5EDA-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": 2,
   "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": 3,
   "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": 3,
     "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": 4,
   "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 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",
    "plt.figure(figsize=(14,8))\n",
    "sns.scatterplot(x=\"FlightNumber\", y=\"LaunchSite\", hue=\"Class\", data=df)\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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x800 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",
    "plt.figure(figsize=(14,8))\n",
    "sns.scatterplot(x=\"PayloadMass\",y=\"LaunchSite\", hue =\"Class\",data=df)\n",
    "plt.xlabel(\"Pay Load Mass (kg)\",fontsize=20)\n",
    "plt.ylabel(\"Launch Site\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now if you observe Payload Vs. Launch Site scatter point chart you will find for the VAFB-SLC  launchsite there are no  rockets  launched for  heavypayload mass(greater than 10000).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  3: Visualize the relationship between success rate of each orbit type\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we want to visually check if there are any relationship between success rate and orbit type.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a `bar chart` for the sucess rate of each orbit\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "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",
    "df.groupby(['Orbit']).mean()\n",
    "sns.barplot(x=\"Orbit\",y=\"Class\",data=df)\n",
    "plt.xlabel(\"Orbit\",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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      "text/plain": [
       "<Figure size 1400x800 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",
    "plt.figure(figsize=(14,8))\n",
    "sns.scatterplot(x=\"FlightNumber\",y=\"Orbit\",hue=\"Class\",data = df)\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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x800 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",
    "plt.figure(figsize=(14,8))\n",
    "sns.scatterplot(x=\"PayloadMass\",y=\"Orbit\",hue=\"Class\",data = df)\n",
    "plt.xlabel(\"PayloadMass\",fontsize=20)\n",
    "plt.ylabel(\"Orbit\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With heavy payloads the successful landing or positive landing rate are more for Polar,LEO and ISS.\n",
    "\n",
    "However for GTO we cannot distinguish this well as both positive landing rate and negative landing(unsuccessful mission) are both there here.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  6: Visualize the launch success yearly trend\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can plot a line chart with x axis to be <code>Year</code> and y axis to be average success rate, to get the average launch success trend.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function will help you get the year from the date:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 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",
    "df['year']=Extract_year(df[\"Date\"])\n",
    "df_year_success=df.groupby(\"year\",as_index=False)[\"Class\"].mean()\n",
    "\n",
    "plt.figure(figsize=(14,8))\n",
    "sns.lineplot(data=df_year_success, x=\"year\", y=\"Class\" )\n",
    "plt.xlabel(\"Year\",fontsize=20)\n",
    "plt.title('Launch success yearly trend')\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": 12,
   "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": 12,
     "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<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>ES-L1</th>\n",
       "      <th>GEO</th>\n",
       "      <th>...</th>\n",
       "      <th>B1048</th>\n",
       "      <th>B1049</th>\n",
       "      <th>B1050</th>\n",
       "      <th>B1051</th>\n",
       "      <th>B1054</th>\n",
       "      <th>B1056</th>\n",
       "      <th>B1058</th>\n",
       "      <th>B1059</th>\n",
       "      <th>B1060</th>\n",
       "      <th>B1062</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>1</td>\n",
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       "      <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",
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       "      <td>...</td>\n",
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       "    <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",
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       "      <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",
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       "      <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",
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       "      <td>0</td>\n",
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       "  </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  ES-L1  GEO  ...  B1048  B1049  B1050  B1051  B1054  B1056  \\\n",
       "0            0      0    0  ...      0      0      0      0      0      0   \n",
       "1            0      0    0  ...      0      0      0      0      0      0   \n",
       "2            0      0    0  ...      0      0      0      0      0      0   \n",
       "3            0      0    0  ...      0      0      0      0      0      0   \n",
       "4            0      0    0  ...      0      0      0      0      0      0   \n",
       "\n",
       "   B1058  B1059  B1060  B1062  \n",
       "0      0      0      0      0  \n",
       "1      0      0      0      0  \n",
       "2      0      0      0      0  \n",
       "3      0      0      0      0  \n",
       "4      0      0      0      0  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HINT: Use get_dummies() function on the categorical columns\n",
    "features_one_hot = features\n",
    "\n",
    "features_one_hot = pd.concat([features_one_hot,pd.get_dummies(df['Orbit'])], axis=1)\n",
    "features_one_hot.drop(['Orbit'], axis = 1,inplace=True)\n",
    "\n",
    "features_one_hot = pd.concat([features_one_hot,pd.get_dummies(df['LaunchSite'])], axis=1)\n",
    "features_one_hot.drop(['LaunchSite'], axis = 1,inplace=True)\n",
    "\n",
    "features_one_hot = pd.concat([features_one_hot,pd.get_dummies(df['LandingPad'])], axis=1)\n",
    "features_one_hot.drop(['LandingPad'], axis = 1,inplace=True)\n",
    "\n",
    "features_one_hot = pd.concat([features_one_hot,pd.get_dummies(df['Serial'])], axis=1)\n",
    "features_one_hot.drop(['Serial'], axis = 1,inplace=True)\n",
    "\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": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HINT: use astype function\n",
    "features_one_hot = features_one_hot.astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now export it to a <b>CSV</b> for the next section,but to make the answers consistent, in the next lab we will provide data in a pre-selected date range.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<code>features_one_hot.to_csv('dataset_part\\_3.csv', index=False)</code>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.linkedin.com/in/nayefaboutayoun/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Nayef Abou Tayoun</a> is a Data Scientist at IBM and pursuing a Master of Management in Artificial intelligence degree at Queen's University.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Log\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Date (YYYY-MM-DD) | Version | Changed By    | Change Description      |\n",
    "| ----------------- | ------- | ------------- | ----------------------- |\n",
    "| 2021-10-12        | 1.1     | Lakshmi Holla | Modified markdown       |\n",
    "| 2020-09-20        | 1.0     | Joseph        | Modified Multiple Areas |\n",
    "| 2020-11-10        | 1.1     | Nayef         | updating the input data |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright © 2020 IBM Corporation. All rights reserved.\n"
   ]
  }
 ],
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