{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zv6wbwOs-mZm"
      },
      "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": {
        "id": "Raf4ov0K-mZq"
      },
      "source": [
        "# **SpaceX  Falcon 9 First Stage Landing Prediction**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_pxtJt-f-mZq"
      },
      "source": [
        "## Assignment: Exploring and Preparing Data\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GEvlxsNM-mZr"
      },
      "source": [
        "Estimated time needed: **70** minutes\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GS-8-Wnj-mZr"
      },
      "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": {
        "id": "CRvYdgGl-mZs"
      },
      "source": [
        "Falcon 9 first stage will land successfully\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BBgQPz0V-mZs"
      },
      "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": {
        "id": "1XxpLG6S-mZt"
      },
      "source": [
        "Several examples of an unsuccessful landing are shown here:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3gpuz-JJ-mZu"
      },
      "source": [
        "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ksKcq_fX-mZv"
      },
      "source": [
        "Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3t4yuDgb-mZv"
      },
      "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": {
        "id": "fEVyTWV4-mZw"
      },
      "source": [
        "***\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "StX752eA-mZw"
      },
      "source": [
        "### Import Libraries and Define Auxiliary Functions\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KxO2bnYW-mZw"
      },
      "source": [
        "We will import the following libraries the lab\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "aZoC2aIi-mZx"
      },
      "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": {
        "id": "jrNZc91h-mZy"
      },
      "source": [
        "## Exploratory Data Analysis\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QGfhRptA-mZy"
      },
      "source": [
        "First, let's read the SpaceX dataset into a Pandas dataframe and print its summary\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "9d-dV754-mZz",
        "outputId": "54296218-9027-4a8e-be28-f678239a54c0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>FlightNumber</th>\n",
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              "  <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",
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              "      <td>False</td>\n",
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              "      <td>None None</td>\n",
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              "      <td>False</td>\n",
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              "      <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",
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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",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>B0007</td>\n",
              "      <td>-80.577366</td>\n",
              "      <td>28.561857</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2013-09-29</td>\n",
              "      <td>Falcon 9</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>PO</td>\n",
              "      <td>VAFB SLC 4E</td>\n",
              "      <td>False Ocean</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>B1003</td>\n",
              "      <td>-120.610829</td>\n",
              "      <td>34.632093</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2013-12-03</td>\n",
              "      <td>Falcon 9</td>\n",
              "      <td>3170.000000</td>\n",
              "      <td>GTO</td>\n",
              "      <td>CCAFS SLC 40</td>\n",
              "      <td>None None</td>\n",
              "      <td>1</td>\n",
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              "  </tbody>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-30b8c349-b8fc-46bf-9bc6-1646e1fe2ec3')\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-30b8c349-b8fc-46bf-9bc6-1646e1fe2ec3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "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": {
        "id": "Oc4yk-g9-mZz"
      },
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274
        },
        "id": "jIPjaGmz-mZ0",
        "outputId": "b6689bef-655b-49c4-cea6-84d137653e4f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1842.38x360 with 1 Axes>"
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "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": {
        "id": "oDYKLbpk-mZ0"
      },
      "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": {
        "id": "S0GstK_k-mZ0"
      },
      "source": [
        "Next, let's drill down to each site visualize its detailed launch records.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YmOY6DdK-mZ1"
      },
      "source": [
        "### TASK 1: Visualize the relationship between Flight Number and Launch Site\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TDQ018K0-mZ1"
      },
      "source": [
        "Use the function <code>catplot</code> to plot <code>FlightNumber</code> vs <code>LaunchSite</code>, set the  parameter <code>x</code>  parameter to <code>FlightNumber</code>,set the  <code>y</code> to <code>Launch Site</code> and set the parameter <code>hue</code> to <code>'class'</code>\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "id": "6zu6k_4w-mZ1",
        "outputId": "23a46303-3a9f-4ee4-83dd-520181116453"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1842.38x360 with 1 Axes>"
            ],
            "image/png": 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8tyRJahw69kkeUiUtmhXwrSOG8KvHpwDQrCBw6VFDU05VC226wHnPwcKpSQGgffbvF1k5Z8u+FbNrny2fTX64Uvuh7IubMcLbtyfvsboNh4O+Ca2y/Iyl69Dk+lIFzSwKqPFauxT+9c2yLXM+eSFZ2e2YX6WTp0Ub6Lt37tcPHlNxadsug6DTgFrHkpSdNG9hGwNsijHeWNoRY3wXIITwFeC10sJm5tgL5a49DJgM3AOcQcXiZmXtSX7OJZlxNgAfVHFeL2Dzq4oY48Rq8v8IOCzGuDJz/krgtq2cew3wfaD8jsWfAW6PMUbg9RBCpxBCrxjjvGqeV/WgoCBw6t59OXXvvjmPcdlxw2nbohkvfbSIEb078J2js7zDso4cNaIH94ybtbndtkUhBw3uWv9BWrTZsq95FX31YENRMXe9MZMp81Zx8JCunLhHFRtf15NhPdszrGfuS4SO6N2Bpy4dzROT5tO1XUuO261nk91vs7zCgmBhs7wWbd2bUpIkSY3eeaMHcfCQrrw/dyX7DepC387pvOesU7XZJ3Pnk+Clq6AkM5u1eRsYNrZucuVi0zrYsBradUsvQ6f+sOSjsnbn7Ffq4pXrkmUpIZkNO2c8nPXwtq+p7OBvJ8tazn4r2U/4yJ/m9zKOi6fBy9fA2sUw8osw4qS0E6khWfxRWWGz1IJJVZ+bDw7/MRSthw+egG5D4ZjfpHcTudSEpVnc3BUYn8MxSAqad5EUC38dQmgeY9yUOXZaCOHgzPfXxRhvCSH8C5gRQngWeBS4q3Qp23JuAO4JIVwEPAPcEmOcW9WTZ2Zpto8xflLV8UrnfgaYE2N8t1LBoQ/JzNNSszN9FjcbiZbNCvnuMcP47jHpFDVLHTmiB1d+bg/ufnMmHVo35+LDd6JzpT0JayqpxZNb8WzA6GQ5zOkvJe12PWCfc3PKUVvfv38ij0xI/njfM24W81as4/zRg1PJUhf6dm7DuYc0jDs835m5jPWbSth3YBcKm/L6uGoSNhaVMG3hagZ0bUObFnm85JkkSUrP2qXQol2ywoXqxM69OrBzrw5px2gYeu4KX3oI3rwZCprDARdBp37pZBl/Kzz5Y9i4CgYeCp+/HVp3qv8cY38P954NG1ZAy45w3O+yH+Pduyu2P3keVi3IbnZt2x2SrXqWTYfWXZJ9hfPVxrVwy3HJ0rqQLH38hXth6DHp5lLD0WuPZK/stYvL+gYfkV6e2mreGo6/KnlISk3efRIXQmhBsk/lt2OMq0IIbwDHkBQtodKytAAxxnNDCLsBRwLfJVlO9pxK5zwZQhgEHEuyZOw7IYQt9tnMMmsbkhmeR9dijPOB8wH69+9fmzhqwmo7CxXgj89+xM0vJvX880cP4uIjhmQ3QOl+e9OehfXLYeixqbx4X7V+E/9+t+J9C3e9OSuvi5sNQXFJ5Nzb3uL5DxYByQcK93xtfzq0cvZko7H4I3jrbxCLk308u+fxXqYbViV3sM97N/lg5YCLoDC7l0TvzlrOubePY9GqDbRv1YxrTxvJETksLS1JqnvvzlrOlHkrOXBwV/rvkN6srSWrN/Dqx0sY1K1t/W8JUceKikt48aNFrN9UwuHDu9OqeWFO48xaupZFqzewR99O3gi3bhncd06yNF/rznDsb2GP09JOpcZo4OjkkaZV8+Gx75TNIP30v/Dy1XDUz+s/y+Ax8J0pyXK/3Ycnq9xkq30PWDSlrN28bW779gF0HpDbdQ3J9JfKCpulJj1gcVNlmreCL94HT/9fsjT2rqfAARemnUpSnkuzuDkZOHUbxw7dyrFjgE7Ae5nZY22AdZQVN6sUY3wvc80dwKdUKm5mzlkK3AncGUJ4FBhNFTNIM3tsrg4hDKpm9uZgYCBQOmuzL/B2CGFfYA5Q/na5vpm+ys91M8meoYwaNSpu62eUtpeXP1rMVU9/uLl91dMfsmf/zhw8JMvlbQsKYWjOtf460bywgNbNC1mT2aMSoEOrvLvPo8F58cNFmwubAFPmreSeN2dx3uiGMaM0n02as4LH35tHr06tOXWvvrRukduHibWych789QhYvyJpv/NP+Mar+ftG/MGvwQePJd9//BysWZT1Xh8/f/R9Fq1KltVZtb6IHz88iTHDulPQ1D+olaSUXffMR1zzTPK6tVlB4MYz9+bIEfV/88n4GUv50t/e3Lwv+kVjdkp9RZdcbSwq4fSbX+PtmcsB6N+lDQ9feBBdslwN5jePT+Hmlz4hRhjUrS13nbc/PTq02h6R88OLVyaFTUgKnf+6GIYcleyzKDU2iz8qK2yWWjil6nPrQ4u2tdtv7/D/g7nvJO+PQgEceXluRdLGokMV+yBX1aemrc9ecM42P76XpKykuRj0c0DLzMxEAEIIu4cQDiEpMB4YQji+3LHRmZmUZwDnxhgHxBgHkBQPj8rMktxCCKFdCOGwcl0jgRlVnHd46RghhPYkhcmZ28j/G+CGzBK1pc9zVvkTYozvxRi7l8s6G9grxjgf+BdwVkjsD6xwv001VO/MXLZF34RZW/blg1bNC7n0qKGb2y0KCyq0lZtFqzds0bd4zZZ9ys4r0xbzmRte4U8vfMxPHp7E2be8mU6Q9x8pK2wCbFqT3ImbjzaugQ8er9iXw88yY8naCu35K9ezoajyiveSpPq0bmMxf/7vtM3topLIH577aBtXbD/XPTttc2ET4KYXP2b52o2pZKmt56Yu2FzYBJi5dC33jpu1jSu29Mmi1dz0YlLYTNpruOm/1e7y0rjNf69iu3gDLJlW9blSvuuzd7L0ank7HZVOlrrQd2+49H0480G45D3Y72tpJ0pXz11h368BmRs9u49wVp4kabtLbbpSjDGGEE4Grg0h/ABYD0wHLokxrgshnJA5di2wCZgI/IBk2dgLyo2zJoTwMnDiVp4qAN8PIdxEMsNzDVXM2gT2Bq4PIRSRFH3/GmN8K4QwABgWQphd7txLgT8D7YC3QgibMhmzWWj7cZLldacBa4EvZ3GtVK/2Hbjl3cP7DtwhhSR149xDBnHo0G68P28l+w/aoWnfMV5Hjty5B53aNGf52mT74+aFgc/s4Z2atXX7a9MpLimbtP/mp0uZPHdF/S9t17rzln2tUtgfpy40awVtuyazNUvlcFfxsbv24B+vl90DdejQbunMqpUkbVZUUsKm4oqL3ZQvMNanles2VWhvKo6s3VhMp/RWyc3Z6g1b/g5Xry+q4sytm79i/RZ981asyzlTo7DTEcnSnKXadIWeu6eXp4FYv6mYpWs20rtT67SjqC61aANnPgDP/gxWzoXdPg/7nJt2qtpp2S75c6zE2N/B/l9P9hHuvWeyNZEkSdtRqmsxxhjnAp/fyrGpJIXMyraossQYTynXvLXSsVUkRcTqsvwe+H0V/dOBrW0a97vMo0YyszdLv4+AtzEpL+w3aAd+euIIbszcXX3BoYOqLHjmkyE92jOkR/u0YzQcm9bBh09Cs5aw05FQmN1emV3atuCBrx/ILa98yrqNJXxhv/6M6J3bnqqbiktYv6mY9u7XSYtmWxbLWjZL4U3iiM/AmzfDnHFJu+dusHue7glVUAjHXgGPXAhF65Mi7dG/zHqYHx8/gk6tW/DKx4vZrU9Hvu0McElKXftWzfnsyD488HbZfalnH7BjKlnO2LcfE2aVzXYcPbRb3hZrjhrRg+7tW7Iwsxx76+aFnLJXdjcG7T2gM707tmJuuSLnSXv0rtOceWf/C5OVMSY9AB37JXsPNm/aN10+MH42P/3XZFZtKGKPfp34y1l707190/6dNCp99oKzHkk7ReOz9BN4955kWdw9z0x3aesuA5NHmoo3wbi/J8sGDzgERn4BgluHSFJjFGJ0G8d8MWrUqDhu3Li0Y0hqbNYuTfZTXJpZGqz3XvCVJ5JCZz27d9wsfvXYFFau38QRw3tw3ekjadsypftwijbCsunJm7Msi711ZeLs5Zx+8+ubZ50cv1svbvjiXqlkoaQYPn0RYjEMPAwK83yv2rVLYdFU6LVH094fR5IakqKNMPlBWD4Ldj4Buu+c/RDFJTzw9mymzFvF6KFdOXx4/e+3Werp9xfwzPsLGNy9LWfuvyNtWuT4b+f6FdCsNTTLbo/LujRvxTrufGMm6zcVc9o+/dipe/Y3Cc5cspY/vTCNRas28Nk9+3BiUy9uqoIVazex32+eYf2msmX+z9i3P785ZbcUU6mhmbt8Hb97YiofLVzNmGHd+eYRQ2iRxs2fDcXiaXDzYbBxVdLuPBC+/moyU7apevhCmPCPsvahP4AxP0ovj+pWSQlMfRQWvg+Dj4B++6SdSGoomuRdHBY384jFTUnbxat/hKd+XLHv1Ftg11OqPn87WbhyPQde8RxF5ZZh/eYRQ9KZETf9Fbjv7GTp0nY94bQ7oN++9Z+DZBm3Z6YsoE+n1hw6tBsFBU3y9YokqSn4x6kw7enk+4Lm8KWHYOAh6WZK08Y18OD5MPUxaNUhmdm39zlppxIkN329dgNMewZ67Aqjv5vubKlGYNKcFZzwx5cr9O0zoDP3XXBgSonUEB3/h5eYPHfl5vYFhw7msuOGp5goZU9fDq9cW7EvhffyDUbRBvh1bygpt3R6+17wnanpZVLdeuw78NZfy9on3wR7nJ5eHqnhaJIfFjbh25skSQBsWL1l38Yq+razDxesrlDYBHi/3BvXevXoJWV7Mq6en7yATknPjq04c/8dGTO8u4VNSVJFJcXwn8vgV73g6hEw8d60E+Vu4dSywiZAySZ448b08jQEr16fzE4gJrM3H/02rJiTdioBPP9rePonyZ6Zr98A952TdqK8N7xne/pUWrp5zPDuKaVRQzRvxboKhU2AZ6YsSClNA1HVaksprMDUYBQ0g5aVVhZo3TmdLKp761fC+Fsr9r16fSpRJDUMFjclqanb4zRo0a6s3a4HDD+h/mP060j7SkvQHrzTDvWegxhhybSKfZXbkiQ1BG/fDm/8GTathZVz4KELYNmMtFPlJlTx1rSp75E1f2LFdiyGhVPSyaKKJj9Ysf3pf2HNknSyNBLNCgu49cv7cOTO3Rnaox3fOmIIXxs9OO1YakC6tG1BpzYVtwsZ1LWJby+x9znJ+/dSvfeEIUenFid1BYVwxOVsnsBU2AIO/0mqkVSHQmCLyWlN/bWi1MTl+YZZkqRa6zIIzn8h+YC0WcvkDVIKy2q1b9Wcv52zD799YioLVq7n5D37cNYBA+o9ByHAsLGZmRIZw8bWfw5Jkqoz682K7VgMc8ZD5x3TyVMb3YYmN1eV/vtb2BIOuCjdTGkbPKbi65EW7d1bqqHo2Ldsv3qAVp22nC2krA3p0Z6/nu3/46pay2aF/Prk3fjBAxNZtb6IQV3b8sOx2e/N3Kh06A0XvgFTHoUWbWH48VDYvPrrGrNRX4ZBh8K8d6Hf/tChV9qJVFdatod9z09WTIDkxriDL003k6RUuedmHnHPTUmqJ+uWw7M/h9lvQf8D4Iif+IGVJKnhGX8r/PtbZe1QCN+aAJ36pxapVoqL4IPHYPnM5APaLoPSTpSukhL472/h3bugfc9kNsqAg9JOJYC57yR7xK5dnBTiT/qDe35J9WTdxmLmrVjHwK5tCc7akpqej56BhZNh0BjotXvaaaSGokn+g2hxM49Y3JQkSZK0WUlJsu/f+NuSPaWOvBx2OzXtVFLTsGl9snTwDjulsuqJJEmSlGFxUw2bxU1JkiRJkiRJkiRlNMniZkHaASRJkiRJkiRJkiSpJixuSpIkSZIkSZIkScoLFjclSZIkSZIkSZIk5QWLm5IkSZIkSZIkSZLygsVNSZIkSZIkSZIkSXnB4qYkSZIkSZIkSZKkvGBxU5IkSZIkSZIkSVJesLgpSZIkSZIkSZIkKS9Y3JQkSZIkSZIkSZKUFyxuSpIkSZIkSZIkScoLFjclSZIkSZIkSZIk5QWLm5IkSZIkSZIkSZLygsVNSZIkSZIkSZIkSXnB4qYkSZIkSZIkSZKkvGBxU5IkSZIkSZIkSVJesLgpSZIkSZIkSZIkKS9Y3JQkSZIkSZIkSZKUFyxuSpIkSZIkSZIkScoLFjclSZIkSZIkSZIk5QWLm5IkSZIkSZIkSZLygsVNSZIkSZIkSZIkSXnB4qYkSfwJHWkAACAASURBVJIkSZIkSZKkvGBxU5IkSZIkSZIkSVJesLgpSZIkSZIkSZIkKS9Y3JQkSZIkSZIkSZKUFyxuSpIkSZIkSZIkScoLFjclSZIkSZIkSZIk5QWLm5IkSZIkSZIkSZLygsVNSZIkSZIkSZIkSXnB4qYkSZIkSZIkSZKkvGBxU5IkSZIkSZIkSVJesLgpSZIkSZIkSZIkKS9Y3JQkSZIkSZIkSZKUFyxuSpIkSZIkSZIkScoLFjclSZIkSZIkSZIk5QWLm5IkSZIkSZIkSZLygsVNSZIkSZIkSZIkSXnB4qYkSZIkSZIkSZKkvFDr4mYIoXMIoV9dhJEkSZIkSZIkSZKkrcmpuBlCaBdCuCqEMB9YDHxa7th+IYTHQwh71VVISZIkSZIkSZIkScq6uBlC6Ai8BlwKzAWmAKHcKe8BhwBn1EVASZIkSZIkSZIkSYLcZm7+L7ALcE6McS/gvvIHY4xrgf8CR9Q+niRJkiRJkiRJkiQlcilungI8GWO8fRvnzAD65BZJkiRJkiRJkiRJkraUS3GzLzCxmnNWAx1zGFuSJEmSJEmSJEmSqpRLcXMV0L2acwYCi3MYW5IkSZIkSZIkSZKqlEtx8y3ghBBC+6oOhhB6AWOBl2sTTJIkSZIkSZIkSZLKy6W4eR2wA/B4CGHn8gcy7fuAVsAfah9PkiRJkiRJkiRJkhLNsr0gxvhkCOFnwOXAJGATQAhhMdAZCMAPYoyv1mVQSZIkSZIkSZIkSU1bLjM3iTH+DDgC+BewDCgGIvA4cGSM8fd1llCSJEmSJEmSJEmSyGHmZqkY4/PA83WYRZIkSZIkSZIkSZK2KuuZmyGEs0IIu1dzzm4hhLNyjyVJkiRJkiRJkiRJFeWyLO2twGerOeck4JYcxpYkSZIkSZIkSZKkKuW052YNFJLswSlJkiRJkiRJkiRJdWJ7FTeHAsu209iSJEmSJEmSJEmSmqBmNTkphPD3Sl2fDSEMqOLUQqA/cAjwWK2SSZIkSZIkSZIkSVI5NSpuAueU+z4CIzOPqkTgDeDS3GNJkiRJkiRJkiRJUkU1LW4OzHwNwCfAtcB1VZxXDCyLMa6pg2ySJEmSJEmSJEmStFmNipsxxhml34cQfgY8X75PkiRJkiRJkiRJkra3ms7c3CzG+LPtEUSSJEmSJEmSJEmStqXa4mYIoX/m2zkxxuJy7WrFGGfmnEySJEmSJEmSJEmSyqnJzM3pQAR2Bj4s165OrOH4kiRJkiRJkiRJklStmhQfbycpVK6o1JYkSZIkSZIkSZKkelNtcTPGeM622pIkSZIkSZIkSZJUHwrSDiBJkiRJkiRJkiRJNVEne2KGELoBBwEBeC3GOL8uxpUkSZIkSZIkSZKkUjWauRlCGBlC+HkIYWQVx74KzAAeAO4HZoQQvlW3MSVJkiRJkiRJkiQ1dTVdlvZM4IfAvPKdIYTdgRuBVsArwBNAMXB1COHAOswpSZIkSZIkSZIkqYmraXHzIGBcjHFBpf6LgELgFzHG0THG44GjM8cuqKOMkiRJkiRJkiRJklTj4mY/YHwV/UcC64ErSjtijC8DzwEH1DqdJEmSJEmSJEmSJGXUtLjZFVhWviOE0AUYALwRY1xX6fzJQJ9ap5MkSZIkSZIkSZKkjJoWNzeRFDjL2zPz9e0qzl8DxFxDSZIkSZIkSZIkSVJlNS1uTiNZgra8o0kKmK9XcX4vYH4tckmSJEmSJEmSJElSBTUtbv4bGBxCuDmEsHsI4VTgApL9Np+s4vz9gU/rKKMkSZIkSZIkSZIk1bi4eRUwA/gq8A5wD9AeuCbGuLL8iSGEEcBw4Lk6zClJkiRJkiRJkiSpiWtWk5NijCtCCAcCvwAOAJYA98QYb6ji9LHAu8BjdZZSkiRJkiRJkiRJUpMXYoxpZ1ANjRo1Ko4bNy7tGJIkSZIkSZIkSUpfSDtAGmq6LK0kSZIkSZIkSZIkpcripiRJUlqWfgpv3AwfPQOupiFJkvLZmsWw9JO0U0iSJKkJqNGem5WFEA4FvgfsC3Sm6iJpjDHmNL4kSVKj9+lL8I//geINSXvkmfDZqrYzlxqRTeuSR5suaSeRJNWlZ38Or1wHJUUw4BA44y5o2T7tVJIkSWqksi4+hhCOBx4GCoGZwAdAUR3nkpTxxKR5PPbefPp2bs15hwyiS9sWaUeSJNWFV64rK2wCTPgnjPkRdOyTXiZpe3rj5uTD742rYNhY+J+/Qou2aaeSJNXW/Enw0lVl7ekvwZs3wyHfSS+TJEmSGrVcZlb+FNgEHB9jfKpu40gq75EJc/jW3RM2t//7wSIe++bBhNAk9wiWpMaleGOljgglm1KJIm13y2bAEz+AWJK0P3gcXv8TjP5eurkkSbW39OMt+5ZU0SdJkiTVkVz23NwVuMfCprT93T9+doX2+/NWMmXeqpTSSJLq1H4XQCj3UmzYWOg8ILU40na1cEpZYbPUgsnpZJGqsXZjEb989H1Ouv5l/u+RSaxY540n0jYNHA0tO1TsG358OlkkSZLUJOQyc3M1sLSug0ja0g6VlqAtCNC5bfOU0kiS6tTwsfDVZ+CDx6DLINjt82knkraf/vtBi3awcXVZ305HppdH2oYfPzyJB9+eA8DE2SuYt2I9fzlrVMqppAasdWc462H47+9h3VLY6yyLm5IkSdquciluPgscUNdBJG3posN34uVpi1m8Olm68PzRg+nVsXXKqSRJdabv3slDauxad4Yv3AvP/RLWLIKRZ8DIL6adSqrS05MXVGg/O2UBxSWRwgK3hpC2qs/e8IW7004hSZKkJiKX4uYPgDdDCD8GfhVjjHWcSVLGTt3b89L3D+f1T5fQr3NrdurePu1IkiRJuRlwEHzlP2mnkKo1oGtb3puzYnO7X5c2FjbVsK2YA81bQ5suaSeRJEmS6kWorjYZQvh7Fd0DgEOBGcAEYHkV58QY41drG1BlRo0aFceNG5d2DEmSJElqtN6euYyv3TGeRas20LlNc67/wl4ctFPXtGNJW9q4Fu47Bz56Egqaw4EXw5GXp51KkiRJ9atJ3olZk+JmSY5jxxhjYS4XhhCeB66IMT5Zru8SYFiM8eshhK7APODiGOON5c6ZDqwCijNd3wDmAlOAD0j+I68Bvhxj/KDScxYA1wKHAxFYD3w+xvhpZtxRMcbFla45DvgF0AbYADwXY/zOVn6mfYDXgNNjjPeHEAaUy1Xq6hjj7Vv7vVjclCRJkqTtb1NxCZ8uXkP/Lm1o1Tynt7XS9vfaDfDkjyr2nf8C9N4zjTSNy+SHYfpLye9yjzOgwL8HJElSg9Uki5s1WZZ24HZPsaW7gNOBJ8v1nQ58P/P954DXgTOAGyteypjyRchMEfHjGOPITPtrwI+AsytddxrQG9g9xlgSQuhLUgitUghhV+B64PgY49QQQiFw/lbOLQR+CzxV6dDmXJKkxmX9pmKufPIDXvhwEcN6tudHY3emTyf3zJUkKR80LyxgaA+3hFADt/ijqvssbtbOi1fCc78oa895G064Or08kiRJ2kK1xc0Y44z6CFLJ/cAvQwgtYowbMwXK3sBLmeNnAN8B7gwh9I0xzs5i7A7Asir6ewHzYowlADUY8/ske45OzZxfDPx5K+deDDwA7JNFTklSHrviP1O59dXpAExbuJoZS9bw6MWHpBtKkiRJjcew42D8LWXt5m1g0GFppWk83vpbxfY7d8CxV0CzFunkkSRJ0hYK0g5QlRjjUuBN4LhM1+nAvTHGGELoB/SKMb4J3Esy47K850MIE0IIb5TrG5zp+xj4NlDVLXf3AidmzrsqhFDdrY67AuOr+1lCCH2Ak6m68Fmaq/SxxafeIYTzQwjjQgjjFi1aVN3TSZIaiOc/WFihPWn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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "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(\"LaunchSite\",fontsize=20)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Explanation**: We can see from the scatter plot that as flight number increases, there are more successful first stage landing. With small flight numbers, launches happens more in the site CCAFS SLC 40 and with much lower success rate. Although there are less launches in VAFB SLC 4E and KSC LC 39A, higher success rate can be seen in these two sites."
      ],
      "metadata": {
        "id": "-f26nQiz_W3b"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jvXbE6GB-mZ1"
      },
      "source": [
        "Now try to explain the patterns you found in the Flight Number vs. Launch Site scatter point plots.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JD1uwAeQ-mZ2"
      },
      "source": [
        "### TASK 2: Visualize the relationship between Payload and Launch Site\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w5lEv7MU-mZ2"
      },
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "id": "My-L4ZXf-mZ2",
        "outputId": "051c77e6-16f7-4a81-f7ef-685e6f95817d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1842.38x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "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(\"PayloadMass\",fontsize=20)\n",
        "plt.ylabel(\"LaunchSite\",fontsize=20)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Explanation**: With higher Payload the success rate is much higher. And in KSC LC39A launchsite we can see much higher success rate with low Payload whereas this rate is mucher lower in CCAFS SLC 40 launchsite. Besides, there no rockets launched in VAFB-SLC for Payload greater than 10000. Furthermore, with Payload more than 9500, we can see very high success rate overall."
      ],
      "metadata": {
        "id": "xV6R_RmvDwKJ"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HSqcrNl1-mZ2"
      },
      "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": {
        "id": "gGFJvcCW-mZ2"
      },
      "source": [
        "### TASK  3: Visualize the relationship between success rate of each orbit type\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pqD2V_pt-mZ3"
      },
      "source": [
        "Next, we want to visually check if there are any relationship between success rate and orbit type.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n2WnO0j--mZ3"
      },
      "source": [
        "Let's create a `bar chart` for the sucess rate of each orbit\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "7zDTwqh4-mZ3"
      },
      "outputs": [],
      "source": [
        "# HINT use groupby method on Orbit column and get the mean of Class column\n",
        "df_success_rate = df.groupby(['Orbit']).mean()\n",
        "df_success_rate.reset_index(inplace=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "1qf32vCm-mZ3",
        "outputId": "08e52c1b-7881-4914-a886-07c7d89a90e5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Orbit  FlightNumber   PayloadMass   Flights  GridFins    Reused      Legs  \\\n",
              "0   ES-L1     13.000000    570.000000  1.000000  1.000000  0.000000  1.000000   \n",
              "1     GEO     83.000000   6104.959412  2.000000  1.000000  1.000000  1.000000   \n",
              "2     GTO     35.037037   5011.994444  1.407407  0.629630  0.333333  0.629630   \n",
              "3     HEO     49.000000    350.000000  1.000000  1.000000  0.000000  1.000000   \n",
              "4     ISS     39.142857   3279.938095  1.238095  0.809524  0.238095  0.857143   \n",
              "5     LEO     20.000000   3882.839748  1.000000  0.571429  0.000000  0.714286   \n",
              "6     MEO     77.666667   3987.000000  1.000000  0.666667  0.000000  0.666667   \n",
              "7      PO     36.333333   7583.666667  1.333333  0.888889  0.333333  0.777778   \n",
              "8      SO     73.000000   6104.959412  4.000000  0.000000  1.000000  0.000000   \n",
              "9     SSO     60.800000   2060.000000  2.400000  1.000000  0.800000  1.000000   \n",
              "10   VLEO     78.928571  15315.714286  3.928571  1.000000  1.000000  1.000000   \n",
              "\n",
              "       Block  ReusedCount   Longitude   Latitude     Class  \n",
              "0   1.000000     0.000000  -80.577366  28.561857  1.000000  \n",
              "1   5.000000     2.000000  -80.577366  28.561857  1.000000  \n",
              "2   3.037037     0.962963  -80.586229  28.577258  0.518519  \n",
              "3   4.000000     1.000000  -80.577366  28.561857  1.000000  \n",
              "4   3.142857     1.285714  -80.583697  28.572857  0.619048  \n",
              "5   2.142857     0.428571  -80.584963  28.575058  0.714286  \n",
              "6   5.000000     0.666667  -80.577366  28.561857  0.666667  \n",
              "7   3.222222     1.555556 -120.610829  34.632093  0.666667  \n",
              "8   5.000000     3.000000  -80.603956  28.608058  0.000000  \n",
              "9   4.600000     3.200000 -112.604136  33.418046  1.000000  \n",
              "10  5.000000     3.928571  -80.586862  28.578358  0.857143  "
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              "      <th></th>\n",
              "      <th>Orbit</th>\n",
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              "      <th>PayloadMass</th>\n",
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              "      <th>0</th>\n",
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              "      <th>1</th>\n",
              "      <td>GEO</td>\n",
              "      <td>83.000000</td>\n",
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              "      <th>2</th>\n",
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              "      <td>3.037037</td>\n",
              "      <td>0.962963</td>\n",
              "      <td>-80.586229</td>\n",
              "      <td>28.577258</td>\n",
              "      <td>0.518519</td>\n",
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              "      <th>3</th>\n",
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              "      <td>28.561857</td>\n",
              "      <td>1.000000</td>\n",
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              "      <th>4</th>\n",
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              "      <td>39.142857</td>\n",
              "      <td>3279.938095</td>\n",
              "      <td>1.238095</td>\n",
              "      <td>0.809524</td>\n",
              "      <td>0.238095</td>\n",
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              "      <td>1.285714</td>\n",
              "      <td>-80.583697</td>\n",
              "      <td>28.572857</td>\n",
              "      <td>0.619048</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
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              "      <td>20.000000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>0.571429</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>28.575058</td>\n",
              "      <td>0.714286</td>\n",
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              "      <th>6</th>\n",
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              "      <th>7</th>\n",
              "      <td>PO</td>\n",
              "      <td>36.333333</td>\n",
              "      <td>7583.666667</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>0.888889</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.777778</td>\n",
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              "      <td>-120.610829</td>\n",
              "      <td>34.632093</td>\n",
              "      <td>0.666667</td>\n",
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              "      <th>8</th>\n",
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              "      <th>9</th>\n",
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              "      <th>10</th>\n",
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          },
          "metadata": {},
          "execution_count": 7
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      ],
      "source": [
        "df_success_rate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
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        "id": "vajiUyZI-mZ4",
        "outputId": "79bfdec5-b950-4b14-ece4-ab7ef49c98a2"
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      "outputs": [
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.barplot(y='Class', x='Orbit', data=df_success_rate)\n",
        "plt.xlabel(\"Orbit\",fontsize=10)\n",
        "plt.ylabel(\"sucess rate\",fontsize=10)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Explanation**: From the Bar Plot we can see for Orbit type ES-L1, GEO, HEO, and SSO have the highest success rate, which is 100%. And we also find in SO orbit, the rate is zero."
      ],
      "metadata": {
        "id": "Jm7pzmj7GRq8"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bq6W_uo5-mZ4"
      },
      "source": [
        "Analyze the ploted bar chart try to find which orbits have high sucess rate.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kLppJSyr-mZ4"
      },
      "source": [
        "### TASK  4: Visualize the relationship between FlightNumber and Orbit type\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vzvcFURz-mZ4"
      },
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        },
        "id": "IYAa1PRv-mZ4",
        "outputId": "a46779b0-cc75-4c13-aa96-31a5808aba5e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1122.38x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "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 = 3)\n",
        "plt.xlabel(\"FlightNumber\",fontsize=20)\n",
        "plt.ylabel(\"Orbit\",fontsize=20)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Explanation**: In ES-L1, GEO, HEO, and SSO orbits, all launches are successful. There is clear relationship between flight number and success rate in LEO orbit since as flightnumber increases, the success rate increases. In contrast, there is no such obvious relationship in GTO orbit."
      ],
      "metadata": {
        "id": "Kl4WI8ojHHE6"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZtWXzlBI-mZ5"
      },
      "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": {
        "id": "mNexbs31-mZ5"
      },
      "source": [
        "### TASK  5: Visualize the relationship between Payload and Orbit type\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pFhSIIcz-mZ5"
      },
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        },
        "id": "1iQvjzZG-mZ5",
        "outputId": "15120ec4-dba6-4f40-8ece-d1ff4c0bff94"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1122.38x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "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 = 3)\n",
        "plt.xlabel(\"PayloadMass\",fontsize=20)\n",
        "plt.ylabel(\"Orbit\",fontsize=20)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k2xFuEt7-mZ5"
      },
      "source": [
        "**Explanation**: 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": {
        "id": "W8VY5_m--mZ6"
      },
      "source": [
        "### TASK  6: Visualize the launch success yearly trend\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S7fhFkBM-mZ6"
      },
      "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": {
        "id": "4B8CKA5g-mZ6"
      },
      "source": [
        "The function will help you get the year from the date:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "Cu5fzS4y-mZ6"
      },
      "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": 12,
      "metadata": {
        "id": "fNDg3grd-mZ6"
      },
      "outputs": [],
      "source": [
        "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\n",
        "year = Extract_year(df['Date'])\n",
        "df['Year']=year"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "iitSY8wx-mZ6"
      },
      "outputs": [],
      "source": [
        "df_year_success = df.groupby('Year').mean()\n",
        "df_year_success.reset_index(inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "fT5kxctm-mZ7",
        "outputId": "0be32b1c-02fa-4591-acee-5b2cfca9362f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.lineplot(y='Class', x='Year', data=df_year_success)\n",
        "plt.xlabel(\"Year\",fontsize=15)\n",
        "plt.ylabel(\"Success rate\",fontsize=15)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "txdXLrpP-mZ7"
      },
      "source": [
        "**Explanation**: you can observe that the sucess rate since 2013 kept increasing till 2020\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ClbCwSUV-mZ7"
      },
      "source": [
        "## Features Engineering\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zNTO9Qyg-mZ7"
      },
      "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": null,
      "metadata": {
        "id": "xvOT82VS-mZ7",
        "outputId": "c37ff22b-582a-4f7f-a4e5-bc9dbc6e1ec2"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>FlightNumber</th>\n",
              "      <th>PayloadMass</th>\n",
              "      <th>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",
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              "    <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",
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              "    <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": 49,
          "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": {
        "id": "WQIioU_1-mZ8"
      },
      "source": [
        "### TASK  7: Create dummy variables to categorical columns\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NjVpLOx8-mZ8"
      },
      "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": null,
      "metadata": {
        "id": "H-rxBRnf-mZ8"
      },
      "outputs": [],
      "source": [
        "# HINT: Use get_dummies() function on the categorical columns\n",
        "df_orbit = pd.get_dummies(features['Orbit'])\n",
        "df_LaunchSite = pd.get_dummies(features['LaunchSite'])\n",
        "df_LandingPad = pd.get_dummies(features['LandingPad'])\n",
        "df_Serial = pd.get_dummies(features['Serial'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b9F8G2-t-mZ8",
        "outputId": "a4e073c4-f4ba-4e91-cc20-1a3a16af7865"
      },
      "outputs": [
        {
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              "      <th>ES-L1</th>\n",
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              "<p>5 rows × 72 columns</p>\n",
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            ],
            "text/plain": [
              "   ES-L1  GEO  GTO  HEO  ISS  LEO  MEO  PO  SO  SSO  ...  B1048  B1049  B1050  \\\n",
              "0      0    0    0    0    0    1    0   0   0    0  ...      0      0      0   \n",
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              "\n",
              "   B1051  B1054  B1056  B1058  B1059  B1060  B1062  \n",
              "0      0      0      0      0      0      0      0  \n",
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              "\n",
              "[5 rows x 72 columns]"
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          },
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "features_one_hot = pd.concat([df_orbit, df_LaunchSite, df_LandingPad, df_Serial], axis=1)\n",
        "features_one_hot.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Un0H3IzJ-mZ8"
      },
      "source": [
        "### TASK  8: Cast all numeric columns to `float64`\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zuq63XpV-mZ8"
      },
      "source": [
        "Now that our <code>features_one_hot</code> dataframe only contains numbers cast the entire dataframe to variable type <code>float64</code>\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JOeOuCW4-mZ9",
        "outputId": "f2d32ba0-3365-4c01-9305-78efc1792c90"
      },
      "outputs": [
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>90 rows × 72 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    ES-L1  GEO  GTO  HEO  ISS  LEO  MEO   PO   SO  SSO  ...  B1048  B1049  \\\n",
              "0     0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  ...    0.0    0.0   \n",
              "1     0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  ...    0.0    0.0   \n",
              "2     0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  ...    0.0    0.0   \n",
              "3     0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  ...    0.0    0.0   \n",
              "4     0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...    0.0    0.0   \n",
              "..    ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...    ...    ...   \n",
              "85    0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...    0.0    0.0   \n",
              "86    0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...    0.0    0.0   \n",
              "87    0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...    0.0    0.0   \n",
              "88    0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...    0.0    0.0   \n",
              "89    0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  ...    0.0    0.0   \n",
              "\n",
              "    B1050  B1051  B1054  B1056  B1058  B1059  B1060  B1062  \n",
              "0     0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
              "1     0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
              "2     0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
              "3     0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
              "4     0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
              "..    ...    ...    ...    ...    ...    ...    ...    ...  \n",
              "85    0.0    0.0    0.0    0.0    0.0    0.0    1.0    0.0  \n",
              "86    0.0    0.0    0.0    0.0    1.0    0.0    0.0    0.0  \n",
              "87    0.0    1.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
              "88    0.0    0.0    0.0    0.0    0.0    0.0    1.0    0.0  \n",
              "89    0.0    0.0    0.0    0.0    0.0    0.0    0.0    1.0  \n",
              "\n",
              "[90 rows x 72 columns]"
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# HINT: use astype function\n",
        "features_one_hot.astype('float64')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wPFL_PJ0-mZ9"
      },
      "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": {
        "id": "w4KJfc1s-mZ9"
      },
      "source": [
        "<code>features_one_hot.to_csv('dataset_part\\_3.csv', index=False)</code>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XxHlgmPx-mZ9"
      },
      "source": [
        "## Authors\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y9waDjQf-mZ9"
      },
      "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": {
        "id": "zjABVJwd-mZ9"
      },
      "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": {
        "id": "8c_iWMY2-mZ9"
      },
      "source": [
        "## Change Log\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kfn9pbM7-mZ-"
      },
      "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": {
        "id": "rikmkOR4-mZ-"
      },
      "source": [
        "Copyright © 2020 IBM Corporation. All rights reserved.\n"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python",
      "language": "python",
      "name": "conda-env-python-py"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.12"
    },
    "colab": {
      "name": "5-jupyter-labs-eda-dataviz.ipynb",
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  "nbformat_minor": 0
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