5-EDA-with-Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "    <img src=\"https://gitlab.com/ibm/skills-network/courses/placeholder101/-/raw/master/labs/module%201/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **SpaceX  Falcon 9 First Stage Landing Prediction**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assignment: Exploring and Preparing Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimated time needed: **70** minutes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this assignment, we will predict if the Falcon 9 first stage will land successfully. SpaceX advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is due to the fact that SpaceX can reuse the first stage.\n",
    "\n",
    "In this lab, you will perform Exploratory Data Analysis and Feature Engineering.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Falcon 9 first stage will land successfully\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing\\_1.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several examples of an unsuccessful landing are shown here:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n",
    "\n",
    "Perform exploratory Data Analysis and Feature Engineering using `Pandas` and `Matplotlib`\n",
    "\n",
    "*   Exploratory Data Analysis\n",
    "*   Preparing Data  Feature Engineering\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries and Define Auxiliary Functions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will import the following libraries the lab\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# andas is a software library written for the Python programming language for data manipulation and analysis.\n",
    "import pandas as pd\n",
    "#NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays\n",
    "import numpy as np\n",
    "# Matplotlib is a plotting library for python and pyplot gives us a MatLab like plotting framework. We will use this in our plotter function to plot data.\n",
    "import matplotlib.pyplot as plt\n",
    "#Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's read the SpaceX dataset into a Pandas dataframe and print its summary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .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": {
      "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(\"Payload Mass (kg)\",fontsize=20)\n",
    "plt.ylabel(\"Launch Site\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now try to explain any patterns you found in the Payload Vs. Launch Site scatter point chart.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Payloads that approach MAX(Payload) tended to launch from CCAFS SLC 40 & KSC LC 39A\n",
    "# Payloads less than 8000 kg tended to fail at a higher rate when launched from CCAFS SLC 40,\n",
    "#  plausibly due to that launch site being used for R&D versus the other two launch \n",
    "#  sites used with less failure-tolerant payloads."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  3: Visualize the relationship between success rate of each orbit type\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we want to visually check if there are any relationship between success rate and orbit type.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a `bar chart` for the sucess rate of each orbit\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# HINT use groupby method on Orbit column and get the mean of Class column\n",
    "df.groupby(\"Orbit\").mean()['Class'].plot(kind='bar')\n",
    "plt.xlabel(\"Orbit Type\",fontsize=20)\n",
    "plt.ylabel(\"Success Rate\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analyze the ploted bar chart try to find which orbits have high sucess rate.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  4: Visualize the relationship between FlightNumber and Orbit type\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each orbit, we want to see if there is any relationship between FlightNumber and Orbit type.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1842.38x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be FlightNumber and y axis to be the Orbit, and hue to be the class value\n",
    "sns.catplot(y=\"Orbit\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"FlightNumber\",fontsize=20)\n",
    "plt.ylabel(\"Orbit\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should see that in the LEO orbit the Success appears related to the number of flights; on the other hand, there seems to be no relationship between flight number when in GTO orbit.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  5: Visualize the relationship between Payload and Orbit type\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, we can plot the Payload vs. Orbit scatter point charts to reveal the relationship between Payload and Orbit type\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\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\",fontsize=20)\n",
    "plt.ylabel(\"Orbit\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should observe that Heavy payloads have a negative influence on GTO orbits and positive on GTO and Polar LEO (ISS) orbits.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  6: Visualize the launch success yearly trend\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can plot a line chart with x axis to be <code>Year</code> and y axis to be average success rate, to get the average launch success trend.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function will help you get the year from the date:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A function to Extract years from the date \n",
    "year=[]\n",
    "def Extract_year(date):\n",
    "    for i in df[\"Date\"]:\n",
    "        year.append(i.split(\"-\")[0])\n",
    "    return year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\n",
    "df1=pd.DataFrame(Extract_year(df['Date']),columns =['year'])\n",
    "df1['Class']=df['Class']\n",
    "sns.lineplot(data=df1, x=np.unique(Extract_year(df['Date'])), y=df1.groupby('year')['Class'].mean())\n",
    "plt.xlabel(\"Year\", fontsize=20)\n",
    "plt.ylabel(\"Success Rate\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you can observe that the sucess rate since 2013 kept increasing till 2020\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features Engineering\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By now, you should obtain some preliminary insights about how each important variable would affect the success rate, we will select the features that will be used in success prediction in the \n",
    "future module.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Orbit</th>\n",
       "      <th>LaunchSite</th>\n",
       "      <th>Flights</th>\n",
       "      <th>GridFins</th>\n",
       "      <th>Reused</th>\n",
       "      <th>Legs</th>\n",
       "      <th>LandingPad</th>\n",
       "      <th>Block</th>\n",
       "      <th>ReusedCount</th>\n",
       "      <th>Serial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>ISS</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>PO</td>\n",
       "      <td>VAFB SLC 4E</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3170.000000</td>\n",
       "      <td>GTO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FlightNumber  PayloadMass Orbit    LaunchSite  Flights  GridFins  Reused  \\\n",
       "0             1  6104.959412   LEO  CCAFS SLC 40        1     False   False   \n",
       "1             2   525.000000   LEO  CCAFS SLC 40        1     False   False   \n",
       "2             3   677.000000   ISS  CCAFS SLC 40        1     False   False   \n",
       "3             4   500.000000    PO   VAFB SLC 4E        1     False   False   \n",
       "4             5  3170.000000   GTO  CCAFS SLC 40        1     False   False   \n",
       "\n",
       "    Legs LandingPad  Block  ReusedCount Serial  \n",
       "0  False        NaN    1.0            0  B0003  \n",
       "1  False        NaN    1.0            0  B0005  \n",
       "2  False        NaN    1.0            0  B0007  \n",
       "3  False        NaN    1.0            0  B1003  \n",
       "4  False        NaN    1.0            0  B1004  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = df[['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights', 'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount', 'Serial']]\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  7: Create dummy variables to categorical columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the function <code>get_dummies</code> and <code>features</code> dataframe to apply OneHotEncoder to the column <code>Orbits</code>, <code>LaunchSite</code>, <code>LandingPad</code>, and <code>Serial</code>. Assign the value to the variable <code>features_one_hot</code>, display the results using the method head. Your result dataframe must include all features including the encoded ones.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Flights</th>\n",
       "      <th>GridFins</th>\n",
       "      <th>Reused</th>\n",
       "      <th>Legs</th>\n",
       "      <th>Block</th>\n",
       "      <th>ReusedCount</th>\n",
       "      <th>Orbit_ES-L1</th>\n",
       "      <th>Orbit_GEO</th>\n",
       "      <th>...</th>\n",
       "      <th>Serial_B1048</th>\n",
       "      <th>Serial_B1049</th>\n",
       "      <th>Serial_B1050</th>\n",
       "      <th>Serial_B1051</th>\n",
       "      <th>Serial_B1054</th>\n",
       "      <th>Serial_B1056</th>\n",
       "      <th>Serial_B1058</th>\n",
       "      <th>Serial_B1059</th>\n",
       "      <th>Serial_B1060</th>\n",
       "      <th>Serial_B1062</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 80 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_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n",
       "0            0            0          0  ...             0             0   \n",
       "1            0            0          0  ...             0             0   \n",
       "2            0            0          0  ...             0             0   \n",
       "3            0            0          0  ...             0             0   \n",
       "4            0            0          0  ...             0             0   \n",
       "\n",
       "   Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             0             0             0             0   \n",
       "2             0             0             0             0             0   \n",
       "3             0             0             0             0             0   \n",
       "4             0             0             0             0             0   \n",
       "\n",
       "   Serial_B1059  Serial_B1060  Serial_B1062  \n",
       "0             0             0             0  \n",
       "1             0             0             0  \n",
       "2             0             0             0  \n",
       "3             0             0             0  \n",
       "4             0             0             0  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HINT: Use get_dummies() function on the categorical columns\n",
    "features_one_hot = pd.get_dummies(features, columns = ['Orbit', 'LaunchSite', 'LandingPad', 'Serial'])\n",
    "features_one_hot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  8: Cast all numeric columns to `float64`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that our <code>features_one_hot</code> dataframe only contains numbers cast the entire dataframe to variable type <code>float64</code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Flights</th>\n",
       "      <th>GridFins</th>\n",
       "      <th>Reused</th>\n",
       "      <th>Legs</th>\n",
       "      <th>Block</th>\n",
       "      <th>ReusedCount</th>\n",
       "      <th>Orbit_ES-L1</th>\n",
       "      <th>Orbit_GEO</th>\n",
       "      <th>...</th>\n",
       "      <th>Serial_B1048</th>\n",
       "      <th>Serial_B1049</th>\n",
       "      <th>Serial_B1050</th>\n",
       "      <th>Serial_B1051</th>\n",
       "      <th>Serial_B1054</th>\n",
       "      <th>Serial_B1056</th>\n",
       "      <th>Serial_B1058</th>\n",
       "      <th>Serial_B1059</th>\n",
       "      <th>Serial_B1060</th>\n",
       "      <th>Serial_B1062</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3170.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>86.0</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>87.0</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>88.0</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>89.0</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>90.0</td>\n",
       "      <td>3681.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 80 columns</p>\n",
       "</div>"
      ],
      "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",
       "2            3.0    677.000000      1.0       0.0     0.0   0.0    1.0   \n",
       "3            4.0    500.000000      1.0       0.0     0.0   0.0    1.0   \n",
       "4            5.0   3170.000000      1.0       0.0     0.0   0.0    1.0   \n",
       "..           ...           ...      ...       ...     ...   ...    ...   \n",
       "85          86.0  15400.000000      2.0       1.0     1.0   1.0    5.0   \n",
       "86          87.0  15400.000000      3.0       1.0     1.0   1.0    5.0   \n",
       "87          88.0  15400.000000      6.0       1.0     1.0   1.0    5.0   \n",
       "88          89.0  15400.000000      3.0       1.0     1.0   1.0    5.0   \n",
       "89          90.0   3681.000000      1.0       1.0     0.0   1.0    5.0   \n",
       "\n",
       "    ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n",
       "0           0.0          0.0        0.0  ...           0.0           0.0   \n",
       "1           0.0          0.0        0.0  ...           0.0           0.0   \n",
       "2           0.0          0.0        0.0  ...           0.0           0.0   \n",
       "3           0.0          0.0        0.0  ...           0.0           0.0   \n",
       "4           0.0          0.0        0.0  ...           0.0           0.0   \n",
       "..          ...          ...        ...  ...           ...           ...   \n",
       "85          2.0          0.0        0.0  ...           0.0           0.0   \n",
       "86          2.0          0.0        0.0  ...           0.0           0.0   \n",
       "87          5.0          0.0        0.0  ...           0.0           0.0   \n",
       "88          2.0          0.0        0.0  ...           0.0           0.0   \n",
       "89          0.0          0.0        0.0  ...           0.0           0.0   \n",
       "\n",
       "    Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n",
       "0            0.0           0.0           0.0           0.0           0.0   \n",
       "1            0.0           0.0           0.0           0.0           0.0   \n",
       "2            0.0           0.0           0.0           0.0           0.0   \n",
       "3            0.0           0.0           0.0           0.0           0.0   \n",
       "4            0.0           0.0           0.0           0.0           0.0   \n",
       "..           ...           ...           ...           ...           ...   \n",
       "85           0.0           0.0           0.0           0.0           0.0   \n",
       "86           0.0           0.0           0.0           0.0           1.0   \n",
       "87           0.0           1.0           0.0           0.0           0.0   \n",
       "88           0.0           0.0           0.0           0.0           0.0   \n",
       "89           0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "    Serial_B1059  Serial_B1060  Serial_B1062  \n",
       "0            0.0           0.0           0.0  \n",
       "1            0.0           0.0           0.0  \n",
       "2            0.0           0.0           0.0  \n",
       "3            0.0           0.0           0.0  \n",
       "4            0.0           0.0           0.0  \n",
       "..           ...           ...           ...  \n",
       "85           0.0           1.0           0.0  \n",
       "86           0.0           0.0           0.0  \n",
       "87           0.0           0.0           0.0  \n",
       "88           0.0           1.0           0.0  \n",
       "89           0.0           0.0           1.0  \n",
       "\n",
       "[90 rows x 80 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HINT: use astype function\n",
    "features_one_hot.astype('float64')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now export it to a <b>CSV</b> for the next section,but to make the answers consistent, in the next lab we will provide data in a pre-selected date range.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<code>features_one_hot.to_csv('dataset_part\\_3.csv', index=False)</code>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.linkedin.com/in/nayefaboutayoun/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Nayef Abou Tayoun</a> is a Data Scientist at IBM and pursuing a Master of Management in Artificial intelligence degree at Queen's University.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Log\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Date (YYYY-MM-DD) | Version | Changed By | Change Description      |\n",
    "| ----------------- | ------- | ---------- | ----------------------- |\n",
    "| 2020-09-20        | 1.0     | Joseph     | Modified Multiple Areas |\n",
    "| 2020-11-10        | 1.1     | Nayef      | updating the input data |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright © 2020 IBM Corporation. All rights reserved.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python",
   "language": "python",
   "name": "conda-env-python-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}