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": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>Date</th>\n",
       "      <th>BoosterVersion</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Orbit</th>\n",
       "      <th>LaunchSite</th>\n",
       "      <th>Outcome</th>\n",
       "      <th>Flights</th>\n",
       "      <th>GridFins</th>\n",
       "      <th>Reused</th>\n",
       "      <th>Legs</th>\n",
       "      <th>LandingPad</th>\n",
       "      <th>Block</th>\n",
       "      <th>ReusedCount</th>\n",
       "      <th>Serial</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-06-04</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0003</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2012-05-22</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <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",
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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",
       "      <td>0</td>\n",
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       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n",
       "0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n",
       "1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n",
       "2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n",
       "3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n",
       "4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n",
       "\n",
       "       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n",
       "0    None None        1     False   False  False        NaN    1.0   \n",
       "1    None None        1     False   False  False        NaN    1.0   \n",
       "2    None None        1     False   False  False        NaN    1.0   \n",
       "3  False Ocean        1     False   False  False        NaN    1.0   \n",
       "4    None None        1     False   False  False        NaN    1.0   \n",
       "\n",
       "   ReusedCount Serial   Longitude   Latitude  Class  \n",
       "0            0  B0003  -80.577366  28.561857      0  \n",
       "1            0  B0005  -80.577366  28.561857      0  \n",
       "2            0  B0007  -80.577366  28.561857      0  \n",
       "3            0  B1003 -120.610829  34.632093      0  \n",
       "4            0  B1004  -80.577366  28.561857      0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\")\n",
    "\n",
    "# If you were unable to complete the previous lab correctly you can uncomment and load this csv\n",
    "\n",
    "# df = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/dataset_part_2.csv')\n",
    "\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's try to see how the `FlightNumber` (indicating the continuous launch attempts.) and `Payload` variables would affect the launch outcome.\n",
    "\n",
    "We can plot out the <code>FlightNumber</code> vs. <code>PayloadMass</code>and overlay the outcome of the launch. We see that as the flight number increases, the first stage is more likely to land successfully. The payload mass is also important; it seems the more massive the payload, the less likely the first stage will return.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1842.38x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(y=\"PayloadMass\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Flight Number\",fontsize=20)\n",
    "plt.ylabel(\"Pay load Mass (kg)\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that different launch sites have different success rates.  <code>CCAFS LC-40</code>, has a success rate of 60 %, while  <code>KSC LC-39A</code> and <code>VAFB SLC 4E</code> has a success rate of 77%.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's drill down to each site visualize its detailed launch records.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK 1: Visualize the relationship between Flight Number and Launch Site\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the function <code>catplot</code> to plot <code>FlightNumber</code> vs <code>LaunchSite</code>, set the  parameter <code>x</code>  parameter to <code>FlightNumber</code>,set the  <code>y</code> to <code>Launch Site</code> and set the parameter <code>hue</code> to <code>'class'</code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1842.38x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be Flight Number and y axis to be the launch site, and hue to be the class value\n",
    "sns.catplot(y=\"LaunchSite\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Flight Number\",fontsize=20)\n",
    "plt.ylabel(\"Launch Site\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now try to explain the patterns you found in the Flight Number vs. Launch Site scatter point plots.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK 2: Visualize the relationship between Payload and Launch Site\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also want to observe if there is any relationship between launch sites and their payload mass.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1842.38x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be Pay Load Mass (kg) and y axis to be the launch site, and hue to be the class value\n",
    "sns.catplot(y=\"LaunchSite\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# HINT use groupby method on Orbit column and get the mean of Class column\n",
    "df_mean_orbit = df.groupby('Orbit').mean().reset_index()\n",
    "# df_mean_orbit\n",
    "sns.barplot(data=df_mean_orbit, y=\"Class\", x=\"Orbit\")\n",
    "plt.xlabel(\"Orbit Type\",fontsize=20)\n",
    "plt.ylabel(\"Success Rate\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analyze the ploted bar chart try to find which orbits have high sucess rate.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  4: Visualize the relationship between FlightNumber and Orbit type\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each orbit, we want to see if there is any relationship between FlightNumber and Orbit type.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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 Type\",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": 29,
   "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 Payload and y axis to be the Orbit, and hue to be the class value\n",
    "sns.catplot(y=\"Orbit\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Payload Mass (kg)\",fontsize=20)\n",
    "plt.ylabel(\"Orbit Type\",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": 30,
   "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": 64,
   "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",
    "df_year_col=pd.DataFrame(Extract_year(df[\"Date\"]), columns=['Year']).astype({'Year': 'int32'})\n",
    "df_year=pd.concat([df,df_year_col],axis=1)\n",
    "df_year.groupby('Year').mean().reset_index()\n",
    "\n",
    "sns.lineplot(data=df_year, y=\"Class\", x=\"Year\")\n",
    "plt.xlabel(\"Year\",fontsize=20)\n",
    "plt.ylabel(\"Success Rate\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you can observe that the sucess rate since 2013 kept increasing till 2020\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features Engineering\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By now, you should obtain some preliminary insights about how each important variable would affect the success rate, we will select the features that will be used in success prediction in the future module.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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": 65,
     "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": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HINT: Use get_dummies() function on the categorical columns\n",
    "features_one_hot = pd.get_dummies(features)"
   ]
  },
  {
   "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": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HINT: use astype function\n",
    "features_one_hot = features_one_hot.astype('float64')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now export it to a <b>CSV</b> for the next section,but to make the answers consistent, in the next lab we will provide data in a pre-selected date range.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<code>features_one_hot.to_csv('dataset_part\\_3.csv', index=False)</code>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">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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