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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2023-01-01\">\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",
    "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",
    "\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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      "text/plain": [
       "<Figure size 2558.88x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(y=\"PayloadMass\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Flight Number\",fontsize=20)\n",
    "plt.ylabel(\"Pay load Mass (kg)\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that different launch sites have different success rates.  <code>CCAFS LC-40</code>, has a success rate of 60 %, while  <code>KSC LC-39A</code> and <code>VAFB SLC 4E</code> has a success rate of 77%.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's drill down to each site visualize its detailed launch records.\n"
   ]
  },
  {
   "cell_type": "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 2558.88x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be Flight Number and y axis to be the launch site, and hue to be the class value\n",
    "sns.catplot(y=\"LaunchSite\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Flight Number\",fontsize=20)\n",
    "plt.ylabel(\"Launch Site\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now try to explain the patterns you found in the Flight Number vs. Launch Site scatter point plots.\n"
   ]
  },
  {
   "cell_type": "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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      "text/plain": [
       "<Figure size 2558.88x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be Pay Load Mass (kg) and y axis to be the launch site, and hue to be the class value\n",
    "sns.catplot(y=\"LaunchSite\", x=\"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 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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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# HINT use groupby method on Orbit column and get the mean of Class column\n",
    "sns.countplot(x='Orbit' ,data=df);"
   ]
  },
  {
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2558.88x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be FlightNumber and y axis to be the Orbit, and hue to be the class value\n",
    "sns.catplot(y=\"Orbit\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Flight Number\",fontsize=20)\n",
    "plt.ylabel(\"Orbit\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should see that in the LEO orbit the Success appears related to the number of flights; on the other hand, there seems to be no relationship between flight number when in GTO orbit.\n"
   ]
  },
  {
   "cell_type": "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2558.88x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be Payload and y axis to be the Orbit, and hue to be the class value\n",
    "sns.catplot(y=\"Orbit\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Payload Mass (kg)\",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",
    "\n",
    "\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": 9,
   "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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "year = Extract_year(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "success_percent=df['Class']*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\n",
    "sns.lineplot(x=year,y=success_percent,data=df)\n",
    "plt.xlabel('Year',fontsize=20)\n",
    "plt.ylabel('Success %',fontsize=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you can observe that the sucess rate since 2013 kept increasing till 2020\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features Engineering \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By now, you should obtain some preliminary insights about how each important variable would affect the success rate, we will select the features that will be used in success prediction in the future module.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Orbit</th>\n",
       "      <th>LaunchSite</th>\n",
       "      <th>Flights</th>\n",
       "      <th>GridFins</th>\n",
       "      <th>Reused</th>\n",
       "      <th>Legs</th>\n",
       "      <th>LandingPad</th>\n",
       "      <th>Block</th>\n",
       "      <th>ReusedCount</th>\n",
       "      <th>Serial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>ISS</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>B0007</td>\n",
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       "    <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",
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       "    </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",
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       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1004</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FlightNumber  PayloadMass Orbit    LaunchSite  Flights  GridFins  Reused  \\\n",
       "0             1  6104.959412   LEO  CCAFS SLC 40        1     False   False   \n",
       "1             2   525.000000   LEO  CCAFS SLC 40        1     False   False   \n",
       "2             3   677.000000   ISS  CCAFS SLC 40        1     False   False   \n",
       "3             4   500.000000    PO   VAFB SLC 4E        1     False   False   \n",
       "4             5  3170.000000   GTO  CCAFS SLC 40        1     False   False   \n",
       "\n",
       "    Legs LandingPad  Block  ReusedCount Serial  \n",
       "0  False        NaN    1.0            0  B0003  \n",
       "1  False        NaN    1.0            0  B0005  \n",
       "2  False        NaN    1.0            0  B0007  \n",
       "3  False        NaN    1.0            0  B1003  \n",
       "4  False        NaN    1.0            0  B1004  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = df[['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights', 'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount', 'Serial']]\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### TASK  7: Create dummy variables to categorical columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the function <code>get_dummies</code> and <code>features</code> dataframe to apply OneHotEncoder to the column <code>Orbits</code>, <code>LaunchSite</code>, <code>LandingPad</code>, and <code>Serial</code>. Assign the value to the variable <code>features_one_hot</code>, display the results using the method head. Your result dataframe must include all features including the encoded ones.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HINT: Use get_dummies() function on the categorical columns\n",
    "features_one_hot = pd.get_dummies(features[['Orbit','LaunchSite','LandingPad','Serial']],dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "    Orbit_ES-L1  Orbit_GEO  Orbit_GTO  Orbit_HEO  Orbit_ISS  Orbit_LEO  \\\n",
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      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_one_hot"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = features.drop(['Orbit','LaunchSite','LandingPad','Serial'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>86</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>87</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>88</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>89</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>90</td>\n",
       "      <td>3681.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 8 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",
       "85            86  15400.000000        2      True    True   True    5.0   \n",
       "86            87  15400.000000        3      True    True   True    5.0   \n",
       "87            88  15400.000000        6      True    True   True    5.0   \n",
       "88            89  15400.000000        3      True    True   True    5.0   \n",
       "89            90   3681.000000        1      True   False   True    5.0   \n",
       "\n",
       "    ReusedCount  \n",
       "0             0  \n",
       "1             0  \n",
       "2             0  \n",
       "3             0  \n",
       "4             0  \n",
       "..          ...  \n",
       "85            2  \n",
       "86            2  \n",
       "87            5  \n",
       "88            2  \n",
       "89            0  \n",
       "\n",
       "[90 rows x 8 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HINT: use astype function\n",
    "features = features.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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2023-01-01\">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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2023-01-01\">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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