EDA-with-visualization.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<center>\n    <img src=\"https://gitlab.com/ibm/skills-network/courses/placeholder101/-/raw/master/labs/module%201/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n</center>\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# **SpaceX  Falcon 9 First Stage Landing Prediction**\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Assignment: Exploring and Preparing\u00a0Data\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\nIn 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\nPerform exploratory Data Analysis and Feature Engineering using `Pandas` and `Matplotlib`\n\n*   Exploratory Data Analysis\n*   Preparing\u00a0Data  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": "# pandas is a software library written for the Python programming language for data manipulation and analysis.\nimport 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\nimport 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.\nimport 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\nimport seaborn as sns"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Exploratory Data Analysis\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "First, let's read the SpaceX dataset into a Pandas dataframe and print its summary\n"
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>Date</th>\n      <th>BoosterVersion</th>\n      <th>PayloadMass</th>\n      <th>Orbit</th>\n      <th>LaunchSite</th>\n      <th>Outcome</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>LandingPad</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Serial</th>\n      <th>Longitude</th>\n      <th>Latitude</th>\n      <th>Class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2010-06-04</td>\n      <td>Falcon 9</td>\n      <td>6104.959412</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0003</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2012-05-22</td>\n      <td>Falcon 9</td>\n      <td>525.000000</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0005</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>2013-03-01</td>\n      <td>Falcon 9</td>\n      <td>677.000000</td>\n      <td>ISS</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0007</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>2013-09-29</td>\n      <td>Falcon 9</td>\n      <td>500.000000</td>\n      <td>PO</td>\n      <td>VAFB SLC 4E</td>\n      <td>False Ocean</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1003</td>\n      <td>-120.610829</td>\n      <td>34.632093</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>2013-12-03</td>\n      <td>Falcon 9</td>\n      <td>3170.000000</td>\n      <td>GTO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1004</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n\n       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n0    None None        1     False   False  False        NaN    1.0   \n1    None None        1     False   False  False        NaN    1.0   \n2    None None        1     False   False  False        NaN    1.0   \n3  False Ocean        1     False   False  False        NaN    1.0   \n4    None None        1     False   False  False        NaN    1.0   \n\n   ReusedCount Serial   Longitude   Latitude  Class  \n0            0  B0003  -80.577366  28.561857      0  \n1            0  B0005  -80.577366  28.561857      0  \n2            0  B0007  -80.577366  28.561857      0  \n3            0  B1003 -120.610829  34.632093      0  \n4            0  B1004  -80.577366  28.561857      0  "
                    },
                    "execution_count": 2,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df=pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\")\n\n# If you were unable to complete the previous lab correctly you can uncomment and load this csv\n\n# df = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/dataset_part_2.csv')\n\ndf.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\nWe can plot out the <code>FlightNumber</code> vs. <code>PayloadMass</code>and overlay the outcome of the launch. We see that as the flight number increases, the first stage is more likely to land successfully. The payload mass is also important; it seems the more massive the payload, the less likely the first stage will return.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [
                {
                    "data": {
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\n",
                        "text/plain": "<Figure size 1842.38x360 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "sns.catplot(y=\"PayloadMass\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Pay load Mass (kg)\",fontsize=20)\nplt.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": 4,
            "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\nsns.catplot(y=\"LaunchSite\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Launch Site\",fontsize=20)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Now try to explain the patterns you found in the Flight Number vs. Launch Site scatter point plots.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK 2: Visualize the relationship between Payload and Launch Site\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "We also want to observe if there is any relationship between launch sites and their payload mass.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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v3cfZ6W8Mjvm8sd7HWafXAVX6dC0toVtpCcvKVq+X2rt7067XrrxxX/ZBixWLoEsP+NRfXZ+gI7r5dHg/X2/i3eezNf4aOaXl9w7dggVLy3jk9dlsul5ffv7JrdosmNxocB/uPntPZi9Yxtq9u1HamCm7O4iPbbEuT523H5NnL2KT9frQvUsRfP8okD/c/wZvf7C4Stv8JctZWZ7oUlp8f3fapckPwz+PYtVsDZNuh68+D916F7QsSZIkSSqId59fs23mBBi555rtUisp5CJU+wArUkp/qWhIKb0AEBEnAU9UBKL5vgerHTsBuA44lqqhaHV9yV7nB/l5lgGv1dBvfeCeStd7qZ76zwP2SSl9lPefD1xdS9/fAt8C/lOp7Qjg2ryetyPiTWBH4Il6rqv6THt6dSBa4dXbDEVVlFaWJ1ZWG/XzYXlvOOKPqxseGQ0P/GT19p7fbNK1tl67nD93vYjIp+bdcdb18PQ2sMuXV/Xp3b0LX95nNBfe+zoA3UpL+NrHNmnS9dqVu89bHSyXLc2mPjYU7VjKV8LMav+01/TDeD3W7t2Nvx4/roWKaprBfdtmZGqh9O7eha2GNe8DIzVZumIlv7v/DZ5+ey5jhw3grP03pl+PrvUf2AHNmLeE3973+hrtR2wzlC6lrhPcZl6+nirT1y+cBZMfgs0OKVRFkiRJklQ4/YbBey9Wbes/vDC1qNMqZCg6Bni2CfsgC0KvIQsZfxYRXVNaNe76MxGxe/78dymlKyPiVuCdiLgfuA24JqVUXu2cfwKui4gzgfuAK1NK79Z08YjoC/RNKdU7715EHA7MSCm9WG0kyVDgyUrb0/O26sefBpwGsMEGG9R3OQGsNQKipNICy8DAjQpWjtrQojnZ1LDzpmZTJ2x+aKEranX9e3blk9sO5d/PTl/VduJuI6p22vMc2HA3mPEcjNgdhmzTtIu9+9yqQHSVdx6vEooCfHW/jdl3s3V4fdYCdh01iPX6t/5ai61uwXvVtmdCeXnz1jxQ2yopze6Ddx5b3TZyj8LVozb3g/9M5Lrx0wB49p0Pmf7hYi49rrABd2t5d94SyqvNkjt6cB9+9omtClNQZ9VnvTXb+tbQJkmSJEmdwSYHwGuVZuyKUtjIUaJqWx3ut7kR0Q34OHBLPkrzKaDy/I3XpZS2yb+uBEgpnQLsBzwNfBO4ovp5U0p3AxsBlwGbAc9HxODayqDKx75rrbUX8F3g+7WcY40yaqjr0pTSuJTSuMGDaytHVay1Iez7PSjJR38M2RZ2O6vJp5syZxGzFyxrmdrUelKCvx8Jj14IE26A6z4HL99Q6KraxAWf2pqLPrMNp+81imtO3ZlPj6vhE1Yb7gq7ntn0QBRgva2htFvVtqHb1dh1zND+fHK7YcURiAJsVW1Ngq2OMhDtiD7xFxi9P/RcCzY7FA77faErargP3oIrDoYfrwN//wTMn9F2114wEx74Kdz5HXivvok02q87J1T9cMN9r86ibGX1z8gVh22GD2DogJ5V2k7fexTduvh9q03tdDoM2nT19jafy6a1lyRJkqTOqPrvFNLKbNZHqQ0VcqToROCoOvbtVcu+g4D+wMv5yMtewGKyNUhrlVJ6OT/m78DbwAk19JkL/Av4V0TcRrZe6RojVvM1RBdFxEYppcl1XHYUMBKoGCU6DHguInYkGxlaObkYBtQ4MlVNsMfXYbvjYfEHMLhpU3cuXFbGKVc/w5OT51JaEpyyx0jOPXjzFi5ULWbmSzDz5aptL/wrC68Kbf50eORX8OE7sMURMO7EFj19aUlw5LZrDDRvkmlzFzPx3flsv+Haa07R2WedLFS6+7uwaHYWFO5yZs0nKjYHXQADhsM7T8CwcbDrVwtdkZpiwAbw+Q76YYmbToMZ47Pnbz0A//1a27yW5Yvhrx+D+dkIS8ZfAafeD+t1vBGHGw7szcsz5q/aHjKgZ9FOJdu1tIR/nboTv7//TWZ+tIQjxg7lqO2HFbqszqfPYDjjCZj2VPZhjHX8OVKSJElSJ9alhsETNbVJraiQoegDZFPfnppSugwgInYgCzn/BZwbEYeklG7P9x0EzCCbOveUlNI1eXtvsjU5e9V0kYjoA4xLKT2UN20DvFNDv32BJ1NKi/PpcUcBU+uo/+fAnyLiM3lI2g84JqV0aUWHPIhdp9I1puS1zMmn9P1XRFwIDAE2JhvJqpbSe2D21UR/e2IKT06eC2TrNl7y8GQO23oIY4a2/DpnagE912aNQdy9BxWqmtXKy7NRXXPytd0mP5hN7bzDyYWtqwb/emoq/3fLy5Qn6NalhEu+sD37bLpO1U5jPpV9la/MpiPtLLp0g93Pzr6qWbB0BT+57VUefXMOY4b243uHbsGwtWr8J6nzWvoRLF8E/dYvdCUd08oVqwPRClOfrLlvS3vjntWBKMDKZfD8P+HgC9rm+vW4+vEp3PDsdNbu3Y2z99+EbYYPqLXv+YdvwWl/e5YPFi2nb48u/OTIMW1XaAFsOLA3v/n02EKXoZLSbLYGSZIkSersdjwVXro2G8gEMGKPbKkvqQ0VLBRNKaWI+ARwUUR8B1gKTAHOSiktiYhD830XASuAl4BvAwcCX6x0nkUR8ShwWC2XCuBbEXEJsARYRA2jRIHtgT9GRBnZtMJ/TSk9ExEjgE0jYnqlvmcDFwN9gGciYkVe428a8fonRsT1wCtAGfDllNLKhh6v1vfOnMVrtn2w2FC0vRowPJse9vE/ZNu9B8Me3yhsTQCzJqwORCtMvLnlQtH3XoJ7v5+vo3ok7H0elDb+W3vZynJ+efekVWvQLS8r59d3v7ZmKFqhMwWi9fjhf1/hhnxN1xnzljDro2Xc8uXdClxVO/LY7+DBn0HZUthoH/j036BHv0JX1bGUds2mgn/3+dVtw3dom2t379uwtgL4zwsz+MGtE1dtPzf1Qx77zr7069G1xv7bb7g2j5+7L2/MWsiowX3o2c3vY5IkSZI6gBVL4JnLYc5rsMlBsNkhha5Iapq1R8KZ42HS7dBrbdj4QIiaVhmUWk+kVO/SmGonxo0bl8aPH19/R7WIBybN4qSrVr/ffbp34dFv78OAXt3qOEoF9/6rMG9a9imjbu1gtN7C2XDh5lC+YnXb2M/CJy5u/rnLlsNFW8HCmavbSrvCsJ3gsN/BoNENPtXSFSvZ8gd3s7J89b8J6/fvwRPn7tf8Opvh6bfn8tM7XmXm/CUcPnYI3z5os3Y33eWOP72P96utO/ziDw6gf8+ag5lO5YO34A/bU2UE997nwd7fLlhJLalsZTnXPjONCTPms8uogRyxTctMY12j2a/BLWfAjGez729H/jmbDri1lZdn6zW//XC23W9YNn1u3/Va/9r1+Oo1z3Pri1VXHvjrceP42BbrFqgiSZIkSWoF1xwLr92xevvQ38K4kwpXj6Ri0SkT6UJOnyu1a/tuti4Xfnos1z49jX49u/KVfUcbiHYE62zevtbs6jMY9vse3P8jKC+DtUbAXt9qmXPPerlqIArZNJvvPAo3ngRffKTBp+rRtZTDxw7h5udnrGr79LjhdRzR+hYtK+Pkq59hwdIyAC7739sM7NOd0/caVdC6qtts/X68v2D2qu1ha/Wkb/fG/fM6/cPFzF6wjK2HDaC0pIh+HpnzBlUCUcg+2VokvnvzBK4bn00te+0z05j6wWK+st/GrXOxwZtmYWRbKymBL9ySTf29bAFsfED7+MAJMGpwnyrbETBqnT619JYkSZKkDmjBrKqBKMCzVxmKSlITGYpKdfjkdsP45HbDCl2GOrrdvgZbfwbmz4Ah27Tc1LNrbwRdekLZkjX3vfditoZjt94NPt0Fn9qKLYf0Y8KM+ew6ahBHjyvs3/0JM+avCkQrPP7WB+0uFP3h4VvypX88y6SZCxjSvwe/PnosJY0INn9x1yT+8vBbpAQbDe7NNafuzLr9imSR+Q13ge79YNlHq9s2PrBw9bSgZWUruen56VXarnl6auuFooVUUgKjCztqvCYn7T6Cp6d8wGNvfkC3LiV8bb+NGTmo4d/z2tSM52DCjdBnXdj+eOjhVPySJEmSGqBrDyjtBiuXr27z/xOS1GSGopLUFvqu1/LTTfZcCw7/PdxxDiydV3Xf4M0bFYgCdO9Syil7bNRy9TXTJuv2pVuXEpaXla9qGzOk/a1FOXJQb+46a0/mLFzGWr26NWqk55Q5i7j4obdWbU+evYhLHp7M9w/bojVKbXs9+sPnb4IHfwqL58A2n4Oxnyl0VS2iNIKeXUtZsXJ1cN+3lrUs6/XEn+Hl66Hv+rDPebDeVi1UZXHr26Mr/zxlZ96dt4Q+PbrUupZowU15FK4+HCqWjp9wA5z6UBY2S5IkSVJdevSH3b8OD1+QbXfpCXsVx5I0klQIhqKS1JFt/WnY4gh4fxLcfV42de56W8GRfyl0Zc22Vu9u/Prosfzov6/wwaJl7L/5upyxT8PXSW1rg/p0b/Qx785fc5Tvu/NqGPnbkQ3fAY67pdBVtLgupSV844BN+cGtE7PtkuDrB2zS+BM99ze4+9x843mY/gyc9TJ07dlyxRa5IQPa+Xs1/srVgShkI/mnPw0b7Fy4miRJkiR1HPucC5sdAnNeh5F7Qp91Cl2RJHVYhqKS1NF16Q5DxsKJt0P5ypabnrcdOHzsEA7Zan2Wl5XTs1vxvK4K4zZcmyH9e/Du/KWr2g4bO6SAFakxjt91BLuNHsiEGR+xw8i1GdqUcO61O6tuL5qdBaMj92yZIlV4NQXcXdvHuqySJEmSOoj1t86+JEnNYigqScWkiALRCqUlUZSBKEC3LiVce9ou/PmhN5n10VKO3HYoh2y9fqHLUiOMXqcvo9fp2/QTDKw2+jlKsvWCVTx2ORNevRWWzs+2Nz/MX2ZIkiRJkiQVQKSUCl2DGmjcuHFp/PjxhS5DkiS1lMVz4V+fyaZT7dID9v0e7HpmoatSS1s8F16/C/qsC6P2hWj42sOSJEmSJEmtoFP+csJQtAMxFJUkqUh9OAV6DICeAwpciCRJkiRJkjqBThmKOn2uJElSoa01otAVSJIkSZIkSUWtpNAFSJIkSZIkSZIkSVJrMhSVJEmSJEmSJEmSVNQMRSVJkiRJkiRJkiQVNUNRSZIkSZIkSZIkSUXNUFSSJEmSJEmSJElSUTMUlSRJkiRJkiRJklTUDEUlSZIkSZIkSZIkFTVDUUmSJEmSJEmSJElFzVBUkiRJkiRJkiRJUlEzFJUkSZIkSZIkSZJU1AxFJUmSJEmSJEmSJBU1Q1FJkiRJkiRJkiRJRc1QVJIkSZIkSZIkSVJRMxSVJEmSJEmSJEmSVNQMRSVJkiRJkiRJkiQVNUNRSZIkSZIkSZIkSUXNUFSSJEmSJEmSJElSUTMUlSRJkiRJkiRJklTUDEUlSZIkSZIkSZIkFTVDUUmSJEmSJEmSJElFzVBUkiRJkiRJkiRJUlEzFJUkSZIkSZIkSZJU1AxFJUmSJEmSJEmSJBU1Q1FJkiRJkiRJkiRJRc1QVJIkSZIkSZIkSVJRMxSVJEmSJEmSJEmSVNQMRSVJkiRJkiRJkiQVNUNRSZIkSZIkSZIkSUXNUFSSJEmSJEmSJElSUTMUlSRJkiRJkiRJklTUDEUlSZIkSZIkSZIkFTVDUUmSJEmSJEmSJElFzVBUkiRJkiRJkiRJUlEzFJUkSZIkSZIkSZJU1AxFJUmSJEmSJEmSJBU1Q1FJkiRJkiRJkiRJRa1LYw+IiK7AfsDmQJ+U0o/z9h5AP2BOSqm8RauUJEmSJEmSJEmSpCZq1EjRiDgImALcDvwGOL/S7m2A94DPtExpkiRJkiRJkiRJktR8DQ5FI2IccAuQgLOBf1Xen1J6Engb+EQL1idJkiRJkiRJkiRJzdKYkaLfAxYD41JKvwfeqKHPM8DYlihMkiRJkiRJkiRJklpCY0LR3YBbUkoz6+gzDVi/eSVJkiRJkiRJkiRJUstpTCjaB5hTT59ejTynJEmSJEmSJEmSJLWqxgSYM4At6+mzDTC5ydVIkiRJkiRJkiRJUgtrTCh6J3BgROxe086IOBjYFbitJQqTJEmSJEmSJEmSpJbQmFD058A84J6I+AWwBUBEHJJv/xt4D7iwpYuUJEmSJEmSJEmSpKbq0tCOKaUZEXEAcD1wTqVdtwIBvAV8MqVU37qjkiRJkiRJkiRJktRmGhyKAqSUnouITYFDgF2AgcB84EngPymlspYvUZIkSZIkSZIkSZKarlGhKEBKaSXZ6NBbW74cSZIkSZIkSZIkSWpZDV5TNCIeiIjj6unz+Yh4oPllSZIkSZIkSZIkSVLLaHAoCuwNjKinz4bAXk0tRpIkSZIkSZIkSZJaWmNC0YboCbiuqCRJkiRJkiRJkqR2o7FriqaaGiMigA2AjwPTmluUJEmSJEmSJEmSJLWUOkeKRkR5RKyMiJV50/kV25W/yEaHTga2Aa5t3ZIlSZIkSZIkSZIkqeHqGyn6CKtHh+4JTAWm1NBvJfABcD/w15YqTpIkSZIkSZIkSZKaq85QNKW0d8XziCgHrkwp/ai1i5IkSZIkSZIkSZKkltKYNUVHAvNaqQ5JkiRJkiRJkiRJahUNDkVTSu+0ZiGSJEmSJEmSJEmS1BpqDUUj4vtk64n+KaU0N99uiJRS+nGLVCdJkiRJkiRJkiRJzRQppZp3ZGuIJmDzlNLr+XZDpJRSaUsVqNXGjRuXxo8fX+gyJEmSJEmSJEmS1HFFoQsohLqmz90nf5xabVuSJEmSJEmSJEmSOoxaQ9GU0sN1bUuSJEmSJEmSJElSR1BS6AIkSZIkSZIkSZIkqTXVNX1unSKiK/AlYF+yuYcfBv6UUlrWQrVJkiRJkiRJkiRJUrPVOVI0Io6LiKkRsV+19hLgNuC3wOHAYcCvgAcioslBqyRJkiRJkiRJkiS1tPqmz90f6As8VK392HzfLOAU4DPAU8DOwMktW6IkSZIkSZIkSZIkNV19oeh2wOMppZXV2j8PJOC4lNIVKaV/AwcA84FPt3yZkiRJkiRJkiRJktQ09YWi6wKTa2jfFZiVUrqvoiGltBC4HRjTcuVJkiRJkiRJkiRJUvPUF4r2AxZVboiI0WRT6j5WQ//pwIAWqUySJEmSJEmSJEmSWkB9oeiHwMhqbTvkj8/X0L8LsLC5RUmSJEmSJEmSJElSS6kvFH0eOCQi1q/UdgzZeqIP19B/Y+C9FqpNkiRJkiRJkiRJkpqtvlD0cqAX8EREXBgRtwGHAW+llKpMnxsRXYA9gBdbpVJJkiRJkiRJkiRJaoIude1MKf07IvYHTgHOypvnA6fW0P0wYC3g3pYsUJIkSZIkSZIkSZKao85QFCCldFpEXAXsBnwA3JVSereGrouBs4FbW7RCSZIkSZIkSZIkSWqGekNRgJTS48Dj9fS5G7i7JYqSJEmSJEmSJEmSpJZS35qikiRJkiRJkiRJktShGYpKkiRJkiRJkiRJKmqGopIkSZIkSZIkSZKKmqGoJEmSJEmSJEmSpKJmKCpJkiRJkiRJkiSpqBmKSg304aLl3P7Se0x8d36hS5EkSZIktXcrlsCKpYWuQpIkSVKuS6ELkDqCF6bN4/N/fYqFy8oA+NLeo/j2QZsVuCpJkiRJUruTEh/d/n3uevoVIhIf32Vbeh/0vUJXJUmSJHV6jhSVGuAP97+xKhAFuOyRyXywcFkBKypCS+fD6/fAvKmFrkSSJEmSmmzeS3dwymP9mF3ei/dW9uWUh7ux4NUHCl2WJEmS1Ok1aqRoROwFnAPsCKxFzaFqSik5AlVFZf6SFVW2y8oTi5atZGCfAhVUbKY+Cf84CpYvgCiBg34BO51W6KokSZIkqdHueWYil3X7Lf1jMQCfK72f+8efw5Gb71vgyiRJkqTOrcHhZUQcAtwClAJTgdeAsrqOkYrFMTtuwPh3Ply1veuogWwwsFcBKyoy9/8oC0QBUjnc/0PY7gvQtWdh65IkSZKkRtqwfNqqQBRgYCxg/ZXvFbAiSZIkSdC4kaLnAyuAQ1JK97ROOVL7dNT2w1irV1funjiTkYP68IVdNix0ScVl0eyq28sXwvLFhqKSJEmSOpwtNtsc3q3aNmbzzQtTjCRJkqRVGrOm6BjgOgNRdVb7bb4uvzxqLF/aexR9ujtDdIsae2zV7Y0PhN4DC1OLJEmSJDVD3x0/R9naG6/aLhu8Bb23PaqAFUmSJEmCxo0UXQjMba1CJHViu58NvQfDm/fBulvCzmcUuiJJkiRJapoe/enypUfh9bugpJQuGx8IXboVuipJkiSp04uUUsM6RlwLbJBS2rV1S1Jtxo0bl8aPH1/oMiRJkiRJkiRJktRxRaELKITGTJ/7bWBURPxfRHTKN0uSJEmSJKleZcvg1dvgtTth5YpCVyNJkiSJOqbPjYgramieCPwQOCkiXgDm1dAnpZRObk5REfEQ8POU0t2V2s4CNkkpnRERg4F3gTNTSpdU6jMFWACszJvOyPu9CrxGlnwvAk5MKb1W7ZolwEXAvkAClgKfTim9nZ93XEppTrVjDgZ+DPTOz31bSumbtbymHYAngc+klG7I21YCL1fqdm1K6YL63yG1hfLyREmJ+b8kSZIkqRGWfgSX7Akfvp1tD9oUTnsIuvUqaFmSJElSZ1fXmqIn1LFvRP5VkwQ0KxQFrgGOAe6u1HYMcE7+/GiygPFY4JKqh7JP5fAyIkYAb6WUtsm3vwicBxxf7bjPAEOArVNK5RExjCxArVFEjAH+CBySUpoUEV2A02rpWwr8otrrAVhSUZfaj6ffnst3bnqJt+csYt9N1+E3nx7LgF6u/yJJkiRJaoAnL14diALMeQ2euwp2PqNgJUmSJEmqe/rckU382qgF6roBODQiusOqYHMI8Gi+/1jgG8CwiBjayHP3Az6soX194L2UUjlASml6SqmmfhW+Bfw0pTQp71+WUvpzLX2/AtwIvN/IWtWWlsyj7IXruPofVzJ59kJSgvsnvc8Fd04qdGWSJEmSpI5i2lNrtr3zRNvXIUmSJKmKWkeKppTeactCql37g4h4GjgI+A/ZKNHrUkopIoYD66WUno6I68lGeF5Y6fAH82lpl6WUdsrbRuXT/fYFegE7sabrgUcjYg/gfuAfKaXn6yhzDPCb+l5LHtp+gmxa3h2q7e6Z11Xh5yml66odfxr5CNQNNtigvsupqeZOhr/uT5fFc/gTcGTX7Tl1xTcAeGHavIKWJkmdyuSH4I5z4MN3YIsj4LCLoFvvQlclSZLUcCN2h7fur9o2cq/C1CJJkiRplbpGihZaxRS65I/XVHp+ff78WrJRo5Xtk1LaplIgCvn0uSmlUcBZwKXVL5ZSmg5sCpwLlAP3R8R+LfA6LgK+nVJaWcO+JXldFV/XVe+QUro0pTQupTRu8ODBLVCOavTkxbB49ZKx+5c+y9h4E4AdRqxdqKokqXNZvhiuPw7mvA4rl8HL18PDvyx0VZIkSY2zwc5rtg0b1/Z1SJIkSQUUEetFxLUR8VZEvBIRd0TEJhExoVA1NTgUjYijI+KBiBhSy/6hEXF/RHyyhWq7BdgvIrYDeqaUnsvbjwVOiIgpwK3A2IjYuBHnvRXYs6YdKaVlKaU7U0rnAD8DjqzjPBOB7RtwvXHAtXm9RwF/joi6zqtCWLZwjaaBXZdz+NghfOugTQtQkCR1QrMnwdL5VdumPV2YWiRJkprq7Uca1iZJkiQVqYgI4GbgoZTSqJTSFsB5wLqFrKsxI0VPAQaklN6taWdKaQbZep2ntERhKaWFwEPAFeSjRCNiU6B3SmloSmlESmkE8HNWjyhtiN2Bt6o3RsR2FYFvRJQAWwN1TSH8K+C8iNik4piI+HoNr2NkpVpvAM5IKd3SiHrVFrY/HkoqzSY9eDOu+MHZ/P7Ybenbo2vh6pKkzmTwZtCjf9W24TsWphZJkqSmGlTD57YHbdL2dUiSJEmFsw+wIqX0l4qGlNILwLSK7YgYERH/i4jn8q9d8/b1I+KRiHghIiZExB4RURoRV+XbL0fE2U0pqtY1RWuwFXBbPX3GA4c1pZBaXAPcxOrQ81iyZLmyG8mm0f1xHeepWFM0gOXUHNyuA1wWEd3z7aeBP1ba/1JElOfPr08pfT0izgKuiYheQAJub8iLqqT6mqJ3pZS+08hzqCVssDOcfC+8/G/oPQi2PxFKG3N7SJKarVsv+PTfqq4pute3Cl2VJElS4wwYUa0hYK0NC1GJJEmSVChjgGfr6fM+sH9KaWk+I+w1ZLOvfha4O6X004goBXoB2wBDU0pjACJiQFOKakzqs3ZeYF0+AAY1pZCapJRuJgsyK7bPr6HPS8AW+fMRNeyfAvRswLXuAu6qZd8a583bb6P+oLhy/xOqbZc29Fi1gaHbZV+SpMLZaG8485lCVyFJktR0b95brSHBG/fCOpsXpBxJkiSpneoK/DEitgFWAhXTqzwDXBERXYFbUkovRMRkYKOI+APZAMV7mnLBxkyfOweob+3OjYF5TSlEkiRJkiSpwxs4es22mqbUlSRJkorXRGD7evqcDcwCxpKNEO0GkFJ6BNgTmAH8PSKOSyl9mPd7CPgy8NemFNWYUPQx4PCI2KymnRGxOXAE8L+mFCJJUr1WroCXb4D//Qben1ToaiRJkqQ1bXEkjDmKbOKrgG0+DxsfWOCiJEmSpDb1ANA9Ik6taIiIHYDK60r0B95LKZUDXwBK834bAu+nlC4DLge2i4hBQElK6Ubge0CTpv2MlFLDOmbFPg7MB35ENtXsDGAocHBeRH9g95TSU00pRnUbN25cGj9+fKHLkKTCueaz8Fq+fHNJV/jCTTByz8LWJEmSJNXko3chSqDveoWuRJIkSaou6u/SzAtEDAEuIhsxuhSYApwF3JxSGpOvI3ojsBh4EPhKSqlPRBwPnAOsABYCxwH9gCtZPdjz3JTSnY2uqaGhaP4CTgX+RJ7WVrMSOCOl1KQhq6qfoaikTm3Om/DHajMubHIwfPbawtQjSZIkSZIkSR1Tq4ei7VGXxnROKV0WEY8CZwA7AQPI1hB9Erg4pfRqSxcoSVKtolP+2y1JkiRJkiRJaqRGhaIAefD5lVaoRZKk2g0aDZsdCpNuy7ZLu8HOZxS2JkmSJEmSJElSh9DoUFSSpII5+mp49VaY904WkA7auNAVSZIkSZIkSZI6gEaHohFRCmwKrEXNa4uSUnqkmXVJkrSm0i4w5pOFrkKSJEmSJEmS1ME0KhSNiO8BZwP96+laY1gqSZIkSZIkSZIkSW2twaFoRHwL+CEwH/g7MA0oa6W6JEmSJEmSJEmSJKlFNGak6KnADGC7lNLsVqpHkiRJkiRJkiRJUhEY8Z3bS4BjgbOA4WSDLi8CrplywSHlTT1vRBwE/I5s9tq/ppQuqO+Ykkacfzhwi4GoJEmSJEmSJEmSpLrkgeiNwCXAOGDd/PES4IZ8f6NFRCnwJ+BgYAvg2IjYor7jGnOxWTRyDVJJkiRJkiRJkiRJndKxwP5A72rtvYEDgGOaeN4dgTdTSpNTSsuBa4Ej6juoMaHo9cD+EdG9iQVKkiRJkiRJkiRJ6hzOYs1AtEJv4Owmnnco2TS8FabnbXVqTCj6feA94IaIGNm42iRJkiRJkiRJkiR1IsObub82UUNbqu+gxkyHOxHoCgwBPh4R84F5NV00pTSqEeeVJEmSJEmSJEmSVFymka0jWtf+pphO1UB1GPBufQc1ZqRoCVAGTM2/5pMlsdW/mrQoqiRJkiRJkiRJkqSicRGwqJZ9i4DfNvG8zwAbR8TIiOhGtjbprfUd1OCRoimlEU0sTJIkSZIkSZIkSVLncg1wFLA/VdcWXQTcA1zblJOmlMoi4kzgbqAUuCKlNLG+4yKleqfYVTsxbty4NH78+EKXIUmSJEmSJEmSpI6rpjU5W8WI79xeQjaS82yyKW+nkY0QvXbKBYeUt1UdYCjaoRiKSpIkSZIkSZIkqZnaLBRtTxo8fW5EHNfQvimlvzWtHEmSJEmSJEmSJElqWQ0ORYGrgPqGlUbex1BUkiRJkiRJkiRJUrvQmFD0xFraBwA7kM0HfCNwezNrkiRJkiRJkiRJkqQW0+BQNKV0dV37I+JKskD0980tSpIkSZIkSZIkSZJaSklLnSildD9wF/CjljqnJEmSJEmSJEmSJDVXY6bPbYjXgdNb+JySJEmSJEmSJEmSOprz+5cAxwJnAcOBacBFwDWcP7+8qaeNiCuAQ4H3U0pjGnJMi40UzW0BpBY+pyRJkiRJkiRJkqSOJAtEbwQuAcYB6+aPlwA35Pub6irgoMYc0OxQNCJKImLDiPgJcDDwv+aeU5IkSZIkSZIkSVKHdiywP9C7Wntv4ADgmKaeOKX0CDC3Mcc0ePrciCin7lGgAXwAnNOYAiRJkiRJkiRJkiQVnbNYMxCt0Bs4G/hXWxXTmDVFH6HmULQc+BB4GrgypTS7JQqTJEmSJEmSJEmS1GENb+b+FtXgUDSltHcr1iFJkiRJkiRJkiSpeEwjW0e0rv1tptlrikqSJEmSJEmSJElSNRcBi2rZtwj4bduVYigqSZIkSZIkSZIkqeVdA9zLmsHoIuAe4NqmnjgirgGeADaNiOkRcXK9x6RU0zKhdV5kB+BAYCjQvYYuKaVU74XVeOPGjUvjx48vdBmSJEmSJEmSJEnquKLNrnR+/xLgGOBssjVEp5GNEL2W8+eXt1kdNCIUjYgArgI+T/ZmJaq+aRXbKaVU2rJlCgxFJUmSJEmSJEmS1GxtF4q2I42ZPvdM4AvA34FxZG/YRcCuwHnAArJhrhu1bImSJEmSJEmSJEmS1HRdGtH3eOC1lNIJANnAUeallJ4EnoyIu4EnyeYGvrKF65QkSZIkSZIkSZKkJmnMSNFNgQeqta0KVVNKzwO3AWe0QF2SJEmSJEmSJEmS1CIaE4oGML/S9iJg7Wp93gA2a25RkiRJkiRJkiRJktRSGhOKzgCGVtqeDGxfrc/GZGGpJEmSJEmSJEmSJLULjQlFn6ZqCHonsGNEfC8itoyILwNHkK0rKkmSJEmSJEmSJEntQmNC0RuB0ogYmW//EngH+CHwEvAHYB7wnZYsUJIkSZIkSZIkSZKao0tDO6aUbgFuqbQ9NyK2BU4FRgFTgL+llN5r2RIlSZIkSZIkSZIkqekaHIrWJKU0H/h1xXZE9IiIfimlj5pdmSRJkiRJkiRJkiS1gMZMn9sQFwNzW/ickiRJkiRJkiRJktRkLR2KAkQrnFOSJEmSJEmSJEmSmqQ1QlFJkiRJkiRJkiRJajcMRSVJkiRJkiRJkiQVNUNRSZIkSZIkSZIkSUXNUFSSJEmSJEmSJElSUTMUlSRJkiRJkiRJklTUutS1MyJWtlUhkiRJkiRJkiRJktQa6gxFgWjCOVNTCpEkSZIkSZIkSZKk1lBnKJpScnpdSZIkSZIkSZIkSR2aoackSZIkSZIkSZKkomYoKkmSJEmSJEmSJKmoGYpKkiRJkiRJkiRJKmqGopIkSZIkSZIkSZKKmqGoJEmSJEmSJEmSpKJmKCpJkiRJkiRJkiSpqBmKSpIkSZIkSZIkSSpqhqKSJEmSJEmSJEmSipqhqCRJkiRJkiRJkqSiZigqSZIkSZIkSZIkqagZikqSJEmSJEmSJEkqaoaikiRJkiRJkiRJkoqaoagkSZIkSZIkSZKkomYoKkmSJEmSJEmSJKmoGYpKkiRJkiRJkiRJKmqGopIkSZIkSZIkSZKKmqGoJEmSJEmSJEmSpKJmKCpJkiRJkiRJkiSpqBmKSpIkSZIkSZIkSSpqhqKSJEmSJEmSJEmSipqhqCRJkiRJkiRJkqSiZigqSZIkSZIkSZIkqagZikqSJEmSJEmSJEkqaoaikiRJkiRJkiRJkoqaoagkSZIkSZIkSZKkomYoKkmSJEmSJEmSJKmoGYpKkiRJkiRJkiRJKmqGopIkSZIkSZIkSZKKmqGoJEmSJEmSJEmSpKJmKCpJkiRJkiRJkiSpqBmKSpIkSZIkSZIkSSpqhqKSJEmSJEmSJEmSipqhqCRJkiRJkiRJkqSiZigqSZIkSZIkSZIkqah1KXQBkiSpHSlfCVECEYWuRJJaxPKycn5x1yTumjCTEYN68d2Pb8EWQ/oVuixJkiRJ6pyWLYTSbtClW6ErUSfkSFFJkgTl5XDnt+FnQ+CXI+HpywpdkaQW8NBr768KBFNKhS6nIP74wBtc/ujbzJi3hMfe/IBTrn6GspXlhS5LkiRJkjqXsmVww8lwwXD41Sh46pJCV6ROyJGikiQJXroOnvpL9rxsKdzxTVi5AvoNgU0OhK49C1ufpEa77JHJ/PSOV1dtn77XKL5z8GYFrKgwHnljTpXtd+cv5c3ZC9lsPUeLSpIkSVKbeeavMOGG7Pmyj7IP54/+GAwcVdi61Kk4UlSSJMGMZ9dsu/tc+Pfx8Jc9YOn8tq9JxWllGUy8GR79Lcx+vdDVFLXLH327yvbVj09hZXnnGy26+fpVw8++3bswfK1eBapGkiRJkjqp916q1pBg5svNPu0HC5cxe8GyZp9HnYOhqCRJgg13rX3fB2/Ai9e1XS0qbjeeDP8+Ae47Hy7eFSY/XOiKilbXLlXXBu5SGnTG1YK/ccAm7DRybQAG9enGr47emt7dnTBHkiRJktrUqH2qbpd2q/v3UfUoL0+ce9NL7PDT+9jxZ/fxjetf7JQfBFbj+NsASZIEYz4Jc97IpjIpXwlLPqi6f/nCwtSl4jL3bXjlltXb5SvgiT/BRnsVrKRi9pV9NuZbN67+JO6X9xlNSUnni0UH9enOdV/chXmLl9Onexe6lPq5UEmSJElqc6Vdq25HCURpk0/3wKT3uebpaau2b3xuOvtutg6HbL1+k8+p4mcoKkkqTinBW/fD7Ndg1H6wTudbR6/R9v529rVwNvx5Z1icr8PXvT9sdXRha1ORqOkTm36Ks7V8eofhbDm0H09NnsvY4f3ZfsO1C11SQQ3o1a3QJUiSJElS5/XGvVW3y5bCO4/CFkc06XRvzV7zA/xvvu+H+lU3Q1FJUnG66zvw1F+y51EKn/k7bHbImv0WzYH3X4Eh20L3vm1bY3vVZzCc9hA8d3U2anS7L8CA4YWuSsVg7Y1g88Pg1f9m2yVdYOczCltTMVu5gi3fvoot33kMyraDIV+Drj0LXZUkSZIkqTMaXMOAhZraGmjvTdfhl3e/tmrK3JKAfTdbp8nnU+cQKfnp/I5i3Lhxafz48YUuQ5LavyUfwq9GQ3nZ6rZhO8Ip1T6R9vINcMsZsHIZdO8Hx/wLRu7RtrVKnc3KsmwK3Q+nZB9UWGfzmvstnguP/Dr70MLGB8BOp0OJ0542yp3fXv3hEIAxR8FRlxeuHkmSJElS57V8MdxwErx+J3TpCXt9C/b4erNOed8rs7jkkbcoT3DqHiM5aIxT5zZC51tfB0PRDsVQVJIaaPFc+NUoSOWr24Zsm41+rFBeDr/ZFBa9X3sfVbViKbx2Rza9yWaHQo9+ha5IxezKj8M7j63e3vu8bHpnNdwvRsKSuau3oxT+b9aa67hIkiRJktRWFs7OZjHq3qfQlXR2nTIU7VAft4+IhZWefzwi3oiIDSJi04h4KCJeiIhXI+LSSv12jIhHIuK1iJgUEX+NiF7Vzrt3RNxWw/W6RsQF+XUmRMTTEXFwDf0uj4gXI+KliLghIvrk7WtFxM15+9MRMabacZ+IiBQRLnQnSS2p19ow9rOVGgJ2ObNqn5XLV6+ZWeGjd1u9tA5rxVK4fH+44US45Uvw511g4fv1Hyc1xYKZVQNRgAk3FqaWjqzfkKrbfdbJpiyWJEmSJKlQ+gw2EFXBdKhQtEJE7Af8ATgopTQV+D3w25TSNimlzfN9RMS6wL+Bb6eUNgU2B+4CGrpo3I+B9YExKaUxwGG1HHt2SmlsSmlrYCpQ8Zv384AX8vbjgN9VO+5Y4FHgmAbWI0lqqMN/D5+6HPY8B06+B7Y6qur+rj2y0Y6VbXV029XX0bx2B8x8afX2R9Ph+b8Xrh4Vtx79symtK+s/rM0uv2T5Sp546wPeX7C0za7ZKg74CXTL/6NZ2h0O+jlEp/wgqCRJkiRJEh3uo+IRsQdwGfDxlNJbefP6wPSKPimll/OnXwauTik9kbcn4IYGXqcXcCowMqW0LD9+FnB99b4ppY/yYwLoCVTMSbwF8PO8z6SIGBER66aUZuWjSXcD9gFuBc5v0BsgSWqYktI1g9DqPvGXbEH3916AkXvCTl9qk9I6pLJlDWtTcXnnCXjkV7B8IYw7Cca20ee4uvaEA38Kt38jG9Xdex342A/a5NIvTpvH8Vc+zbzFK+hSEvz4yDEcu+MGbXLtlrKsbCUpQY9R+8DXX4F3X4B1x0DvgYUuTZIkSTWZOxmWLYD1xxa6EkmSilpHC0W7A/8B9k4pTarU/lvggYh4HLgHuDKlNA8YA1zdxGuNBqZWBJ71iYgrgY8DrwDfyJtfBD4JPBoROwIbAsOAWcCRwF0ppdcjYm5EbJdSeq6G854GnAawwQYd6xdyktTudesN+3630FV0DJsdAv2Hw/xp2Xb3/jD22MLWpNa1YCb8/RNQtiTbnvYU9B4Eoz/WNtff7jjY9OPwwVswZBvo0r1NLvvLuycxb/EKAMrKEz+7/VU+se1QenQtbZPrN9ev7p7E5Y++TXmCE3YdwbkHb0ZstFehy5IkSVJt/vNleP4f2fNhO8Dnb4Ie/eo+RpIkNUlHmz53BfA4cHLlxpTSlWRT4/4b2Bt4MiLa5jdnq2s4ERgCvAp8Jm++AFgrIl4AvgI8D5Tl+44Frs2fX5tv13TeS1NK41JK4wYPHtxK1UtSI5SXw+SH4K0HoXxly533xevg4t2yr5fWGJSvQuvRD059ED52Puz1HTj9EVh7ZKGrUmua/NDqQLTCa3e2bQ29B8EGO7VZIAowc37VKXMXLCtj4bKyWnq3L/97YzZ/evAtlq4oZ3lZOZc+Mpn7XnXtX0mSpHbrnSdWB6IA05+BZ68sXD2SJBW5jjZStBz4NHBfRJyXUvpZxY6U0rvAFcAVETGBbJToRGB7stGljfUmsEFE9E0pLWjIASmllRFxHXAO2WjVj4ATYdXUum8Db0fEQGBfYExEJKAUSBHxrXyKX0lqn1YsgasOhRnjs+0h28IJt2cjLptj+rNw82mrt286DQaOhqHbNe+8all9BsPuZxe6CrWVgaMb1lZkjthmKBfe+/qq7d1HD2JQnzb9rF2TTZix5gQnE9+dz/5brFuAaiRJklSvipl4KptXQ5skSWoRHW2kKCmlxcChwOci4mSAiDgoIrrmz9cDBgIzgD8Cx0fEThXHR8Tn8z4Nuc7lwO8jolt+7PoR8fnK/SIzuuI5cBgwKd8eUHEscArwSB6UHgX8LaW0YUppREppOFlgunvT3hVJaiMTb1kdiAK8+zxMuLH5533rgWoNCSY/2PzzSmq6YeNg169A5NPGjt4ftj+hoCW1hTP3Gc2PjtiSfTdbh9P3GsWfPtdxPpyx2+g11wzdbfSgAlQiSZKkBhn9sWxpklUCtvxEwcqRJKnYdbSRogCklOZGxEHAIxExB9gL+F1EVMx3dk5KaSZARBwD/Doi1iEbafoIcFMNp90vIqZX2j4a+D/gJ8Ar+bkXAd+vdlwAV0dEv/z5i8CX8n2bA3+LiJVka41WTPt7LNnUupXdCHwW+F8D3wZJantL5q7ZtriGtsZab8yabevW0CapbR3wE9jtLFixGAZ0jrXNS0qC43YZwXG7jCh0KY229bAB/ProsVz80JukBKfuuRE7jFi70GVJkiSpNr3WhhNvh8d+B8sWwPYnwojdCl2VJLWe8nJ49znouRYMHFXoatQJhbO1dhzjxo1L48ePr7+jJLWW+dPhTzvD8nxW8W594EuPw1obNu+8KcFd34FnLocI2OFUOPCn2XNJkiRJkiRJHduiD+Dqw+D9idn29ifAYb8raEmdXKf8xauhaAdiKCqpXZj9Gjzz1yzI3OFkWGfzljv3soXZY/c+LXdOSZIkSZIkSYX14M/g4V9UbTv1QRjacZatKTKdMhTtkNPnSpIKaPCm8PFftc65DUMlSZLUEaxYCk9fCjNfgo32hm0+5ywnkiRJdZk9ac22j2YYiqpNGYpKkiRJkiQ1xn/OgAk3Zs9f/jd89B7sdU5ha5IkSWrPymuYtbS0e9vXoU6tpNAFSJIkSZIkdRjLFsLEm6u2Pf+3wtQiSZLUUfQZtGabM22ojRmKSpIkSZIkNVRpN+jWt2pbr4GFqUWSJKmj2P4E6NJj9fagTbNlCKQ25PS5kiRJkiRJDdWlG3zs+3DHOZDKs1/u7fu9QlclSZLUvq0/Fk57CF66HnquBdt9AUq7FroqdTKRUg3zOKtdGjduXBo/fnyhy5AkSZIkSXPfhlkTYINdoHcN08FJkiRJ7VennLvYkaKSJEmSJEmNtfbI7EuSJElSh+CaopIkSZIkSZIkSZKKmqGoJEmSJEmSJEmSpKJmKCpJkiRJkiRJkiSpqBmKSpIkSZIkSZIkSSpqhqKSJEmSJEmSJEmSipqhqCRJkiRJkiRJkqSiZigqSZIkSZIkSZIkqagZikqSJEmSJEmSJEkqaoaikiRJkiRJkiRJkoqaoagkSZIkSZIkSZKkomYoKkmSJEmSJEmSJKmoGYpKkiRJkiRJkiRJKmqGopIkSZIkSZIkSZKKmqGoJEmSJEmSJEmSpKJmKCpJkiRJkiRJkiSpqBmKSpIkSZIkSZIkSSpqhqKSJEmSJEmSJEmSipqhqCRJkiRJkiRJkqSiZigqSZIkSZIkSZIkqagZikqSJEmSJEmSJEkqaoaikiRJkiRJkiRJkoqaoagkSZIkSZIkSZKkomYoKkmSJEmSJEmSJKmoGYpKkiRJkiRJkiRJKmqGopIkSZIkSZIkSZKKmqGoJEmSJEmSJEmSpKJmKCpJkiRJkiRJkiSpqBmKSpIkSZIkSZIkSSpqhqKSJEmSJEmSJEmSipqhqCRJkiRJkiRJkqSiZigqSZIkSZIkSZIkqagZikqSJEmSJEmSJEkqaoaikiRJkiRJkiRJkoqaoagkSZIkSZIkSZKkomYoKkmSJEmSJEmSJKmoGYpKkiRJkiRJkiRJKmqGopIkSZIkSZIkSZKKmqGoJEmSJEmSJEmSpKJmKCpJkiRJkiRJkiSpqBmKSpIkSZIkSZIkSSpqkVIqdA1qoIiYDbxT6DoaYBAwp9BFSO2c94lUN+8RqX7eJ1L9vE+kunmPSPXzPpHq530i1a093iNzUkoHFbqItmYoqhYXEeNTSuMKXYfUnnmfSHXzHpHq530i1c/7RKqb94hUP+8TqX7eJ1LdvEfaD6fPlSRJkiRJkiRJklTUDEUlSZIkSZIkSZIkFTVDUbWGSwtdgNQBeJ9IdfMekernfSLVz/tEqpv3iFQ/7xOpft4nUt28R9oJ1xSVJEmSJEmSJEmSVNQcKSpJkiRJkiRJkiSpqBmKSpIkSZIkSZIkSSpqhqJqMRFxUES8FhFvRsR3Cl2P1JYiYnhEPBgRr0bExIj4Wt6+dkTcGxFv5I9rVTrm3Px+eS0iDqzUvn1EvJzv+31ERCFek9QaIqI0Ip6PiNvybe8RqZKIGBARN0TEpPzflF28T6SqIuLs/OetCRFxTUT08D5RZxcRV0TE+xExoVJbi90XEdE9Iq7L25+KiBFt+gKlZqrlHvlV/jPXSxFxc0QMqLTPe0SdTk33SaV934yIFBGDKrV5n6jTqe0+iYiv5PfCxIj4ZaV275N2xlBULSIiSoE/AQcDWwDHRsQWha1KalNlwDdSSpsDOwNfzu+B7wD3p5Q2Bu7Pt8n3HQNsCRwE/Dm/jwAuBk4DNs6/DmrLFyK1sq8Br1ba9h6RqvodcFdKaTNgLNn94n0i5SJiKPBVYFxKaQxQSnYfeJ+os7uKNf8Ot+R9cTLwYUppNPBb4Bet9kqk1nEVa94j9wJjUkpbA68D54L3iDq1q6jh56GIGA7sD0yt1OZ9os7qKqrdJxGxD3AEsHVKaUvg13m790k7ZCiqlrIj8GZKaXJKaTlwLdk3AqlTSCm9l1J6Ln++gOyX2EPJ7oOr825XA0fmz48Ark0pLUspvQ28CewYEesD/VJKT6SUEvC3SsdIHVpEDAMOAf5aqdl7RMpFRD9gT+BygJTS8pTSPLxPpOq6AD0jogvQC3gX7xN1cimlR4C51Zpb8r6ofK4bgP0cXa2OpKZ7JKV0T0qpLN98EhiWP/ceUadUy78lkAUz3wJSpTbvE3VKtdwnXwIuSCkty/u8n7d7n7RDhqJqKUOBaZW2p+dtUqeTT2uwLfAUsG5K6T3IglNgnbxbbffM0Px59XapGFxE9h+p8kpt3iPSahsBs4ErI5tm+q8R0RvvE2mVlNIMsk9eTwXeA+anlO7B+0SqSUveF6uOyUOk+cDAVqtcansnAXfmz71HpFxEHA7MSCm9WG2X94m02ibAHvl0tw9HxA55u/dJO2QoqpZS06cVUg1tUlGLiD7AjcBZKaWP6upaQ1uqo13q0CLiUOD9lNKzDT2khjbvERW7LsB2wMUppW2BReRTHdbC+0SdTmRrIh4BjASGAL0j4vN1HVJDm/eJOrum3BfeMypaEfFdsiVx/lnRVEM37xF1OhHRC/gu8P2adtfQ5n2izqoLsBbZkmrnANfnozu9T9ohQ1G1lOnA8Erbw8imsZI6jYjoShaI/jOldFPePCufEoH8sWL6hNrumemsnrKncrvU0e0GHB4RU8imWN83Iv6B94hU2XRgekrpqXz7BrKQ1PtEWu1jwNsppdkppRXATcCueJ9INWnJ+2LVMfnU1f2peYpFqUOJiOOBQ4HP5VMYgveIVGEU2QfRXsz/Lz8MeC4i1sP7RKpsOnBTyjxNNkPaILxP2iVDUbWUZ4CNI2JkRHQjW0D41gLXJLWZ/NM/lwOvppQurLTrVuD4/PnxwH8qtR8TEd0jYiTZgtpP59NaLYiInfNzHlfpGKnDSimdm1IallIaQfZvxAMppc/jPSKtklKaCUyLiE3zpv2AV/A+kSqbCuwcEb3yv9/7ka3l7n0irakl74vK5zqK7Gc5Ry2oQ4uIg4BvA4enlBZX2uU9IgEppZdTSuuklEbk/5efDmyX/7/F+0Ra7RZgX4CI2AToBszB+6Rd6lLoAlQcUkplEXEmcDdQClyRUppY4LKktrQb8AXg5Yh4IW87D7iAbMqEk8l+iXc0QEppYkRcT/bL7jLgyymllflxXwKuAnqSrWlSsa6JVIy8R6SqvgL8M/+Q2WTgRLIPMnqfSEBK6amIuAF4juzv/fPApUAfvE/UiUXENcDewKCImA78gJb9Oety4O8R8SbZaIVj2uBlSS2mlnvkXKA7cG/2O2meTCmd7j2izqqm+ySldHlNfb1P1FnV8u/JFcAVETEBWA4cnweZ3iftUBgyS5IkSZIkSZIkSSpmTp8rSZIkSZIkSZIkqagZikqSJEmSJEmSJEkqaoaikiRJkiRJkiRJkoqaoagkSZIkSZIkSZKkomYoKkmSJEmSJEmSJKmoGYpKkiRJUicXESdERIqIEwpdS2V5TQ8Vuo72KCIeioiXI6KkUlur/jlGxNCIWBIRP26N80uSJElSazIUlSRJkqRWlIdUlb9WRsSciHggIj5X6PqKSaX3uDwiRtXR78FKfU9owxJbREQcBewF/CClVN5W100pzQD+AnwjIoa31XUlSZIkqSUYikqSJElS2/hh/nUB8BCwJ/CPiLiwkEUVoTIggJNr2hkRG5MFimVtWVRLiYgAfgK8DtxcgBJ+BXQDvleAa0uSJElSkxmKSpIkSVIbSCmdn399N6V0FHAgkICzImJEYasrKrOA8cCJEdGlhv2nkIWmt7VpVS3nY8CmwNUppdTWF08pvQvcC3wuIvq39fUlSZIkqakMRSVJkiSpAFJK9wOTyAK6HQAi4siI+EdEvB4RiyJiYUQ8GxFfrbx2ZN732nz61z1rOn9EHJXv/0Nz6oyI7SPixoh4PyKWRcQ7EfHniFi/hr6bRMQFETE+ImZX6n9pRAyr5fzdIuJ7EfFW3v/tiPhJRHRvRtmXAesBh1a7VlfgeOBxYGIdr/d3EfFiRMyNiKUR8UZE/CYi1qql/q9GxHMR8WFELI6IKRHxn4j4WLW+e0TEfyNiev5aZ0bEkxHxg0a8tooRsNc19ICIWCsi/pdPK3xutX07RMQ9EbEgIj6KiPsiYpeIOD//+7N3Dae8FugFHNOIuiVJkiSpoAxFJUmSJKlwIn+sGPF3AbAd8BTwB+DvQB/gd8DV1Y79c/74xVrOfVr+eGmTi4s4lCxAPAy4D7gQeA34EjC+hhGunwROB6YB1+Sv4RWy0ZnPRMTQaucP4HrgR2TvwR/JRnCelLc31TXAovy6lR0OrEsWmtbmVLKw7zXgSrI1NN8Dvg48FhF9q/W/iuzPpyvwN+D3wCPAVsBBFZ0i4iCyaZN3B+4HfgPcAiwDzmjIi8rfr32BmSmltxp4zAbAY8BOwHEppZ9X2rdHXuu+wB1k7/8S4EFgxzpO+1j+uH9DapAkSZKk9qCmqYQkSZIkSa0sH0W4KVkY+EzefEj1sCsfIXolcFxE/DGl9BRASumRiJgIfCoivpZSmlPpmJFk06w+nlJ6uYn19SEL/LoAe6eU/ldp37fJAtxLgQMqHfZ34LcppWXVznUAcCfwf2SBaoVjgSOAJ4F9UkpL8/4/YPV70mgppQURcS1wQkQMSylNz3edCnxEFrieV8vhPwe+nFJaWe01nAz8lSzA/EXe1p8sQH0W2KmGYwZW2jyV7IPJe6eUXqzWb1ADX9qmwGAaOPVvRIwle997Ax9PKd1XaV8JcAXQI993Z6V9pwMX13belNKbETGPbF1cSZIkSeoQHCkqSZIkSW0gn470/Ij4aUTcANxFNlL0opTSOwA1jf5LKZWTjUSEbB3Syi4GupNNCVvZafm5L2lGyUcAA4HrKgeiud8AU4D985GIFbXOqB6I5u33kE1XW73+E/PH8yoC0bz/XODHzagdstGgpWSjTomIDclGNv4zpbS4toNSSu9UDzdzV5AFqpVfQyJ7n5cB5TWc64MazrOkhn5zauhXk4r3+r36Ouahe8Wf256VA9HcrsBo4MHKgWjuUuD1ei4xExgcET3qq0WSJEmS2gNDUUmSJElqGz/Iv84lm670f8AXUkpfr+gQEQPzNTlfytcTTRGRyEYiAgytds6/AQtZPVVuxbqZJwAf0rwpaLfLHx+oviOlVEY27SrAtpWuHRHx+XxdytkRUVbpNWxVQ/3bkYWJj9Zw/YeaUTv5iNqXgZPyUZGnkP0fuK6pc4mIrhFxZkQ8mq8pujKvvxzoV/k1pJQ+Av5LFjC+EBHfj4h9IqJXDaf+Z/74VET8JSI+U9s6q3WoGHn6YT39jiKbDnc6sHP1kam5ij+3Nd77PIh/vJ5rzM0fGzrKVZIkSZIKyulzJUmSJKkNpJSirv0RMYBsytiRwNNkgedcoAwYAHyNbFRo5XMuiIh/AKdHxD4ppQfJRniuRzYCdSlN1z9/rG1UYkX7gEptFwJn5fvuBmawemTkCcCGNVxjbkppRQ3nn9moamt2GdkanweRjUp9NqX0fD3HXAd8ApgM/Cevo2L061lU+zMAPgN8G/gs8MO8bWk+GvibKaVZACmlm/I1Wr9BNnr1iwAR8Sxwbkrp3ga8nor3sr7RmbuQrXH6JNn6rjWp+POdVcv+2tor9KxWkyRJkiS1a4aikiRJktQ+nEIWiP4wpXR+5R0RsQtZKFqTi4HTyUK2B1k9avTSZtYzP39cr5b961fuFxHrAF8FJgC7ppQWVO4cEcfWco21I6JrDcFobddtjL+Trf95CdkIzx/V1TkixpEFoveRrbO5otK+EuBb1Y9JKS0BzgfOj4jhZOtsngB8HhgB7FGp7+3A7RHRG9gJOJRsjdXbImLblNIr9bye9/PHgXX2ytZL/ThZEFwSESfloz8r+yh/XLeWc9TWXmEgWWA/t55+kiRJktQuOH2uJEmSJLUPo/PHG2vYt1dtB6WUXgIeAz4RETsBHwMeSSm92sx6KkZU7l19R0R0AXbPN5/LHzci+z/mPTUEosPy/dU9lx+zew371rhuY6WU5gE3AMOARcA19RxS8Wdwaw0h7Y6sHh1Z2/WmpZT+Sbbu6BvA7hGxRoCZUlqUUnognzr5Z0A34OB6aoNsXdaVwGb19FtGNoXuv8nWm/1H/mdWWcWf7xrvfR4A71rbyfNQdyjwUkopNaBuSZIkSSo4Q1FJkiRJah+m5I97V26MiG3J1iGty8VkwdqNQAB/aYF6biEbBXhsROxcbd9ZZCHnfSmlqXnblPxx94goregYEX3IprGtaaaiK/PHn0ZEj0rHrA38XzPrr/B/ZKM/D6we1tZgSv64d+XGfBTsn6p3jojBeRBdXW+gL9lIyuV53/0ioqZQtWJE5uJ6aiOlNB94Adi6lnNV7rsCOBb4R/54Xb7ebIXHgLeAfSKieiB7GrBJHaffESglG5ksSZIkSR2C0+dKkiRJUvvwN+Ac4KKI2IdspOHGZFOs3kS2dmVt/g38lmz03py8f7OklBZGxEn5uR+OiH8DU4HtgQPI1tr8YqX+MyPiWuAY4IWIuIds3cr9gaVkYd421S5zTf66DgcmRMR/yNbCPIpsfdVRLfA6puZ1N8QzZGHhJyPiceBRstDyYOA14N1q/YcCT0bEq2SjXqcB/cj+zNYDfl8piP0NMCIiHiILX5eTvZf7Au8A1zawxhsrHXd7XR1TSisj4niy9/8U4KaIOCqltCylVB4RpwB3AbdGxI1kIenWZH9md+avu/q0u5D9+VfUIkmSJEkdgiNFJUmSJKkdSCm9S7b+5O1kU5qeCWwInAF8p55jlwP/zDevSikta6Ga/gPsBtxBNiXsN4HNyUaibp9SmlztkJPJpoPtCXw5P+Y2sqlY51frSz716tHAD8j+f3omWUB6JfDplngNjZFSWplf/2JgCNkaqbsDfyV7LdWn1J1CVvtMYB/g68AngbeBz5KNqK3wM7KgcUuygPJ0ssD1Z8AOKaUPG1jm5WSB6nENfE3lZCM//0gW1t5aMco0pfQQ2dTMDwGH5K+3Z/5aKv5sP6p8vnxq3c8DL6aUnmhgzZIkSZJUcOHyH5IkSZLU8eUjEPcENk0pvVHgctSKIuISsrVCR6SUZrbSNR4DdgL6p5QWVWo/DLgV+EJK6R+tcW1JkiRJag2OFJUkSZKkDi4idiQb8Xe3gWin8H2y0aLfbc5JIqJXRAyoof0EstG991QLRAP4ITCe1SOTJUmSJKlDcE1RSZIkSeqgIuJLZOtanki29uMPCluR2kJKaVZEfB7YMiJK8ilym2ID4PmIuBd4k+x3BNuSTRk8D/hGtf7rkY0SvSU57ZQkSZKkDsbpcyVJkiSpg4qIKcAwsvUfz08p/auwFakjiYi1gF+RjTJeD+hOtj7qfcBPU0pvFbA8SZIkSWpRhqKSJEmSJEmSJEmSipprikqSJEmSJEmSJEkqaoaikiRJkiRJkiRJkoqaoagkSZIkSZIkSZKkomYoKkmSJEmSJEmSJKmoGYpKkiRJkiRJkiRJKmr/D6L3XYXoM4dqAAAAAElFTkSuQmCC\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\nsns.catplot(y=\"LaunchSite\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Pay load Mass (kg)\",fontsize=20)\nplt.ylabel(\"Launch Site\",fontsize=20)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Now if you observe Payload Vs. Launch Site scatter point chart you will find for the VAFB-SLC  launchsite there are no  rockets  launched for  heavypayload mass(greater than 10000).\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK  3: Visualize the relationship between success rate of each orbit type\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Next, we want to visually check if there are any relationship between success rate and orbit type.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Let's create a `bar chart` for the sucess rate of each orbit\n"
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Orbit</th>\n      <th>SuccRate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>ES-L1</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>GEO</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>HEO</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>SSO</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>VLEO</td>\n      <td>0.857143</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>LEO</td>\n      <td>0.714286</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>MEO</td>\n      <td>0.666667</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>PO</td>\n      <td>0.666667</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>ISS</td>\n      <td>0.619048</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>GTO</td>\n      <td>0.518519</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>SO</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "    Orbit  SuccRate\n0   ES-L1  1.000000\n1     GEO  1.000000\n3     HEO  1.000000\n9     SSO  1.000000\n10   VLEO  0.857143\n5     LEO  0.714286\n6     MEO  0.666667\n7      PO  0.666667\n4     ISS  0.619048\n2     GTO  0.518519\n8      SO  0.000000"
                    },
                    "execution_count": 6,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# HINT use groupby method on Orbit column and get the mean of Class column\ndf_avg_succ = df.groupby(\"Orbit\").mean()[\"Class\"].reset_index().sort_values(\"Class\", ascending=0).rename(columns={\"Class\":\"SuccRate\"})\ndf_avg_succ"
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<AxesSubplot:xlabel='Orbit', ylabel='SuccRate'>"
                    },
                    "execution_count": 7,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": "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\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "sns.barplot(x=\"Orbit\", y=\"SuccRate\", data=df_avg_succ)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Analyze the ploted bar chart try to find which orbits have high sucess rate.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK  4: Visualize the relationship between FlightNumber and Orbit type\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "For each orbit, we want to see if there is any relationship between FlightNumber and Orbit type.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\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\nsns.catplot(y=\"Orbit\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "You should see that in the LEO orbit the Success appears related to the number of flights; on the other hand, there seems to be no relationship between flight number when in GTO orbit.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK  5: Visualize the relationship between Payload and Orbit type\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Similarly, we can plot the Payload vs. Orbit scatter point charts to reveal the relationship between Payload and Orbit type\n"
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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                        "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\nsns.catplot(y=\"Orbit\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Pay load Mass (kg)\",fontsize=20)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "With heavy payloads the successful landing or positive landing rate are more for Polar,LEO and ISS.\n\nHowever 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": 13,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "['2010',\n '2012',\n '2013',\n '2013',\n '2013',\n '2014',\n '2014',\n '2014',\n '2014',\n '2014',\n '2014',\n '2015',\n '2015',\n '2015',\n '2015',\n '2015',\n '2015',\n '2016',\n '2016',\n '2016',\n '2016',\n '2016',\n '2016',\n '2016',\n '2016',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2019',\n '2019',\n '2019',\n '2019',\n '2019',\n '2019',\n '2019',\n '2019',\n '2019',\n '2019',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020']"
                    },
                    "execution_count": 13,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# A function to Extract years from the date \nyear=[]\ndef Extract_year(df):\n    for i in df[\"Date\"]:\n        year.append(i.split(\"-\")[0])\n    return year\nExtract_year(df)\n# df.insert(1, \"Year\", year)"
        },
        {
            "cell_type": "code",
            "execution_count": 15,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\ndf2 = df.merge(df_avg_succ, on=\"Orbit\")\nsns.lineplot(y=\"SuccRate\", x=\"Year\", data=df2)\nplt.xlabel(\"Year\",fontsize=20)\nplt.ylabel(\"Sucess Rate\",fontsize=20)\nplt.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": 16,
            "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  \\\n0             1  6104.959412   LEO  CCAFS SLC 40        1     False   False   \n1             2   525.000000   LEO  CCAFS SLC 40        1     False   False   \n2             3   677.000000   ISS  CCAFS SLC 40        1     False   False   \n3             4   500.000000    PO   VAFB SLC 4E        1     False   False   \n4             5  3170.000000   GTO  CCAFS SLC 40        1     False   False   \n\n    Legs LandingPad  Block  ReusedCount Serial  \n0  False        NaN    1.0            0  B0003  \n1  False        NaN    1.0            0  B0005  \n2  False        NaN    1.0            0  B0007  \n3  False        NaN    1.0            0  B1003  \n4  False        NaN    1.0            0  B1004  "
                    },
                    "execution_count": 16,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "features = df[['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights', 'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount', 'Serial']]\nfeatures.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": 17,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "(64, 12)"
                    },
                    "execution_count": 17,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df_feat = features.dropna()\ndf_feat.shape"
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "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>...</th>\n      <th>42</th>\n      <th>43</th>\n      <th>44</th>\n      <th>45</th>\n      <th>46</th>\n      <th>47</th>\n      <th>48</th>\n      <th>49</th>\n      <th>50</th>\n      <th>51</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>11</th>\n      <td>12</td>\n      <td>2395.0</td>\n      <td>ISS</td>\n      <td>CCAFS SLC 40</td>\n      <td>1</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>5e9e3032383ecb761634e7cb</td>\n      <td>1.0</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>0.0</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>14</td>\n      <td>1898.0</td>\n      <td>ISS</td>\n      <td>CCAFS SLC 40</td>\n      <td>1</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>5e9e3032383ecb761634e7cb</td>\n      <td>1.0</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>0.0</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>16</td>\n      <td>2477.0</td>\n      <td>ISS</td>\n      <td>CCAFS SLC 40</td>\n      <td>1</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>5e9e3032383ecb6bb234e7ca</td>\n      <td>1.0</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>0.0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>17</td>\n      <td>2034.0</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>1</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>5e9e3032383ecb267a34e7c7</td>\n      <td>1.0</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>0.0</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>18</td>\n      <td>553.0</td>\n      <td>PO</td>\n      <td>VAFB SLC 4E</td>\n      <td>1</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>5e9e3033383ecbb9e534e7cc</td>\n      <td>1.0</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>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 64 columns</p>\n</div>",
                        "text/plain": "    FlightNumber  PayloadMass Orbit    LaunchSite  Flights  GridFins  Reused  \\\n11            12       2395.0   ISS  CCAFS SLC 40        1      True   False   \n13            14       1898.0   ISS  CCAFS SLC 40        1      True   False   \n15            16       2477.0   ISS  CCAFS SLC 40        1      True   False   \n16            17       2034.0   LEO  CCAFS SLC 40        1      True   False   \n17            18        553.0    PO   VAFB SLC 4E        1      True   False   \n\n    Legs                LandingPad  Block  ...   42   43   44   45   46   47  \\\n11  True  5e9e3032383ecb761634e7cb    1.0  ...  0.0  0.0  0.0  0.0  0.0  0.0   \n13  True  5e9e3032383ecb761634e7cb    1.0  ...  0.0  0.0  0.0  0.0  0.0  0.0   \n15  True  5e9e3032383ecb6bb234e7ca    1.0  ...  0.0  0.0  0.0  0.0  0.0  0.0   \n16  True  5e9e3032383ecb267a34e7c7    1.0  ...  0.0  0.0  0.0  0.0  0.0  0.0   \n17  True  5e9e3033383ecbb9e534e7cc    1.0  ...  0.0  0.0  0.0  0.0  0.0  0.0   \n\n     48   49   50   51  \n11  0.0  0.0  0.0  0.0  \n13  0.0  0.0  0.0  0.0  \n15  0.0  0.0  0.0  0.0  \n16  0.0  0.0  0.0  0.0  \n17  0.0  0.0  0.0  0.0  \n\n[5 rows x 64 columns]"
                    },
                    "execution_count": 18,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# HINT: Use get_dummies() function on the categorical columns\nfrom sklearn.preprocessing import OneHotEncoder\nenc = OneHotEncoder(handle_unknown='ignore')\n\ndf_features_enc = pd.DataFrame(enc.fit_transform(df_feat[[\"Orbit\", \"LaunchSite\", \"LandingPad\", \"Serial\"]]).toarray())\nfeatures_one_hot = df_feat.merge(df_features_enc, left_index=True, right_index=True)\nfeatures_one_hot.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "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>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_GEO</th>\n      <th>Orbit_GTO</th>\n      <th>...</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_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>11</th>\n      <td>12</td>\n      <td>2395.0</td>\n      <td>1</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>14</td>\n      <td>1898.0</td>\n      <td>1</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>16</td>\n      <td>2477.0</td>\n      <td>1</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>17</td>\n      <td>2034.0</td>\n      <td>1</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>18</td>\n      <td>553.0</td>\n      <td>1</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\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      <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>86</td>\n      <td>15400.0</td>\n      <td>2</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5.0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>87</td>\n      <td>15400.0</td>\n      <td>3</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5.0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>88</td>\n      <td>15400.0</td>\n      <td>6</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5.0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>89</td>\n      <td>15400.0</td>\n      <td>3</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5.0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>90</td>\n      <td>3681.0</td>\n      <td>1</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>5.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>64 rows \u00d7 60 columns</p>\n</div>",
                        "text/plain": "    FlightNumber  PayloadMass  Flights  GridFins  Reused  Legs  Block  \\\n11            12       2395.0        1      True   False  True    1.0   \n13            14       1898.0        1      True   False  True    1.0   \n15            16       2477.0        1      True   False  True    1.0   \n16            17       2034.0        1      True   False  True    1.0   \n17            18        553.0        1      True   False  True    1.0   \n..           ...          ...      ...       ...     ...   ...    ...   \n85            86      15400.0        2      True    True  True    5.0   \n86            87      15400.0        3      True    True  True    5.0   \n87            88      15400.0        6      True    True  True    5.0   \n88            89      15400.0        3      True    True  True    5.0   \n89            90       3681.0        1      True   False  True    5.0   \n\n    ReusedCount  Orbit_GEO  Orbit_GTO  ...  Serial_B1047  Serial_B1048  \\\n11            0          0          0  ...             0             0   \n13            0          0          0  ...             0             0   \n15            0          0          0  ...             0             0   \n16            0          0          0  ...             0             0   \n17            0          0          0  ...             0             0   \n..          ...        ...        ...  ...           ...           ...   \n85            2          0          0  ...             0             0   \n86            2          0          0  ...             0             0   \n87            5          0          0  ...             0             0   \n88            2          0          0  ...             0             0   \n89            0          0          0  ...             0             0   \n\n    Serial_B1049  Serial_B1050  Serial_B1051  Serial_B1056  Serial_B1058  \\\n11             0             0             0             0             0   \n13             0             0             0             0             0   \n15             0             0             0             0             0   \n16             0             0             0             0             0   \n17             0             0             0             0             0   \n..           ...           ...           ...           ...           ...   \n85             0             0             0             0             0   \n86             0             0             0             0             1   \n87             0             0             1             0             0   \n88             0             0             0             0             0   \n89             0             0             0             0             0   \n\n    Serial_B1059  Serial_B1060  Serial_B1062  \n11             0             0             0  \n13             0             0             0  \n15             0             0             0  \n16             0             0             0  \n17             0             0             0  \n..           ...           ...           ...  \n85             0             1             0  \n86             0             0             0  \n87             0             0             0  \n88             0             1             0  \n89             0             0             1  \n\n[64 rows x 60 columns]"
                    },
                    "execution_count": 19,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "features_one_hot = pd.get_dummies(df_feat, columns=[\"Orbit\", \"LaunchSite\", \"LandingPad\", \"Serial\"])\nfeatures_one_hot"
        },
        {
            "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": 20,
            "metadata": {},
            "outputs": [],
            "source": "# HINT: use astype function\ndf_ohe_feat = 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": "code",
            "execution_count": 21,
            "metadata": {},
            "outputs": [],
            "source": "df_ohe_feat.to_csv('dataset_part_3_ouso.csv', index=False)"
        },
        {
            "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 \u00a9 2020 IBM Corporation. All rights reserved.\n"
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.8",
            "language": "python",
            "name": "python3"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "file_extension": ".py",
            "mimetype": "text/x-python",
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
            "version": "3.8.12"
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    "nbformat": 4,
    "nbformat_minor": 4
}