EDA with Data Visualization.ipynb

{
    "cells": [
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "# **SpaceX  Falcon 9 First Stage Landing Prediction**\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Assignment: Exploring and Preparing\u00a0Data\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Estimated time needed: **70** minutes\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Falcon 9 first stage will land successfully\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing\\_1.gif)\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Several examples of an unsuccessful landing are shown here:\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans.\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "***\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### Import Libraries and Define Auxiliary Functions\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "We will import the following libraries the lab\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# andas 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",
            "execution_count": 1,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Exploratory Data Analysis\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "First, let's read the SpaceX dataset into a Pandas dataframe and print its summary\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "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)",
            "execution_count": 5,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 5,
                    "data": {
                        "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  ",
                        "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>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "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()",
            "execution_count": 6,
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 1842.38x360 with 1 Axes>",
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XKhQsZ5Y2PTZs2XRGNqxfnZUrKhvWr86WTWdMOhKHa+PWZN3ZyYqVg+PGrZNOtDCWy0xVAAAAAGDJWznC2POSXJTkGUnOTvK6qrosyW919/ULkA2WpYVY2vTG2+/Kxdt25rrdd+TMdauyZdMZOWXNCUd8Xw7ulDUn5MoXPWXSMRiH1euTC6+adIqFt/aswczNmW0AAAAAgEVo3jM4u3tbdz8ryaOT/FqSf0zyM0k+XVUfqqrzqup+C5QTlo2FWNrUrFDgkJbLTFUAAJY9W8MAACx91d2Hd2HVyiTPzT2zOpPkK0kuzWBW5+fGknACpqamenp6etIxWKYWYrblaa+4Knv33fO9vnJF5YZfPvdIowIAAMCSc94l12THrj3/1N6wfrUVeACApaomHWBSRlmi9l66e2+SbUm2VdWGJO9KclKSn01ycVW9L8kru/tjY0kKy8RCLG165rpV9/o/b+OYFQoAAABL0UJsDQMAwNE17yVqZ1NV319Vv5PkwxkUN29L8utJ/jzJuUk+WlX/9ohTAkdky6YzsmH96qxcUdmwfnW2bDpj0pEAADgW7NmVXHpu8uo1g+OeXZNOBHBIC7E1DAAAR9fIS9RW1eokF2SwNO1jMpj+enWSNyb53e7+1nDchiS/l+Rr3f0d4wy90CxRCwAAMA+Xnpvsvvqe9rqzkwuvmlwegHlYiK1hAAAmxBK1h1JV35PkRUl+JMkDkvx9kkuSvLG7P3Hg+O7eUVWXJnnpmLICAACwmNx07dxtgEVoIbaGAQDg6BplidqPJHl+ks8l+Ykkj+ru/zhbcXOGLw4fh6Wqjquqj1fVe4bt1VX1gar67PC4asbYl1fVDVV1fVU9e0b/k6rqE8Nzr6+qZVvNBgAAGKu1Z83dBgAAgAUwSoHznUm+v7v/RXe/obv//lAXdPdvdvf6w4+Xn0rymRntlyX5YHefnuSDw3aq6vFJzk/yhCTnJHlDVR03vOaNGSyne/rwcc4R5AEAAGC/jVsHy9KuWDk4btw66UTAYmS/XgAAxmzkPTiPlqo6OcnlSX4pyc909w9W1fVJntbdt1TViUn+tLsfW1UvT5Lu/pXhtX+c5FVJvpDkT7r7ccP+5w2vf9Fcr20PTgAAAIAxsV8vAMBCWbarlo4yg/No+/UkP5tk34y+R3T3LUkyPD582H9SkptmjLt52HfS8PmB/fdRVRdV1XRVTd92223jeQcAAAAAy539egE4lliZABaFlaNeUFXfneTZGRQKj59lSHf3C48kVFX9YJJbu/tjVfW0+VwyW445+u/b2f2mJG9KBjM45xkVAAAAgLmsPeveMzjt1wvAUrZ98z3/u7b76kHbygRw1M27wFlVleSyJD+WQeHwwAJiz+g/ogJnkrOT/FBVnZvkAUkeXFW/neTLVXXijCVqbx2OvznJ2hnXn5zkS8P+k2fpBwAAAOBo2Lh18Mffm64dFDft1wv8/+3de7hcd13v8fc3DbeAhyah5dYkDVJuopEQW2q5VK4l+hCQ0lNQKJcjPVLlZlQuWko5QpUcQR6jFIFW8UafIE3VForQKnJKQiwNtLSFam6FAiW7tGgUCfmdP9ba7WRnrbVn/dbsPZf9fj3PfmbP7JnP/s7a3/yyZv1m/UYaZ65MII2ENkvU/grwUuAjwDqKycz3Aj8NvAX4HvDXwCO6FpVSenNK6biU0vHAmcBnUkq/CFwGnFXe7Sxga/n9ZcCZEXGfiFgNnABsL5ex/V5EPKmcoH1Zz2MkSZIk6UguOaVRZ49q3CxbXZzZcu7+4nLZ6mFXJElSvpkrEbgygTQUbSY4zwJuTim9PKV0bXnbd1NKn08pXQD8DPBC4OmDLrLHBcCzIuJrwLPK66SUbgAuAb4CfAI4J6X0w/Ixvwx8ELgF+FfgijmsT5IkSXPFA/qaL9NLTh06eM+SU9IosUclSZKGZ8NmWHUKLFpcXLoygQbF4x6tREr9fdxkRHwP+LOU0jnl9UPA/0kpndtzn48BK1NKPzUXxc6XdevWpR07dgy7DEmSJPW6aP3hn9+16hQ/50Rz4/zlxcTRtEWLi7OOpFFhj0qSJEmTJ++4R8x2h0nV5gzOAO7suf4fwLIZ9/ka8JiuRUmSJElH8HNONF9cckqjzh6VJEmSJo/HPVppM8H5deDhPdf/DXjijPucQDHxKUmSJA2WB/Q1X1xySqPOHpUkSZImj8c9WmmzRO1HgCeklB5fXr8A+HXgPOBvgFOBPwD+LqX0/Lkodr64RK0kSdIImtpVfM7cvm3FTv6GzbBs9bCrkiRJkiRJ6i7vuMeCXaK2zQTn84F3AetTSrsiYhmwAzgeSBQbcQp4ckrpprkpd344wSlJkiRJkiRJkqQRt2AnOBf3e8eU0qXApT3XpyLiCcAvAT8K7Ab+LKV026CLlKbt3X+AjVt2cu2eO1i7aimbTl/DyuVLhl2WJEmSJEmSJEmS5klfZ3BGxErgpyjO1PxCSmnfXBc2TJ7BObrOuPAatu+auvv6iauXccnZJw+xIkmSJEmSJEmSpKHwDM46EbEJeD33bKQUEe9JKf36nFYmVbh2zx2N1yVJkiRJkiRJkjTZFjX9MCJeAryRYnLzJuDm8vs3RsSL5748jbu9+w9wxoXX8Mi3XM4ZF17D3v0HOuWtXbW08bokSZIkSZIkSZImW+MEJ/Aq4CDwzJTSj6WUHgc8BzhU/kxqtHHLTrbvmuLgocT2XVNs3LKzU96m09dw4uplLF4UnLh6GZtOXzOgSiVNlKldcNF6OH95cTm1a9gVjWZNkiRJkiRJkjSGGj+DMyJuB65KKZ0x4/YtwKkppQfNcX1D4WdwDs4j33I5Bw/d02OLFwW3vHP9ECuStCBctB72fO6e66tOgVdcPrx6YDRrkiRJkiRJkjTOFuxncM52BudSimVpZ7oJOHrw5WjSuKSspKHYt635+jCMYk2SJElSFVcfkSRJ0oibbYJzEfCDitt/wAKeFVb/XFJW0lCsOKn5+jCMYk2SJElSla3nFKuPHDpYXG49Z9gVSZIkSYdZ3Md96tewlWaxcvkSLjn75GGXIWmh2bC5OAizb1sxkbhh87ArGs2aJI2WqV1HjhPLVg+7KknSQuTqI5IkSRpxs30G5yHaT3CmlFI/E6cjy8/glCRJ0rzzs3olSaPC/5MkSZLGxYJdbXW2JWqh2DhtvvrJlCRJktTLs2UkSV0N6rMzN2wuJjUXLS4uXX1EkiRJI6bxTMuUkpOVkiRJ0nxYcdLhZ8v4Wb3SSNi7/wAbt+zk2j13sHbVUjadvoaVy5cMuyyp2vRnZ8I9n52Zc+blstWesSlJkqSR5gSmJEmS5t6gziiZZJ4tI42kjVt2sn3XFAcPJbbvmmLjlp3DLkmq52oAkiRJWiDG+rMyJUmSNCYGdUbJJPNsGWkkXbvnjsbr0khxNQBJkiQtEJ7BKUmSpLnnGSWSxtTaVUsbr0sjZcJXA9i7/wBnXHgNj3zL5Zxx4TXs3X9g2CVJkiRpSJzglCRJ0tybeQaJZ5RIGhObTl/DiauXsXhRcOLqZWw6fc2wS5LqTa8GcO7+4nLZ6mFXNFAuGS1JkqRpLlErSSNu7/4DbNyyk2v33MHaVUvZdPoaVi5fMuyypPEztatYFnXftmJybcPmiTvoN9I2bD5y+0vSGFi5fAmXnH3ysMuQhEtGt+b+ryRJmmCewSlpaFxeqD++S1kakOnPgDx08J7PgNT8mfAzSiRJBffxNZdcMrol938lSdIEc4JT0tA4cdcf36UsDYifASlJ0pxzH19zySWjW3L/V5IkTTCXqNXC5nItQ+XEXX/WrlrK9l1Th12XlGHFScU713uvS5KkgXIfX3PJJaNbcv9XkiRNMM/g1MLmci1D5fJC/fFdyjrC1C64aD2cv7y4nNo17IrGw4bNsOoUWLS4uPQzICVJGjj38aURstD2f32dJEnSghIppWHXMHLWrVuXduzYMewyNB/OX15Mbk5btLj4bDAdZu/+A2zcspNr99zB2lVL2XT6GlYuXzKyudLEu2j94e/EXnVK8ZmGkqTR4CohWsDcx5c0NL5OkiQtTDHsAobFCc4KTnAuIO789uWMC685bInUE1cvc1kgaZh8c4YkjTb3MSVJmn++TpIkLUwLdoJzJJeojYgVEXFVRNwYETdExOvK25dFxKci4mvl5dKex7w5Im6JiJsj4jk9tz8xIr5c/ux9EbFg/9iqsNCWa8nk5+hII2bmZ+f4WTqSNFr2bWu+LkmSBs/XSZIkLSgjOcEJHAR+LaX0WOBJwDkR8TjgTcCnU0onAJ8ur1P+7Ezgx4DTgD+KiKPKrD8GXg2cUH6dNp9PRCNu2eri3fTn7i8uXTqskp+jM4H8bJLx5pszJGm0eYBVkqT55+skSZIWlLFYojYitgJ/WH6dmlK6LSIeClydUnp0RLwZIKX0rvL+nwTOA3YDV6WUHlPe/uLy8Wc3/T6XqJUO5+foTCCXzpMkae74GZySJEmayX1ESXNjwa5aunjYBcwmIo4HngBsAx6cUroNoJzkPLa828OBz/c87Nbyth+U38+8XVILK5cvWVCfubkgJnRdOk+SpLkzvUqIJEmSNG3rOfe82XzP54rr7jNKUrZRXaIWgIh4APAx4PUppbua7lpxW2q4vep3vToidkTEjttvv719sZImxsYtO9m+a4qDhxLbd02xccvOYZc0eC6dJ0mSJEmSNH98s7kkDdTITnBGxL0oJjf/IqX0N+XN3yqXpqW8/HZ5+63Aip6HHwd8o7z9uIrbj5BS+kBKaV1Kad0xxxwzuCciaexcu+eOxusTwc8mkSRJkiRJmj++2VySBmokJzgjIoAPATemlH6/50eXAWeV358FbO25/cyIuE9ErAZOALaXy9l+LyKeVGa+rOcxklRp7aqljdcnwvTSeefuLy79zAdJkiRJkqS545vNJWmgIqXKFVuHKiKeDHwW+DJwqLz5LRSfw3kJsBLYC7wopTRVPuatwCuBgxRL2l5R3r4OuBi4H3AF8Ktplie9bt26tGPHjgE/K0njYkF8BqckSZIkSZIkadxVfVTjgjCSE5zD5gSnJEmSJEmSJEmSRtyCneAcySVqJUmSJEmSJEmSNERTu+Ci9XD+8uJyatewK5Lu5gSnJEmSJEmSJEmSDrf1HNjzOTh0sLjces6wK5Lu5gSnJEmSJEmSJEmSDrdvW/N1aYic4JQkSZIkSZI0v1z2UJJG34qTmq9LQ+QEpyRJkiRJkqT55bKHkjT6NmyGVafAosXF5YbNw65IutviYRcgjb2pXcVO+L5txTtYNmyGZauHXZUkSZIkSdLoctlDSRp9y1bDKy4fdhVSJc/glLryHYeSJEmSJEntuOyhJEnqwAlOqSvfcShJkiRJktSOyx5KkqQOXKJW6mrFScWZm73XJUmSJEmSVM9lDyVJUgeewSl15TsOJUmSJEmSJEmS5o1ncEpd+Y5DSZIkSZIkSZKkeeMZnJIkSZIkSZIkSZLGhhOckiRJkiRJkiRJksaGE5ySJEmSJEmSJEmSxoYTnJIkSZIkSZIkSZLGhhOckiRJkiRJkiRJksaGE5ySJEmSJEmSJEmSxsbiYRegwdu7/wAbt+zk2j13sHbVUjadvoaVy5cMuyxJkiRJkiRJkiSpM8/gnEAbt+xk+64pDh5KbN81xcYtO4ddkiRJkiRJkiRJkjQQTnBOoGv33NF4XZpIU7vgovVw/vLicmrXsCtaEPbuP8AZF17DI99yOWdceA179x8YdkmSJEmSJEmSpAnnBOcIGPQEwdpVSxuvD6Mmac5tPQf2fA4OHSwut54z7IoWBM8YlyRJkiRJkiTNNyc4R8CgJwg2nb6GE1cvY/Gi4MTVy9h0+pqh1yTNuX3bmq9rTnjGuCSNMFc3kCRJkiRJE2rxsAvQ4CcIVi5fwiVnn9wpw0kLjZ0VJxVnbvZe15xbu2op23dNHXZdkjQiplc3gHtWN3jF5cOtSZIkSZIkaQA8g3MEDGJJ2UEbxZqkRhs2w6pTYNHi4nLD5mFXtCAM4oxxSdIccXUDaTR5drUkSZIkdRYppWHXMHLWrVuXduzYMW+/b+/+A2zcspNr99zB2lVL2XT6GlYuXzJvv39capIkSVILF60/fHWDVad4Bqc0Cvy3KUmSJGlwYtgFDIsTnBXme4JTkiRJGripXcWytPu2FUu3b9gMy1YPuypJ5y+HQwfvub5oMZy7f3j1SJIkSRpnC3aC08/glCRJkibRstWeFSaNIj87XpIkSZI68zM4JUmSJEmaL352vCRJkiR1tiDO4IyI04A/AI4CPphSumDIJUmSJEmSFiLPrpYkSZKkzib+DM6IOArYDDwXeBzw4oh43HCrkiRJkiRJkiRJkpRj4ic4gROBW1JK/5ZS+m/gr4ENQ65JkiRJkiRJkiRJUoaFMMH5cGBfz/Vby9sOExGvjogdEbHj9ttvn7fiJEmSJEmSJEmSJPVvIUxwRsVt6YgbUvpASmldSmndMcccMw9lSZIkSZIkSZIkSWprIUxw3gqs6Ll+HPCNIdUiSZIkSZIkSZIkqYOFMMH5BeCEiFgdEfcGzgQuG3JNkiRJkiRJkiRJkjIsHnYBcy2ldDAifgX4JHAU8OGU0g1DLkuSJEmSJEmSJElShomf4ARIKV0OXD7sOiRJkiRJkiRJkiR1sxCWqJUkSZIkSZIkSZI0IZzglCRJkiRJkiRJkjQ2IqU07BpGTkTcDuzp464PAr4zoF9rlllmjU7WoPPMMsusycwadJ5ZZpk1mVmDzjPLLLMmM2vQeWaZZdZkZg06zyyzzJrMrEHnmTXaWd9JKZ02oN83Vpzg7CAidqSU1pllllmTlTXoPLPMMmsyswadZ5ZZZk1m1qDzzDLLrMnMGnSeWWaZNZlZg84zyyyzJjNr0HlmTUbWJHKJWkmSJEmSJEmSJEljwwlOSZIkSZIkSZIkSWPDCc5uPmCWWWZNZNag88wyy6zJzBp0nllmmTWZWYPOM8sssyYza9B5Zpll1mRmDTrPLLPMmsysQeeZNRlZE8fP4JQkSZIkSZIkSZI0NjyDU5IkSZIkSZIkSdLYcIJT21ZsyAAAGUFJREFUkiRJkiRJkiRJ0thwgjNDRHw4Ir4dEdd3zFkREVdFxI0RcUNEvK5D1n0jYntE7Cyz3t6ltjLzqIj4YkT83QCydkfElyPiuojY0THr6IjYEhE3ldvu5MycR5f1TH/dFRGv71DXG8ptf31E/FVE3LdD1uvKnBva1lTVnxGxLCI+FRFfKy+Xdsh6UVnXoYhY17Gud5d/xy9FxMcj4ugOWe8oc66LiCsj4mG5WT0/2xgRKSIe1KGu8yLi6z19tr5LXRHxqxFxc/k3+L0OdX20p6bdEXFdP1kNeT8ZEZ+f/jceESd2yFoTEdeUY8bfRsT/6COncjzN6f2GrNa935CV2/t1ea37vy6r5+d9939DXa37v6mutv3fUFfr/m/Iat37DVk5vV/5/39m79dl5fR+XVbr3m/Iyun7xv2lln1fV1dO39fW1bbvZ6ktp/frsnJ6vy6rde+XjztsXzWn72fJy9rnqcnKGvdrsrL2eaqyem5vtc9TU1fWPk9dXTm9X1NXl32emVlZ+zs1WVl9Xz72iNdWuf1fk5W7v1+VlbvPU5WVu79f+1q0be/X1JW7v19ZV07v19SV1fs1Wbn7+lVZuWP+EccBOvR9VVZu31dl5fZ9VVZu39ceN2nb9w215fZ+ZW2ZvV9VV27vV2Xl9n5VVs6+fuVxq5zeb8jK2devy8rZ16/LytnXbzzO16b3G+rK2devratt3zfUldv3dXk5+/p1WTm9f8Qx1py+nyUvd9yvysod96uycsf92uPSbXq/oa7cMb+yrra931BXbu9XZeWO+VVZufs7R8wJdOn9BSGl5FfLL+CpwFrg+o45DwXWlt//CPBV4HGZWQE8oPz+XsA24Ekd63sj8JfA3w1gm+0GHjSg7f+nwP8qv783cPQAMo8Cvgmsynz8w4FdwP3K65cAL8/MejxwPbAEWAz8A3BCi8cf0Z/A7wFvKr9/E/C7HbIeCzwauBpY17GuZwOLy+9/t2Nd/6Pn+9cC78/NKm9fAXwS2NNv79bUdR6wMaMPqrJ+puyH+5TXj+3yHHt+/n+BczvWdiXw3PL79cDVHbK+ADyt/P6VwDv6yKkcT3N6vyGrde83ZOX2fl1e6/6vy8rp/4a6Wvd/Q1br/m96jm37v6Gu1r3fkJXT+5X//2f2fl1WTu/XZbXu/YasnL6v3V/K6Pu6unL6vi4rd9yfdb+wRe/X1ZbT+3VZrXu/vO9h+6o5fT9LXtY+T01W1rhfk5W1z1OVldP7DXW17v2GrKzer3uObfu+oa6s/Z2arKy+L++/e+bfKrf/a7Jy9/ersnL3eaqycvf3j8gqb8/Z36+qK6v3a7Jyx/3K59jz8757v6au3H39qqzcMf+I4wAd+r4qK7fvq7Jy+74qK7fvK4+b5PR9Q225vV+Vldv7jceHWvZ+VV25vV+VlT3ul4+5+7hVbu/XZGXv71RkZe/vVGRl7+/MzOrS+xV1ZfV9TVb2/k7Vc8zp+4basvd5KrJa9T41x1hz+74hL+d1bl1Wzuvcuqyc17m1x6Xb9n5DXa17vyEr5/jOrMfe++39hrpyXuPWZeUc36mcE8jt/YXy5RmcGVJK/wRMDSDntpTSteX33wNupPhHkZOVUkr/Xl69V/mVcmuLiOOAnwU+mJsxF8p3OzwV+BBASum/U0rfHUD0M4B/TSnt6ZCxGLhfRCymGIi+kZnzWODzKaUDKaWDwD8CL+j3wTX9uYFiB5vy8vm5WSmlG1NKN/dbzyxZV5bPEeDzwHEdsu7quXp/+uz/hn/P7wF+o9+cWbJaq8n6ZeCClNL3y/t8u2tdERHAGcBfdawtAdPvRnogffZ/TdajgX8qv/8U8MI+curG09a9X5eV0/sNWbm9X5fXuv9n+T+oVf8P+P+zuqzW/T9bXW36vyGrde83ZOX0ft3//zm9X5mV2ft1Wa17vyErp++b9pfa9v3A9r0asnLH/cbaWvZ+XVZO79dlte79mn3VrP2durzcfZ6arKxxvyYra5+nYf++9T7PIF8r1GRl9X5TXW33eWqysvZ3arJa9/0ssvt/ptzer8nK6v2arKzeb9C69+dBVu83ydnfr5DV+zVyxvy64wCt+74uK6fvG7Ja931DVuu+n+W4Sc6YP7DjMA1ZrXt/trra9H5DVuveb8jqOu73HrfqOubfnTWAMb83q+uY35vVdcyfeZyvy5g/iGOGVVldx/wj6uo45vfmdR33e7Nyer/qGGuXvj8ir0PvV2Xl9n5VVm7v1x2Xzun9QR3jrsvK7f3aujJ6vyort++rsnL6vm5OYGD7+ZPICc4RERHHA0+geCd7bsZR5WnY3wY+lVLKzgLeSzH4HeqQ0SsBV0bEv0TEqzvkPAK4HbgoiiWePhgR9x9AfWfS4QVfSunrwCZgL3AbcGdK6crMuOuBp0bE8ohYQvGOkRW5tZUenFK6raz1NuDYjnlz4ZXAFV0CIuJ3ImIf8AvAuR1yngd8PaW0s0s9PX4liuUlPtxxGYFHAU+JiG0R8Y8R8VMDqO0pwLdSSl/rmPN64N3l9t8EvLlD1vXA88rvX0TL/p8xnnbq/UGMzX1kZfX+zLwu/d+b1bX/K55ndv/PyOrU/zXbP6v/Z2R16v0ZWVm9X/P/f1bvD3Jfoo+svnu/Liun76uycvu+4Tm27vuarOy+n2X7t+r9mqys3q/Jyun9qn3VLmP+IPd9Z8tqM+5XZmWO+UdkdRjz655jzphflZXb+03bvu2YX5WVO+ZXZXXZ36l6bZXb/4N6ndZPVpver8zK7P0jsjr0ft1zzOn9qqzc3m/a9m17vyort/ersnJ6v+44QE7fD/KYQj9Z/fZ9bVZG31dmdej7pufZtvfrsnJ6f7bt36b367Jyer8uq9PrXA4/btX1GE+nY2B9ZuW8zj0sK3PMPyKrQ+9X1kW3Yzy9WV2P8VRt+y7HeHrzuh7j6c1q1fsNx1iz+n6Qx2z7zOqr95uy2vZ+XVZO78/yHFv1fkNW697vY9v33fsNWa37viErZ8yvmxMYh+P6w5NG4DTScfwCjqfjErU9WQ8A/gX4+QHlHQ1cBTw+8/E/B/xR+f2pDGaJ2oeVl8cCO4GnZuasAw4CJ5XX/4CWy3pUZN4b+A7FYJGbsRT4DHAMxdkIlwK/2CHvVcC1FO/0eD/wnpaPP6w/ge/O+PkduVk9t19N++Xa6rLeCnwciK5Z5c/eDLw9J4vinTbbgAeW13fTbumemdv+wRRLcywCfgf4cIes64H3USzxdyLFEgh9bbOGbf/HwK9l9OjM2t4HvLD8/gzgHzpkPYZiWYh/Ad4G7G+Rddh42rH3K8fmzN6vy2rd+0155c/a9v/dWQPo/5nbv0v/z8zq0v912791/1fU1aX3Z2Zl9375+Lv//+/S+zOzuvR+Q1Zu71fu47Tt+xlZP9Gl7yu2fXbfV2Rl9/0s2z937O+tLbv3K7Ja9T41+6q5fV+Xl9P7fWT13fuzZbXp/aosMsf8hu3fuvcbslr3fh/bvu++b6irdd83ZHXZ3znitVWH/q99ndam9/vIajXuN2W16f2G7ZU17tdkZY37NVlZ4/4s277VmF9TV9aYX5PVuvepOQ6Q0/d1WTl930dWmzF/1mMd/fZ9Tda7O/R93fbPGffrsnLG/dm2f5txv66unHG/LqvLuH/Ycauc3q/Lyun9PrJyjvHUHpvrt/ersuj+Gnfmtu/yGndmVpfXuHXbPnc/f2ZtXV7nzsxqu69feYw1t+/r8nJ6v4+sNuP+rMeS++39mqyX5fR+w/bPGfPrsnLG/Nm2fZsxv66unDG/LitrzKdiTiC39xfK19ALGNcvBjTBWTb+J4E3Dri+t5H/GTjvAm4tB75vAgeAPx9gbed1qO0hwO6e608B/r5jPRuAKztmvAj4UM/1l1EeyBjA9non8JqWjzmsP4GbgYeW3z8UuDk3q+f2qxnABCdwFnANsKRrVs/PVrX598nhE5w/TnFWye7y6yDFu3AeMoC6Wo0bFX/HTwCn9lz/V+CYDtt+MfAt4LiMvpxZ252UOyMUOyh3Dehv+Shge585R4ynub1fldXzs1a9X5fVofcb/99o0/8zs7r0fx919d3/NX/LrP5v2P6t+7+mrqze72N79d37Mx73NmBjbu9XZfVcb9X7dVm5vV9XV3lbq3F/RtZv5/Z9H3X13fc1f8fscb9h+2eP/TNqyx73Z9lms/Y+NfuquX1fl9fz8757vymrbe/PVleb3q/J+lhO7/dZV1+93/C3bN37s2z7Vn3fUFfrvu9ze2WN+eVjz2Nw4/55DG7cvzurbe/PVleb3q/JGtS4X1VXX73f8HfsPO7P2PZdx/zpugYx5ldtr756n5rjADl9X5fVc73vvm/Katv3s9VV3tbvmF+V9encvu+ztr56v+FvmTPuN23/tuN+XV05434/26vVuM+M41Y5vV+XldP7TVlte3+2utr0flUW3Y/xNNXVV983/B27HOOp2vZdjvHMrK3LMZ6mbdbPvn7lMdbcvq/L67ned+83ZbXt/dnqKm/rd9yvyroqp/f7rKuv3m/4W+aM+U3bvu2YX1dXzpjfz/bKPb7zTuA1ub2/UL5conaIIiIo1uS/MaX0+x2zjomIo8vv7wc8E7gpJyul9OaU0nEppeMplhT4TErpFzvUdv+I+JHp7yk+ePn6zNq+CeyLiEeXNz0D+EpubaUX031pjr3AkyJiSfl3fQbFZ6pliYhjy8uVFGdVda3vMor/aCkvt3bMG4iIOA34TeB5KaUDHbNO6Ln6PPL7/8sppWNTSseX/wZuBdaWvZdT10N7rr6AzN4vXQo8vcx9FPe8Ky7XM4GbUkq3dsiY9g3gaeX3Tweyl7zt6f9FwG9RvGNptsfUjaete3/AY3NlVm7vN+S17v+qrNz+b6irdf83bP/W/T/L37JV/zdkte79hu2V0/t1///n9P7A9iXqsnJ6vyErp++rsr6Y2fd1deX0fd22zxr3Z/lbtu39uqyc3q/bZq16v2FfNWt/Z5D7vnVZOb3fkNW692uyXpjT+w11te79hm3fuvdn+Tu26vuGrNZ937C9Wo/55f3rXlvljPsDe51Wl5U57tdl5Yz7VVlfyBz36+rKGffrtn3O/k7T37HtmF+XlTPm122v1r3fcBygdd8P8phCXVbmmF+XlTPmV2Vdm/s6t6G2nHG/bvvnjPtNf8u2435dVs64X7e9ssb90szjVl2O8QziGFhlVsdjPDOzuhzjuTtrAMd4ZtbV5RjPzG3f5RhP1d+xyzGemXldjvHM3GZte7/uGGtu3w/ymG1lVmbv12Xl9H5V1t9k9n5dXTm9X7ftc3q/6e/YtvfrsnL6vm575e7rV80JjORx/ZHRZjbUr+KLorFuA35AMTi8KjPnyRSfSfEl4Lrya31m1k8AXyyzrgfOHdBzPZWOS9RSfP7AzvLrBuCtHfN+EthRPtdLgaUdspYA+ylP1+9Y19sp/tO5HvgIcJ8OWZ+l2JneCTyja38Cyynesfm18nJZh6wXlN9/n+LdMZ/skHULsK+n/9/fIetj5bb/EvC3wMNzs2b8fDf9L91TVddHgC+XdV1G+Y6bzKx7U5w9cD3FcgVP7/IcgYuB/53Rn1W1PZli2YWdFEtgPLFD1uuAr5ZfF9Df0h6V42lO7zdkte79hqzc3q/La93/dVk5/d9QV+v+b8hq3f9Nz7Ft/zfU1br3G7Jyer/y///M3q/Lyun9uqzWvd+QldP3s+4vtej7urpy+r4uK3fcr32eGb1fV1tO79dlte79nsxTuWfJz6z9nYa8rH2emqyscb8mK2ufpyorp/cb6sra56nJyur9uufYtu8b6sra36nJyup7al5b5fR/Q1bOuF+XlTPu12XljPuzvhbtt/cb6soZ9+uycvZ3ap9j295vqCtnzK/Lyu39I44D5PR9Q1bu69yqrNx9/aqs3Ne5jcdN+u37WWrLfa1blZW7z1P5PNv2fkNdua9zq7Jye/+I41Yder8qK7f3q7Jye78qK7f3G4/zten9mrpy+74qK7fvK59jTt831Jbb+1VZOa9zjzjGmtv3DXm5vV+Vldv7VVm5vd94XLpl71fVldv7VVm5vV/5HHN6v6au3L6vysod84+YE+jS+wvha/qUW0mSJEmSJEmSJEkaeS5RK0mSJEmSJEmSJGlsOMEpSZIkSZIkSZIkaWw4wSlJkiRJkiRJkiRpbDjBKUmSJEmSJEmSJGlsOMEpSZIkSZIkSZIkaWw4wSlJkiRJQxQRF0dEiojje247vrzt4gHk746I3V1zJl1EnFdu81OHXYskSZIkqZkTnJIkSZI0YOVEWdPXy4ddY50uk6sRcXX52B9GxI/X3Gd6QveZnYuVJEmSJC1Ii4ddgCRJkiRNsLfX3H7dLI/7OvBY4M7BljNvFgHvBk4bdiGSJEmSpMnjBKckSZIkzZGU0nmZj/sBcNNgq5lXtwDPiYhnpZQ+NexiJEmSJEmTxSVqJUmSJGnENC0TGxGPioiPRcQdEfEfEfH/IuJnI+LlTcvfRsSSiHh3ROyNiO9HxC0R8ZsRET33OQ/YVV49q8Oyum8BEvDuiOjrdWf5O66u+Vnj55RGxI9GxJaI2B8R34uIKyPi8eX9jomID0TEbRHxXxHxhYj4mVlqOSsivhgR/xkR346ID0fEQ2ruuywi3hURN5b3vzMiPh0Rz664791/o4g4rVzS986ISP1sI0mSJElSwTM4JUmSJGlMRMRjgM8By4C/B74EPAL4OHB5w0PvBVwJPAy4AjgIPB+4ALgv9yylezVwNPA6YCdwaU/GbMvq9voi8OfAS4GzgItaPLat44FtwI3AxeX1FwBXR8TJwCeAu4CPUmy3M4ErIuJRKaW9FXlvAJ5d3v8TwJOBVwCnRsRJKaXbp+8YEasottnxwGfL+98f+DngExFxdkrpTyp+x+kUy/deAby/fLwkSZIkqU9OcEqSJEnSHCnPiJxpd0rp4szIzRSTdK9JKf1xz+95Ls0TnA+jmLB8VkrpP8vHvB34KvCGiHhnSukHKaWrI2I3xQTndblL7JbeCrwIeEdEfDSldKBDVpOnAb+VUvqd6Rsi4reB8ykmPi+h2F6Hyp99CvgzionMN1TkPRc4KaX0xZ689wCvp5gQflXPff8UWAW8OKX01z33P5pi4vN9EXFZSulbM37HemB9SukTWc9YkiRJkhY4l6iVJEmSpLnztoqvl+cERcQK4OkUn295Ye/PUkpXAP8wS8Rrpyc3y8d8G9gKPBB4dE5NTVJK+4D3Ag8H3jjo/B67KSYee/1peXkf4NenJzdLf0lxButP1uR9pHdys3QecCfwkoi4D0BErKGYXP1Y7+QmQErpuxR/6/sCL6z4HVud3JQkSZKkfJ7BKUmSJElzJKUUs9+rb9MTctfMmLCb9s/AM2see2dK6ZaK2/eVl0u7FlfjXRRnPP5GRPxJxZmMg3BdSumHM277Rnn51ZTS93p/kFL6YUR8CziuJu8fZ96QUrozIq6jmNB8LMVyvSeXP35gzZm6x5SXj6342faa3y1JkiRJ6oMTnJIkSZI0Hh5YXtZNEjZNHn635vaD5eVRWRXNIqV0V7kU7h9SnAX5y3Pwa+6s+L0HI6LyZ6WDFJ9LWqVuO36zvJz+OywvL59VftV5QEOWJEmSJCmDS9RKkiRJ0ni4q7x8cM3P624ftgspPuvzlyKi6mzGaYn6N+EePfCq6tVtx4eUl3fOuHxdSikavl5RkZUGWrEkSZIkLTBOcEqSJEnSeJj+XMiTI6LqtdyTB/R7ppd7HchZnSmlg8Bvlnm/13DXO4AVM2+MiKOo/7zMufC0ihoeWNbwX8CN5c2fLy+fMk91SZIkSZJKTnBKkiRJ0hhIKe0DrgYeCZzd+7OIOI36z99s6w6KMwxXDiiPlNKlwGeBnwNOqbnbdmBlRDx7xu2/BawaVC19eGlEPGHGbedRLE37Vyml7wOklHZQPKefj4hXVgVFxI9HxLFzWawkSZIkLUR+BqckSZIkjY9zgM8BfxQR64EvAY8AXghsBTYAh7r8gpTSv0fENuApEfEXFMvL/hC4LKX0pQ7RGynOenxkzc83Ac8BtkbER4Ep4KeB1RQTu6d2+N1tXAF8LiIuAW6jODP2ycBu4E0z7vsS4DPAhyLitcA2is87PQ74CeDxwMnAt+elckmSJElaIDyDU5IkSZLGRErpKxQTZh+nWBr19cDxwAuAfy7vdlflg9t5KfD3wGnA24B3AGu7BKaUtgMfbfj5p4HnAzcAZwJnUUwqngjs6fK7W3oP8BqKJWlfDzwGuBj46ZTSYROVKaVbgScCb6WYBP4F4LUUE7N7Kc60/fJ8FS5JkiRJC0WklIZdgyRJkiSpo/Jsy5cAj0kp3TzseiRJkiRJmiuewSlJkiRJYyIiFkXEQypufwbwP4GvOLkpSZIkSZp0fganJEmSJI2PewP7IuIq4CbgIPBjwLOA/6b4jE5JkiRJkiaaS9RKkiRJ0piIiKOA9wJPB44DlgDfAf4JuCCl9MUhlidJkiRJ0rxwglOSJEmSJEmSJEnS2PAzOCVJkiRJkiRJkiSNDSc4JUmSJEmSJEmSJI0NJzglSZIkSZIkSZIkjQ0nOCVJkiRJkiRJkiSNDSc4JUmSJEmSJEmSJI2N/w9bZJqMeXfz+QAAAABJRU5ErkJggg==\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Next, let's drill down to each site visualize its detailed launch records.\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### TASK 1: Visualize the relationship between Flight Number and Launch Site\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "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(\"LaunchSite\",fontsize=20)\nplt.show()",
            "execution_count": 7,
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 1842.38x360 with 1 Axes>",
                        "image/png": 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                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Now try to explain the patterns you found in the Flight Number vs. Launch Site scatter point plots.\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### TASK 2: Visualize the relationship between Payload and Launch Site\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "We also want to observe if there is any relationship between launch sites and their payload mass.\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "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(\"PayloadMass\",fontsize=20)\nplt.ylabel(\"LaunchSite\",fontsize=20)\nplt.show()",
            "execution_count": 8,
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 1842.38x360 with 1 Axes>",
                        "image/png": 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                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Now try to explain any patterns you found in the Payload Vs. Launch Site scatter point chart.\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Most launches with payload mass under 10,000 kg are from any launch site, but heavier ones happens mainly at CCAFS SLC 40 and KSC LC 39A"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### TASK  3: Visualize the relationship between success rate of each orbit type\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Next, we want to visually check if there are any relationship between success rate and orbit type.\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Let's create a `bar chart` for the sucess rate of each orbit\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# HINT use groupby method on Orbit column and get the mean of Class column\ndf.groupby('Orbit')['Class'].mean().plot.bar()",
            "execution_count": 9,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 9,
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f2169a52ad0>"
                    },
                    "metadata": {}
                },
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 432x288 with 1 Axes>",
                        "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEYCAYAAABMVQ1yAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAW2UlEQVR4nO3de7SldX3f8ffHAQRFgsp46QAOiXgZLaNxRLNiKpek5aJFjVkBa0hslVIl1bZWqclKvKykKLqqVBRpMl5aI20iKupEGnWhJSoyGFBA0QnXkTQOeK3Gy8i3fzzP0c12zzl7hv2cPed33q+1Zq39XM7+/p4z+3zOc37P7/k9qSokSSvfvebdAEnSbBjoktQIA12SGmGgS1IjDHRJasQ+8yp8yCGH1Pr16+dVXpJWpKuuuuqOqlo7advcAn39+vVs3bp1XuUlaUVKcsuuttnlIkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhqxZKAn2Zzka0mu3cX2JDkvybYkn0/yi7NvpiRpKdOcob8DOGGR7ScCR/b/zgDees+bJUnaXUsGelV9Evj6IrucAryrOp8BDk7y0Fk1UJI0nVncKboOuG1keXu/7u/Gd0xyBt1ZPIcffviib7r+7A/vcYNuPufkPf7aedWdp9V4zFodVttnexYXRTNh3cTHIFXVhVW1qao2rV07cSoCSdIemkWgbwcOG1k+FLh9Bu8rSdoNswj0S4DT+9EuTwa+VVU/090iSRrWkn3oSd4DHAMckmQ78IfAvgBVdQGwBTgJ2AZ8D3jeUI2VJO3akoFeVactsb2AF82sRZKkPeKdopLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1Yp95N0CSWrP+7A/v8dfefM7Je/y1nqFLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGjFVoCc5IckNSbYlOXvC9p9L8sEk1yS5LsnzZt9USdJilgz0JGuA84ETgQ3AaUk2jO32IuD6qtoIHAO8Icl+M26rJGkR05yhHw1sq6obq+qHwEXAKWP7FHC/JAEOBL4O7JxpSyVJi5om0NcBt40sb+/XjXoz8GjgduALwIur6q7xN0pyRpKtSbbu2LFjD5ssSZpkmkDPhHU1tvzPgKuBfwQ8DnhzkoN+5ouqLqyqTVW1ae3atbvdWEnSrk0T6NuBw0aWD6U7Ex/1PODi6mwDbgIeNZsmSpKmMU2gXwkcmeSI/kLnqcAlY/vcChwPkOTBwCOBG2fZUEnS4pacD72qdiY5C7gUWANsrqrrkpzZb78AeA3wjiRfoOuieXlV3TFguyVJY6Z6wEVVbQG2jK27YOT17cA/nW3TJEm7wztFJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktSIqSbnklqz/uwP7/HX3nzOyTNsiTQ7nqFLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjvFNUWkb35A5VuGd3qXp3bPs8Q5ekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEZMdadokhOANwFrgD+pqnMm7HMM8EZgX+COqnrqDNupRnn3ojQ7SwZ6kjXA+cCvAduBK5NcUlXXj+xzMPAW4ISqujXJg4ZqsCRpsmm6XI4GtlXVjVX1Q+Ai4JSxfZ4DXFxVtwJU1ddm20xJ0lKmCfR1wG0jy9v7daMeAdw/yWVJrkpy+qwaKEmazjR96Jmwria8zxOA44EDgE8n+UxVfflub5ScAZwBcPjhh+9+ayVJuzTNGfp24LCR5UOB2yfs85Gq+m5V3QF8Etg4/kZVdWFVbaqqTWvXrt3TNkuSJpgm0K8EjkxyRJL9gFOBS8b2+QDwK0n2SXIf4EnAF2fbVEnSYpbscqmqnUnOAi6lG7a4uaquS3Jmv/2Cqvpiko8AnwfuohvaeO2QDZck3d1U49CraguwZWzdBWPL5wLnzq5pkqTd4Z2iktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSI6aay0XLw+drSronPEOXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJasRUgZ7khCQ3JNmW5OxF9ntikh8nefbsmihJmsaSgZ5kDXA+cCKwATgtyYZd7Pda4NJZN1KStLRpztCPBrZV1Y1V9UPgIuCUCfv9LvBe4GszbJ8kaUrTBPo64LaR5e39up9Isg54JnDBYm+U5IwkW5Ns3bFjx+62VZK0iGkCPRPW1djyG4GXV9WPF3ujqrqwqjZV1aa1a9dO20ZJ0hT2mWKf7cBhI8uHAreP7bMJuCgJwCHASUl2VtX7Z9JKSdKSpgn0K4EjkxwBfBU4FXjO6A5VdcTC6yTvAD5kmEvS8loy0KtqZ5Kz6EavrAE2V9V1Sc7sty/aby5JWh7TnKFTVVuALWPrJgZ5Vf3OPW+WJGl3eaeoJDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqRFTBXqSE5LckGRbkrMnbP8XST7f//tUko2zb6okaTFLBnqSNcD5wInABuC0JBvGdrsJeGpVHQW8Brhw1g2VJC1umjP0o4FtVXVjVf0QuAg4ZXSHqvpUVX2jX/wMcOhsmylJWso0gb4OuG1keXu/blf+FfCXkzYkOSPJ1iRbd+zYMX0rJUlLmibQM2FdTdwxOZYu0F8+aXtVXVhVm6pq09q1a6dvpSRpSftMsc924LCR5UOB28d3SnIU8CfAiVV152yaJ0ma1jRn6FcCRyY5Isl+wKnAJaM7JDkcuBj4rar68uybKUlaypJn6FW1M8lZwKXAGmBzVV2X5Mx++wXAHwAPBN6SBGBnVW0artmSpHHTdLlQVVuALWPrLhh5/Xzg+bNtmiRpd3inqCQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNWKqQE9yQpIbkmxLcvaE7UlyXr/980l+cfZNlSQtZslAT7IGOB84EdgAnJZkw9huJwJH9v/OAN4643ZKkpYwzRn60cC2qrqxqn4IXAScMrbPKcC7qvMZ4OAkD51xWyVJi0hVLb5D8mzghKp6fr/8W8CTquqskX0+BJxTVZf3yx8DXl5VW8fe6wy6M3iARwI37GG7DwHu2MOvvafmVdtjXh21V1vdedZeqcf8sKpaO2nDPlN8cSasG/8tMM0+VNWFwIVT1Fy8QcnWqtp0T99nJdX2mFdH7dVWd561WzzmabpctgOHjSwfCty+B/tIkgY0TaBfCRyZ5Igk+wGnApeM7XMJcHo/2uXJwLeq6u9m3FZJ0iKW7HKpqp1JzgIuBdYAm6vquiRn9tsvALYAJwHbgO8BzxuuycAMum1WYG2PeXXUXm1151m7uWNe8qKoJGll8E5RSWqEgS5JjTDQJakR04xDX9WS7A88nG5c/d9W1ffn3CQ1KMmBQFXVd5expp/txqzoM/T+h2Co994nyevoxti/E/gfwG1JXpdk36HqjtTfL8nzkrw+ybn963sPXbevfWKSTya5I8mOJJ9IctLANZ+e5GEjy3+Q5JoklyQ5Ysjafb1lP+a+7guT3ArcQvf5uiXJCweuOdfPdt+GY5OcleRFSY5dppoPSvKqJH+R5M/71w9ehrqPTfKuJFuTXJnknUmOGqLWig504PoB3/tc4AHAEVX1hKp6PPALwMHA6wesSz/52fXAMcCtdD94xwDXTZgYbda1XwC8Bngl8PN0x/wq4JX91A1D+SNgR9+GpwHPBf4l3T0OFwxYd27HnOT3gacBx1TVA6vqAcCxwIn9tqHM87O9LskV/PR7/XC67/Nnk6wbsO4v091TA/Auul9iAFf024aqewrwPuAyus/z84FPAO/tt8223t4+bDHJv9/VJuD3+h+CIep+BXhEjX2D+tknv1RVRw5Rt6/xMbq5cf5qbP2v0h3zYGc0Sa4HnlJVXx9b/0Dg8qp69EB1r6mqjf3rzcANVfXafvlzVTXYlMxzPOYbgI3jXR1JDgCuqapHDFR3np/t9wEfqKp3jK0/Hfj1qpp5yPXv/xng31TV34ytfxzwtqp60kB1rwFOqaqbx9avp/s+bJxlvZVwhv7HwP2B+439O5Bh21/jH/h+5Y+ZME/NjK0bD/O+9keBhwxcO+PB1te+c+i6SQ5Mci/geOBjI9v2H7r2nI6ZSf3WVfUPwF3Dlp3bZ3vDeJj3td8FPGrAugeNh3lf92q6PBnKvuNh3te9GZh599ZKuCj6OeD9VXXV+IYkzx+w7vVJTu8/aKM1nwt8acC6APdKcu+q+sFY7f0Z/v/s20k2VtU1Y7U3At8ZsO4bgauBbwNfXJipM8njgaGnkZjXMW9PcnxVjf7yIslxDHvM8/xsr5m0sv9FPnHbjCTJ/avqG2MrH8CwJ4Y/SnJ4Vd06VvdhwM5ZF1sJXS6PBO6sqp+ZajLJg6vq7wequw64GPgH4Cq6M5cnAgcAz6yqrw5Rt6/9+8CTgbMWfrv3f6KdB2ytqlcPWPspwLuBt3P34/5t4LkLUyQPVHsd8CC67oa7+nUPAfYb/4GYcd25HHOSxwAfAC4fq/vLdH+mXzdQ3Xl+tt8I3Bd4ycKIniT3Bf4L8P2q+rcD1T0DeAHwUrqTRIAnAK+lm87kbQPVfQbwOrqehtHv9dl0U4y/f6b19vZAX0yS11fVSweucRzwGLo+++vGz6YGrHsW8DLgPv2q7wKvr6r/ugy1Hwy8iJHjBs6vqv87YM2HAd+sqm/1y8cCz6Ab/fHm/uEqg5nHMfd19weeM1b33csxhHAen+1+FM0f0833dAtdwD2MbrTNK4b8f+4vtr+M7pih+16fW1UfHKpmX3cj8B/46ff6WuAN438RzqTWCg/0W6vq8IHe+7iq+nj/+oiqumlk27Oq6uIh6o6/f5L7AVTVkH/6j9Y+qKq+vYttP/On4wzrXkF3dnh7f6Hqo8B/Bo4CflT9A1YGqj2vY35UVX2pf323LrYkT67u6V9D1N0fOJNuhMkXgD+tqpn/+b+L2k+kG7X1zb7+sXQjfb4EvHLStYxWJdln1t/3lXBRdDGTHqwxK6PDt947tm3IIWV3e/+q+s5yhXnvsoUX/WibUTP983DMAVW1MIf+c+n+DH4D3Znc0QPWhfkd85+NvP702La3DFj3ncAmujA/kYGHKo55G/CD/sLv/em6Ht4GfIsBZz9M8oIkR44sb07yrQz8UPskl4+8/u9jmz8763p7/UXR/qLFxE0MG+jZxetJyy0ZPbbx7/1yfb+PA/4TQFXdlQz+7d4bjnk5P2MbquofAyT5UwYIlkWsGTkL/03gwqp6L9247KsHrPti4B0ASZ4DbKQbB/944E3ArwxU974jrx87tm3m/8d7faDz0wsJkw5+yH7V2sXrScuz9qgkn5+wPnRDzga5y6w3r+P+eJL/RTe64/7AQnfXQxn2/xnmd8zzqvujnxTpnncwYKmfsWakq+F4fvqMYRg2j3ZW1cJxP43uofZ3Ah9Nd9fsUJb1/3ivD/SqGvy27134+SSX0IXowmv65aHbdBPw9IFr7MqD+pu5MvKafnnig2ln5CV0Z2wPpbvJZ+GH7yHA7w1YF+Z3zIcmOa+vs/B6oe5gd00CG5MsXDMIcEC/vHDCcNCAtd8DfCLJHXSjbP4PQJKH03W7DOWu/uTgG3S/SP5oZNsBA9Y9OMkz6bq3D07yrH59gJ+bdbEVeVE0ySur6pUD13jqhNUL36xU1ScGrD3onZFL1P7DxbZX1auWqy3LZV7HnOS3l6j7ziHqzlu6x1Q+FPjfI0MXHwEcWFWfW/SL97zm0+j66tcAH6yqF/Trnwq8rKpOHqju2xfbXlUzfbrbSg30wQMv3TwLh1bV+f3yZ+nO1opu/OifD1j7WuCtI7Wv4Kdnii+rqr8Yqva8JPkOk/8EXY6zxl1K8pKqeuM8amt2+tE1fw98p6q+kX6qgX7dq4Yce79Im369v34wu/dcoYH+N9VNKDRkjb8GTq2q2/rlq+n+VLsv8PaqOn7A2p8CfnNOtc9bbPtQN37srQYeGjv+sPW7qap/PkTd1SjJ54BfraqvJ/knwEXA7wKPAx5dVc+eQ5tm/tna6/vQd2E5uiP2WwjU3uX9RZQ7+zvbhrTvHGuPTrHwKmDR7ohVYMgrhr8E3EbXr3zFwLVWu3mNrlnM6hvlkm5q08uq6ivpLsdvBp6V5Bbgd4bqc6MbafETVXXWyOKQF8rmWnu037bvbmiyH3c3DPkn7EOAXwNOo7tb9MPAe4a65X+Vm9fomsWsvlEujIwfpfvgH8XyjB+9IskLquq/ja5M8q8ZftzuPGuPWnn9cXtgif77wUZAVDe74UeAj6R7eMlpwGVJXr0cUzysMnMZXZPkC+z6szXzh2vs9X3oSa6uqsf1r/8MuKKq3tQvD3ZxNMmD6O4S/AF3n8zn3sAzhpoUbN61x9oxt9E2q0Uf5CfThfl6ugd6bJ7HRbrWzWl0zYfp5q75KhOCvapumWm9FRDon6P7wH+DbjKf4xb+JE3yxRro4QMj9RcmMIJuAqOPD1lv3rXHzlbvA3xvYRNzHG3SoiTvpLt78C+Bi6rq2jk3STOW5MXAqXS/SP4nXZfaYH32KyHQ5zJ+VBpakrvoZtGEu5+9+cuzMelmEz21/7c/XRfQRVX15ZnW2dsDHbpZyYD71cjk9P1oj1TV/5tfyyRp96R7aMtm4KiqmulDPfb62RaTvKyqdvY3A/zGwvq+D+wVc2yaJE0lyb5Jnp7k3XRdbF+mu7FptnX29jP00Qtz4xfpvGgnaW+WZGFY6sl0I9Quonuk5ncX/cI9tBKGLa7WaWwlrXyvoJv3/qXL8fCOlRDo85zGVpL2WFUdu5z1VkKXy4/pRgIs3OQxOoxu/6rad15tk6S9yV4f6JKk6ez1o1wkSdMx0CWpEQa6Vo0khyb5QJKvJPnbJG9Kst+E/Y5J8qFdvMeWJAf3/144fKul6RnoWhX6qZcvphsDfCTwCOBA7v5syYW7knepqk6qqm8CBwMGuvYqK2HYojQLxwHfr6q3Qzd1bZJ/B9yU5CbgWLo5Nu4LvBo4KMn7gEcCnwReWFV3JbkZ2AScA/xC/3CEv6qq/7jsRySNMdC1WjyGuz+Niar6dpJb6X4Ofolubo2vJzkGOBrYQDfD50eAZwGjz3I9G3jswtTO0t7ALhetFmGRh1DTnWWP3sn32aq6sX8IxXuApyxDG6V7xEDXanEdXVfJTyQ5CDgMWLh5bZR3JWvFMdC1WnwMuE+S0wGSrAHeQPd4w+9N2P/oJEckuRfdQ4UvH9v+HeB+wzVX2n0GulaF6m6JfibwG0m+Qjd96ffZ9RTMn6a78HktcBPwvrH3uxP46yTXJjl3sIZLu8Fb/yWpEZ6hS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUiP8PG3erj0C8atYAAAAASUVORK5CYII=\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Analyze the ploted bar chart try to find which orbits have high sucess rate.\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### TASK  4: Visualize the relationship between FlightNumber and Orbit type\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "For each orbit, we want to see if there is any relationship between FlightNumber and Orbit type.\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "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(\"FlightNumber\",fontsize=20)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.show()",
            "execution_count": 10,
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 1842.38x360 with 1 Axes>",
                        "image/png": 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                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### TASK  5: Visualize the relationship between Payload and Orbit type\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Similarly, we can plot the Payload vs. Orbit scatter point charts to reveal the relationship between Payload and Orbit type\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "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(\"PayloadMass\",fontsize=20)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.show()",
            "execution_count": 11,
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 1842.38x360 with 1 Axes>",
                        "image/png": 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                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "You should observe that Heavy payloads have a negative influence on GTO orbits and positive on GTO and Polar LEO (ISS) orbits.\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### TASK  6: Visualize the launch success yearly trend\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "The function will help you get the year from the date:\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# A function to Extract years from the date \nyear=[]\ndef Extract_year(date):\n    for i in df[\"Date\"]:\n        year.append(i.split(\"-\")[0])\n    return year\n\nExtract_year(df['Date'])",
            "execution_count": 12,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 12,
                    "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']"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\ntemp_df = df.copy()\ntemp_df['Year'] = year\ntemp_df.groupby('Year')['Class'].mean().plot()\n",
            "execution_count": 13,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 13,
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f2168e1c410>"
                    },
                    "metadata": {}
                },
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 432x288 with 1 Axes>",
                        "image/png": 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\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "you can observe that the sucess rate since 2013 kept increasing till 2020\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Features Engineering\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "features = df[['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights', 'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount', 'Serial']]\nfeatures.head()",
            "execution_count": 14,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 14,
                    "data": {
                        "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  ",
                        "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>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "pd.get_dummies(df, columns=['Orbit', 'LaunchSite','LandingPad', 'Serial'], drop_first=True)",
            "execution_count": 15,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 15,
                    "data": {
                        "text/plain": "    FlightNumber        Date BoosterVersion   PayloadMass      Outcome  \\\n0              1  2010-06-04       Falcon 9   6104.959412    None None   \n1              2  2012-05-22       Falcon 9    525.000000    None None   \n2              3  2013-03-01       Falcon 9    677.000000    None None   \n3              4  2013-09-29       Falcon 9    500.000000  False Ocean   \n4              5  2013-12-03       Falcon 9   3170.000000    None None   \n..           ...         ...            ...           ...          ...   \n85            86  2020-09-03       Falcon 9  15400.000000    True ASDS   \n86            87  2020-10-06       Falcon 9  15400.000000    True ASDS   \n87            88  2020-10-18       Falcon 9  15400.000000    True ASDS   \n88            89  2020-10-24       Falcon 9  15400.000000    True ASDS   \n89            90  2020-11-05       Falcon 9   3681.000000    True ASDS   \n\n    Flights  GridFins  Reused   Legs  Block  ...  Serial_B1048  Serial_B1049  \\\n0         1     False   False  False    1.0  ...             0             0   \n1         1     False   False  False    1.0  ...             0             0   \n2         1     False   False  False    1.0  ...             0             0   \n3         1     False   False  False    1.0  ...             0             0   \n4         1     False   False  False    1.0  ...             0             0   \n..      ...       ...     ...    ...    ...  ...           ...           ...   \n85        2      True    True   True    5.0  ...             0             0   \n86        3      True    True   True    5.0  ...             0             0   \n87        6      True    True   True    5.0  ...             0             0   \n88        3      True    True   True    5.0  ...             0             0   \n89        1      True   False   True    5.0  ...             0             0   \n\n    Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n0              0             0             0             0             0   \n1              0             0             0             0             0   \n2              0             0             0             0             0   \n3              0             0             0             0             0   \n4              0             0             0             0             0   \n..           ...           ...           ...           ...           ...   \n85             0             0             0             0             0   \n86             0             0             0             0             1   \n87             0             1             0             0             0   \n88             0             0             0             0             0   \n89             0             0             0             0             0   \n\n    Serial_B1059  Serial_B1060  Serial_B1062  \n0              0             0             0  \n1              0             0             0  \n2              0             0             0  \n3              0             0             0  \n4              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[90 rows x 82 columns]",
                        "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>Outcome</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>Block</th>\n      <th>...</th>\n      <th>Serial_B1048</th>\n      <th>Serial_B1049</th>\n      <th>Serial_B1050</th>\n      <th>Serial_B1051</th>\n      <th>Serial_B1054</th>\n      <th>Serial_B1056</th>\n      <th>Serial_B1058</th>\n      <th>Serial_B1059</th>\n      <th>Serial_B1060</th>\n      <th>Serial_B1062</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <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>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</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>1</th>\n      <td>2</td>\n      <td>2012-05-22</td>\n      <td>Falcon 9</td>\n      <td>525.000000</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</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>2</th>\n      <td>3</td>\n      <td>2013-03-01</td>\n      <td>Falcon 9</td>\n      <td>677.000000</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</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>3</th>\n      <td>4</td>\n      <td>2013-09-29</td>\n      <td>Falcon 9</td>\n      <td>500.000000</td>\n      <td>False Ocean</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</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>4</th>\n      <td>5</td>\n      <td>2013-12-03</td>\n      <td>Falcon 9</td>\n      <td>3170.000000</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</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>2020-09-03</td>\n      <td>Falcon 9</td>\n      <td>15400.000000</td>\n      <td>True ASDS</td>\n      <td>2</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5.0</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>2020-10-06</td>\n      <td>Falcon 9</td>\n      <td>15400.000000</td>\n      <td>True ASDS</td>\n      <td>3</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5.0</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>2020-10-18</td>\n      <td>Falcon 9</td>\n      <td>15400.000000</td>\n      <td>True ASDS</td>\n      <td>6</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5.0</td>\n      <td>...</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      <td>0</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>89</td>\n      <td>2020-10-24</td>\n      <td>Falcon 9</td>\n      <td>15400.000000</td>\n      <td>True ASDS</td>\n      <td>3</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5.0</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>2020-11-05</td>\n      <td>Falcon 9</td>\n      <td>3681.000000</td>\n      <td>True ASDS</td>\n      <td>1</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>5.0</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>90 rows \u00d7 82 columns</p>\n</div>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### TASK  7: Create dummy variables to categorical columns\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# HINT: Use get_dummies() function on the categorical columns\nfeatures_one_hot = pd.get_dummies(df, columns=['Orbit', 'LaunchSite','LandingPad', 'Serial'], drop_first=False)\nfeatures_one_hot.head()",
            "execution_count": 16,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 16,
                    "data": {
                        "text/plain": "   FlightNumber        Date BoosterVersion  PayloadMass      Outcome  Flights  \\\n0             1  2010-06-04       Falcon 9  6104.959412    None None        1   \n1             2  2012-05-22       Falcon 9   525.000000    None None        1   \n2             3  2013-03-01       Falcon 9   677.000000    None None        1   \n3             4  2013-09-29       Falcon 9   500.000000  False Ocean        1   \n4             5  2013-12-03       Falcon 9  3170.000000    None None        1   \n\n   GridFins  Reused   Legs  Block  ...  Serial_B1048  Serial_B1049  \\\n0     False   False  False    1.0  ...             0             0   \n1     False   False  False    1.0  ...             0             0   \n2     False   False  False    1.0  ...             0             0   \n3     False   False  False    1.0  ...             0             0   \n4     False   False  False    1.0  ...             0             0   \n\n   Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n0             0             0             0             0             0   \n1             0             0             0             0             0   \n2             0             0             0             0             0   \n3             0             0             0             0             0   \n4             0             0             0             0             0   \n\n   Serial_B1059  Serial_B1060  Serial_B1062  \n0             0             0             0  \n1             0             0             0  \n2             0             0             0  \n3             0             0             0  \n4             0             0             0  \n\n[5 rows x 86 columns]",
                        "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>Outcome</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>Block</th>\n      <th>...</th>\n      <th>Serial_B1048</th>\n      <th>Serial_B1049</th>\n      <th>Serial_B1050</th>\n      <th>Serial_B1051</th>\n      <th>Serial_B1054</th>\n      <th>Serial_B1056</th>\n      <th>Serial_B1058</th>\n      <th>Serial_B1059</th>\n      <th>Serial_B1060</th>\n      <th>Serial_B1062</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <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>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</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>1</th>\n      <td>2</td>\n      <td>2012-05-22</td>\n      <td>Falcon 9</td>\n      <td>525.000000</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</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>2</th>\n      <td>3</td>\n      <td>2013-03-01</td>\n      <td>Falcon 9</td>\n      <td>677.000000</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</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>3</th>\n      <td>4</td>\n      <td>2013-09-29</td>\n      <td>Falcon 9</td>\n      <td>500.000000</td>\n      <td>False Ocean</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</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>4</th>\n      <td>5</td>\n      <td>2013-12-03</td>\n      <td>Falcon 9</td>\n      <td>3170.000000</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</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  </tbody>\n</table>\n<p>5 rows \u00d7 86 columns</p>\n</div>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### TASK  8: Cast all numeric columns to `float64`\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Now that our <code>features_one_hot</code> dataframe only contains numbers cast the entire dataframe to variable type <code>float64</code>\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# HINT: use astype function\n## WEIRD\nfeatures_one_hot = features_one_hot.astype('float64', errors='ignore')",
            "execution_count": 19,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "features_one_hot.to_csv('dataset_part_3.csv', index=False)",
            "execution_count": 20,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "<code>features_one_hot.to_csv('dataset_part\\_3.csv', index=False)</code>\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Authors\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Change Log\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "| Date (YYYY-MM-DD) | Version | Changed By | Change Description      |\n| ----------------- | ------- | ---------- | ----------------------- |\n| 2020-09-20        | 1.0     | Joseph     | Modified Multiple Areas |\n| 2020-11-10        | 1.1     | Nayef      | updating the input data |\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Copyright \u00a9 2020 IBM Corporation. All rights reserved.\n"
        }
    ],
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