eda-dataviz.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
    "    </a>\n",
    "</p>\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": [
    "### 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": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'piplite'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_24528\\2628191766.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mpiplite\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mawait\u001b[0m \u001b[0mpiplite\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minstall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'numpy'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mawait\u001b[0m \u001b[0mpiplite\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minstall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'pandas'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mawait\u001b[0m \u001b[0mpiplite\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minstall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'seaborn'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'piplite'"
     ]
    }
   ],
   "source": [
    "import piplite\n",
    "await piplite.install(['numpy'])\n",
    "await piplite.install(['pandas'])\n",
    "await piplite.install(['seaborn'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pandas 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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting js\n",
      "  Downloading js-1.0.tar.gz (2.5 kB)\n",
      "  Preparing metadata (setup.py): started\n",
      "  Preparing metadata (setup.py): finished with status 'done'\n",
      "Collecting fanstatic\n",
      "  Downloading fanstatic-1.2-py2.py3-none-any.whl (31 kB)\n",
      "Requirement already satisfied: setuptools in c:\\users\\ali\\anaconda3\\lib\\site-packages (from js) (63.4.1)\n",
      "Collecting WebOb>=1.2\n",
      "  Downloading WebOb-1.8.7-py2.py3-none-any.whl (114 kB)\n",
      "     ------------------------------------ 115.0/115.0 kB 104.7 kB/s eta 0:00:00\n",
      "Collecting shutilwhich\n",
      "  Downloading shutilwhich-1.1.0.tar.gz (2.3 kB)\n",
      "  Preparing metadata (setup.py): started\n",
      "  Preparing metadata (setup.py): finished with status 'done'\n",
      "Building wheels for collected packages: js, shutilwhich\n",
      "  Building wheel for js (setup.py): started\n",
      "  Building wheel for js (setup.py): finished with status 'done'\n",
      "  Created wheel for js: filename=js-1.0-py3-none-any.whl size=2884 sha256=db3974cb21143b982a3c043541a011141c175bda54ae3e9b5748aa6ab0138bc0\n",
      "  Stored in directory: c:\\users\\ali\\appdata\\local\\pip\\cache\\wheels\\6f\\91\\12\\9fc79cc62b07127faf39b5f3afcc6606e659bb54743a00bebb\n",
      "  Building wheel for shutilwhich (setup.py): started\n",
      "  Building wheel for shutilwhich (setup.py): finished with status 'done'\n",
      "  Created wheel for shutilwhich: filename=shutilwhich-1.1.0-py3-none-any.whl size=2767 sha256=cc64feda2defad73cf9d1f7b05b6cadcb837acf6e044c8f25067315a508a1885\n",
      "  Stored in directory: c:\\users\\ali\\appdata\\local\\pip\\cache\\wheels\\84\\c7\\f5\\fed66dce1ed897b44e0da776b6a592dfad0a70f7dd61f73a9d\n",
      "Successfully built js shutilwhich\n",
      "Installing collected packages: shutilwhich, WebOb, fanstatic, js\n",
      "Successfully installed WebOb-1.8.7 fanstatic-1.2 js-1.0 shutilwhich-1.1.0\n"
     ]
    }
   ],
   "source": [
    "! pip install js"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\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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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from js import fetch\n",
    "# import io\n",
    "\n",
    "URL = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\"\n",
    "# resp = await fetch(URL)\n",
    "# dataset_part_2_csv = io.BytesIO((await resp.arrayBuffer()).to_py())\n",
    "df=pd.read_csv(URL)\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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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/JyLtCTc5tjrCKctmT4Rac+HVfgQiwwLBBBarBYLDhw83bdu2NTt27PDk/Y8ePWoKCwvNypUrzcMPP2yaNWtmNm7caD3nyy+/NBkZGebjjz8u3+bFAsGqDhw4YDIzM82ECROsv3e9evVMz549K2275557zIUXXmg9q0xubq4ZMGCAZ+8/Y8YM07p1azNjxgzzySefmL/+9a+madOmZtq0adazNm/ebC655BIjySQlJZkePXqYIUOGmOzsbGsZwRYI7ty5s9Lrfv7zn5v+/ftby6kolgsEjx07ZgYNGmTOO+88U1xc7EnOgQMHTGFhoVm2bJm59dZbTbt27czu3butZq1atcpkZmZW2lnxaoFgVTt37jT16tUz//rXv6xmff3110aS+elPf1rpdT/60Y/MjTfeaC2nqrPPPtuMHDky4vd3ynrmmWdMp06dzOzZs826devMc889Zxo3bmzmz59vNWfVqlWmW7du5X3Rv39/c9VVV5mrrroq4pxA31+96gin7+W2esIpx2ZHhMqy2ROBcrzqCLf7XNH2RKAcrzrCzZxs9ESwHC86IliWrZ4Itm/sRT+42Q+30Q9ucmz1g1OWrX4IluNFP4RzvBRNPwTL8aIf3M4p2n4IlWO7H0Jl2eoHp2NaWz0RzrFztB0RTlY0PeE2x0ZHOGXZ6olIznFE2hFOWbZ6Itw5RdMRbrJs9ISbHC+ONYypfD7Kq+OMqjkVefFowGBZNo8zguV4cS6iapZXxxlVcwKxdS6iapZXxxlVc6qyeS4iUJYXxxmBcmz2g9N5ZFs9Ec756mh7IpysaHrCbY6NnnDKstUTkVxXiLQnnLJs9US4c4qmJ9xk2egJNzle7EdUvQbk1X5EqGtNtvcjQmXZ3I8IluPFfkTVLK/2I9xcE7S1H1E1y6v9CKc52dyPCJTlxX5EoBxb/eB03dZmR4RzjTianggnJ9qOcJsVbU845djsiEiu5UfSE045Njsi3DlF2hNucmx1hJssWz0Ras2Fl+cjED4WCCawWCwQHDlypGndurXZunWrpzkV9evXz9xxxx3W3/eNN94oL6WyD0nG5/OZpKQkc+LECeuZZS6//HJz1113WX/fM844w9x2222VtuXl5ZmsrCzrWcYYs337dlOnTh3z5ptvevL+xhjTunVrM2nSpErbnnjiCXP22Wd7lnngwIHyb0o33HCDufrqq629d9V/p1u2bDGSzJo1ayq9buDAgebmm2+2llNRrBYIHjt2zFx77bXm3HPPtXLHTLcd16FDh6jvMlk16w9/+EN5N1Tsizp16pi2bdtaywmmQ4cOZvz48RHnBMo6evSoqVu3rnniiScqve5Xv/qV6dWrl7WcihYvXmwkVboYFo2qWYcOHTL16tUz+fn5lV532223ebbg9rvvvjN79uwxxhhz/vnnm+HDh0eUEez7qxcd4eZ7uY2ecMqx2RHh7p9E2hPBcrzoiEjmFElPBMvxoiPczMlGTwTL8aIj3MzJVk+UKds39mofIlBWRV5c3K+aY3sfIlRWVTb2IyrmeLUPESgrGBv7ERVzvNqHCJRVke39iIo5Xu1DBMqqKNp+cDqm3bx5s5WeCOfYOdqOcJsVbU9Eej4gko5wyvr9739vpSeimVO4HeGUdeTIESs9Ec6cou0Ip6wDBw5Y6Ylw5mR7H8KY/56P8no/ItB5Ly/2IQJlebUf4XQuz9Y+RMUsr/cj3MzJxj5ExSyv9yMCzcmLfYiKWV7vRwSak41+cDqPbKsnwjlfHW1PuM2KticiPQcfSU84ZdnqiWjmFG5POGXZ6olw5hRtTzhl2eqJcOZkaz8i0DUgL/YjnK412dyPCJVlcz8inOtn0e5HBMryYj8i3DlFsx8RKMuL/QinOdncjwiU5cV+hNOcou0Hp+u2NjsinGvE0fSE2xwbHRHpde9we8Ipx2ZHRDOncHrCKcdmR4Qzp2h6winHZkeEMydb+xGB1lzE4roG3KsjIABjjEaOHKnXX39d77//vtq3bx/T7LJnntvUr18/rV+/Xh9//HH5R/fu3TVkyBB9/PHHSkpKsp4p+Z/fXlBQoJYtW1p/74suukibNm2qtO3zzz9X27ZtrWdJ0tSpU5WRkaFrrrnGk/eXpEOHDqlOncrVlJSUpNLSUs8yGzVqpJYtW2rfvn2aN2+eBg0a5FlW+/bt1aJFC82fP79827Fjx7Ro0SL16tXLs1yvHT9+XDfccIMKCwv17rvv6vTTT49ZthedcdNNN+mTTz6p1BdZWVl68MEHNW/ePKtZVX377bfasWOH9c6oX7++evToEdPOeOmll5STk6Nu3bp58v7Hjx/X8ePHY9oZ6enpat68uQoLC7Vq1aqw+8Lp+6vNjojV93I3ObY6ItI5hdsTTjk2OyKSOUXSE045NjsinDlF0xNOOTY7Ipw5RdsTgbKPHj0ak30Ir/bDQ+V4vQ/hNCdbcy57n1jsQ4Qas839iLKcWOxDBJqTF/sRZTmx2IcINKdo+8HpmPbMM8+00hOxPHZ2k2WjJyKdUyQd4ZQ1bNgwKz0RyZwi7QinrOTkZCs9Ec6cou0Ip6yTJ09a6Ylw5mR7H6Li+Sgv9yO8PO/llOXVfoSbOdnah6iY5eV+hNOcbO5DVMzycj8i2Jy82IeomOXlfkSwOdnoB6fzyLZ6Ipbnq91k2eiJSOcUSU84ZdnqiUjmFGlPOGXZ6olw5hRtTzhl2eqJcOZkaz8i0DUgL/YjYnGtySnL9n5EOHOKdj8iUJYX+xFu52RjPyJQlhf7EU5zsrkfESjLi/0IpzlF2w9O121tdkSsrhG7ybHVEZHOKdyecMqx2RGRzCmSnnDKsdkR4cwpmp5wyrHZEeHMydZ+RKA1FzV1bUTCisUqRNizf/9+s3btWrN27Vojqfx56l988YXVnLvvvtukp6ebhQsXml27dpV/HDp0yGrOI488YhYvXmy2bdtmPvnkE/PrX//a1KlTx/z73/+2mhOMF48YHjVqlFm4cKHZunWrWb58uRkwYIBJTU0127dvt5pjjDEfffSRqVu3rnnyySdNYWGhefXVV01KSoqZPn269ayTJ0+aM844wzz00EPW37uiW265xbRq1crk5+ebbdu2mddff900a9bM/OpXv7Ke9c4775i5c+earVu3mn//+9+mW7du5vzzzzfHjh2L6n2d/p2OHz/epKenm9dff92sX7/e/PSnPzUtW7Y0JSUlVnO+/fZbs3btWjNnzhwjyfztb38za9euNbt27bI6p+PHj5uBAwea1q1bm48//rhSZxw9etRazoEDB8wjjzxili1bZrZv325Wr15tbrvtNpOcnGw2bNhgdU6BRHqr7VA5+/fvN6NGjTIffvih2bZtm1mwYIHp2bOnadWqVdh/H9zM6fXXXzf16tUzU6ZMMYWFhea5554zSUlJZsmSJVZzjDGmuLjYpKSkmOeffz7seYSTdemll5ouXbqYBQsWmK1bt5qpU6eaBg0amLy8PKs5f//7382CBQvMli1bzJtvvmnatm1rrrvuurDn4+b7q62OcJNloyeccmx2hFOWrZ6IZD8o0o5wyrLVE27mZKsj3P7+RdsTbnJsdYSbLBs94bRvbKsf3GTZ2o8IlWOzH5yybO5HhHsME80jfUJl2dyPcJqTrX5wk2WMnf0Ipxxb/eAmy9Z+RCBVj2lt9kSoHJvHGqGybPdEsBzbxxqhsgKx9QjRijm2jzVCZRljtydC5Rhj71jDKctmT4TKsdURTuejbPWDU47NfgiVZbMfQuXY7odwzxtG2g+hcmz3g9OcbPWDm987W/3glGWrH5xybO5DuDmPbKMn3OTY6gmnLFs94ZRjsyciOd8fSU845djsCTdzstETbn/vbPSEmywbPeEmx2ZPhLoGZPM4I1SO7eOMYFm2jzOC5XhxnBHOtbpojjOC5XhxnBFqTjaPM5x+72weZ4TKsnmcESrHVj+4uW5rqyPcZNnoCaccmx3hlGWrJyK5vh5pRzhl2eoJN3Oy1RFuf/+i7Qk3ObY6wk2WrZ5wWnPh1flKhI8FgglmwYIFRtIpH7fccovVnEAZkszUqVOt5tx6662mbdu2pn79+qZ58+amX79+MVscaIw3CwQHDx5sWrZsaerVq2eysrLMddddZzZu3Gg1o6K33nrLdO3a1SQnJ5vOnTubKVOmeJIzb948I8ls2rTJk/cvU1JSYn7xi1+YM844wzRo0MCceeaZZvTo0VFf/Alk5syZ5swzzzT169c3LVq0MCNGjDDfffdd1O/r9O+0tLTUjBkzxrRo0cIkJyebSy65xKxfv956ztSpUwN+fcyYMVazym7jHehjwYIF1nIOHz5sfvzjH5usrCxTv35907JlSzNw4EDz0UcfhT0fp6xAIt1RDpVz6NAhk5uba5o3b27q1atnzjjjDHPLLbeYL7/80rM5vfTSS6ZDhw6mQYMGplu3bhE9MtxNzgsvvGAaNmwY9b8pp6xdu3aZYcOGmaysLNOgQQNz9tlnmwkTJpjS0lKrOc8++6xp3bp1+Z/To48+GlEvufn+aqsj3GTZ6AmnHJsd4ZRlqyci2Q+KtCOcsmz1hNs52egIt1nR9oSbHFsd4SbLRk847Rvb6gc3Wbb2I0Ll2OwHpyyb+xHhHsNEc0I+VJbN/Qg3c7LRD26zbOxHOOXY6gc3Wbb2IwKpekxrsydC5dg81giVZbsnguXYPtYIlRWIFwsEbR9rhMoqY6snnHJsHWs4ZdnsiVA5tjrC6XyUrX5wyrHZD6GybPZDqBzb/RDuecNI+yFUju1+cDMnG/3gJsdWPzhl2eoHpxzb+xBO55Ft9YRTjs2eCJVlsydC5djuiXDP90faE6FybPeEmznZ6Ak3ObZ6winLVk845djsiVDXgGweZ4TKsX2cESzL9nFGsBwvjjPCuVYXzXFGsBwvjjOc5mTrOMMpx+ZxRqgsm8cZoXJs9YOb67a2OsJNlo2ecMqx2RFOWbZ6IpLr65F2hFOWrZ5wOycbHeE2K9qecJNjqyP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      "text/plain": [
       "<Figure size 2558.88x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "### TASK 1: Visualize the relationship between Flight Number and Launch Site"
   ]
  },
  {
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2558.88x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### TASK 2: Visualize the relationship between Payload and Launch Site"
   ]
  },
  {
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2558.88x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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=\"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 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": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Orbit\n",
       "ES-L1    1.000000\n",
       "GEO      1.000000\n",
       "GTO      0.518519\n",
       "HEO      1.000000\n",
       "ISS      0.619048\n",
       "LEO      0.714286\n",
       "MEO      0.666667\n",
       "PO       0.666667\n",
       "SO       0.000000\n",
       "SSO      1.000000\n",
       "VLEO     0.857143\n",
       "Name: Class, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### TASK  3: Visualize the relationship between success rate of each orbit type\n",
    "df_groupby_orbits = df.groupby('Orbit').Class.mean()\n",
    "df_groupby_orbits"
   ]
  },
  {
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# HINT use groupby method on Orbit column and get the mean of Class column\n",
    "grouped_orbits = df.groupby(by=['Orbit'])['Class'].mean().sort_values(ascending=False).reset_index()\n",
    "fig, ax=plt.subplots(figsize=(12,6))\n",
    "ax = sns.barplot(x = 'Orbit', y = 'Class', data=grouped_orbits)\n",
    "ax.set_title('Plot of success rate by class of each Orbits', fontdict={'size':12})\n",
    "ax.set_ylabel('Class', fontsize = 10)\n",
    "ax.set_xlabel('Orbits', fontsize = 10)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), fontsize = 10, rotation=90)\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": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "### TASK  4: Visualize the relationship between FlightNumber and Orbit type"
   ]
  },
  {
   "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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2558.88x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "### TASK  5: Visualize the relationship between Payload and Orbit type"
   ]
  },
  {
   "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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2558.88x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": 28,
   "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>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</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</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</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</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</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FlightNumber  Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n",
       "0             1  2010       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n",
       "1             2  2012       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n",
       "2             3  2013       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n",
       "3             4  2013       Falcon 9   500.000000    PO   VAFB SLC 4E   \n",
       "4             5  2013       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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A function to Extract years from the date \n",
    "year=[]\n",
    "def Extract_year():\n",
    "    for i in df[\"Date\"]:\n",
    "        year.append(i.split(\"-\")[0])\n",
    "    return year\n",
    "\n",
    "Extract_year()\n",
    "df['Date'] = year\n",
    "df.head()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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_copy = df.copy()\n",
    "df_copy['Extracted_year'] = pd.DatetimeIndex(df['Date']).year\n",
    "\n",
    "# plot line chart\n",
    "fig, ax=plt.subplots(figsize=(12,6))\n",
    "sns.lineplot(data=df_copy, x='Extracted_year', y='Class')\n",
    "plt.title('Plot of launch success yearly trend');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you can observe that the sucess rate since 2013 kept increasing till 2020\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": 30,
   "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": 30,
     "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": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Flights</th>\n",
       "      <th>GridFins</th>\n",
       "      <th>Reused</th>\n",
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       "      <th>ReusedCount</th>\n",
       "      <th>Orbit_GEO</th>\n",
       "      <th>Orbit_GTO</th>\n",
       "      <th>...</th>\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_GEO  Orbit_GTO  ...  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          1  ...             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",
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       "\n",
       "[5 rows x 76 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### TASK  7: Create dummy variables to categorical columns\n",
    "features_one_hot = pd.get_dummies(features, drop_first=True)\n",
    "features_one_hot.head()"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HINT: Use get_dummies() function on the categorical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FlightNumber    float64\n",
       "PayloadMass     float64\n",
       "Flights         float64\n",
       "GridFins        float64\n",
       "Reused          float64\n",
       "                 ...   \n",
       "Serial_B1056    float64\n",
       "Serial_B1058    float64\n",
       "Serial_B1059    float64\n",
       "Serial_B1060    float64\n",
       "Serial_B1062    float64\n",
       "Length: 76, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### TASK  8: Cast all numeric columns to `float64`\n",
    "features_one_hot =  features_one_hot.astype(float)\n",
    "features_one_hot.dtypes"
   ]
  },
  {
   "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": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HINT: use astype function\n",
    "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": [
    "[Pratiksha Verma](https://www.linkedin.com/in/pratiksha-verma-6487561b1/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2022-01-01)\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",
    "| 2022-11-09        | 1.0     | Pratiksha Verma | Converted initial version to Jupyterlite|\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <h3 align=\"center\"> IBM Corporation 2022. All rights reserved. <h3/>\n"
   ]
  }
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