EDA with Data Visualization.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "    <img src=\"https://gitlab.com/ibm/skills-network/courses/placeholder101/-/raw/master/labs/module%201/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **SpaceX  Falcon 9 First Stage Landing Prediction**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assignment: Exploring and Preparing Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimated time needed: **70** minutes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this assignment, we will predict if the Falcon 9 first stage will land successfully. SpaceX advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is due to the fact that SpaceX can reuse the first stage.\n",
    "\n",
    "In this lab, you will perform Exploratory Data Analysis and Feature Engineering.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Falcon 9 first stage will land successfully\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing\\_1.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several examples of an unsuccessful landing are shown here:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n",
    "\n",
    "Perform exploratory Data Analysis and Feature Engineering using `Pandas` and `Matplotlib`\n",
    "\n",
    "*   Exploratory Data Analysis\n",
    "*   Preparing Data  Feature Engineering\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries and Define Auxiliary Functions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will import the following libraries the lab\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": "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": {
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       "\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",
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       "      <td>B0005</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2013-03-01</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>ISS</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0007</td>\n",
       "      <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",
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       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
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      "text/plain": [
       "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n",
       "0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n",
       "1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n",
       "2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n",
       "3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n",
       "4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n",
       "\n",
       "       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n",
       "0    None None        1     False   False  False        NaN    1.0   \n",
       "1    None None        1     False   False  False        NaN    1.0   \n",
       "2    None None        1     False   False  False        NaN    1.0   \n",
       "3  False Ocean        1     False   False  False        NaN    1.0   \n",
       "4    None None        1     False   False  False        NaN    1.0   \n",
       "\n",
       "   ReusedCount Serial   Longitude   Latitude  Class  \n",
       "0            0  B0003  -80.577366  28.561857      0  \n",
       "1            0  B0005  -80.577366  28.561857      0  \n",
       "2            0  B0007  -80.577366  28.561857      0  \n",
       "3            0  B1003 -120.610829  34.632093      0  \n",
       "4            0  B1004  -80.577366  28.561857      0  "
      ]
     },
     "execution_count": 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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\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=\"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": "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": 6,
   "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>2010-06-04</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",
       "    </tr>\n",
       "  </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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check head of dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group df by Orbits and find the mean of Class column\n",
    "df_groupby_orbits = df.groupby('Orbit').Class.mean()\n",
    "df_groupby_orbits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 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);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Analyze the ploted bar chart try to find which orbits have high success rate.\n",
    "\n",
    "Looking at the plot, we can see that `ES-L1`, `GEO`, `HEO`, `SSO`, and `VLEO` are the Orbits that have high success rate. The `SO` has the least success rate amongst the orbits."
   ]
  },
  {
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1842.38x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be FlightNumber and y axis to be the Orbit, and hue to be the class value\n",
    "sns.catplot(y=\"Orbit\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Flight Number\",fontsize=20)\n",
    "plt.ylabel(\"Orbit\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should see that in the LEO orbit the Success appears related to the number of flights; on the other hand, there seems to be no relationship between flight number when in GTO orbit.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  5: Visualize the relationship between Payload and Orbit type\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, we can plot the Payload vs. Orbit scatter point charts to reveal the relationship between Payload and Orbit type\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1842.38x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be Payload and y axis to be the Orbit, and hue to be the class value\n",
    "sns.catplot(y=\"Orbit\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"PayloadMass\",fontsize=20)\n",
    "plt.ylabel(\"Orbit\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With heavy payloads the successful landing or positive landing rate are more for Polar,LEO and ISS.\n",
    "\n",
    "However for GTO we cannot distinguish this well as both positive landing rate and negative landing(unsuccessful mission) are both there here.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  6: Visualize the launch success yearly trend\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can plot a line chart with x axis to be <code>Year</code> and y axis to be average success rate, to get the average launch success trend.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function will help you get the year from the date:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A function to Extract years from the date \n",
    "year=[]\n",
    "def Extract_year(date):\n",
    "    for i in df[\"Date\"]:\n",
    "        year.append(i.split(\"-\")[0])\n",
    "    return year\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\n",
    "df_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": "markdown",
   "metadata": {},
   "source": [
    "## Features Engineering\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By now, you should obtain some preliminary insights about how each important variable would affect the success rate, we will select the features that will be used in success prediction in the future module.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 13,
     "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <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_GEO</th>\n",
       "      <th>Orbit_GTO</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",
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       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>...</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3170.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
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       "<p>5 rows × 76 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   FlightNumber  PayloadMass  Flights  GridFins  Reused   Legs  Block  \\\n",
       "0             1  6104.959412        1     False   False  False    1.0   \n",
       "1             2   525.000000        1     False   False  False    1.0   \n",
       "2             3   677.000000        1     False   False  False    1.0   \n",
       "3             4   500.000000        1     False   False  False    1.0   \n",
       "4             5  3170.000000        1     False   False  False    1.0   \n",
       "\n",
       "   ReusedCount  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",
       "4             0             0             0  \n",
       "\n",
       "[5 rows x 76 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HINT: Use get_dummies() function on the categorical columns\n",
    "features_one_hot = pd.get_dummies(features, drop_first=True)\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": 15,
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HINT: use astype function\n",
    "features_one_hot =  features_one_hot.astype(float)\n",
    "features_one_hot.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# export csv\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": [
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.linkedin.com/in/nayefaboutayoun/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Nayef Abou Tayoun</a> is a Data Scientist at IBM and pursuing a Master of Management in Artificial intelligence degree at Queen's University.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Log\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Date (YYYY-MM-DD) | Version | Changed By    | Change Description      |\n",
    "| ----------------- | ------- | ------------- | ----------------------- |\n",
    "| 2021-10-12        | 1.1     | Lakshmi Holla | Modified markdown       |\n",
    "| 2020-09-20        | 1.0     | Joseph        | Modified Multiple Areas |\n",
    "| 2020-11-10        | 1.1     | Nayef         | updating the input data |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright © 2020 IBM Corporation. All rights reserved.\n"
   ]
  }
 ],
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