W2B_Exploratory_Data_Analysis_with_Pandas.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": "<p style=\"text-align:center\">\n    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\" target=\"_blank\">\n    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n    </a>\n</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# **SpaceX  Falcon 9 First Stage Landing Prediction**\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Assignment: Exploring and Preparing Data\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 Data  Feature Engineering\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": "import piplite\nawait piplite.install(['numpy'])\nawait piplite.install(['pandas'])\nawait piplite.install(['seaborn'])",
      "metadata": {
        "trusted": true
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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",
      "metadata": {
        "trusted": true
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "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": "from js import fetch\nimport io\n\nURL = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\"\nresp = await fetch(URL)\ndataset_part_2_csv = io.BytesIO((await resp.arrayBuffer()).to_py())\ndf=pd.read_csv(dataset_part_2_csv)\ndf.head(5)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 3,
      "outputs": [
        {
          "execution_count": 3,
          "output_type": "execute_result",
          "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": {}
        }
      ]
    },
    {
      "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()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 4,
      "outputs": [
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          },
          "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",
      "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(data=df, x=\"FlightNumber\", y=\"LaunchSite\", hue=\"Class\", aspect = 5)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Launch Site\", fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x4398078>",
            "image/png": 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I/n5cMvO/N55VrpLs94tki+NL2xctZr2PrfrXkk7eZd0cPmTNPDHq/XTt0DY7DeqZyjY2jqIJCoVknV1qH7A86NQjOfQPyc3fSOb/9/uRylWSg3+bdFmttL01RbFYu3jCU79PauYmhYra7bMPvjRpu0qpu1uxDdghefPB+vXOfZKeg1q/H+DT7fyd5I1/JfNmfFSrqEx2P7vxc2AFUigWi/bAolmqq6tTVVWVadOmpWvX5XulLsrTW5NnZmL1nGy4RlU6t5dXLntvP5EM3afhscEHJ0de27r9UFYmVs/JL+55Lfe+PD7tKity8GZr5Mw910unFvzaMGvegox4rzq9urTPWj0aX6WClnPjc2Pz7RterFdv26aQx8/aPb26tG/eBa7YPXn32QYGCslZbycdmhksnDs9ueFLtT9YJrVvxG1+bHLAJbUr9i3vrj+2dquKj9vw0OSIoaXpZwnMr1mY/7nhxdw67L1FtZ0G9cxlxw7xPQMAQIlNrJ6Tn9/1au76z/i0qSjkwE1Xz/f22SDdOtpeb5laMDe5eMOPQn8fKlQkpzzdMh9Y18xPXvxb8vo9SWWHZNOjk0F7NH9eAGhpc6Ylr99bu1rbenuVzwq4zw5N7ji9fn2bk5J9/1+rt7NSmf1BcuWeyfsjP1Ys1O7KsunnS9YW8Ckmvpo8/n/J+BeT7usk256S9N+m1F1BqxD8o9kE/4Al8sHo5NebNjy2zTeSfX/equ1QPubMr8l+v34kb06uu2LEDuv2yF++0oIrk9Hq5i6oyVG/fzLD3plap37KrgPznb03aP4Frtgtefe5BgYKyfdGJ6t0a978t38ree6a+vW9f5Zsd3Lz5m4NNQuSF65NXr6l9g79DT+XbH5ceYQW/2vUpBl5ddz0rNWjo5U0AQBYub12V/K3Rj6U/uz3kl3/t3nzL1yY/PXI5I37PjH3Wcmu32/e3FAO3h9VewPgahslbdxwBiwjf9glee+F+vV2XZKzxiQVVmNdpmZ/kDxzVfL2Y0nn1ZIhXxYgAmC55acSAFrXqgOS9fZJXr+7br1Nu2TIl0rREWXin8PH1Qv9Jcljb7yfF8Z8kM37l8ndmtTTvrJN/vKVbfKnJ9/Og69OTOf2lTl8yJrZd+PVW+YCgw9pOPg3cNfmh/4W1iQv/b3hsWF/LY/gX5vKZMsTah9lamCvzhnYq3Op2wAAYEXyyu3JQ79IJo5IegxKdjwj2fSoUnf16RbMaXxs/uzmz//63fVDf0nyyEW1P1OUw/aJsDSmjU1u/Eoy5ona5136JvtdmHzmgNL2BayYZn/QcH3e9KRmXlLRoXX7Wdmssmqy8/8k+Z9SdwIAn8rtAAC0vkP/kGx0WFLx3/x5j3WTz/816d0CK3uxwho5cUbjYxMaH6M8dGpfmZM+OzDXf327XPWlrVou9Jck23w9GbRX3Vq3/sn+v2r+3AtrGv/wbN705s8PAAC0vtfuSq4/Lhn/UrJwQTLpleTmryUvXl/qzj7dOrsklas0PLb+fs2f/62HG64vnF+7Kg6sqK77wkehvySZ/l5yw5eSySMbPQVgqa2za8P1/tsnbYX+AICPLPfBv/Hjx+eb3/xm1llnnbRv3z79+vXLgQcemPvvv7/OcS+88EKOOOKIrLbaaunQoUMGDRqUr371q3n99dfrzbn33nunTZs2eeaZZ+qNfelLX0qhUKj3eOONN5IkL774Yg466KD07t07HTp0yIABA3LUUUdl4sSJjb6Gt956K8ccc0z69u2bDh06ZM0118zBBx+cV199ddExhUIht9xyS6NzFIvF/OEPf8g222yTzp07p1u3btlyyy1zySWXZNasWZ/225j3338/a665ZgqFQqZOnVpn7KWXXspOO+2UDh06pF+/fvnFL37xqfPBhyZOn5NL/vV6TvnL8/nVfa9nQvVi7iqmvMz+IHnid8md/5M898dk3qd/rWmyDlXJ4Vcn33kjOX14cuqzyaA9W27+Mjd7Xk2ue3pMfnTLf3LlI2/mg5nzSt3ScmFQ78ZX8xq0mpW+WIzK9skXbki+9M9kzx8nhw9NTn0u6b52C8zdLlnnsw2PDdq7+fMDAACt79FLkhQbqLfAzUPL2iqr1q5CVvjEW/9bfTVZa7vmz9+px2LGejV/flgevfdCMm5Y/frC+ckLf271doCVwM7fSbquWbfWrkuy149L0w8AsNxarrf6HT16dHbYYYd069YtF154YTbeeOPMnz8/99xzT0455ZRFwbk77rgjhx12WPbee+/85S9/ycCBAzNx4sTccMMN+dGPfpTrr//oTswxY8bk8ccfz6mnnpqrr746W221Vb3r7rPPPhk6dGidWq9evTJp0qTsvvvuOeCAA3LPPfekW7duGT16dG677bbMnFl/68EkmT9/fvbcc8+sv/76uemmm7L66qtn7Nixueuuu+oF8BbnuOOOy0033ZQf/vCH+e1vf5tevXrlxRdfzCWXXJIBAwbkkEMOWez5J554YjbZZJO8++67derV1dXZa6+9sscee+Tyyy/P8OHDc8IJJ6Rbt2752te+1uT+WDmNmjQjR17+RN7/WCjpT0+Mzt+/vl0GrdalhJ3RbJNeT67ZP5n5sVDz4/+XfPmfSefeLXedVVatfbDI5Blzc+Tvn8ibkz76d+Xyh0blb1/ddqX//2q/jVfPbx94o952vzus28M2vzTNgB1qHy1t758m1xyQzJ7yUa3Huv/dDgIAACg7k+vfTF5bL5OVvbY4Lum/XTL8hmTB7NqV/vpv2zJzb3p08vBFtfN+XI9ByYAdW+YasLyZ+X7jY7Mmt14flK9isfbfkHYdk6o1P/14ysaMuQvywpgPUrVK22yyZreWm7hqjeSkR5Ln/5i8N6z2BuYtT6jdxQRontlTk4kjkq59k1UHlLobgGYrFIvFBm5dXD7st99+eemll/Laa6+lU6dOdcamTp2abt26ZdasWVlrrbWy44475uabb643x4fHfei8887Lq6++mnPOOSfbbrttxo0bl1VW+Wjrgy996UuZOnVqg6vv3XLLLTniiCMye/bsVFY2LTM5bNiwbL755hk9enTWWmutRo8rFAq5+eabGwzw/f3vf89RRx2VW265JQcffHCdsWKxmOrq6lRVVTU692WXXZbrr78+Z599dnbfffd88MEHi35PLrvssvzgBz/I+PHj065duyTJWWedlVtuuaXOioSL8+H1p02blq5duzbpHFYMp/zl+dw5fFy9+l6DV8sfjt+yBB3RYv5yRDLy3vr1rb9We9c4y8y5t72cax4fXa/+2fV65Y8nbN36DS1nJlbPyS/vfS33vDwh7SorcvCmfXPGnuulU/vl+l4GVgYzJ9fe5f/BW0mfTZJNjkraW4kSAADK0jUHJKMfqV9fbePkG4+2fj/LmzfuT24/PZk2pvb5GkOSQ69IegwsaVuwzMyemvzqM8n8BnZEOfSKZJMjW70lysibDyV3nJFMGVX7fMBOyecuFwBcAfz1qTH56T9fyYy5C5Ikg1fvmsuPHZL+PTqWuDOgUQ/9Inn04v/+m16ovUHmc5cnHWQcgPK13G71O2XKlNx999055ZRT6oX+kiwKrt1zzz2ZPHlyvvvd7zY4z8dDf8ViMUOHDs2xxx6bDTbYIOuuu27+8Y9/NLmnPn36ZMGCBbn55pvT1Lxkr169UlFRkX/84x+pqalp8rU+7i9/+UvWX3/9eqG/pDYwuLjQ34gRI3L++efn2muvTUVF/T/uJ554IjvvvPOi0F9SuxXya6+9lg8++KDBOefOnZvq6uo6D1ZOj4yc1EjdXY6t5cV3pmboY2/lnpfHZ37NwpaZdGFN8sa/Gh577e6WuQaNeuDVhreOf3jkpJb7My5jvbt2yC8O3zQvnrNXnvnBHvnhAYOF/lg+dOqZ7Hh6cuCvk61OFPoDgHI0a0oy/B/JK3ck8+eUuhugiT6YOS9z5i/d+66N2unMpNCmfv2z32nZ65SrdXdPvvVi8vWHk1OfTb76gNAfK7ZVuiW7/qB+fa0dksGHtHY3lJNpY5O/ff6j0F9SGyz/6+dL1xMtYtg7U/ODW4YvCv0lyYhx1Tn5r8+VsCtgsf5zU/LgBR8L8heT1+5M7vpeSdsCaK7lNvj3xhtvpFgsZoMNNljscSNH1m6v8GnHJcm//vWvzJo1K3vvvXeS5Nhjj81VV11V77g77rgjnTt3XvQ44ogjkiTbbrtt/vd//zfHHHNMevbsmX333TcXXnhhJkyY0Og111hjjfzmN7/J2WefnVVXXTW77bZbfvzjH+fNN9/81H4//hrXX3/9Jh//oblz5+boo4/OhRdemP79G176efz48VlttdXq1D58Pn78+AbP+dnPfpaqqqpFj379+i1xb6wYunVs10i9bSt3svJZULMw3/jzczn40sdy3u0j8vU/PZc9fvVQ3pnSwF2nS6pQkVSu0vBYO3eqLWsd2zXwwUKSDpVt0qZQaOVuAABgJfHcH2tX8rnxxOT6LyQXD07efrzUXa0UZs1bkJ/d9Up2+PkD2fan9+fc217OtFnzS93W8mP6+OShC5NbT0meuTKZO6PUHS03Hh05Oftc8nA2//F92fz8+/LDW4a3XABw4G7Jsf9I1toxWWXVpN82yef/lgyuf2P2SquiIll906TnoFJ3Aq1j+1OT429LNvl87epAB1ycHHtTUtnwe+SUmXkzawMhw/5Wu6tDSxn2t4ZXipwwPBnzZMtdZ8bE5PV7kwkvt9ycLNY/nnsnDa0R8593q/PKOAumwHLp+T82XP/PP2r/HQAoU8tt8K+pK+otyU7FV199dY466qhF2/QeffTReeyxxzJq1Kg6x+26664ZNmzYosdvfvObRWMXXHBBxo8fn8svvzwbbrhhLr/88mywwQYZPnx4o9c95ZRTMn78+PzlL3/JdtttlxtuuCEbbrhh7rvvvhZ/jR/3/e9/P5/5zGdy7LHHLtX5i5t32rRpix7vvPNOi85P+Thqq4ZDn5/fquGgKS3nz0+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          },
          "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(data=df, x=\"PayloadMass\", y=\"LaunchSite\", hue=\"Class\", aspect = 1.5)\nplt.xlabel(\"Payload Mass (kg)\",fontsize=10)\nplt.ylabel(\"Launch Site\")\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x5446290>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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",
      "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\nplt.figure(figsize=(6,4))\n\nsuccess_rate = df.groupby('Orbit')['Class'].mean()*100\nsuccess_rate.plot.bar( x = 'Orbit', y = 1)\n\nplt.xlabel('Orbit')\nplt.ylabel('Success Rate (%)')\nplt.tight_layout()\n\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x477fa18>",
            "image/png": 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"
          },
          "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\nplt.figure()\n\ncolors = {0:'tab:blue', 1:'tab:orange'}\ndf.plot.scatter(x='FlightNumber', y='Orbit', c=df['Class'].map(colors))\n\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x938d298>",
            "image/png": 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"
          },
          "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(data=df, x=\"PayloadMass\", y=\"Orbit\", hue=\"Class\", aspect = 1.5)\n\nplt.xlabel(\"Payload Mass (kg)\",fontsize=10)\nplt.ylabel(\"Orbit\")\n\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x9a20408>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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",
      "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():\n    for i in df[\"Date\"]:\n        year.append(i.split(\"-\")[0])\n    return year\nExtract_year()\ndf['Year'] = year\ndf.head()\n    ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 55,
      "outputs": [
        {
          "execution_count": 55,
          "output_type": "execute_result",
          "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  Year  \n0            0  B0003  -80.577366  28.561857      0  2010  \n1            0  B0005  -80.577366  28.561857      0  2012  \n2            0  B0007  -80.577366  28.561857      0  2013  \n3            0  B1003 -120.610829  34.632093      0  2013  \n4            0  B1004  -80.577366  28.561857      0  2013  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>Date</th>\n      <th>BoosterVersion</th>\n      <th>PayloadMass</th>\n      <th>Orbit</th>\n      <th>LaunchSite</th>\n      <th>Outcome</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>LandingPad</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Serial</th>\n      <th>Longitude</th>\n      <th>Latitude</th>\n      <th>Class</th>\n      <th>Year</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <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      <td>2010</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      <td>2012</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      <td>2013</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      <td>2013</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      <td>2013</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "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\nsucc_rate_by_year = df.groupby('Year')['Class'].mean().to_frame().reset_index()\nsucc_rate_by_year.columns = ['Year', 'Success Rate']\nprint(succ_rate_by_year)\n\nsns.lineplot(data=succ_rate_by_year, x='Year', y='Success Rate')\n\nplt.grid(True)\nplt.tight_layout()\n\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 77,
      "outputs": [
        {
          "name": "stdout",
          "text": "   Year  Success Rate\n0  2010      0.000000\n1  2012      0.000000\n2  2013      0.000000\n3  2014      0.333333\n4  2015      0.333333\n5  2016      0.625000\n6  2017      0.833333\n7  2018      0.611111\n8  2019      0.900000\n9  2020      0.842105\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0xa1d9208>",
            "image/png": 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"
          },
          "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()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 78,
      "outputs": [
        {
          "execution_count": 78,
          "output_type": "execute_result",
          "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": {}
        }
      ]
    },
    {
      "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_onehot = pd.get_dummies(features, prefix=['Orbits', 'LaunchSite', 'LandingPad', 'Serial'])\nfeatures_onehot.head()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 84,
      "outputs": [
        {
          "execution_count": 84,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   FlightNumber  PayloadMass  Flights  GridFins  Reused   Legs  Block  \\\n0             1  6104.959412        1     False   False  False    1.0   \n1             2   525.000000        1     False   False  False    1.0   \n2             3   677.000000        1     False   False  False    1.0   \n3             4   500.000000        1     False   False  False    1.0   \n4             5  3170.000000        1     False   False  False    1.0   \n\n   ReusedCount  Orbits_ES-L1  Orbits_GEO  ...  Serial_B1048  Serial_B1049  \\\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_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 80 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>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>Orbits_ES-L1</th>\n      <th>Orbits_GEO</th>\n      <th>...</th>\n      <th>Serial_B1048</th>\n      <th>Serial_B1049</th>\n      <th>Serial_B1050</th>\n      <th>Serial_B1051</th>\n      <th>Serial_B1054</th>\n      <th>Serial_B1056</th>\n      <th>Serial_B1058</th>\n      <th>Serial_B1059</th>\n      <th>Serial_B1060</th>\n      <th>Serial_B1062</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>6104.959412</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <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>1</th>\n      <td>2</td>\n      <td>525.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <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>677.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <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>500.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <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>3170.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <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 × 80 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "### TASK  8: Cast all numeric columns to `float64`",
      "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\nprint('Before:\\n', features_onehot.dtypes)\n\nfeatures_onehot = features_onehot.astype('float64')\n\nprint('\\nAfter:\\n', features_onehot.dtypes)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 90,
      "outputs": [
        {
          "name": "stdout",
          "text": "Before:\n FlightNumber    float64\nPayloadMass     float64\nFlights         float64\nGridFins        float64\nReused          float64\n                 ...   \nSerial_B1056    float64\nSerial_B1058    float64\nSerial_B1059    float64\nSerial_B1060    float64\nSerial_B1062    float64\nLength: 80, dtype: object\n\nAfter:\n FlightNumber    float64\nPayloadMass     float64\nFlights         float64\nGridFins        float64\nReused          float64\n                 ...   \nSerial_B1056    float64\nSerial_B1058    float64\nSerial_B1059    float64\nSerial_B1060    float64\nSerial_B1062    float64\nLength: 80, dtype: object\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "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": "[Pratiksha Verma](https://www.linkedin.com/in/pratiksha-verma-6487561b1/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2022-01-01)\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| 2022-11-09        | 1.0     | Pratiksha Verma | Converted initial version to Jupyterlite|\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### <h3 align=\"center\"> IBM Corporation 2022. All rights reserved. <h3/>\n",
      "metadata": {}
    }
  ]
}