02_jupyter-labs-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": [
    "***\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries and Define Auxiliary Functions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will import the following libraries the lab\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# andas is a software library written for the Python programming language for data manipulation and analysis.\n",
    "import pandas as pd\n",
    "#NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays\n",
    "import numpy as np\n",
    "# Matplotlib is a plotting library for python and pyplot gives us a MatLab like plotting framework. We will use this in our plotter function to plot data.\n",
    "import matplotlib.pyplot as plt\n",
    "#Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's read the SpaceX dataset into a Pandas dataframe and print its summary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .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": 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": {
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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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      "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 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be Pay Load Mass (kg) and y axis to be the launch site, and hue to be the class value\n",
    "sns.catplot(y=\"LaunchSite\", x=\"PayloadMass\", hue=\"Class\", data=df)\n",
    "plt.xlabel(\"Pay Load Mass (kg)\",fontsize=20)\n",
    "plt.ylabel(\"Launch Site\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now if you observe Payload Vs. Launch Site scatter point chart you will find for the VAFB-SLC  launchsite there are no  rockets  launched for  heavypayload mass(greater than 10000).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  3: Visualize the relationship between success rate of each orbit type\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we want to visually check if there are any relationship between success rate and orbit type.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a `bar chart` for the sucess rate of each orbit\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# HINT use groupby method on Orbit column and get the mean of Class column\n",
    "t = df.groupby(['Orbit', 'Class'])['Class'].agg(['mean']).reset_index()\n",
    "sns.barplot(y=\"Class\", x=\"Orbit\", data=t)\n",
    "\n",
    "plt.xlabel(\"Orbit\",fontsize=20)\n",
    "plt.ylabel(\"Class\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analyze the ploted bar chart try to find which orbits have high sucess rate.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  4: Visualize the relationship between FlightNumber and Orbit type\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each orbit, we want to see if there is any relationship between FlightNumber and Orbit type.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 402.375x360 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)\n",
    "plt.xlabel(\"FlightNumber\",fontsize=20)\n",
    "plt.ylabel(\"Orbit\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should see that in the LEO orbit the Success appears related to the number of flights; on the other hand, there seems to be no relationship between flight number when in GTO orbit.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  5: Visualize the relationship between Payload and Orbit type\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, we can plot the Payload vs. Orbit scatter point charts to reveal the relationship between Payload and Orbit type\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 402.375x360 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)\n",
    "plt.xlabel(\"Payload\",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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\n",
    "sns.lineplot(data=df, x=\"Date\", y=\"Class\")\n",
    "plt.xlabel(\"Date\",fontsize=20)\n",
    "plt.ylabel(\"Success Rate\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you can observe that the sucess rate since 2013 kept increasing till 2020\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features Engineering\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By now, you should obtain some preliminary insights about how each important variable would affect the success rate, we will select the features that will be used in success prediction in the future module.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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": 14,
     "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Flights</th>\n",
       "      <th>GridFins</th>\n",
       "      <th>Reused</th>\n",
       "      <th>Legs</th>\n",
       "      <th>Block</th>\n",
       "      <th>ReusedCount</th>\n",
       "      <th>Orbit_ES-L1</th>\n",
       "      <th>Orbit_GEO</th>\n",
       "      <th>...</th>\n",
       "      <th>Serial_B1048</th>\n",
       "      <th>Serial_B1049</th>\n",
       "      <th>Serial_B1050</th>\n",
       "      <th>Serial_B1051</th>\n",
       "      <th>Serial_B1054</th>\n",
       "      <th>Serial_B1056</th>\n",
       "      <th>Serial_B1058</th>\n",
       "      <th>Serial_B1059</th>\n",
       "      <th>Serial_B1060</th>\n",
       "      <th>Serial_B1062</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>1</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "<p>5 rows × 80 columns</p>\n",
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      ],
      "text/plain": [
       "   FlightNumber  PayloadMass  Flights  GridFins  Reused   Legs  Block  \\\n",
       "0             1  6104.959412        1     False   False  False    1.0   \n",
       "1             2   525.000000        1     False   False  False    1.0   \n",
       "2             3   677.000000        1     False   False  False    1.0   \n",
       "3             4   500.000000        1     False   False  False    1.0   \n",
       "4             5  3170.000000        1     False   False  False    1.0   \n",
       "\n",
       "   ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n",
       "0            0            0          0  ...             0             0   \n",
       "1            0            0          0  ...             0             0   \n",
       "2            0            0          0  ...             0             0   \n",
       "3            0            0          0  ...             0             0   \n",
       "4            0            0          0  ...             0             0   \n",
       "\n",
       "   Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             0             0             0             0   \n",
       "2             0             0             0             0             0   \n",
       "3             0             0             0             0             0   \n",
       "4             0             0             0             0             0   \n",
       "\n",
       "   Serial_B1059  Serial_B1060  Serial_B1062  \n",
       "0             0             0             0  \n",
       "1             0             0             0  \n",
       "2             0             0             0  \n",
       "3             0             0             0  \n",
       "4             0             0             0  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HINT: Use get_dummies() function on the categorical columns\n",
    "features_one_hot = pd.get_dummies(features, columns=['Orbit', 'LaunchSite', 'LandingPad', 'Serial'])\n",
    "features_one_hot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  8: Cast all numeric columns to `float64`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that our <code>features_one_hot</code> dataframe only contains numbers cast the entire dataframe to variable type <code>float64</code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "    FlightNumber   PayloadMass  Flights  GridFins  Reused  Legs  Block  \\\n",
       "0            1.0   6104.959412      1.0       0.0     0.0   0.0    1.0   \n",
       "1            2.0    525.000000      1.0       0.0     0.0   0.0    1.0   \n",
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       "4            5.0   3170.000000      1.0       0.0     0.0   0.0    1.0   \n",
       "..           ...           ...      ...       ...     ...   ...    ...   \n",
       "85          86.0  15400.000000      2.0       1.0     1.0   1.0    5.0   \n",
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       "87          88.0  15400.000000      6.0       1.0     1.0   1.0    5.0   \n",
       "88          89.0  15400.000000      3.0       1.0     1.0   1.0    5.0   \n",
       "89          90.0   3681.000000      1.0       1.0     0.0   1.0    5.0   \n",
       "\n",
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       "    Serial_B1059  Serial_B1060  Serial_B1062  \n",
       "0            0.0           0.0           0.0  \n",
       "1            0.0           0.0           0.0  \n",
       "2            0.0           0.0           0.0  \n",
       "3            0.0           0.0           0.0  \n",
       "4            0.0           0.0           0.0  \n",
       "..           ...           ...           ...  \n",
       "85           0.0           1.0           0.0  \n",
       "86           0.0           0.0           0.0  \n",
       "87           0.0           0.0           0.0  \n",
       "88           0.0           1.0           0.0  \n",
       "89           0.0           0.0           1.0  \n",
       "\n",
       "[90 rows x 80 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HINT: use astype function\n",
    "features_one_hot.astype(float)"
   ]
  },
  {
   "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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">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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