05- EDA with Visualization.ipynb

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   "source": [
    "<center>\n",
    "    <img src=\"https://gitlab.com/ibm/skills-network/courses/placeholder101/-/raw/master/labs/module%201/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **SpaceX  Falcon 9 First Stage Landing Prediction**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assignment: Exploring and Preparing Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimated time needed: **70** minutes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this assignment, we will predict if the Falcon 9 first stage will land successfully. SpaceX advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is due to the fact that SpaceX can reuse the first stage.\n",
    "\n",
    "In this lab, you will perform Exploratory Data Analysis and Feature Engineering.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Falcon 9 first stage will land successfully\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing\\_1.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several examples of an unsuccessful landing are shown here:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n",
    "\n",
    "Perform exploratory Data Analysis and Feature Engineering using `Pandas` and `Matplotlib`\n",
    "\n",
    "*   Exploratory Data Analysis\n",
    "*   Preparing Data  Feature Engineering\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries and Define Auxiliary Functions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will import the following libraries the lab\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# andas is a software library written for the Python programming language for data manipulation and analysis.\n",
    "import pandas as pd\n",
    "#NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays\n",
    "import numpy as np\n",
    "# Matplotlib is a plotting library for python and pyplot gives us a MatLab like plotting framework. We will use this in our plotter function to plot data.\n",
    "import matplotlib.pyplot as plt\n",
    "#Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's read the SpaceX dataset into a Pandas dataframe and print its summary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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     "data": {
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>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",
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       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
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       "    <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",
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       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0007</td>\n",
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       "      <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",
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       "    <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",
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       "      <td>1.0</td>\n",
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       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
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      "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  "
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     "execution_count": 2,
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     "output_type": "execute_result"
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   ],
   "source": [
    "df=pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\")\n",
    "\n",
    "# If you were unable to complete the previous lab correctly you can uncomment and load this csv\n",
    "\n",
    "# df = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/dataset_part_2.csv')\n",
    "\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's try to see how the `FlightNumber` (indicating the continuous launch attempts.) and `Payload` variables would affect the launch outcome.\n",
    "\n",
    "We can plot out the <code>FlightNumber</code> vs. <code>PayloadMass</code>and overlay the outcome of the launch. We see that as the flight number increases, the first stage is more likely to land successfully. The payload mass is also important; it seems the more massive the payload, the less likely the first stage will return.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1842.38x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(y=\"PayloadMass\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Flight Number\",fontsize=20)\n",
    "plt.ylabel(\"Pay load Mass (kg)\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that different launch sites have different success rates.  <code>CCAFS LC-40</code>, has a success rate of 60 %, while  <code>KSC LC-39A</code> and <code>VAFB SLC 4E</code> has a success rate of 77%.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's drill down to each site visualize its detailed launch records.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK 1: Visualize the relationship between Flight Number and Launch Site\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the function <code>catplot</code> to plot <code>FlightNumber</code> vs <code>LaunchSite</code>, set the  parameter <code>x</code>  parameter to <code>FlightNumber</code>,set the  <code>y</code> to <code>Launch Site</code> and set the parameter <code>hue</code> to <code>'class'</code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1842.38x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be 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.grid(True, which='both', axis='x')\n",
    "plt.minorticks_on()\n",
    "plt.grid(b=True, which='minor', color='#999999', linestyle='-', alpha=0.2)\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": [
    "### Answer:\n",
    "Cape Canaveral (CCAFS) is the most frequented launch site. It has even after the initial launches a higher failure rate. "
   ]
  },
  {
   "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": 12,
   "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 in kg\",fontsize=20)\n",
    "plt.ylabel(\"Launch Site\",fontsize=20)\n",
    "plt.grid(True, which='both', axis='x')\n",
    "plt.minorticks_on()\n",
    "plt.grid(b=True, which='minor', color='#999999', linestyle='-', alpha=0.2)\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": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# HINT use groupby method on Orbit column and get the mean of Class column\n",
    "df_orbit = df[['Orbit', 'Class']]\n",
    "df_orbit = df_orbit.groupby('Orbit').mean()\n",
    "df_orbit.head(20)\n",
    "df_orbit.plot(kind = 'bar', figsize=(15,5))\n",
    "\n",
    "plt.xlabel('Orbit')\n",
    "plt.ylabel('Success rate')\n",
    "plt.grid(True, which='Major', axis='y', alpha = 0.5)\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": [
    "### Answer:\n",
    "The success rate for L1, GEO, HEO and SOO have very high success rates"
   ]
  },
  {
   "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": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1842.38x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be FlightNumber and y axis to be the Orbit, and hue to be the class value\n",
    "sns.catplot(y=\"Orbit\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Flight No.\",fontsize=20)\n",
    "plt.ylabel(\"Orbit\",fontsize=20)\n",
    "plt.grid(True, which='both', axis='x')\n",
    "plt.grid(b=True, which='Major', color='#999999', linestyle='-', alpha=0.2)\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": 57,
   "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 Payload and y axis to be the Orbit, and hue to be the class value\n",
    "sns.catplot(y=\"Orbit\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Payload in kg\",fontsize=20)\n",
    "plt.ylabel(\"Orbit\",fontsize=20)\n",
    "plt.grid(True, which='both', axis='x')\n",
    "plt.grid(b=True, which='Major', color='#999999', linestyle='-', alpha=0.2)\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": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A function to Extract years from the date \n",
    "year=[]\n",
    "def Extract_year(date):\n",
    "    for i in df[\"Date\"]:\n",
    "        year.append(i.split(\"-\")[0])\n",
    "    return year\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 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",
    "year=[]\n",
    "year = Extract_year(df[['Date']])\n",
    "df_year = pd.DataFrame(year, columns = ['Year'])\n",
    "df_year['Class'] = df[['Class']]\n",
    "df_year.tail()\n",
    "df_year = df_year.groupby('Year').mean()\n",
    "df_year.head(10)\n",
    "\n",
    "df_year.plot(kind = 'line', figsize=(15,5))\n",
    "\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Success rate')\n",
    "plt.grid(True, which='Major', axis='both', alpha = 0.5)\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": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Orbit</th>\n",
       "      <th>LaunchSite</th>\n",
       "      <th>Flights</th>\n",
       "      <th>GridFins</th>\n",
       "      <th>Reused</th>\n",
       "      <th>Legs</th>\n",
       "      <th>LandingPad</th>\n",
       "      <th>Block</th>\n",
       "      <th>ReusedCount</th>\n",
       "      <th>Serial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>ISS</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>PO</td>\n",
       "      <td>VAFB SLC 4E</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3170.000000</td>\n",
       "      <td>GTO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FlightNumber  PayloadMass Orbit    LaunchSite  Flights  GridFins  Reused  \\\n",
       "0             1  6104.959412   LEO  CCAFS SLC 40        1     False   False   \n",
       "1             2   525.000000   LEO  CCAFS SLC 40        1     False   False   \n",
       "2             3   677.000000   ISS  CCAFS SLC 40        1     False   False   \n",
       "3             4   500.000000    PO   VAFB SLC 4E        1     False   False   \n",
       "4             5  3170.000000   GTO  CCAFS SLC 40        1     False   False   \n",
       "\n",
       "    Legs LandingPad  Block  ReusedCount Serial  \n",
       "0  False        NaN    1.0            0  B0003  \n",
       "1  False        NaN    1.0            0  B0005  \n",
       "2  False        NaN    1.0            0  B0007  \n",
       "3  False        NaN    1.0            0  B1003  \n",
       "4  False        NaN    1.0            0  B1004  "
      ]
     },
     "execution_count": 87,
     "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": 90,
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Flights</th>\n",
       "      <th>GridFins</th>\n",
       "      <th>Reused</th>\n",
       "      <th>Legs</th>\n",
       "      <th>Block</th>\n",
       "      <th>ReusedCount</th>\n",
       "      <th>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>Orbit_VLEO</th>\n",
       "      <th>LaunchSite_CCAFS SLC 40</th>\n",
       "      <th>LaunchSite_KSC LC 39A</th>\n",
       "      <th>LaunchSite_VAFB SLC 4E</th>\n",
       "      <th>LandingPad_5e9e3032383ecb267a34e7c7</th>\n",
       "      <th>LandingPad_5e9e3032383ecb554034e7c9</th>\n",
       "      <th>LandingPad_5e9e3032383ecb6bb234e7ca</th>\n",
       "      <th>LandingPad_5e9e3032383ecb761634e7cb</th>\n",
       "      <th>LandingPad_5e9e3033383ecbb9e534e7cc</th>\n",
       "      <th>Serial_B0003</th>\n",
       "      <th>Serial_B0005</th>\n",
       "      <th>Serial_B0007</th>\n",
       "      <th>Serial_B1003</th>\n",
       "      <th>Serial_B1004</th>\n",
       "      <th>Serial_B1005</th>\n",
       "      <th>Serial_B1006</th>\n",
       "      <th>Serial_B1007</th>\n",
       "      <th>Serial_B1008</th>\n",
       "      <th>Serial_B1010</th>\n",
       "      <th>Serial_B1011</th>\n",
       "      <th>Serial_B1012</th>\n",
       "      <th>Serial_B1013</th>\n",
       "      <th>Serial_B1015</th>\n",
       "      <th>Serial_B1016</th>\n",
       "      <th>Serial_B1017</th>\n",
       "      <th>Serial_B1018</th>\n",
       "      <th>Serial_B1019</th>\n",
       "      <th>Serial_B1020</th>\n",
       "      <th>Serial_B1021</th>\n",
       "      <th>Serial_B1022</th>\n",
       "      <th>Serial_B1023</th>\n",
       "      <th>Serial_B1025</th>\n",
       "      <th>Serial_B1026</th>\n",
       "      <th>Serial_B1028</th>\n",
       "      <th>Serial_B1029</th>\n",
       "      <th>Serial_B1030</th>\n",
       "      <th>Serial_B1031</th>\n",
       "      <th>Serial_B1032</th>\n",
       "      <th>Serial_B1034</th>\n",
       "      <th>Serial_B1035</th>\n",
       "      <th>Serial_B1036</th>\n",
       "      <th>Serial_B1037</th>\n",
       "      <th>Serial_B1038</th>\n",
       "      <th>Serial_B1039</th>\n",
       "      <th>Serial_B1040</th>\n",
       "      <th>Serial_B1041</th>\n",
       "      <th>Serial_B1042</th>\n",
       "      <th>Serial_B1043</th>\n",
       "      <th>Serial_B1044</th>\n",
       "      <th>Serial_B1045</th>\n",
       "      <th>Serial_B1046</th>\n",
       "      <th>Serial_B1047</th>\n",
       "      <th>Serial_B1048</th>\n",
       "      <th>Serial_B1049</th>\n",
       "      <th>Serial_B1050</th>\n",
       "      <th>Serial_B1051</th>\n",
       "      <th>Serial_B1054</th>\n",
       "      <th>Serial_B1056</th>\n",
       "      <th>Serial_B1058</th>\n",
       "      <th>Serial_B1059</th>\n",
       "      <th>Serial_B1060</th>\n",
       "      <th>Serial_B1062</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>False</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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  Orbit_GTO  Orbit_HEO  Orbit_ISS  \\\n",
       "0            0            0          0          0          0          0   \n",
       "1            0            0          0          0          0          0   \n",
       "2            0            0          0          0          0          1   \n",
       "3            0            0          0          0          0          0   \n",
       "4            0            0          0          1          0          0   \n",
       "\n",
       "   Orbit_LEO  Orbit_MEO  Orbit_PO  Orbit_SO  Orbit_SSO  Orbit_VLEO  \\\n",
       "0          1          0         0         0          0           0   \n",
       "1          1          0         0         0          0           0   \n",
       "2          0          0         0         0          0           0   \n",
       "3          0          0         1         0          0           0   \n",
       "4          0          0         0         0          0           0   \n",
       "\n",
       "   LaunchSite_CCAFS SLC 40  LaunchSite_KSC LC 39A  LaunchSite_VAFB SLC 4E  \\\n",
       "0                        1                      0                       0   \n",
       "1                        1                      0                       0   \n",
       "2                        1                      0                       0   \n",
       "3                        0                      0                       1   \n",
       "4                        1                      0                       0   \n",
       "\n",
       "   LandingPad_5e9e3032383ecb267a34e7c7  LandingPad_5e9e3032383ecb554034e7c9  \\\n",
       "0                                    0                                    0   \n",
       "1                                    0                                    0   \n",
       "2                                    0                                    0   \n",
       "3                                    0                                    0   \n",
       "4                                    0                                    0   \n",
       "\n",
       "   LandingPad_5e9e3032383ecb6bb234e7ca  LandingPad_5e9e3032383ecb761634e7cb  \\\n",
       "0                                    0                                    0   \n",
       "1                                    0                                    0   \n",
       "2                                    0                                    0   \n",
       "3                                    0                                    0   \n",
       "4                                    0                                    0   \n",
       "\n",
       "   LandingPad_5e9e3033383ecbb9e534e7cc  Serial_B0003  Serial_B0005  \\\n",
       "0                                    0             1             0   \n",
       "1                                    0             0             1   \n",
       "2                                    0             0             0   \n",
       "3                                    0             0             0   \n",
       "4                                    0             0             0   \n",
       "\n",
       "   Serial_B0007  Serial_B1003  Serial_B1004  Serial_B1005  Serial_B1006  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             0             0             0             0   \n",
       "2             1             0             0             0             0   \n",
       "3             0             1             0             0             0   \n",
       "4             0             0             1             0             0   \n",
       "\n",
       "   Serial_B1007  Serial_B1008  Serial_B1010  Serial_B1011  Serial_B1012  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             0             0             0             0   \n",
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       "3             0             0             0             0             0   \n",
       "4             0             0             0             0             0   \n",
       "\n",
       "   Serial_B1013  Serial_B1015  Serial_B1016  Serial_B1017  Serial_B1018  \\\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_B1019  Serial_B1020  Serial_B1021  Serial_B1022  Serial_B1023  \\\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_B1025  Serial_B1026  Serial_B1028  Serial_B1029  Serial_B1030  \\\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_B1031  Serial_B1032  Serial_B1034  Serial_B1035  Serial_B1036  \\\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_B1037  Serial_B1038  Serial_B1039  Serial_B1040  Serial_B1041  \\\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_B1042  Serial_B1043  Serial_B1044  Serial_B1045  Serial_B1046  \\\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_B1047  Serial_B1048  Serial_B1049  Serial_B1050  Serial_B1051  \\\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_B1054  Serial_B1056  Serial_B1058  Serial_B1059  Serial_B1060  \\\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_B1062  \n",
       "0             0  \n",
       "1             0  \n",
       "2             0  \n",
       "3             0  \n",
       "4             0  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HINT: Use get_dummies() function on the categorical columns\n",
    "features_one_hot = pd.get_dummies(features)\n",
    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)\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": 93,
   "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": "markdown",
   "metadata": {},
   "source": [
    "<code>features_one_hot.to_csv('dataset_part\\_3.csv', index=False)</code>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.linkedin.com/in/nayefaboutayoun/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Nayef Abou Tayoun</a> is a Data Scientist at IBM and pursuing a Master of Management in Artificial intelligence degree at Queen's University.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Log\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Date (YYYY-MM-DD) | Version | Changed By    | Change Description      |\n",
    "| ----------------- | ------- | ------------- | ----------------------- |\n",
    "| 2021-10-12        | 1.1     | Lakshmi Holla | Modified markdown       |\n",
    "| 2020-09-20        | 1.0     | Joseph        | Modified Multiple Areas |\n",
    "| 2020-11-10        | 1.1     | Nayef         | updating the input data |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright © 2020 IBM Corporation. All rights reserved.\n"
   ]
  }
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