jupyter-labs-eda-dataviz.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
    "    </a>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **SpaceX  Falcon 9 First Stage Landing Prediction**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assignment: Exploring and Preparing Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimated time needed: **70** minutes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this assignment, we will predict if the Falcon 9 first stage will land successfully. SpaceX advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is due to the fact that SpaceX can reuse the first stage.\n",
    "\n",
    "In this lab, you will perform Exploratory Data Analysis and Feature Engineering.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Falcon 9 first stage will land successfully\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing\\_1.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several examples of an unsuccessful landing are shown here:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n",
    "\n",
    "Perform exploratory Data Analysis and Feature Engineering using `Pandas` and `Matplotlib`\n",
    "\n",
    "*   Exploratory Data Analysis\n",
    "*   Preparing Data  Feature Engineering\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries and Define Auxiliary Functions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will import the following libraries the lab\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# andas is a software library written for the Python programming language for data manipulation and analysis.\n",
    "import pandas as pd\n",
    "#NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays\n",
    "import numpy as np\n",
    "# Matplotlib is a plotting library for python and pyplot gives us a MatLab like plotting framework. We will use this in our plotter function to plot data.\n",
    "import matplotlib.pyplot as plt\n",
    "#Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's read the SpaceX dataset into a Pandas dataframe and print its summary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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(\"dataset_part_2.csv\")\n",
    "#df=pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\")\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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\n",
      "text/plain": [
       "<Figure size 1842.38x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(y=\"PayloadMass\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Flight Number\",fontsize=20)\n",
    "plt.ylabel(\"Pay load Mass (kg)\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that different launch sites have different success rates.  <code>CCAFS LC-40</code>, has a success rate of 60 %, while  <code>KSC LC-39A</code> and <code>VAFB SLC 4E</code> has a success rate of 77%.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's drill down to each site visualize its detailed launch records.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK 1: Visualize the relationship between Flight Number and Launch Site\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the function <code>catplot</code> to plot <code>FlightNumber</code> vs <code>LaunchSite</code>, set the  parameter <code>x</code>  parameter to <code>FlightNumber</code>,set the  <code>y</code> to <code>Launch Site</code> and set the parameter <code>hue</code> to <code>'class'</code>\n"
   ]
  },
  {
   "cell_type": "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": [
    "##### Launch success rates improve as the Flight numbers increase.\n",
    "##### VAFB SLC 4E Shows improvement after the Flight number 30 mark.\n",
    "##### Remaining sites show improvement after the Flight number 70 mark."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK 2: Visualize the relationship between Payload and Launch Site\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also want to observe if there is any relationship between launch sites and their payload mass.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1842.38x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be Pay Load Mass (kg) and y axis to be the launch site, and hue to be the class value\n",
    "sns.catplot(y=\"LaunchSite\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Payload Mass(kg)\",fontsize=20)\n",
    "plt.ylabel(\"Launch Site\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now 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": {
      "text/plain": [
       "Orbit\n",
       "ES-L1    100.000000\n",
       "GEO       50.000000\n",
       "GTO       36.842105\n",
       "HEO      100.000000\n",
       "ISS       50.000000\n",
       "LEO       71.428571\n",
       "MEO       66.666667\n",
       "PO        50.000000\n",
       "SO         0.000000\n",
       "SSO       41.666667\n",
       "VLEO      21.818182\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bar = df.groupby('Orbit').sum().astype('float')['Class']/df.groupby('Orbit').sum().astype('float')['Flights']*100\n",
    "bar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XiFgiaV1gk2YGambWzupONJJGAp8HdiYtTrkEmAn8d0TMbWRwPRgGLIyI1wAiYkGO7+3AUuD5XP586WczM+t/dSUaSf8OnA6sQVrrpWQscKqkkyPinKpPbrwrgf+T9GHSjAT/HREzgXuBJ4G5km4Gpnk9GjOz5ql59mZJnwN+ACwDvk0aHLBdvv92Lv+BpIMKiHMVuQXzLuBk4DXgZkl7RcQKYB/gM8BDwLmSJlY+X9I4SZ2SOl/oj4DNzNqUosYRL5I6SbMD7FLt4s3cZTUdeCQiClt4rDQYoEr5ccCWEfG1ivIO0tQ57+lqn8OlOKrxoZrVxKPObLCSND0iOnqqV896NNsDV3Y1Q0A+P3MlsEMd++w1SbtIGp5/Xg3YEZgvabikXcqqjgI8q4GZWZPUc45mKT3PdfYs8Fzvw6nLpsCFktbKj+8BfgJsBpyVk9BLwCLgK/0Uk5mZVagn0dwIfJx0TmQVefGzj+V6hSl1m0XEDcANVarMJ11bY2ZmA0A952iGAXcCdwMnlXehSXobcCawK/DBiHiigFgL09HREZ2dnc0Ow8xsUKn1HE13k2reUqX4WeCzwIGSHiUNI96MtIzzEGA2cBmwV2+CNjOz1tNd19mYHp73jnwrtxOvr8hpZmbW7Vxn9YxIMzMzq8rJxMzMClXPzAArJF1eZDBmZtZ66mnRLMUXPpqZWZ3qSTQzSbMDmJmZ1ayeRHMm8ElJexcVjJmZtZ56ZgbYlHQl/m8lXQ38GXiCKsOZI+LSxoRnZmaDXT0zA7xGSiqq2FS+AwEREUMaE17/8OzNZtaO+jpzeJ9nBqjiiD7EU7fK5QAkHQ50RMQxeX2ZL5MmzCwZExHPStoNOAdYP5efExGT+ilsMzOrUHOiiYgpRQbSC+dGxFnlBZLeClwOjI2IGZI2Bn4n6bGI+E1TojQza3OtdsHm0cDkiJgBEBGLgROAk5oalZlZG6un6wxJuwMfAoaTzs0sBP4YEbcXENvakmaVPX4LcG3Z4/GSDs0//zMi9iAtulbZ8uqknxZjMzOzVdWUaHKC+SnwrlJRvo+8/QHgqw1OOC9GxKiyGA4Hyk86rdJ1luOqdnZrlTJJ44BxABv0OVQzM+tKj4lG0oHAFbnu48BtwD9IX+ojSLM8bwf8XtLBETGtqGBrMIeUjMpbPqOB+ysr5gECkyCNOuuX6MzM2lC3iSYvhzwFeBX4GnBRRKyoqLMa8EXgPOBSSXdHxOMFxduT84E/SZoWEbMkbUS60PTbTYrHzKzt9TQY4FhgKHBIRPysMskARMRrEXEhcEiu+43Gh1nVeEmzym4jI2IhcChwYe7OuxO4OCKu66eYzMysQrcXbEqaDSyLiA/UtDPpLmCdiNixQfH1C1+waWbtaKBcsLklcFEdr3sn6ULKQWX46NFM6OxsdhhmZi2pp66zNYCX69jfK8Cgmn7GzMyK1VOiWQi8p4797UCaaNPMzAzoOdHcAewtaduediRpO+Dj+TlmZmZAz4nmJ6Tus19L6nLRs5xkriN1m53fuPDMzGyw63YwQERMl/QD4HhghqRpwM2kCzYDeBvwUeDTwJrA2RHhs+pmZrZSjzMDRMSJkpYBpwIHAwdVVBGwAvgOMLHRAZqZ2eBW01xnEfFtSVOAI0mTag4jJZiFwP+RZkyeW1iUZmY2aNWzHs18YEKBsZiZWQuqeSnnVuaZAVpTX696NrPu1TozQKstfGZmZgPMgE00kjaTdLmkv0uaLukuSQeVTaL5vKQH88+X5ufsJukeSQ/k27hmH4eZWbura4XN/iJJwNXAlIj411y2JbB/aTE0SbcBx5WGU0t6K3A5MDYiZkjaGPidpMci4jfNOA4zMxu4LZo9gZcj4r9KBRExPyJ+3M1zjiaNfpuR6y8GTgBOKjRSMzPr1kBNNDsAM3rxnOkVZZ25fBWSxknqlNT5Qi8CNDOz2gzURPMGks6XdK+kP3dXjTRbQaWqQ48iYlJEdEREx9CGRGlmZtUM1EQzB9il9CAijgb2Ajbp4TmVw+xGA/c3PDozM6vZQE00twBvkvRvZWU9NTzOBw6XVBossBFwJvD9YkI0M7NaDMhRZxERksYC50o6AVgELANO7OY5CyUdClwoaT1SV9p5EXFdvwRtZmZVeWYAoKOjIzq9lLOZWV08M4CZmQ0ITjRmZlYoJxozMyuUE42ZmRXKicbMzArlRGNmZoVyojEzs0I50ZiZWaGcaMzMrFCeGQAYLsVRzQ7C2tYE/w3aIDXoZwboYinnT0saI2lJ2ZLOsyR9ND9nhKRrJD0s6RFJP5S0ZrOPxcysnQ3IRFO2lPMdEfGOiBgNHAyMyFX+EBGjym435edMA66OiK2BbYB1gdObcQxmZpYMyERD75Zy3hN4KSIuyfVXAOOBIyV5bTMzsyYZkMsE0PNSzh+WNKvs8YFUWco5Ip6T9CjwTmB2w6M0M7MeDdRE8waSzgd2A14Gjid1ne1XUaerpZyrlksaB4wD2KDRAZuZ2UoDteusIUs5S1of2AJ4pLJyREyKiI6I6HC/mplZcQZqounNUs43A0MlHQYgaQhwNjA5Il4oJkwzM+vJgEw0kS7uGQvsLmmupHuAKby+lPOHK4Y3fyY/59PAv0h6GHgIeAn4ZjOOwczMkgF7jiYiFpKGNFdT9bRKRPwD+FRhQZmZWd0GbKLpT8NHj2ZCZ2ezwzAza0kDsuvMzMxahxONmZkVyonGzMwK5URjZmaFcqIxM7NCOdGYmVmhnGjMzKxQTjRmZlYoJxozMyuUwuuVM1yKo5odhJlZP5vQx+9/SdMjoqOnek1t0UhaUTE55km5fD9JMyXdK+l+SVXzgKR5kjauKNtW0l2Slks6rj+Ow8zMutbsuc5ejIhR5QWS1gAmAe+LiAWS1gJG1rHPZ4Cvk2Z/NjOzJhuI52jWIyXApwEiYnlEPFjrkyPiqYj4M/BKQfGZmVkdmp1o1q7oOjsoIp4BrgXmS7pC0iGSmh2nmZn10oDrOgOIiC9Jeg/wUeA4YG/g8Ea+sKRxwDjoYnEbMzNriAHbUoiI+yLiXFKSOVDSkLKWz7cbsP9JEdERER09rRFtZma91+wWzSokrQt0RMRtuWgUMD8iVuSfzcxsEGl2ollb0qyyxzcApwMnSPoZ8CKwjO67zWZLei3/fCXwfaATWB94TdKxwPYR8Vyjgzczs541NdFExJAuNn2yxueP7GLTiF4FZGZmDdfsFs2AMHz0aCZ0djY7DDOzljRgBwOYmVlrcKIxM7NCOdGYmVmhnGjMzKxQXiYAkLQUqHk+tRa0MbC42UE0kY+/fY+/nY8d+n78W0bEJj1V8qiz5MFa1lRoVZI6ffw+/mbH0QztfOzQf8fvrjMzMyuUE42ZmRXKiSaZ1OwAmszH397a+fjb+dihn47fgwHMzKxQbtGYmVmh2j7RSNpH0oOS/ibppGbHUyRJW0i6VdJfJc2R9I1c/hZJv5f0cL5/c7NjLVJe22impF/nx2+X9Kd8/FMlrdnsGIsiaUNJV0l6IP8efKCdPn9J4/Pv/l/yCr5vauXPX9LFkp6S9Jeysqqft5If5e/C2ZJ2aVQcbZ1oJA0Bzgc+AWwPfE7S9s2NqlCvAv8eEdsB7weOzsd7EnBzRGwN3Jwft7JvAH8te3wmcG4+/n8CX2xKVP3jh8ANEbEtsBPpfWiLz1/S5sDXSetdvRsYAhxMa3/+k4F9Ksq6+rw/AWydb+OAnzYqiLZONMD7gL9FxN8j4mXgf4ADmhxTYSJiYUTMyD8vJX3JbE465im52hRgbHMiLJ6kEcC+wEX5sYA9gatylZY9fknrAx8Bfg4QES9HxLO00edPunZwbUmrA0OBhbTw5x8RdwDPVBR39XkfAFwayd3AhpKGNSKOdk80mwP/KHu8IJe1PEkjgZ2BPwGbRcRCSMkI2LR5kRXuPOAEoLRY3kbAsxHxan7cyr8D7wAWAZfkrsOLJK1Dm3z+EfEYcBbwKCnBLAGm0z6ff0lXn3dh34ftnmhUpazlh+Hl5bL/Fzi2nVYelbQf8FRETC8vrlK1VX8HVgd2AX4aETuTVq9tyW6yavK5iAOAtwPDgXVI3UWVWvXz70lhfwvtnmgWAFuUPR4BPN6kWPqFpDVISeayiJiWi58sNZHz/VPNiq9gHwL2lzSP1E26J6mFs2HuSoHW/h1YACyIiD/lx1eREk+7fP4fBeZGxKKIeAWYBnyQ9vn8S7r6vAv7Pmz3RPNnYOs86mRN0onBa5scU2Hy+YifA3+NiHPKNl0LfCH//AXgmv6OrT9ExMkRMSIvAX4wcEtEHALcCnwmV2vl438C+Iekd+WivYD7aZPPn9Rl9n5JQ/PfQun42+LzL9PV530tcFgeffZ+YEmpi62v2v6CTUmfJP1XOwS4OCJOb3JIhZG0G/AH4D5eP0fxTdJ5miuBt5H+GP8lIipPILYUSWOA4yJiP0nvILVw3gLMBA6NiOXNjK8okkaRBkKsCfwdOIL0D2dbfP6STgMOIo3AnAl8iXQeoiU/f0lXAGNIszQ/CUwArqbK552T709Io9ReAI6IiIascd/2icbMzIrV7l1nZmZWMCcaMzMrlBONmZkVyonGzMwK5URjZmaFcqIxM7NCOdGYWVNJuk3SfZIK+T6SdKCkkLRXEfu3njnRWMPkdV6+LOl2Sc9IeiWvhTE7T+C4f7NjHKzyF2X5bUV+j2+TdHi+2K4RrzMvT9HTLyR9BtgdmBARr5WVrynpu5LmSlqitI5S1fVRJO2d35N9u3iZacAM4Jyikpl1zxdsWkPktX1+Tbqq+FngN6S5k94CbAV8AJgREbs1LchBTFLpD/W0fL8G8E7g0/nn8yPimAa8zjyAPE1PoXJy/CtpMsdto+zLSNI5wHjSvHwLgM+TJgXdtnxalDxB7F+AP0TE57t5rc8CU4FDIuLyAg7HuuFEYw0h6VDgF8C9wO4RsaRi+1Bg14i4tRnxDXalRBMRqij/EHAH6ct6q4iY28fXmZdfZ2Rf9lPja+0N3AicEhHfKysX8DwwNSKOzGW7A7cBJ0bE98vqng8cCGzf3bQ5ktYCngDm+J+d/udmpDXKB/P95MokAxARL1QmGUkTc5fHmMr6kkbmbZOrbBsq6URJnZKWSnpeaVniH0narI91T5Y0S9KyXPcuSZ+rEoMkfUHSnZIWSXpJ0j8k/U7SQRV1d1RaNniepOW5/gxJ5ynNpt1rEfFH4AFSohld8bprSjpG0vWS5ufXfkbSTZI+UVF3TE5mWwJbVnTTTa6ou62kyfl4l0t6UtLlen2yzlqVVrKcWlG+CWlRsnvKyko/b1kWx0eAfwOO7mlutjx32dXAhyRtW2ec1ker91zFrCZP5/ttinwRpTVFbiUtQ/wgcDHwMql77khSf/yTvai7IXALaTG4GbnuasDHgcsl7RARp5aFcjpwMjCXNEHhEmAY8F7gX8hfnpJ2JE1aGqTZcecC65O6vb4KnAq80te3Jd9X7uctpKWb7wR+T1r0bBjwKeB6SV+OiIty3Xmkbrlj8+PzyvYza+ULSfuQ3rc1gOuAv5Gmk/9/wL6S9iit4tptwFq5sukTEfFIxeZFwIu8MXF25Pv5+flrkyYH/d+I+N+eXi/7I3A4abmAB2p8jjVCRPjmW59vpC/ol0mzQv+C9MWzZQ/PmUj6Ah5TZdvIvG1yRfnlufynwGoV29YDNuhl3cm57gkV9d4E3JCPa1RZ+dOkcwdDq8S+cdnPZ+f9HlCl3psr4+rmvYr057pK+UeAFcByYFjFtrWAEVWeswHpvMYzwNoV2+YB87qI4c3AP4HFpK6q8m07kLq7ZtR4PNvmY7qui+3n5ff8SuDc/JrPAZuXva+LgU3r+B3dKb/mlc38W2nHm7vOrCEiYiZwKKmFcCjpJO48SU9L+pWkT/X1NSRtSprifSFpiv/XyrdHxNLI3XZ11t0ox9wZZf3/ud5LwImkVsO/VoT0CulLnornLK4S/otV6v2zMq6e5O7GiZJOlzQVuCnHdlxUrB0SEcsjYkGV111CarG9mdQCq9VhwIakEWL3V+xzDnAhsLOk7WvY19vyfVfrnZwInAm8j9TFNgfYKyIek7QrqeX1DWCxpAmSFkp6VdL0fN6qmicqXtv6ibvOrGFOCL5rAAAFi0lEQVQi4kpJvwL2AHYjtXJ2A8YCYyVdChwe+d/LXngvqTvrjohY1uC6Q4CQNLHK9tJ5lO3Kyi4DvgbMkfRL4Hbgrlj1/NRU0hfi1ZKuIiWGP8aq3UW1mlDxOIAvRsQl1SpL2gE4ntTyGUZqoZWrZ034D+T7nbp4n0rdptuRFhTrzkb5/p/VNkY6p3Jyvq2ktEDhxcD1EXGZpGNJLePTSF1jpwA3SHpnRDxZsdvSeZyNe4jNGsyJxhoq0hK5N+ZbadjzgaQvh8OAX5FOyvbGhvn+sQbXLX3pvZfu/8Nft+zn8cAjpHM9J+Xbq5KuB/49Iv4GEBH3SPow6QvwM6Rhukh6EDgtIq6oIb6VIo86k7QO6Yv/58B/SZofEbeU11VaJfEW0t/5zaRzRM+RuwGBA0jda7UqvU9f7qHeuj1sh9dbeJWJrycTSMnxY/nx8cDNETERVr6v84CjgW9VPHftite2fuKuMytURKyIiFI/O6QTwCWlbqNq//BsWKXs2Xxfy3/h9dQttULOjQh1c9uj9IR8XD+MiJ2AzUjJ9FfA/qT/qNcqq3tXROxH6qr6EPCd/JzLJX20hvhWERHLIuIm0on9IcAUpSHk5U4lfbl+LCI+ERHHRsS38pfyn3rxsqX3aace3qcpNeyrtE79Rt3WKiNpZ+AEUiJ/TNL6wHDS4A0AIuJR0rmbHarsovRaT1XZZgVyorH+sjTfl18HUuo22aJK/Y4qZfeQktNH8n/03elN3Q/3UK+qiHgqIqZFxGdJLYitgHdXqbc8Iu6MiG8BX8/FB/TmNcv2OZt0bmQEqZVV7p3AMxFxW5Wn7t7FLleQElc1d+f7Xr1PFebk16ppqLGk1Umt4lsj4ucVmytbZV21kkqvNauL7VYQJxprCEmfU5oKZJXfKUlv5fXuljvKNpWujTgif5GU6m/Bqt0eRMQi0truw4CzKl9L0rqSNuhF3adI51w6JP1HeSxl9beS9Pb881qS9spDdMvrrEEaUgxpzXUkfbj0OhU2K6/XR98FXgKOy0O6S+YBb8lDrMvj/CJp2HY1TwOb5OHDlS4htRQnSHpf5UZJq6nKNVHV5HNZs4Adu3itSieTEufKbruIeI7UNbpP6TNTurBzPVIiq/T+fO+Lhvtbs4e9+dYaN9Jw1CCNIvol8P18+yXpyzRI52ZU8bzb87YZwFmkodHPkk6iVxve/GbS7ANBmr7kvLLXeY6yodJ11l0fuCvXfYj03/MZwBRSQgzg4Fx3w/x4LimZnUm6XuX+XH5N2X6vzsd/PfAT4D9J15+8Sjo5vVWN72/V4c1V3v8zysr2yWXPka45OTu/3yvyexCkwRnl+zkjl99O6uI7FfhU2fa9eP08z+/z654DXEX60n+pjt+Zk/Nr7dtDvR1Iw7ePrrLt63kfd+VYFpFaz5tVqXsXqRW9ypB034q9NT0A31rjRur+Opp0nuLB/GX0MinxXE8aPrzKNSP5S/tCUr/5ctL1HePo4jqa/Jx1SCfXZ+cv8aX5S/48Kq6rqLPumsAxpAscl+R4HiWdSD8W2CjXW4N0ruC3eftL+QvubuArwJpl+/wYqSVwf97nsvz+/IgerjOqiK2nRLNZ3vey8i9ZYL8c11JSAr+RNALt8C4SzTqk644WkJJhtWQ/kpQ0H87H/hzpAshfAGPrOKZN83s8tZs6Q0jnk+6g4p+UvF2kZLgg7+se4ENV6m2Tj+W8Zv+ttOPNc52ZWdNI+hnwBWBkRDzRU/0+vM7ZpH8itouIvxf1Oladz9GYWTN9i9TyPaWoF5A0jDQn2o+dZJrDicbMmibSRZWHAo9XG0jSICNJ59G+W9D+rQfuOjMzs0K5RWNmZoVyojEzs0I50ZiZWaGcaMzMrFBONGZmVignGjMzK9T/B2HvV6aW7BFcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# HINT use groupby method on Orbit column and get the mean of Class column\n",
    "bar = df.groupby('Orbit').sum().astype('float')['Class']/df.groupby('Orbit').sum().astype('float')['Flights']*100\n",
    "bar.plot(kind='barh',color='maroon')\n",
    "plt.xlabel(\"Success Rate (%)\",fontsize=20)\n",
    "plt.ylabel(\"Orbit Type\",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": [
    "##### The Orbits HEO and ES-L1 have the highest success rates(100%) however, they together account for only 7 launches and are tailed by LEO with 76% success rate."
   ]
  },
  {
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1842.38x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be FlightNumber and y axis to be the Orbit, and hue to be the class value\n",
    "sns.catplot(y=\"Orbit\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Flight Number\",fontsize=20)\n",
    "plt.ylabel(\"Orbit type\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should see that in the LEO orbit the Success appears related to the number of flights; on the other hand, there seems to be no relationship between flight number when in GTO orbit.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  5: Visualize the relationship between Payload and Orbit type\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, we can plot the Payload vs. Orbit scatter point charts to reveal the relationship between Payload and Orbit type\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1842.38x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be Payload and y axis to be the Orbit, and hue to be the class value\n",
    "sns.catplot(y=\"PayloadMass\", x=\"Orbit\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Orbit type\",fontsize=20)\n",
    "plt.ylabel(\"Payload Mass in (kg)\",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": 11,
   "metadata": {
    "tags": []
   },
   "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",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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",
       "      <td>2013</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  Year  \n",
       "0            0  B0003  -80.577366  28.561857      0  2010  \n",
       "1            0  B0005  -80.577366  28.561857      0  2012  \n",
       "2            0  B0007  -80.577366  28.561857      0  2013  \n",
       "3            0  B1003 -120.610829  34.632093      0  2013  \n",
       "4            0  B1004  -80.577366  28.561857      0  2013  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "Extract_year(df[\"Date\"])\n",
    "temp_df = df.copy(deep=False)\n",
    "temp_df['Year'] = year\n",
    "temp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\n",
    "line_data = temp_df.groupby('Year').sum().astype('float')['Class']/temp_df.groupby('Year').sum().astype('float')['Flights']*100\n",
    "line_data.plot(kind='line')\n",
    "plt.xlabel(\"Year\",fontsize=20)\n",
    "plt.ylabel(\"Success Rate(%)\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you can observe that the sucess rate since 2013 kept increasing till 2020\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features Engineering\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By now, you should obtain some preliminary insights about how each important variable would affect the success rate, we will select the features that will be used in success prediction in the future module.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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": 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": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Flights</th>\n",
       "      <th>GridFins</th>\n",
       "      <th>Reused</th>\n",
       "      <th>Legs</th>\n",
       "      <th>Block</th>\n",
       "      <th>ReusedCount</th>\n",
       "      <th>Orbit_ES-L1</th>\n",
       "      <th>Orbit_GEO</th>\n",
       "      <th>...</th>\n",
       "      <th>Serial_B1048</th>\n",
       "      <th>Serial_B1049</th>\n",
       "      <th>Serial_B1050</th>\n",
       "      <th>Serial_B1051</th>\n",
       "      <th>Serial_B1054</th>\n",
       "      <th>Serial_B1056</th>\n",
       "      <th>Serial_B1058</th>\n",
       "      <th>Serial_B1059</th>\n",
       "      <th>Serial_B1060</th>\n",
       "      <th>Serial_B1062</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>1</td>\n",
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       "      <td>False</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\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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       "<p>5 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   FlightNumber  PayloadMass  Flights  GridFins  Reused   Legs  Block  \\\n",
       "0             1  6104.959412        1     False   False  False    1.0   \n",
       "1             2   525.000000        1     False   False  False    1.0   \n",
       "2             3   677.000000        1     False   False  False    1.0   \n",
       "3             4   500.000000        1     False   False  False    1.0   \n",
       "4             5  3170.000000        1     False   False  False    1.0   \n",
       "\n",
       "   ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n",
       "0            0            0          0  ...             0             0   \n",
       "1            0            0          0  ...             0             0   \n",
       "2            0            0          0  ...             0             0   \n",
       "3            0            0          0  ...             0             0   \n",
       "4            0            0          0  ...             0             0   \n",
       "\n",
       "   Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             0             0             0             0   \n",
       "2             0             0             0             0             0   \n",
       "3             0             0             0             0             0   \n",
       "4             0             0             0             0             0   \n",
       "\n",
       "   Serial_B1059  Serial_B1060  Serial_B1062  \n",
       "0             0             0             0  \n",
       "1             0             0             0  \n",
       "2             0             0             0  \n",
       "3             0             0             0  \n",
       "4             0             0             0  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HINT: Use get_dummies() function on the categorical columns\n",
    "features_one_hot = pd.get_dummies(features,columns = ['Orbit','LaunchSite','LandingPad','Serial'])\n",
    "features_one_hot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  8: Cast all numeric columns to `float64`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that our <code>features_one_hot</code> dataframe only contains numbers cast the entire dataframe to variable type <code>float64</code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FlightNumber    float64\n",
       "PayloadMass     float64\n",
       "Flights         float64\n",
       "GridFins        float64\n",
       "Reused          float64\n",
       "                 ...   \n",
       "Serial_B1056    float64\n",
       "Serial_B1058    float64\n",
       "Serial_B1059    float64\n",
       "Serial_B1060    float64\n",
       "Serial_B1062    float64\n",
       "Length: 80, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HINT: use astype function\n",
    "features_one_hot = features_one_hot.astype('float64')\n",
    "features_one_hot.dtypes"
   ]
  },
  {
   "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": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_one_hot.to_csv('dataset_part_3.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<code>features_one_hot.to_csv('dataset_part\\_3.csv', index=False)</code>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.linkedin.com/in/nayefaboutayoun/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">Nayef Abou Tayoun</a> is a Data Scientist at IBM and pursuing a Master of Management in Artificial intelligence degree at Queen's University.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Log\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Date (YYYY-MM-DD) | Version | Changed By    | Change Description      |\n",
    "| ----------------- | ------- | ------------- | ----------------------- |\n",
    "| 2021-10-12        | 1.1     | Lakshmi Holla | Modified markdown       |\n",
    "| 2020-09-20        | 1.0     | Joseph        | Modified Multiple Areas |\n",
    "| 2020-11-10        | 1.1     | Nayef         | updating the input data |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright © 2020 IBM Corporation. All rights reserved.\n"
   ]
  }
 ],
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