jupyter-labs-eda-dataviz.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\">\n",
    "    </a>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **SpaceX  Falcon 9 First Stage Landing Prediction**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Assignment: Exploring and Preparing Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimated time needed: **70** minutes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this assignment, we will predict if the Falcon 9 first stage will land successfully. SpaceX advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is due to the fact that SpaceX can reuse the first stage. \n",
    "\n",
    "In this lab, you will perform Exploratory Data Analysis and Feature Engineering.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Falcon 9 first stage will land successfully\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing_1.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several examples of an unsuccessful landing are shown here:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  ## Objectives\n",
    "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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in c:\\users\\p\\anaconda 3\\envs\\new_env_name\\lib\\site-packages (0.13.2)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in c:\\users\\p\\anaconda 3\\envs\\new_env_name\\lib\\site-packages (from seaborn) (1.26.4)\n",
      "Requirement already satisfied: pandas>=1.2 in c:\\users\\p\\anaconda 3\\envs\\new_env_name\\lib\\site-packages (from seaborn) (2.2.2)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in c:\\users\\p\\anaconda 3\\envs\\new_env_name\\lib\\site-packages (from seaborn) (3.8.4)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\p\\anaconda 3\\envs\\new_env_name\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.2.1)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\p\\anaconda 3\\envs\\new_env_name\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\p\\anaconda 3\\envs\\new_env_name\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.51.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\p\\anaconda 3\\envs\\new_env_name\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\p\\anaconda 3\\envs\\new_env_name\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (24.0)\n",
      "Requirement already satisfied: pillow>=8 in c:\\users\\p\\anaconda 3\\envs\\new_env_name\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.3.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\p\\anaconda 3\\envs\\new_env_name\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.1.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\p\\anaconda 3\\envs\\new_env_name\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\p\\anaconda 3\\envs\\new_env_name\\lib\\site-packages (from pandas>=1.2->seaborn) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\p\\anaconda 3\\envs\\new_env_name\\lib\\site-packages (from pandas>=1.2->seaborn) (2024.1)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\p\\anaconda 3\\envs\\new_env_name\\lib\\site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install seaborn\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": {
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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(\"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": [
    {
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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": [
    "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 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": 6,
   "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(x=\"PayloadMass\", y=\"LaunchSite\", hue=\"Class\", data=df, aspect=5) \n",
    "plt.xlabel(\"Pay Load Mass Kg\", fontsize=20) \n",
    "plt.ylabel(\"LaunchSite\", 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": 8,
   "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",
    "orb_type = df[[\"Orbit\", \"Class\"]].groupby(\"Orbit\").mean() \n",
    "plt.bar(orb_type.index.values, orb_type[\"Class\"]) \n",
    "plt.xlabel(\"Orbit\")\n",
    "plt.ylabel(\"Success rate\")\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": 24,
   "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",
    "\n",
    "sns.catplot(x=\"FlightNumber\", y=\"Orbit\", 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": 26,
   "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(x=\"PayloadMass\", y=\"Orbit\", hue=\"Class\", data=df, aspect=5) \n",
    "plt.xlabel(\"Pay Load Mass\", 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": 39,
   "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(str(i).split(\"-\")[0])\n",
    "    return year\n",
    "df['year'] = Extract_year('')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'year')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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",
    "df[[\"Class\", \"year\"]].groupby(\"year\").mean().plot()\n",
    "plt.title(\"success rate per year\") \n",
    "plt.ylabel(\"success rate\") \n",
    "plt.xlabel(\"year\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can observe that the success rate since 2013 kept increasing till 2017 (stable in 2014) and after 2015 it started increasing.\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": 41,
   "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>1.0</td>\n",
       "      <td>6217.796914</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCSFS SLC 40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>B0003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCSFS SLC 40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>B0005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>ISS</td>\n",
       "      <td>CCSFS SLC 40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>B0007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>PO</td>\n",
       "      <td>VAFB SLC 4E</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>B1003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3170.000000</td>\n",
       "      <td>GTO</td>\n",
       "      <td>CCSFS SLC 40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>B1004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FlightNumber  PayloadMass Orbit    LaunchSite  Flights GridFins Reused  \\\n",
       "0           1.0  6217.796914   LEO  CCSFS SLC 40      1.0    False  False   \n",
       "1           2.0   525.000000   LEO  CCSFS SLC 40      1.0    False  False   \n",
       "2           3.0   677.000000   ISS  CCSFS SLC 40      1.0    False  False   \n",
       "3           4.0   500.000000    PO   VAFB SLC 4E      1.0    False  False   \n",
       "4           5.0  3170.000000   GTO  CCSFS SLC 40      1.0    False  False   \n",
       "\n",
       "    Legs LandingPad  Block  ReusedCount Serial  \n",
       "0  False        NaN    1.0          0.0  B0003  \n",
       "1  False        NaN    1.0          0.0  B0005  \n",
       "2  False        NaN    1.0          0.0  B0007  \n",
       "3  False        NaN    1.0          0.0  B1003  \n",
       "4  False        NaN    1.0          0.0  B1004  "
      ]
     },
     "execution_count": 41,
     "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": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HINT: Use get_dummies() function on the categorical columns\n",
    "dummies = pd.get_dummies(features[[\"Orbit\", \"LaunchSite\", \"LandingPad\", \"Serial\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Orbit_ES-L1</th>\n",
       "      <th>Orbit_GEO</th>\n",
       "      <th>Orbit_GTO</th>\n",
       "      <th>Orbit_HEO</th>\n",
       "      <th>Orbit_ISS</th>\n",
       "      <th>Orbit_LEO</th>\n",
       "      <th>Orbit_MEO</th>\n",
       "      <th>Orbit_PO</th>\n",
       "      <th>Orbit_SO</th>\n",
       "      <th>Orbit_SSO</th>\n",
       "      <th>...</th>\n",
       "      <th>Serial_B1060</th>\n",
       "      <th>Serial_B1062</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6217.796914</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4.0</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3170.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Orbit_ES-L1  Orbit_GEO  Orbit_GTO  Orbit_HEO  Orbit_ISS  Orbit_LEO  \\\n",
       "0        False      False      False      False      False       True   \n",
       "1        False      False      False      False      False       True   \n",
       "2        False      False      False      False       True      False   \n",
       "3        False      False      False      False      False      False   \n",
       "4        False      False       True      False      False      False   \n",
       "\n",
       "   Orbit_MEO  Orbit_PO  Orbit_SO  Orbit_SSO  ...  Serial_B1060  Serial_B1062  \\\n",
       "0      False     False     False      False  ...         False         False   \n",
       "1      False     False     False      False  ...         False         False   \n",
       "2      False     False     False      False  ...         False         False   \n",
       "3      False      True     False      False  ...         False         False   \n",
       "4      False     False     False      False  ...         False         False   \n",
       "\n",
       "   FlightNumber  PayloadMass  Flights  GridFins  Reused   Legs  Block  \\\n",
       "0           1.0  6217.796914      1.0     False   False  False    1.0   \n",
       "1           2.0   525.000000      1.0     False   False  False    1.0   \n",
       "2           3.0   677.000000      1.0     False   False  False    1.0   \n",
       "3           4.0   500.000000      1.0     False   False  False    1.0   \n",
       "4           5.0  3170.000000      1.0     False   False  False    1.0   \n",
       "\n",
       "   ReusedCount  \n",
       "0          0.0  \n",
       "1          0.0  \n",
       "2          0.0  \n",
       "3          0.0  \n",
       "4          0.0  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_one_hot = pd.concat([dummies, features[[\"FlightNumber\", \"PayloadMass\", \"Flights\", \"GridFins\", \"Reused\", \"Legs\", \"Block\",\"ReusedCount\"]]], axis=1) \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": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HINT: use astype function\n",
    "features_one_hot = features_one_hot.astype(\"float64\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now export it to a <b>CSV</b> for the next section,but to make the answers consistent, in the next lab we will provide data in a pre-selected date range. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_one_hot.to_csv('dataset_part_3.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\">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/\">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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