Lg/aB7Y4ufwxtGLd/GV64OnvcoRt85nYYMCLfmiQVWp7hZoNEREfgGOC8lNL8iHgaOJJsGVuotSwtQErpjIjYCTgc+AbZErGn1TrnnojYChgJHA28EBHD1lYGqzcNW2etXYHvleqra4za1hgzIs4EzgQYNGjQ+p5STeQ3973G/a9md/JNnr2Yr1zzAk9/9zBngEltxdzJcM0p8P5o6NwDjv0N7HRC3lU1zJsPVQWbAG89nLX3/Hx54/QcBMf+unG1dOnZ+H1y7/thFmxC9mHA/y4oP9x8+i812y/+G476edmzLjp3aMf+22y6/hMlaUPq2K1VLNcPwKJZ8PD/wdQxsPXhsO+Xy55Z//8+tjPfuXk0oybOZvignlz0UZen1wbw4EVZsAmwfBHc9S3Y8UPQZZN86xLPvz17ne026b2X4Kk/w6oVsOeZMHCP5q9h1ptVwSZAWgVPXVp2uNkkttwXvjYO3n85W9Umr/9u67oZqWP38sYY9tFs79CXroFol/3z3ergJimvLLW3HwFYvrjsYfbbelMe/dYhLF6+km6dGvGR9Q4fbNjNuNpw5kyCF/5Z1V6+EB7/XXYDtFTdiqWwcHo2y15ajzzDzTHA2j4pHgMctJZjI4EewMulmZBdgUVUhZt1Sim9XLrmauAtaoWbpXNmAf8G/h0Rd5Dt57nGDNLSHpsLI2KrlNKb63jaocAQoHLW5gDg+YjYk2ym5sBq5w4A1pgpmlK6jGzPUEaMGNGQrdLVAC9OnlujPXfxcibOXMj2/VxuSC3P1U+9zaUPjmdVgs8fuBWn7z8k75IabOy78/jWTS8x9t157Lf1pvz6xF3ou3EOy7jdf2EWbEJ2R+9/zs32DelU5pvNlqD6UrKr+95u/jqawqpVsLjWB1SLZpY/Tu396Np1aBl7h0hSW3f9p6uWL5/4KCxbUPZNMf16dObKz+65AYqTqpk5vmZ7xRKYO8VwswXYbctNVs/crGwX2uI58Oqd2Y0s2x2TrahSjrmT4YqjsyABYOxtcNajDduqojEq6liOOo+lYCt17AaD9s7v+QH2OgteuRkWlfaV3Oao8oPninbZ7Mgjf5o97pzTagH9d8++ppQ+Qq1oDyM+16ChKiqiccGmWqbli1ljTs+yRXWeqjZs7G3wn69mn/v02wlOvibb4khaizw3E3wA6BQRq6eORMQeEXEQWcC4b0QcW+3YyNLsy1OAM1JKg1NKg8nCwyNLsyTXEBHdI+Lgal3DgTU+1Y2IQyvHKC07OxSo4xPh1S4CLomIjUvXbFyaZblaSunllFLfarVOBnZLKb0P3A6cHBGdImIIsA3wzDqeT81o761qLl/VZ6NObLVpAUMNtXrPT5rND259hXfnLuH9eUu48I6xPDFhRm71zFiwlFemzGXlqobdi/Hla57nlSnzWJXg0Tdm8KPbxzRxhfU0/dWa7WXzYV6dK5W3fNscmS2XVikqYIcP5VdPY1RUwPBP1Ozb9dTyxznwm9mdzZX2/XL2AYckKT8LZ9TclxmyD32llmj7Y2q2NxlS9/KQana/OmEX9hzSi/YVwZ5DevGrE3bJu6SGm/cuXLIX3HZ2tqTrlSNh5fL1X1fduDuqgk2AlUthzK1NWma99BwIu1TbkqJdx+xv8Las91D48ig4/jI49aZsmd6G6torv2ATIAI+fVu2RPd+X4Uz7ochB+RXj1qePtvBkAOrdQTscUZu5agFWr44W7q48ob291/OVuqS1iG3W2FSSikijgd+FxHnA0uAicC5KaXFEXFc6djvgOXAaODbwFHAF6qNszAiHgPWtt5AAN+KiL8Ai4GF1DFrE9gd+GNErCALfS9PKT0bEYOB7SJicrVzzwMuBboDz0bE8lKN9V6vL6U0JiKuB8YCK4BzUqpcZ095O/ewbZmzcDl3j3mfwb278qMPfYCO7fO8F6Bxxrw7l8seeZOFS1dy6t6DONg9HluNp9+cVWffvkObf6nMvz32Fr+4axzLVyYG9+7K1afvxcBe9d/3a+6i5UyYvrBG3/OTclpGatuR8N6LVe1eQ6H31rmU8r+xU7nqyYl0aFfBWQcNZc8hZe4dtvHm8Jn/ZEu+LF+SvYEYWOAZLcf8CvrsAFNGZUtK7XZa+WPscByc80y2hFO/nfK/a1uSBJ02gs49Ycmcqr6ebsuhFmqfL8OqlTDu9izYPPT7+ezZpzUM6t219eyx+dzfYcH7Ve0pz8H4/5W3h+BGdexNuVG/RpfWIB/+E3zgeJj1VrYcba/irjgEwBv/g1f/A722ghGnN2yVny6btJ6l5TttBHt/Me8q1JKdcl22NO3sidlS7r4PV3Xz38tWTquu9sQDqZZIyZVOi2LEiBFp1KhReZehgpmxYCmH/PIh5i9dAUBFwA1n7cvuRV+eRwA8OWEmp/z1qRp9V352Dw5p5gB75oKl7HPRAyxbuWp13wm7D+BXJ5Z3p/QRv3mYN6YtWN0+dqfNueSTuzVZnfW2cjk89ItsCahNt4bDf5zdWdvMXpg0m49d+gSVE2E7tq/g/q8dVFZoLElSYbx0bbYU1Yol0K0vnHojbF7gWVfS+sx5B8beCl17Z6FPhy55V6SW5N4fwBO/r9l3wpXZPov1tXI5/PskmHB/1h60D3zqFv9da6zR18PNn69qDzkwu6FUktQwKcEle8KM16v69v8aHP6j/Goqlsi7gDy4iLnUyj302vTVwSbAqgR3jn7PcLOV2Gdob7551Hb8+aEJrEqJ0w/YqtmDTYCp85bWCDYB3plV/v4Jf/zEbpx/82jGvDuP/bfelAs+9IGmKrE87TrAYT/IvnJ079ipVF/hd9mKVTz42jQ+vc/g3GqSJKlOb9wHd30r29/tAx+FD/6u/A/Pdzk5W0591pvZzPraeyRLrcm0cXD5Edn2BwDP/g1Ov88ZoKqy66dg1BXZ/sMAmwzOVpgpR7sO8Kmb4d0XstnGA0Y0eZlt0nN/r9l+6xGYOSGXG2Il5WDVyuzmk1f/m63ydej3oMeAvKsqtohsee77fggz3oDtRsLB38m7KrVwhptSK7dFj85r9vVcs6+teWL8DB5+fTrbbrYRHx6+Be3bFfdDhHMO2ZqzDx5KSlBRkc+NOtv324it+nTjzWrLyh6z0+Zlj7Ndv4245ez9mrK0QtuyjhmaW/Z2b0hJUguzZB7ccFrVB/Cjr82WlD30e+WP1bVX9iW1ds9eXhVsQrbk/sRHYauD8qtJLUufbeELj2Sz2jt2y8LOjg1cwWWLXZu2trau08Y129EuW5ZVaokWzYKXb8hmxu10AnRr/m2MWp3HfgsPXJg9nvxMtq3R2U/mWlKr0HsonPyvvKtQgRhuShvQM2/N4ubnJ9OrW0dO23cwfTdu/lBxn6G9+eiu/bn5hSkA7L7lJpy8Z9vev+j6Z9/hWzeNXt1+8s2ZZS+f2tJEBJHjAgQVFcE/Prcnv7//DSbNWsQxO23Op/beMr+CWonjd+vPvWOn8sCr04iAE3YbwIHb+EZEktTCTBtXFWxWeufpfGqRCs1tg1RL76ENu1FEG9aB38xuRqj8f9/eX4Tuzb+CkrRei2fDXw6Eue9k7ccvhrMeNeBsrFfvqNmeNhZmjM+2NZLUbNxzs0Dcc7NYnnpzJp/461Orl5Qc1Ksr//vaQXRsn88MwTenL2DRspUM698jl+dvSY6++FHGvTdvdbsi4IUfHEmPrh1yrEpau4kzFtKhfQX9e7o3jiSpBVo6H369Q81ZaAedD4e4lJS0VtPGweWHV4UjW+wGZ9zvsrRSUSycCW8+mAXQzoxVS/Xs3+DOr9XsO+oi2OfsfOppLW74LIy5uardoRt84zVncCtP7rkpqencMGpyjb3yJs1axBMTZnBwDvshAmzVp3suz9sS1Q6Y21dU0K5dm/x/gApi8KYuRStJasE6bQQfvwruPr9qz839z8u7Kqll67tDtoTdmFuga+/svxuDTak4uvXOlviUiib8f02jHfaDbCnaWW9C+y5w9P8ZbEo5MNyUNpCedcwC3KRrxxwqUW1fOmRrvvjP51hRSp8/t/8Qunfy5VCSJKnBtj4MvvRs3lVIxdJzEOz31byrkCS1VsM+Bo//DuZMytob94edTsy1pFah11bwpedg+quw8RbQpWfeFUltksvSFojL0hbLlDmLOeHSJ3hv7hIAjt15cy75xG45V6VKb05fwOPjZ7DtZhux11a98y5HkiRJkiRJalqLZ8MrN0FKWdjZtVfeFUlqem1ySULDzQIx3CyeJctX8ugbM+jVrSO7b7lJ3uVIkiRJkiRJkqTWo02Gm67DKG1AnTu044gdN8u7DEmSJEmSJEmSpFbBHYQlSZIkSZIkSZIkFYLhpiRJkiRJkiRJkqRCMNyUJEmSJEmSJEmSVAiGm5IkSZIkSZIkSZIKwXBTkiRJkiRJkiRJUiEYbkqSJEmSJEmSJEkqBMNNSZIkSZIkSZIkSYVguClJkiRJkiRJkiSpEAw3JUmSJEmSJEmSJBWC4aYkSZIkSZIkSZKkQjDclCRJkiRJkiRJklQIhpuSJEmSJEmSJEmSCsFwU5IkSZIkSZIkSVIhGG5KkiRJkiRJkiRJKgTDTUmSJEmSJEmSJEmFYLgpSZIkSZIkSZIkqRAMNyVJkiRJkiRJkiQVguGmJEmSJEmSJEmSpEIw3JQkSZIkSZIkSZJUCIabkiRJkiRJkiRJkgrBcFOSJEmSJEmSJElSIRhuSpIkSZIkSZIkSSoEw01JkiRJkiRJkiRJhWC4KUmSJEmSJEmSJKkQDDclSZIkSZIkSZIkFYLhpiRJkiRJkiRJkqRCMNyUJEmSJEmSJEmSVAiGm5IkSZIkSZIkSZIKwXBTkiRJkiRJkiRJUiEYbkqSJEmSJEmSJEkqBMNNSZIkSZIkSZIkSYVguClJkiRJkiRJkiSpENqXe0FEdAAOA3YAuqeULiz1dwY2BmaklFY1aZWSJEmSJEmSJEmS2ryyZm5GxEhgInAn8GvggmqHhwPvASc1TWmSJEmSJEmSJEmSVKXe4WZEjABuBRJwHvDv6sdTSk8BbwHHN2F9kiRJkiRJkiRJkgSUN3PzB8AiYERK6ffAG3Wc8yywS1MUJkmSJEmSJEmSJEnVlRNu7gfcmlJ6fx3nvANs3riSJEmSJEmSJEmSJGlN5YSb3YEZ6zmna5ljSpIkSZIkSZIkSVK9lBNETgE+sJ5zhgNvNrgaSZIkSZIkSZIkSVqLcsLNu4CjImL/ug5GxNHAvsAdTVGYJEmSJEmSJEmSJFVXTrh5ETAHuDci/g/YESAiji21bwDeA37T1EVKkiRJkiRJkiRJUvv6nphSmhIRRwLXA9+sduh2IIAJwEdTSuvbl1OSJEmSJEmSJEmSylbvcBMgpfR8RGwHHAvsA/QG5gJPAbellFY0fYmSJEmSJEmSJEmSVGa4CZBSWkk2W/P2pi9HkiRJkiRJkiRJkupW7z03I+KBiPj0es45NSIeaHxZkiRJkiRJkiRJklRTvcNN4GBg8HrO2RI4qKHFSJIkSZIkSZIkSdLalBNu1kcXwH03JUmSJEmSJEmSJDW5cvfcTHV1RkQAg4BjgHcaW5QkSZIkSZIkSZIk1bbOmZsRsSoiVkbEylLXBZXt6l9kszXfBIYD127YkiVJkiRJkiRJkiS1ReubufkIVbM1DwQmARPrOG8lMBO4H7i8qYqTJEmSJEmSJEmSpErrDDdTSgdXPo6IVcCVKaWfbOiiJEmSJEmSJEmSJKm2cvbcHALM2UB1SJIkSZIkSZIkSdI61TvcTCm9vSELkSRJkiRJkiRJkqR1WWu4GRE/JNtv85KU0qxSuz5SSunCJqlOkiRJkiRJkiRJkkoipVT3gWyPzQTskFJ6vdSuj5RSatdUBarKiBEj0qhRo/IuQ5IkSZIkSZIkSfmLvAvIw7qWpT2k9H1SrbYkSZIkSZIkSZIkNbu1hpsppYfX1ZYkSZIkSZIkSZKk5lSRdwGSJEmSJEmSJEmSVB/rWpZ2nSKiA/BF4FCyNX0fBi5JKS1totokSZIkSZIkSZIkabV1ztyMiE9HxKSIOKxWfwVwB/Bb4EPAB4FfAg9ERIMDU0mSJEmSJEmSJElam/UtS3sEsBHwUK3+U0rHpgJnACcBTwN7A6c3bYmSJEmSJEmSJEmStP5wczfgiZTSylr9pwIJ+HRK6YqU0g3AkcBc4ONNX6YkSZIkSZIkSZKktm594eZmwJt19O8LTE0p/a+yI6W0ALgTGNZ05UmSJEmSJEmSJElSZn3h5sbAwuodEbE12VK1j9dx/mSgZ5NUJkmSJEmSJEmSJEnVrC/cnA0MqdW3R+n7C3Wc3x5Y0NiiJEmSJEmSJEmSJKm29YWbLwDHRsTm1fpOJttv8+E6zt8GeK+JapMkSZIkSZIkSZKk1dYXbv4N6Ao8GRG/iYg7gA8CE1JKNZaljYj2wAHASxukUkmSJEmSJEmSJEltWvt1HUwp3RARRwBnAOeWuucCn6/j9A8CmwD3NWWBkiRJkiRJkiRJkgTrCTcBUkpnRsTfgf2AmcDdKaV36zh1EXAecHuTVihJkiRJkiRJkiRJ1CPcBEgpPQE8sZ5z7gHuaYqiJEmSJEmSJEmSJKm29e25KUmSJEmSJEmSJEktguGmJEmSJEmSJEmSpEIw3JQkSZIkSZIkSZJUCIabkiRJkiRJkiRJkgrBcFOSJEmSJEmSJElSIRhuSpIkSWoeMyfA6/fA0vl5VyJJkiRJkgqqfd4FSJIkSWoDHvklPPAzIEHnnvDp22CL4TkXJUmSJEmSisaZm5IkSZI2rEWz4OH/B6SsvWQOPPSLPCuSJEmSJEkFVVa4GREHRcQdETEtIpZHxMo6vlZsqGIlSZIkFdDSebByWc2+hdPzqUWSJEmSJBVavZeljYhjgVuBdsAk4DXAIFOSJKmhViyDtx6Bzj1g4B55VyNtOJsMhi33g7cfr+obfko+taxaCc9cBhMehH47wf7nQqeN8qlFktTyLFsE//tRtkd03x3hqJ9B76F5VyVJkqRqytlz8wJgOXBsSuneDVOOJElSG7FgGlxxFMx6M2tvfxyc9E+IyLcuaUM5+d/w1J9gxhuw/bGw0wn51PHgz+DRX2eP37gHpr4Cn7gun1okSS3P/T/OboIBmPM2zJ4I5zyVa0mSJEmqqZxwcxhwrcGmJElSE3jmr1XBJsCrd2Sz2gbvn19N0obUpScc8t28q4CXb6jZfv0eWDI3m0EtSdL4+2u2p4+DuVOgR/986pEkSdIaytlzcwEwa0MVIkmS1KYsmlm/PrVt4++Ha06BGz4LU57Pu5rWYaMtara79IQOXXMppcUYcwv8YXf41bbw4EWQUt4VSVJ+NtuxZrtbH+jeN59aJEmSVKdyws37gX02VCGSJEltyi6nQLSranfvB1sfnl89ankmPwf/OgFe+y+MuRn+fhzMey/vqorviB9XzdJs1xGO+jm065BvTXmaPRFuPB1mjocFU+HhX8DLN+ZdlSTl58ifwua7ZI+7bwYfubRt/39CkiSpBSpnWdpvA89ExPeBn6Xk7bySJEkNNnAP+Ox/4YWroXNP2OsL0LFb3lWpJRl7K6RVVe3lC+H1u2HEZ3MrqVUYtDecNxbefR76bO9snElPQ1pZs2/io7DzifnUI0l56zkIvvBItj96l17QrpyPziRJktQc1voXWkRcUUf3GODHwOci4kVgTh3npJTS6Y0pKiIeAi5KKd1Tre9cYNuU0tkR0Qd4F/hSSukv1c6ZCMwHKt+dn106bxzwGhDAQuCzKaXXaj1nBfA74FAgAUuAj6eU3iqNOyKlNKPWNUcDFwLdSmPfkVL6xlp+pj2Ap4CTUko3lvpWAi9XO+3alNIv1v8bkiRJrcKgvbMvqS49B9XRN7D562iNOnWHIQfmXUXLsMWuZG9lqt272n/3vKqRpJajrd/8IkmS1IKt6/az09ZxbHDpqy4JaFS4CVwDnAzcU63vZOCbpccnkgWFpwB/qXkph1QPISNiMDAhpTS81P4C8F3gM7WuOwnYAtg5pbQqIgaQBaF1iohhwB+BY1NKr0ZEe+DMtZzbDvi/Wj8PwOLKuqS1mbtoOf995T3aVwTH7LQ53Tp516gkSW3C8E9meyG+/XjW3ulEGHpYvjWp9emzLRz7K3jgp7BsEex6avYlSZIkSVILta6UZEizVbGmG4GfRkSnlNLSUkC5BfBY6fgpwNeBf0dE/5TSlDLG3hiYXUf/5sB7KWVrf6WUJq9nnG+RLc/7aun8FcCf1nLul4GbgD3KqFNi5oKlHPeHx3hv7hIALn14Av/50v4GnFJBzFuynIdem86m3Tqyz9DeRETeJUkqko5ds6WL338F2neGTbfOuyK1VnucAbt/LlsG2eUXJUm1jbkV/vcjWDQ7uwHmyAuhot16L5MkSdpQ1vrONaX0dnMWUuu5Z0bEM8BI4DayWZvXpZRSRAwE+qWUnomI68lmXP6m2uUPlpZ7XZpS2qvUN7S0jO5GQFdgL9Z0PfBYRBwA3A/8M6X0wjrKHAb8en0/S0T0B44nW+62drjZpVRXpYtSStfVuv5MSjNCBw2qY2kytWo3PT95dbAJ8Ob0hdz1yvucsPuAHKuSVB8TZyzkY5c+wcyFywA46gOb8ZdPjci5KkmF1G9Y3hWoLaioACryrkKS1NLMexduOgNWLc/aT10CvYfCHo1dtE2SJKnhWvK718qlaSl9v6ba4+tLj68lm8VZ3SEppeHVgk0oLUubUhoKnAtcVvvJSjM1twO+A6wC7o+Iplj363fAt1NKK+s4trhUV+XXdbVPSCldllIakVIa0adPnyYoR0WyYlVao2/lqlU5VCKpXFc8/tbqYBPgnjFTGT15Tn4FSZIkKbNgGoy9HWa9lXclUss3eVRVsFlp0pP51CJJknIREf0i4tqImBARYyPivxGxbUS8kldN9Q43I+LEiHggIrZYy/H+EXF/RHy0iWq7FTgsInYDuqSUni/1nwKcFhETgduBXSJimzLGvR04sK4DKaWlKaW7UkrfBH4OfGQd44wBdq/H840Ari3VewLwp4hY17jSah/ddQC9unVc3e7fswsjh22eY0WS6mvBkhX16pMkSVIzeuN/8Lud4PpPwR92g2f/lndFUsvWfzeoqLXw24A986lFkiQ1u8j22boFeCilNDSltCPwXWCzPOsqZ+bmGUDPlNK7dR0s7Xu5cem8RkspLQAeAq6gNGszIrYDuqWU+qeUBqeUBgMXUTXDsz72BybU7oyI3SqD24ioAHYG1rU07y+B70bEtpXXRMTX6vg5hlSr9Ubg7JTSrWXUqzasX4/O3PXVAzj/6O35/rE78J8v70+PLh3yLktSPZy0x0DaVVTtsbl13+7sOaRXjhVJkqT6emvGQq56YiKPj5+Rdylqag/8BFaUtv5Iq+D+n8DK5eu+RmrLegyAj/wZNtoi2wN8jzNgxOfyrkqSJDWfQ4DlKaU/V3aklF4E3qlsR8TgiHg0Ip4vfe1b6t88Ih6JiBcj4pWIOCAi2kXE30vtlyPivIYUtdY9N+uwE3DHes4ZBXywIYWsxTXAzVSFl6eQJcTV3US2PO2F6xincs/NAJZRdwDbF/hrRHQqtZ8B/ljt+OiIqFwP9PqU0tci4lzgmojoCiTgzvr8UNXU3nPz7pTS+WWOoVZus407c9ZBQ/MuQ1KZ9tqqNzd9cV9ufWEKm3bvyCf32pL27VryavCSJAngwdem8fmrRq3eIuKz+w3mRx/8QM5VqcksmlWzvXR+Fm628yZSaa12PjH7kiRJbdEw4Ln1nDMNOCKltKS00uo1ZKuafgK4J6X0s4hoB3QFhgP9U0rDACKiZ0OKKifc7FUqcF1mAps2pJC6pJRuIQskK9sX1HHOaGDH0uPBdRyfCHSpx3PdDdy9lmNrjFvqv4P1B77Vzz+tVrtdfa+VJBXP8IE9GT6wZ95lSJKkMlz64ITVwSbA1U++zVcP24aeXTuu4yoVxq6nwkMXVbWHfRQ6ds2vnsZ6/Pfw5B8h2sGBX89m1UmSJEnNqwPwx4gYDqwEti31PwtcEREdgFtTSi9GxJvAVhHxB7IJg/c25AnLCTdnAOvb23IbYE5DCpEkSZIkKW9LV66q0V6ZUo2wUwV30LezZTbffBg23wX2/HzeFTXchAfgvh9Ute/8Omw+HAaMyK0kSZIktTpjgBPWc855wFRgF7LtMJcApJQeiYgDgWOBqyPilymlf0TELsBRwDnAx4Gy17wvZ328x4EPRcT2dR2MiB2ADwOPlluEJEmSJEktwef2G1yj/aFdtmDT7p3qPlnFE5HN3vzYX2HfL0H7Av+zffuJ+vU1hwXT4X8/hpu/AG/cl08NkiRJ2hAeADpFxOq7AiNiD2DLauf0AN5LKa0CPgW0K523JTAtpfRX4G/AbhGxKVCRUroJ+AGwW0OKKmfm5q+AjwKPRcRPyJZwnQL0B44uFdGudJ4kSZIkSYXz4eH92bxHFx54dRrb9O3Oh4dvkXdJUt0G7FG/vg1t1Sq46oMwfVzWHn0tnPQv2OG45q9FkiRJTSqllCLieOB3EXE+2azMicC51U77E3BTRJwIPAgsLPUfDHwzIpYDC4BPk2WKV0ZE5eTL7zSkrkip/svrlJLZSyilrrWsBM5OKV3ekEK0fiNGjEijRo3KuwxJkiRJktQSPHgRPHkJVFTAAV+H/b7a/DW88yz87fCafdsdA6dc0/y1SJIktT2RdwF5KGfmJimlv0bEY8DZwF5AT7I9Np8CLk0pjWvqAiVJkiRJklSHQ74DB5+fLbebl6696ujr3fx1SJIkqc0oK9wEKAWYX94AtUiSJEmSJKkceQabAL2Hwh5nwLOlhby69YX9z8u3JkmSJLVqZYebkiRJkiRJ0mrH/hp2+wzMexeGHAgdu+ZdkSRJklqxssPNiGgHbAdsQt17b5JSeqSRdUmSJEmSJKkoNt85+5IkSZI2sLLCzYj4AXAe0GM9p9YZekqSJEmSJEmSJElSQ9U73IyIbwE/BuYCVwPvACs2UF2SJEmSJEmSJEmSVEM5Mzc/D0wBdkspTd9A9UiSJEmSJEmSJElqIQaff2cFcApwLjCQbALk74BrJv7i2FWNGTsiRgIXk60Ke3lK6Rfru6aijPEHArcabEqSJEmSJEmSJEmtXynYvAn4CzAC2Kz0/S/AjaXjDRIR7YBLgKOBHYFTImLH9V1XzhNOpcw9OiVJkiRJkiRJkiQV1inAEUC3Wv3dgCOBkxsx9p7A+JTSmymlZcC1wIfXd1E54eb1wBER0amBBUqSJEmSJEmSJEkqjnNZM9is1A04rxFj9ydb4rbS5FLfOpUTbv4QeA+4MSKGlFebJEmSJEmSJEmSpIIZ2Mjj6xJ19KX1XVTOMrNjgA7AFsAxETEXmFPXk6aUhpYxriRJkiRJkiRJkqSW5x2yfTbXdbyhJlMzHB0AvLu+i8qZuVkBrAAmlb7mkiWqtb8avHGoJEmSJEmSJEmSpBbjd8DCtRxbCPy2EWM/C2wTEUMioiPZ/p23r++ies/cTCkNbnhtkiRJkiRJkiRJkgrmGuAE4Ahq7r25ELgXuLahA6eUVkTEl4B7gHbAFSmlMeu7LlJa79K1aiFGjBiRRo0alXcZkiRJkiRJkiRJyl9de1Y2ucHn31lBNqvyPLJlZN8hm7F57cRfHLuqOWqoznCzQAw3JUmSJEmSJEmSVNIs4WZLU+9laSPi0/U9N6X0j4aVI0mSJEmSJEmSJEl1q3e4CfwdWN80zyidY7gpSZIkSZIkSZIkqUmVE25+di39PYE9yNbavQm4s5E1SZIkSZIkSZIkSdIa6h1uppSuWtfxiLiSLNj8fWOLkiRJkiRJkiRJkqTaKppqoJTS/cDdwE+aakxJkiRJkiRJkiRJqlTOsrT18TpwVhOPKUmSJEmSJEmSJCkPF/SoAE4BzgUGAu8AvwOu4YK5qxo6bERcARwHTEspDavvdU02c7NkRyA18ZiSJEmSJEmSJEmSmlsWbN4E/AUYAWxW+v4X4MbS8Yb6OzCy3IsaHW5GREVEbBkRPwWOBh5t7JiSJEmSJEmSJEmScncKcATQrVZ/N+BI4OSGDpxSegSYVe519V6WNiJWse5ZmQHMBL5ZbhGSJEmSJEmSJEmSWpxzWTPYrNQNOA/4d7NVQ3l7bj5C3eHmKmA28AxwZUppelMUJkmSJEmSJEmSJClXAxt5vMnVO9xMKR28AeuQJEmSJEmSJEmS1LK8Q7bP5rqON6tG77kpSZIkSZIkSZIkqVX6HbBwLccWAr9tvlIyhpuSJEmSJEmSJEmS6nINcB9rBpwLgXuBaxs6cERcAzwJbBcRkyPi9Hpdl1Jd22iu84n2AI4C+gOd6jglpZTq9eQqz4gRI9KoUaPyLkOSJEmSJEmSJEn5i2Z5lgt6VAAnA+eR7bH5DtmMzWu5YO6qZqmhmnqHmxERwN+BU8l+WYmav7TKdkoptWvaMgWGm5IkSZIkSZIkSVqtecLNFqacZWm/BHwKuBoYQfYL+x2wL/BdYD7Z1NOtmrZESZIkSZIkSZIkSYL2ZZz7GeC1lNJpANlETuaklJ4CnoqIe4CnyNbdvbKJ65QkSZIkSZIkSZLUxpUzc3M74IFafavD0ZTSC8AdwNlNUJckSZIkSZIkSZIk1VBOuBnA3GrthUCvWue8AWzf2KIkSZIkSZIkSZIkqbZyws0pQP9q7TeB3Wudsw1Z6ClJkiRJkiRJkiRJTaqccPMZaoaZdwF7RsQPIuIDEXEO8GGyfTclSZIkSZIkSZIkqUmVE27eBLSLiCGl9v8D3gZ+DIwG/gDMAc5vygIlSZIkSZIkSZIkCaB9fU9MKd0K3FqtPSsidgU+DwwFJgL/SCm917QlSpIkSZIkSZIkSVIZ4WZdUkpzgV9VtiOic0RsnFKa1+jKJEmSJEmSJEmSJKmacpalrY9LgVlNPKYkSZIkSZIkSZIkNXm4CRAbYExJkiRJkiRJkiRJbdyGCDclSZIkSZIkSZIkqckZbkqSJEmSJEmSJEkqBMNNSZIkSZIkSZIkSYVguClJkiRJkiRJkiSpEAw3JUmSJEmSJEmSJBVC+3UdjIiVzVWIJEmSJEmSJEmSJK3LOsNNIBowZmpIIZIkSZIkSZIkSZK0LusMN1NKLlsrSZIkSZIkSZIkqUUwvJQkSZIkSZIkSZJUCIabkiRJkiRJkiRJkgrBcFOSJEmSJEmSJElSIRhuSpIkSZIkSZIkSSoEw01JkiRJkiRJkiRJhWC4KUmSJEmSJEmSJKkQDDclSZIkSZIkSZIkFYLhpiRJkiRJkiRJkqRCMNyUJEmSJEmSJEmSVAiGm5IkSZIkSZIkSZIKwXBTkiRJkiRJkiRJUiEYbkqSJEmSJEmSJEkqBMNNSZIkSZIkSZIkSYVguClJkiRJkiRJkiSpEAw3JUmSJEmSJEmSJBWC4aYkSZIkSZIkSZKkQjDclCRJkiRJkiRJklQIhpuSJEmSJEmSJEmSCsFwU5IkSZIkSZIkSVIhGG5KkiRJkiRJkiRJKgTDTUmSJEmSJEmSJEmFYLgpSZIkSZIkSZIkqRAMNyVJkiRJkiRJkiQVguGmJEmSJEmSJEmSpEIw3JQkSZIkSZIkSZJUCIabkiRJkiRJkiRJkgrBcFOSJEmSJEmSJElSIRhuSpIkSZIkSZIkSSoEw01JkiRJkiRJkiRJhWC4KUmSJEmSJEmSJKkQDDclSZKkDSEleO8lmPdu3pVIkiRJklqahTNg2aK8q5AKqX3eBUiSJEmtzoJp8I+PwLQxEBWw/9fgsB/kXZUkSZIkKW9L58MNn4Xx90HH7nDYD2GvL+RdlVQoztyUJEmSmtrjF2fBJkBaBY/+GmZOyLcmSZIkSVL+nvhjFmwCLFsAd58Ps9/OtyapYAw3JUmSpKY2Z1KtjlRHnyRJkiSpzZk2tmY7rYIZr+dTi1RQhpuSJElSU/vA8TXb3fvBoH3yqUUbxrg74KYz4P4LYdGsvKuRJEmSVBTbHFGz3WljGLhnPrVIBeWem5IkSVJTG/ZRWLkMXroWum8GB34TOnTOuyo1ldHXw82fr2qP/x984eH86pEkSZJUHLt+KrtB8qVroFsfOPQH0LlH3lVJhRIppbxrUD2NGDEijRo1Ku8yJEmS1rR4Njz9l2zp1Q8cv+adqFJr8o8Pw5sP1ew763HoNyyXciRJkhrqnVmLuOqJiSxevpKT9xjETgMMWJrEa3fBKzdBjwGw9znQvU/eFUlqvSLvAvLgzE1JkiQ13tXHw7svZI9f/Bec+Pc1l2aVWouum9ZsRwV07VX+OLPeyj706tITdj4JOm3UJOVJkiTVx9zFyzn+T48zY8EyAG4YNZnbvrQfO2y+cc6V5W/ZilV0bN/AHd3G3AI3nFbVfv2e7Ea4CneIk6Sm4iuqJEmSGuf9l6uCzUrPX51PLVJzOPAbNQPOfb8MG29R3hjTxsGl+8EDF8KdX4crjoaVK5q2TkmSpHW4f9zU1cEmwLKVq7j1hSk5VpS/afOW8Im/PsW237+LQ3/9EM+93YC91V+8ptagY+G9F+o+V5LUIIUKNyNiQbXHx0TEGxExKCK2i4iHIuLFiBgXEZdVO2/PiHgkIl6LiFcj4vKI6Fpr3IMj4o46nq9DRPyi9DyvRMQzEXF0Hef9LSJeiojREXFjRHQv9W8SEbeU+p+JiGG1rjs+IlJEbN8Uvx9JkqRcdO7JGqugdNkkj0oAWLRsBc+9PZv5S5bnVoNaub47wLmj4RPXw9lPwxE/KX+MUVfC8oVV7akvr7nUrSRJ0gbUs2uHNfp61NHXllx45ziemDATgDenL+Qr17zIylVlbuvWrdYqHwR07d00BUqSgIKFm5Ui4jDgD8DIlNIk4PfAb1NKw1NKO5SOERGbATcA304pbQfsANwN1He9pwuBzYFhKaVhwAfXcu15KaVdUko7A5OAL5X6vwu8WOr/NHBxretOAR4DTq5nPZIkSS1Pz4Gw1xeq2l02gQO+lkspT06Yyd4/v5+PXfoEe/38fv43dmoudagN6NgNtj0K+jbwPsWo462YS5VJkqRmdNC2fdl/66ogbqs+3Thlj0E5VpS/l96ZU6M9Zc5iZi5YWt4g+38Num9W1d77i7DJ4EbXJkmqUrg9NyPiAOCvwDEppQml7s2ByZXnpJReLj08B7gqpfRkqT8BN9bzeboCnweGpJSWlq6fClxf+9yU0rzSNQF0ASpv59kRuKh0zqsRMTgiNkspTS3N7twPOAS4HbigXr8ASZKklujo/4Phn4A5k2DIQdA5n316fnLHWOYtyZb2XLRsJT+6fQyH77jZeq6ScrDHGfDSv2HJ3Kzdf0T2347U1CY/B0/+AVYuhz3PhK3890xqFpNHwSs3Z8uW7/4Z91VWi9SuIrj69D15+q1ZLF62kv223rTh+0y2Entv1YtJsxatbm/Vpxt9NupU3iCbbg1feRHefhw27g+b7di0RUqSChdudgJuAw5OKb1arf+3wAMR8QRwL3BlSmkOMAy4qoHPtTUwqTK4XJ+IuBI4BhgLfL3U/RLwUeCxiNgT2BIYAEwFPgLcnVJ6PSJmRcRuKaXnG1irJElS/jbfJfvK0ZTZi2q035+3hJWrEu0qYi1XSDnZdGs45xkYezt06Qk7fAgq2uVdlVqbOe/AVcfB8tJr4+t3w5kPQb+dci1LavUmPAD//BikVVl7zM1wxv0Q/j2ilici2Hsrl0yt9L1jd2Tx8lU8/No0tu+3MRd+ZBjRkP92O3aFbY5o+gIlSUDxlqVdDjwBnF69M6V0JdmSszcABwNPRUSZt9Q0Tkrps8AWwDjgpFL3L4BNIuJF4MvAC8CK0rFTgGtLj68ttdcQEWdGxKiIGDV9+vQNVL0kSWrLXp48lzP/MYqTL3uS216cknc5jXLszlvUaI8c1s9gUy3XRv1grzNh549Dh855V6PW6PW7q4JNgFUrYOxt+dUjtRWjrqgKNgGmPAfvej+7VAQ9unTgD6fsyugLjuL6s/Zhu37OupaklqhoMzdXAR8H/hcR300p/bzyQErpXeAK4IqIeIVs1uYYYHey2Z7lGg8MioiNUkrz63NBSmllRFwHfJNs9ug84LOwesnat4C3IqI3cCgwLCIS0A5IEfGt0tK51ce8DLgMYMSIEWXuXi1JkrRucxYt4xN/fYr5S7P7r556cxYbd+nAIdv1zbmyhrngQzuy2cadeOatWewysCdfOmTrvEuSpPxs3H/Nvh4Dmr8Oqa3p0K1+fZIk5W35Enj1DlixBLY/LltVRiqAos3cJKW0CDgO+GREnA4QESMjokPpcT+gNzAF+CPwmYjYq/L6iDi1dE59nudvwO8jomPp2s0j4tTq50Vm68rHwAeBV0vtnpXXAmcAj5QCzxOAf6SUtkwpDU4pDSQLPvdv2G9FkiSpYR4bP2N1sFnpnlfez6maxuvUvh3nHr4t//783nx75PZ069SIe/mS95VJKrhtj8qWPK405EDY+aS1ny+paez7ZejUo6o97GPQd/v86pEkqS4rlsLfDoebTofbzoFL94X5U/OuSqqXos3cBCClNCsiRgKPRMQM4CDg4ohYUjrlmyml9wEi4mTgVxHRl2zm5yPAzXUMe1hETK7WPhH4PvBTYGxp7IXAD2tdF8BVEbFx6fFLwBdLx3YA/hERK8n24qxcTvcUsiVrq7sJ+ATwaD1/DZIkSY22Za81ZxEM6t01h0pakEWzsjd2r98NvYbCBy+GwfvlXVX+li3KlrfstmnelUiqr4p2cNLVMP01WLkc+g3LuyKpbeg3DL7yPLxxL2y8BQw5KO+KJEla02v/hfdfrmrPmwIvXA0HfiO/mqR6iuQd6YUxYsSINGrUqLzLkCRJLcnUsfDs5dnjPc6AzXYse4if3TmWvz32FqsS7DmkF1ectgfdGzPjsej+81V47u9V7W594WtjoV2H3EpqjMfHz+DShyawbMUqPrPvYI7defPyB3n6Mrj/x7BsAWx9BJx4JXTKYf+h5YvhyT/ClOdhy/1gr7OgXRv+d1WSJElS2zX9NXj+H9CuI4z4HPQcWN71L10Ht5xZs+/Ab8Gh32u6GtUcIu8C8mC4WSCGm5IkqYY5k+BP+2SBE0DH7vDFJ2CTLcseatq8JSxYuoKt+nRv4iLLtHI5LJgGPerYJ665XLofTH2lZt+Xn4feQ/OppxHembWIw379MMtWrgIgAm74wj6MGNyr/oPMmQQX7wJpVVXfwd+Bg89v4mrr4abPw8vXV7X3PgdG/rz565AkSZI2tPH3ZzdddtoY9vsK9Nku74rUksycAH8+AJYvzNrd+sI5T0PXMt7rLZ0Pf9oX5k7K2p16wBcehl5Dmr5ebUhtMtws3J6bkiRJKhlza1WwCdnjsbc2aKi+G3fOP9gcfz/8Zkf47Y5wyV4wY3w+dQzap2Z7oy2gZ/mBcUvw0GvTVgebkG0jet+4MvdQmf5azWATYOqYJqiuTCuXw5hau0uMvrb565AkSZI2tLefhH+dAONuhxf/CVccBYvn5F2VWpLR11UFmwALp8Grd5Q3RqeN4MwH4fAfZzewnvWIwaYKw3BTkiSpqLr2rqOvoPshrlqZ7XO5cFrWnv4q3POdfGo57Iew04nQoRtsPhxO+mdhlz6tK7AeummZIfbAPbO7xavb5ohGVNVAFe3X/Pe7e7/mr0OSWpvnr4ZfbQc/HwD3/TC7E0aSlK9Xbqx5g+Hi2TD+f/nVo5anrm1CGrJ1SLdNYf9zs5V5Nhnc2KqkZmO4KUmSVFTDPgoD9qhq9x+R9RXR4jkw/72afdNezaUUOm8MH7scvvdutiTPgN3zqaMJ7Lf1pnxq7y2pKC1SM/ID/fjIrmUu+du5B3zyRhh8AGy6HRz2I9j1U01f7PpEZEvQtuuYtTt0hSMvbP46JKk1mf463P5lWPA+LJsPj18Mo69f/3WSpA1ro83X7Nt4i+avQy3X8E9Cr2pbpwzYA7Y7Nr96pGbmnpsF4p6bkiRpDatWwduPAwm23B8qCnzv2mWHwLvPV7VHnA7H/Sa/elqR6fOXsmLVKjbv0SXvUhpvwXSY+jJssSt02STvaiSp2F74F9x2ds0+//8rSflbPBuu+iC8/3LW3vkk+Ohl+daklmf5Ehh/H7TrBEMPLeyKQ2q0NrnnpuFmgRhuSpKkVm3OO3Dv92HqK9kbs8MvgI7d8q5KkqTWa8Yb8Mc9gGqfDX30r7Dzx3MrSZJUkhJMHpUtNdp3+7yrkdRyGW6qZTPclCRJkiRJTeqFf8EDP4VlC2CP07Plx6NNfkYmSZJURG3yDzfnKUuSJEmSJLVVu34y+5IkSZIKosCbMkmSJEmSJEmSJElqSww3JUmSJEmSJEmSJBWC4aYkSZIkSZIkSZKkQjDclCRJkiRJkiRJklQIhpuSJEmSJEmSJEmSCsFwU5IkSZIkSZIkSVIhGG5KkiRJkiRJkiRJKgTDTUmSJEmSJEmSJEmFYLgpSZIkSZIkSZIkqRAMNyVJkiRJkiRJkiQVguGmJEmSJEmSJEmSpEIw3JQkSZIkSZIkSZJUCIabkiRJkiRJkiRJkgrBcFOSJEmSJEmSJElSIRhuSpIkSZIkSZIkSSoEw01JkiRJkiRJkiRJhWC4KUmSJEmSJEmSJKkQDDclSZIkSZIkSZIkFYLhpiRJkiRJkiRJkqRCMNyUJEmSJEmSJEmSVAiGm5IkSZIkSZIkSZIKwXBTkiRJkiRJkiRJUiEYbkqSJEmSJEmSJEkqBMNNSZIkSZIkSZIkSYVguClJkiRJkiRJkiSpEAw3JUmSJEmSJEmSJBWC4aYkSZIkSZIkSZKkQjDclCRJkiRJkiRJklQIhpuSJEmSJEmSJEmSCsFwU5IkSZIkSZIkSVIhGG5KkiRJkiRJkiRJKgTDTUmSJEmSJEmSJEmFYLgpSZIkSZIkSZIkqRAMNyVJkiRJkiRJkiQVguGmJEmSJEmSJEmSpEIw3JQkSZIkSZIkSZJUCIabkiRJkiRJkiRJkgrBcFOSJEmSJEmSJElSIURKKe8aVE8RMR14O+86msmmwIy8i5CkVsbXVklqer62SlLT8nVVkpqer61S6zUjpTQy7yKam+GmWqSIGJVSGpF3HZLUmvjaKklNz9dWSWpavq5KUtPztVVSa+OytJIkSZIkSZIkSZIKwXBTkiRJkiRJkiRJUiEYbqqluizvAiSpFfK1VZKanq+tktS0fF2VpKbna6ukVsU9NyVJkiRJkiRJkiQVgjM3JUmSJEmSJEmSJBWC4aYkSZIkSZIkSZKkQjDcVIsTESMj4rWIGB8R5+ddjyQVTUQMjIgHI2JcRIyJiK+W+ntFxH0R8Ubp+yZ51ypJRRMR7SLihYi4o9T2tVWSGiEiekbEjRHxaunv1318bZWkhouI80qfBbwSEddERGdfVyW1NoabalEioh1wCXA0sCNwSkTsmG9VklQ4K4Cvp5R2APYGzim9lp4P3J9S2ga4v9SWJJXnq8C4am1fWyWpcS4G7k4pbQ/sQvYa62urJDVARPQHvgKMSCkNA9oBJ+PrqqRWxnBTLc2ewPiU0psppWXAtcCHc65JkgolpfReSun50uP5ZB8Q9Sd7Pb2qdNpVwEdyKVCSCioiBgDHApdX6/a1VZIaKCI2Bg4E/gaQUlqWUpqDr62S1BjtgS4R0R7oCryLr6uSWhnDTbU0/YF3qrUnl/okSQ0QEYOBXYGngc1SSu9BFoACfXMsTZKK6HfAt4BV1fp8bZWkhtsKmA5cWVry+/KI6IavrZLUICmlKcCvgEnAe8DclNK9+LoqqZUx3FRLE3X0pWavQpJagYjoDtwEnJtSmpd3PZJUZBFxHDAtpfRc3rVIUivSHtgNuDSltCuwEJdKlKQGK+2l+WFgCLAF0C0iTs23KklqeoabamkmAwOrtQeQLZ0gSSpDRHQgCzb/lVK6udQ9NSI2Lx3fHJiWV32SVED7AR+KiIlkWyccGhH/xNdWSWqMycDklNLTpfaNZGGnr62S1DCHA2+llKanlJYDNwP74uuqpFbGcFMtzbPANhExJCI6km14fXvONUlSoUREkO1bNC6l9Jtqh24HPlN6/BngtuauTZKKKqX0nZTSgJTSYLK/UR9IKZ2Kr62S1GAppfeBdyJiu1LXYcBYfG2VpIaaBOwdEV1Lnw0cBozD11VJrUyk5Iqfalki4hiy/YzaAVeklH6Wb0WSVCwRsT/wKPAyVfvCfZds383rgUFkb3hOTCnNyqVISSqwiDgY+EZK6biI6I2vrZLUYBExHLgc6Ai8CXyW7GZ8X1slqQEi4sfAScAK4AXgDKA7vq5KakUMNyVJkiRJkiRJkiQVgsvSSpIkSZIkSZIkSSoEw01JkiRJkiRJkiRJhWC4KUmSJEmSJEmSJKkQDDclSZIkSZIkSZIkFYLhpiRJkiRJkiRJkqRCMNyUJEmSpFYgIv4eESkiBlfrG1zq+3sTjD8xIiY2dpzWLiIuKP3OD867FkmSJElqjQw3JUmSJKmFKoVk6/o6Le8a16YxwWpEPFS6dmVE7LSWcyrD3MMbXawkSZIkqTDa512AJEmSJGm9fryW/hfXc90UYAdgbpNW03wqgF8CI/MuRJIkSZLUMhhuSpIkSVILl1K6oIHXLQdebdpqmtV44KiIOCKldF/exUiSJEmS8ueytJIkSZLUSq1radiI2DYiboqI2RGxMCKeiIhjI+K0dS15GxFdI+KXETEpIpZGxPiI+HZERLVzLgDeKjU/04ildL8LJOCXEVGv96+l53hoLcfWuS9pRAyNiBsjYmZEzI+IeyNiWOm8PhFxWUS8FxFLIuLZiDhkPbV8JiJeiIjFETEtIq6IiH5rObdXRFwUEeNK58+NiPsj4sg6zl39zygiRpaW8Z0bEak+vyNJkiRJKjJnbkqSJElSGxMR2wOPA72AO4HRwFbALcB/13FpB+BeYAvgLmAF8BHgF0BnqpbPfQjoCXwVeAm4tdoYL5ZR6gvAP4FPAZ8Brizj2nINBp4GxgF/L7WPBx6KiH2Au4F5wHVkv7eTgbsiYtuU0qQ6xjsPOLJ0/t3A/sBngYMjYq+U0vTKEyNiS7Lf2WDg0dL53YDjgLsj4gsppb/W8RwnkC3Zexfw59L1kiRJktSqGW5KkiRJUgtXmglZ28SU0t8bOOQlZAHd2SmlS6s9z9GsO9zcgiysPCKltLh0zY+B14HzIuLnKaXlKaWHImIiWbj5YkOX1S35HnAicGFEXJdSWtSIsdblIOD7KaWfVXZExA+An5CFnteT/b5WlY7dB/yDLMQ8r47xjgb2Sim9UG283wLnkoXBp1c79ypgS+CUlNK11c7vSRZ6/j4ibk8pTa31HMcAx6SU7m7AzytJkiRJheSytJIkSZLU8v2ojq/TGjJQRAwEDiXbz/Iv1Y+llO4C/reeIb5SGWyWrpkG3Ab0ALZrSE3rklJ6B/gd0B/4WlOPX81EstCxuqtK3zsB36wMNkv+TTZzdfhaxru6erBZcgEwF/hERHQCiIhdyILVm6oHmwAppTlk/6w7Ax+r4zluM9iUJEmS1NY4c1OSJEmSWriUUqz/rHobXvr+ZK2wrtJjwOFruXZuSml8Hf3vlL5v0sja1uYispmO34qIv9Yxg7EpvJhSWlmr793S99dTSvOrH0gprYyIqcCAtYz3cO2OlNLciHiRLMzcgWyJ3n1Kh3usZYZun9L3Heo49sxanluSJEmSWi3DTUmSJElqW3qUvq8tIFxXcDhnLf0rSt/bNaSg9UkpzSstf/tHstmPX9wATzO3juddERF1HitZQbYPaV3W9nt8v/S98p9D79L3I0pfa9N9HWNJkiRJUpvhsrSSJEmS1LbMK33fbC3H19aft7+Q7e35+YioaxZjpcTab+Tt2dRFrcPafo/9St/n1vr+1ZRSrOPrs3WMlZq0YkmSJEkqAMNNSZIkSWpbKveB3Cci6npPuH8TPU/lEq9NMpszpbQC+HZpvP+3jlNnAwNrd0ZEO9a+P+aGcFAdNfQo1bAEGFfqfqr0/YDmKUuSJEmSis1wU5IkSZLakJTSO8BDwNbAF6ofi4iRrH2/zXLNJptZOKiJxiOldCvwKHAcsN9aTnsGGBQRR9bq/z6wZVPVUg+fiohda/VdQLYc7TUppaUAKaVRZD/TRyPic3UNFBE7RUTfDVmsJEmSJBWFe25KkiRJUttzDvA48KeIOAYYDWwFfAy4DfgwsKoxT5BSWhARTwMHRMS/yJaUXQncnlIa3Yihv0E223HrtRz/FXAUcFtEXAfMAvYFhpCFugc34rnLcRfweERcD7xHNiN2f2AicH6tcz8BPAD8LSK+AjxNtr/pAGBnYBiwDzCtOQqXJEmSpJbMmZuSJEmS1MaklMaShWW3kC2Hei4wGDgeeKx02ry6ri3Tp4A7gZHAj4ALgd0aM2BK6RngunUcvx/4CDAGOBn4DFmguCfwdmOeu0y/Bc4mW4b2XGB74O/AvimlGiFlSmkysDvwPbIA+JPAV8hC2UlkM2xfbp6yJUmSJKlli5RS3jVIkiRJklqI0izLTwDbp5Rey7seSZIkSZKqc+amJEmSJLUxEVEREf3q6D8MOAkYa7ApSZIkSWqJ3HNTkiRJktqejsA7EfEg8CqwAvgAcASwjGxPTkmSJEmSWhyXpZUkSZKkNiYi2gG/Aw4FBgBdgRnAI8AvUkov5FedJEmSJElrZ7gpSZIkSZIkSZIkqRDcc1OSJEmSJEmSJElSIRhuSpIkSZIkSZIkSSoEw01JkiRJkiRJkiRJhWC4KUmSJEmSJEmSJKkQDDclSZIkSZIkSZIkFcL/B2af9oZUTzfjAAAAAElFTkSuQmCC\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": [
    "### 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": 5,
   "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(\"Payload Mass (kg)\",fontsize=20)\n",
    "plt.ylabel(\"Launch Site\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now try to explain any patterns you found in the Payload Vs. Launch Site scatter point chart.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Payloads that approach MAX(Payload) tended to launch from CCAFS SLC 40 & KSC LC 39A\n",
    "# Payloads less than 8000 kg tended to fail at a higher rate when launched from CCAFS SLC 40,\n",
    "#  plausibly due to that launch site being used for R&D versus the other two launch \n",
    "#  sites used with less failure-tolerant payloads."
   ]
  },
  {
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "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",
    "df.groupby(\"Orbit\").mean()['Class'].plot(kind='bar')\n",
    "plt.xlabel(\"Orbit Type\",fontsize=20)\n",
    "plt.ylabel(\"Success Rate\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "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": [
    "### 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": 8,
   "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(\"FlightNumber\",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": 9,
   "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(\"Payload\",fontsize=20)\n",
    "plt.ylabel(\"Orbit\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should observe that Heavy payloads have a negative influence on GTO orbits and positive on GTO and Polar LEO (ISS) orbits.\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": 10,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\n",
    "df1=pd.DataFrame(Extract_year(df['Date']),columns =['year'])\n",
    "df1['Class']=df['Class']\n",
    "sns.lineplot(data=df1, x=np.unique(Extract_year(df['Date'])), y=df1.groupby('year')['Class'].mean())\n",
    "plt.xlabel(\"Year\", fontsize=20)\n",
    "plt.ylabel(\"Success Rate\", fontsize=20)\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 \n",
    "future module.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 18,
     "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>Orbit_ES-L1</th>\n",
       "      <th>Orbit_GEO</th>\n",
       "      <th>...</th>\n",
       "      <th>Serial_B1048</th>\n",
       "      <th>Serial_B1049</th>\n",
       "      <th>Serial_B1050</th>\n",
       "      <th>Serial_B1051</th>\n",
       "      <th>Serial_B1054</th>\n",
       "      <th>Serial_B1056</th>\n",
       "      <th>Serial_B1058</th>\n",
       "      <th>Serial_B1059</th>\n",
       "      <th>Serial_B1060</th>\n",
       "      <th>Serial_B1062</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "<p>5 rows × 80 columns</p>\n",
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      ],
      "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  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n",
       "0            0            0          0  ...             0             0   \n",
       "1            0            0          0  ...             0             0   \n",
       "2            0            0          0  ...             0             0   \n",
       "3            0            0          0  ...             0             0   \n",
       "4            0            0          0  ...             0             0   \n",
       "\n",
       "   Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             0             0             0             0   \n",
       "2             0             0             0             0             0   \n",
       "3             0             0             0             0             0   \n",
       "4             0             0             0             0             0   \n",
       "\n",
       "   Serial_B1059  Serial_B1060  Serial_B1062  \n",
       "0             0             0             0  \n",
       "1             0             0             0  \n",
       "2             0             0             0  \n",
       "3             0             0             0  \n",
       "4             0             0             0  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HINT: Use get_dummies() function on the categorical columns\n",
    "features_one_hot = pd.get_dummies(features, columns = ['Orbit', 'LaunchSite', 'LandingPad', 'Serial'])\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": 25,
   "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",
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       "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",
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       "4            0.0           0.0           0.0           0.0           0.0   \n",
       "..           ...           ...           ...           ...           ...   \n",
       "85           0.0           0.0           0.0           0.0           0.0   \n",
       "86           0.0           0.0           0.0           0.0           1.0   \n",
       "87           0.0           1.0           0.0           0.0           0.0   \n",
       "88           0.0           0.0           0.0           0.0           0.0   \n",
       "89           0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "    Serial_B1059  Serial_B1060  Serial_B1062  \n",
       "0            0.0           0.0           0.0  \n",
       "1            0.0           0.0           0.0  \n",
       "2            0.0           0.0           0.0  \n",
       "3            0.0           0.0           0.0  \n",
       "4            0.0           0.0           0.0  \n",
       "..           ...           ...           ...  \n",
       "85           0.0           1.0           0.0  \n",
       "86           0.0           0.0           0.0  \n",
       "87           0.0           0.0           0.0  \n",
       "88           0.0           1.0           0.0  \n",
       "89           0.0           0.0           1.0  \n",
       "\n",
       "[90 rows x 80 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HINT: use astype function\n",
    "features_one_hot.astype('float64')"
   ]
  },
  {
   "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",
    "| 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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