jupyter-labs-eda-dataviz.ipynb.jupyterlite.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": 22,
      "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": 23,
      "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": 25,
      "outputs": [
        {
          "execution_count": 25,
          "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": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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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": "code",
      "source": "### TASK 1: Visualize the relationship between Flight Number and Launch Site\nsns.catplot(y=\"LaunchSite\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 6,
      "outputs": [
        {
          "execution_count": 6,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<seaborn.axisgrid.FacetGrid at 0x4b23460>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 2558.88x500 with 1 Axes>",
            "image/png": 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AljWzpyfXH1r3roc505Nbf5SsvFmy/KrF620pN2N2Vc6686V6q7099c5nuXn0+zlw437FaWxh9d8i2eHXyQNnJVUza2vdBtb+wtoM7LPBSlm+Y5tcOertfDBpetbt2zVHbzMgq3QvzCqEv95zaAb36Zwbn34v02ZVZdvBPfO9rVct/fAmC7agVU7bL990fQAAy6aPX17A2CtN18diqpg+q8F6VXVNpsycI+RH8ZSVJRsdWfuAZUBNTU0efe2TBscefvXjJu5mGfXg2cmoPyWzp9ZuGb7WfsluFySt2xW7M4Bl02fvJJXvJz2HLJmgULsuSdxAwjKmuiqZNSVp26X2d65C6LlGstJGyXtPfmmgzDbYLJOE/ACWNa/d0/CyxjVVyQs3Jlv/tOl7Wka88H5FJn3pjum5/v3aJ6Uf8kuSLX6crHtI8s5jtXekrbxF4X5QbwLbDu6ZbQcvmTvnysrKcvAmK+fgTVZeIvNTJIN2TDqvkEz+oG69dcfaC/IAAEtSzyHzH+sxuOn6WExbDeqRJ9+u//vnqt07ZqXlbA0J0FTKysrSqW2rVEyvf12qS/vWRehoGTPmH8nDv/v8efWc5Nl/1t5YuNPZxesLYFk0c3Jy8/eTl+9IUlO709WWxyXbnFLszqB5+/cFyeMXJVM/Spbrn2xzarLONwsz977DkxGHJe8/Xfu8bXmy45nJihsUZn5oRloUuwEAmticmYs3tgyprq7JQ698lKtGvZ0n355YsHm7dpj/RdMFjZWcjt2SIXsk/bdcIgG/T6bMzKg3Psl7n00r+NywyFq1SQ6+vu6H6OV9k29ek3TqUby+AIBF99YjyX8uTl67N6muLnY3C6fXkGT1XevXu67cLG44OGzz/lm9V+c6tTYtW+RXuw9JWTO5WeidT6fm93e/klNvej7/GvN+Zlc1k68dgC/Zf8OVGqzvt0HDdQroqeEN15/5W+2KNwA0nbtOSV6+Pcn/tlyaMz156Ozk+RuK2hY0a6P+lNx3Wm3AL0k+ezu5+XvJy3cWZv6ufZOjHkiOHpUcfkdy4svJBocVZm5oZooe8pswYUJ+9KMfZdVVV03btm3Tt2/f7L777rn//vvrHDd69Ojst99+6dWrV9q1a5dBgwblqKOOyquvvlpvzmHDhqVly5Z58skvL9mZHH744SkrK6v3eP3115Mkzz77bPbYY4/07Nkz7dq1S//+/XPAAQfko48+mu97eOutt3LQQQdlhRVWSLt27bLSSitlzz33zMsvf76lSllZWW655Zb5zlFTU5NLL700m2yySTp16pSuXbtmww03zAUXXJBp07465PDpp59mpZVWSllZWSZNmlRn7LnnnstWW22Vdu3apW/fvjnnnHO+cj5gKTZgu6Rl24bHBjfw4dEy5pMpM7Prn/6dw4c/mdNufTH7XfJ4vnX5fzNjduMvuA3s2Tkbrlx/688WZckBG/Vt9PzNXU1NTc68fWw2P/uBHPT//putz3kwP/rn6IL82UOj9F4rOea/yfceTb7zQPLjZ5MB2xa7KwBgYc2ckgzfJblq92TkKck1+yaXbp1MbXjbwpKz7xXJlickXVZM2nVN1jko+fadSdtOxe7sK5W3b52bfrB5zthjaHZfZ4V8Z8tVcuePt8o2qy+Z1bUL7f6XPswOf3gkf37w9fzziXH58bVj8q3L/5uZc/yOAjQ/J+64enZdu8+8+zVbtijLgRv3y3e2WrW4jZWSD8Ykj/w+efKyZFrhbvzNtPn8zDFrcjJnRuHOA8CCzZqWPD+i4bFnrmraXpZVs6Ylz1ydjPxZ8vRVyaypxe6IQnj8okWrL65eQ2sXIGnTobDzQjNS1O1633777WyxxRbp2rVrzj333Ky11lqZPXt27r777hxzzDHzQnK333579tlnnwwbNizXXHNNBgwYkI8++igjRozIL3/5y1x33XXz5hw3blxGjRqVH/7wh7niiiuy0UYb1TvvTjvtlOHD69451aNHj3z88cfZbrvtsttuu+Xuu+9O165d8/bbb+fWW2/N1KkN/w9m9uzZ2WGHHbL66qvnpptuSp8+ffLee+/lrrvuqhe2W5Bvfetbuemmm/KLX/wif/7zn9OjR488++yzueCCC9K/f//stddeC3z9kUcembXXXjvvv/9+nXplZWV23HHHbL/99rnkkkvy/PPP54gjjkjXrl3z3e9+d6H7A5YiHbslu5yT3H58UvOFFQg2+2GywnrF66tE/Ob2sXlpfGWd2qOvfZK/PPRGTthhtUbP/+eD1s8P//FMnnqndsuqrh1a52e7rJG1V+ra6Lmbu7/9551c/u+35j2vrklue/aDdO/UJqftPrSIncH/9Fm72B0AAIvjkXOSdx6rW5vwfHLvr5K9/lKcnhZF63bJ9qfVPpqhjm1b5bDN++ewzfsXu5VFUlVdk1/c8kJmfWnlvv+8OTE3PP1eDt5k5SJ1BrB42rVumYsOWj/jPp2Wtz6dmtV6dUqfclunz3P78clTV3z+/N7TalfxX3Wbxs+9ytbJxDfr11fcMGnTsfHzA0vEix9UZPhjb+ftT6ZmjT5dcuSWq6R/d//NNmuzpyVV89nRatpnTdvLsqjyg2T4zrWrvM316HnJt+9KylcsWls00pyZyeTxDY9Neqdpe4FlQFlNTU1NsU6+yy675Lnnnssrr7ySjh3r/lA0adKkdO3aNdOmTcvKK6+cLbfcMjfffHO9OeYeN9cZZ5yRl19+Oaeddlo23XTTjB8/Pu3bf/6L6uGHH55JkyY1uKreLbfckv322y/Tp09Pq1YLl38cM2ZM1ltvvbz99ttZeeX5X9wrKyvLzTff3GBY7/rrr88BBxyQW265JXvuuWedsZqamlRWVqa8vHy+c1988cW57rrr8qtf/SrbbbddPvvss3l/JhdffHF+/vOfZ8KECWnTpk2S5JRTTsktt9xSZ6XBL5o5c2Zmzvz8B5zKysr07ds3FRUV6dKly3z7AJqZT15PXrix9m7RwbslK21Q7I6Krrq6JoN/NTKz5tTffmnV7h3zwE+2Kdi5Xv9ocj6bNjtrrViedq1bFmze5myXPz6asV8KWCZJ57at8tzpOzab7bwAACgxF6yVTBpXv966Q/Lz+VyIZpn34gcV2fXCfzc4tv0aPXPZYfVvLAagmXr1nuQf+9Wvd14hOe75pGUj18uY9G5y+Y7J5A8+r7XukBxyY7Ly5o2bG1giRr3xSQ6/4sk6N3x0btcqNx69eVbr1bmIndFoF2+ZfPh8/frmP0p2/E3T97Msufno5Nl/1K+vc2DyjUuavh8K5y+bJR+NrV8fvFvtTRNAwRRtu96JEydm5MiROeaYY+oF/JLMC6ndfffd+eSTT3LSSSc1OM8XA341NTUZPnx4DjnkkAwePDgDBw7MDTfcsNA99e7dO3PmzMnNN9+chc0+9ujRIy1atMgNN9yQqqrF26rjmmuuyeqrr14v4JfUhgMXFPAbO3Zsfv3rX+fqq69Oixb1/zoff/zxbL311vMCfkntdsavvPJKPvus4TsSzj777JSXl8979O1rC8lS9vQ7E/PDfzyTb/zlsZxx24t577Ov3t6Z5uFfY97PvhePytbnPJgTr382b31S4CWruw9Mtjm5djWIJRHw+/SN5D8X1y67PX1S4edfAmpSG/RrSFWBM/EDe3bORv2XF/D7gorpsxusT545J3Pm8/cCAABfaX4/y9fUv7kH5urUdv6BjgWNAdAMjf1Xw/XJHyTvPdn4+bv2Tb73SLLNqclqOyeb/iD53qMCfkuTaROTd59Mps5na2aanXPvfqXeis6TZ8zJhfe/VqSOKJidfpu0+tJKtsuvmmz+4+L0syx55c6G6y/Pp07zse3Pk7IvZVVatU+2/klx+oGlWNFCfq+//npqamoyePDgBR732mu1Pyx91XFJct9992XatGkZNmxYkuSQQw7J5ZdfXu+422+/PZ06dZr32G+/2ju0Nt100/zsZz/LQQcdlO7du2fnnXfOueeemw8//HC+51xxxRVz4YUX5le/+lWWW265fP3rX8+ZZ56ZN99sYOn1BbzH1VdffaGPn2vmzJk58MADc+6556Zfv34NHjNhwoT06tWrTm3u8wkTJjT4mlNPPTUVFRXzHu++++4i90bTGPnChOz/1//k9ufGZ/S4SRn+2NvZ88+P5d2Jgn7N3aWPvJEfXzsmT73zWcZNnJYbn3kve//lsbw/aXqxW1s4D56d/GmDZOQpya0/Ss4fmrx+f7G7+kotW5Rlx6G9Ghzbec0+TdzNsmfr1bo3WN9s1W5p3bJoP7JQIO9Pmp7f3fVyjrzyyfzfyJfzQXP5fgYANH9D6t9UmSRZY4+m7YNmZeVuHbPhyss1OLbvBm6IBViqNLCAwudjBbpBt1OPZJtTkoOuTXY6u/YGbJq/6upk5KnJeYOTy7ev/eftJyTVi7coSNFM/TSptML1XFXVNRk9blKDY0+9bUvXZm+VrZOjH6tduW/IXskOZyZHPVj7fZolq1W7huut51On+Vhjt+RbNycDd0iWH5AM3Ts58u5khfWK3RksdYr2ifnCrpS3KLsJX3HFFTnggAPmbbV74IEH5rHHHssbb7xR57htt902Y8aMmfe48MIL542dddZZmTBhQi655JIMHTo0l1xySQYPHpznn29g2d7/OeaYYzJhwoRcc8012WyzzTJixIgMHTo09957b8Hf4xedeuqpWWONNXLIIYcs1uvnp23btunSpUudB6WnpqYm54x8OVVfWt3q06mzcsnDb8znVTQHM2ZX5aIH6/8dfjZtdob/+60idLSI3n0yefh3qV0X739mTUlu+m4yZ+Z8X1YqfrHrkPTv1qFObd2+XXPMtgOK1NGy40dfH5QVyuv+Mte5Xav8bJc1itQRhfLyhMrsfMEjueThN3L/yx/l4ofeyM5/fDSvfji52K0BAMuCr52UrLB+3Vr31ZIdzyxOPzQbfzxwvQzp8/l1sbatWuTknQZny0EN36C0rHl34rSc9q8X8o2/PJZj/zk6z4zzoTfQTA3du+F6137Jihs2bS80L4//OfnPX5Kq/133rp6dPHV58uh5xe1rYVW8n/x9n+TcAckfBieXbpN8MLrYXRVdyxZl6d6pbYNjvbo0XKeZ6Tagdmve/a9Ktjg2ad+12B2VjllTk/9emlx/WHLnScmHLxZu7nUOaLi+9v6FOwfFs+o2ySE3JMc+k+w3POmzTrE7gqVSWc3iJswaaeLEienevXvOOuusnHrqqfM97uabb87ee++dUaNGZbPNNlvgfCussEJmz56dsrKyefWqqqr87Gc/y1lnnZUkOfzwwzNp0qTccsstC9XnrFmzst5662XDDTfMVVddtVCvqampybBhwzJz5sw8/PDDSWq33b355puz11571Tt+zz33zMsvv5xXXnlloeafa911183zzz8/7/3W1NSkuro6LVu2zM9//vOcccYZOfTQQ1NZWVnn/T744IP5+te/nokTJ2a55Rq+I/mLKisrU15enoqKCoG/EvLplJnZ4Df3NTg2uHfnjDxu6ybuiEJ5/aPJ2f4PjzQ4tumqy+fa787/e2FJGPmz5D8XNTx28I3JoO2btp/FMGtOdUa+OCHvfDI1Q1fskm1W65kWLcq++oU02mdTZ+W6p97Nix9UZuXlO+SgTfplha7tv/qFlLTvXPVk7nvpo3r1HYf0yqWHuliepDYE/f4zSdtOSe+1Cjt3TU3y1sPJe0/VfkCxxh7ujgRg2VNdnbx+b/LhC0m3gcnquyQtWxe7K5qJ0eM+y8Sps7J+v+WyXMc2hZ186qfJy7cnNVW1X5edexd2/iXkrU+m5ht/eSyTps2eV2vZoiyXfmuDbLdGwyvkA5S0e09LHvtj5t243H755KDrkr4bF7UtStyF6ycTG1h0ocuKyQljm76fRVFTk1y8RfLRlwI87bomx45OOixflLZKxZ/ufy3n3ftqvfof9l8ne6+/UhE6giYwc3IyfOdkwhcWP2rRKtl3eDKkACvhz55eGx587e7PawN3SPa/OmnTYf6vA2CeVsU68fLLL59hw4bloosuyrHHHpuOHTvWGZ80aVK6du2aHXfcMd27d88555yTm2++ud48c4+75pprstJKK9UL791zzz0577zz8utf/zotWy76supt2rTJgAEDMnXq1IV+TVlZWQYPHpxRo0Yt1PEHHXRQvvnNb+Zf//pX9tyz7hYyNTU180J2X3bjjTdm+vTPt7p78sknc8QRR+TRRx/NgAG1K05tttlm+fnPf57Zs2endevai9f33ntvVl999YUK+FG6OrVrlU5tW2XKzDn1xnp18cF9c9azS7u0bdUiM+dU1xtbefmODbyi1CwoO16UXPkia9OqRfZYZ4Vit7FMWq5jm3z/a1ZNXNqMeuPTRaovc164Kbnzp8m0T2qf91472e/K2jtKG2v2jOSf30zefPDzWpczksNuLcz8STLhheSJvyYT36q9O2+T7yddbWEHQIlp0SJZbVjtg4bV1CRlbm5qyHr9ltA1tBduSm45Opkzo/b5nSclu5ybbPjtJXO+AvrLg6/XCfgltVvb/d/Il4X8msKsqckzf0vefjTp2D1Z/7BkxfW/+nXA/O1wRrL+ockbDyTtl6sNXgsc8FWmzefa1vzqpeStR+oH/JJkxqTk+RHJJt9r8pZKyTHbDszUWVX52+NvZ+qsqizXoXWO3maAgB+l4d0na28UatkmWXOfpOfgwsz75OV1A35JUj2ndlvywbs2fgv71u2Tg69Pxj+bfPxK0mN1q70BLKKihfyS5KKLLsoWW2yRjTfeOL/+9a+z9tprZ86cObn33ntz8cUX56WXXkrHjh1z2WWXZb/99ssee+yRY489NgMHDswnn3yS66+/PuPGjcu1116byy+/PPvuu2/WXHPNOufo27dvTj311IwcOTK77rrrAvu5/fbbc+211+ab3/xmVltttdTU1OS2227LnXfemeHDhzf4mjFjxuS0007Lt771rQwZMiRt2rTJww8/nCuuuCInn3xynWPfeuutjBkzpk5t0KBB2X///XPzzTfnwAMPzC9+8YvsuOOO6dGjR55//vmcf/75+dGPftTgCoBzg3xzffJJ7QfDa6yxRrp27ZqkNkB4xhln5Mgjj8zJJ5+cF154IX/84x9z/vnnL/DPgtLXtlXLfHOjvrmsge1bD9+8f9M3RMF0adc6B27cL1eOertOvU3LFjmsOfzdDtmrdouCL+vQLVnFCpOwLFq+Y5tMmzW9Xr1bpwKvgtIcffJactNRtRdL5prwXHLtwckPHm/8B+3/vaRuwC9JKt9L7jghOfRfjZs7Sd58OLlmv8+3pXn70eTZfyZH3luwEOGTb0/MdU++m0nTZmfr1bpn/w37pl3rRl5QAgA+N/Gt5J5fJK/enbRqV7tV0vanJ+3s5rBETZtYN+CX1G7xd8cJyYCvJ8utXLzeFsLT89ma99UPp6Ryxux0aWelzCVm5pTkyl1qPxyd65mrk2/81VZn0FjdBhTuhjiWDatsnbx0awP1rzV9L4uq8oMFjL3fdH2UqBYtynLKzoNz7HYD81HlzPTp2i5tWxX4etSUj5Lnrk+mT6z9mlm1GXzdUHz3/DIZdeHnzx85t/ZGoY2Pavzcbz7UcL3yvdrr2IUKE/ZZR7gPYDEVNeS36qqr5plnnslZZ52VE088MePHj0+PHj2ywQYb5OKLL5533J577plRo0bl7LPPzkEHHZTKysr07ds3X//61/Ob3/wmTz/9dJ599tn8v//3/+qdo7y8PNttt10uv/zyrwz5DRkyJB06dMiJJ56Yd999N23bts2gQYNy2WWX5Vvf+laDr1lppZXSv3//nHHGGXn77bdTVlY27/nxxx9f59gTTjih3usfffTRbLnllvnHP/6RSy+9NFdccUXOOuustGrVKoMGDcqhhx6aYcMW/y7z8vLy3HPPPTnmmGOywQYbpHv37vnVr36V7373u4s9J6Xj5J0HZ051Ta59clxmzK5Oz85tc8IOq2XbwT2L3RqN9Itd10jHti3z9/+MS8X02Vl7pfKcvNPgDFmhGXzI0m+TZKsTk0fP+7zWukPtxeZWbYvXFzR3VbOTMdckL99Zu7Xc2vsnQ/b86teVgAM37pdz736lXv2gjfsVoZsSM+aaugG/uT5+KXn3idrvqY0xdj5BvjcfTqZPStp3bdz89532ecBvrmmfJo/8PvnGxQ2/ZhH87T/v5Je3vPD56V76MDc9836u/e6mgn4AUAgzJydX7vr5B7mzZidPXZ58+lpy2G3F7W1p98qddQN+c9VU1/4Mt8WxTd/TIujdpV3e/Lj+zidd2rVKBz+nLVnPXFU34JfUft3c/fNk6DdsRQ7QlL7+i+Ttf9eGtOZqV55s96vCnaO6Knn9/mTKhKTfZkn3QYWZt+/GScrS4O47K9mmeq4ObVqlf/cl8HH6Gw8m1x6UzJ5W+/zR82r/P77PFbWrkENDPhhdN+CXJKlJ7v5Z7QIcnXo0bv7281vBvKzx15EBKIiympqa5rF3IkUzd7vgioqKdOnSDAJGy6CpM+dk4tRZ6VPeLq1a+uF/aVJdXZNZVdXNM8jw8avJa/ckbTrWBpE6LF/sjqD5qqmp3XL11ZF165v+INnp7OL0tAiqqmty5u1j848nxmXWnOq0adUiB2/SL7/cdUhatFjGt4S77cfJ01c2PHbgdcnqOzVu/v+3XfL+Uw0MlCWnvFN74XlxzZ6RnDWfrdi6rpwc99ziz51k2qw52eSs+zN5Zv0Q5O/2XivfFBIFgMZ78vLaleMactSDtv9ckp65Orn1Rw2PbX9GsuVxTdrOorr7xQn53t+erlc/epsBOXmnAq3wQcOu2T957e6Gx773iFVRAJpa5Qe1P1N99FLSY7VkwyOTrn0LM/fEN5O/71P7z7k2ODzZ7YLG7/6QJLcdlzz9pZ3M+m2eHH5747flZP6qq5I/rpNUvFt/bN8rardfhYY8eHby8O8aHtvrkmTdAxs3/5sPJVc3sLDAajslB13XuLkBKIiiruQHFEbHtq3Ssa3/nJdGLVqUpV1z/WW6x2q1jyXgzY+n5OKH3sjodydlha7t8+3N+1vBkqXbG/fXD/gltVuxbvzdZPlVmr6nRdCyRVlO32NofrzdoLwzcVpWXr5Dlutoq94kyarbNhzya90h6bdp4+dfc++GQ34Dt29cwC9JWrapvbtzegNbtXXu3bi5kzz3XkWDAb8keeyNT4X8AKAQPn1jwWNCfkvOajvX/jxVNetLA2XJGrsXpaVFMWxo7/xmrzXzx/tfy8eTZ6ZDm5Y5aON+OXGHJXMdgC/o2H0+A2VJezdYLg1eGl+Zp975LH26tMs2q/dwUzc01sS3kkd/X7viXseeyYZHND4I80VdVki2+2Xh5vuiW46pG/BLaq8j9ds8WeeAxs+/2/nJShvWbhk7Z2YyeJdko6ME/Ja08c82HPBLkpduF/Jj/ha0W1brdo2ff9Vtkp3PSR74TTKz8vPann9p/NwAFIRUEADNytufTM1eFz2Wyhm1wY/XP5qSR179OL/fb53su8FKRe4OlpC3H2u4XlOdvDOq5EN+cy3XsY1w35etsXsycIfk9Xvr1rc/vTBbIGz83dqvn1fu+Ly2/IBktz80fu4WLWovjH9xe/a5Njyy0dN3W8DXyoLGAIBF0Gvo4o3ReJ161H6wftuPk+q5NzaUJcPOSroNKGprC+uQTVfOARv1zYSKGenWqU06tHGpuUmsf1gy5h+pt73iwO0Kt3IURVFVXZOfjHg2N49+f16tf7cOufqITdKvW4cidgZL1viK6Tn7zpdz94sT0qpFWfZYd4WcstMaKe9QgO3HKz9ILt8xmfpR7fPP3k7eeyKpfD/Z+ieNn39Jqng/GTeq4bHnRxQm5FdWlqx3SO2DptNyAde1FhTigjX3SR486wu/P/xPu67JoGGFOccm30vWPTiZ8FzSqVez+d0EYFnhygsAzcpfH3lzXsDvi/5wzyv5xnorpuWyvvV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          },
          "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",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "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": "code",
      "source": "sns.catplot(y=\"PayloadMass\", x=\"LaunchSite\", hue=\"Class\", data=df, aspect = 5)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 27,
      "outputs": [
        {
          "execution_count": 27,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<seaborn.axisgrid.FacetGrid at 0x4718bd0>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 2558.88x500 with 1 Axes>",
            "image/png": 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          },
          "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",
      "metadata": {
        "trusted": true
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "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": "code",
      "source": "sns.barplot(x = \"Orbit\", y = \"Class\", data = df.groupby([\"Orbit\"]).mean().reset_index()[[\"Orbit\", \"Class\"]])",
      "metadata": {
        "trusted": true
      },
      "execution_count": 29,
      "outputs": [
        {
          "name": "stderr",
          "text": "<ipython-input-29-37bae9181e0a>:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n  sns.barplot(x = \"Orbit\", y = \"Class\", data = df.groupby([\"Orbit\"]).mean().reset_index()[[\"Orbit\", \"Class\"]])\n",
          "output_type": "stream"
        },
        {
          "execution_count": 29,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<AxesSubplot:xlabel='Orbit', ylabel='Class'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "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",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "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": "code",
      "source": "sns.catplot(y=\"Orbit\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 30,
      "outputs": [
        {
          "execution_count": 30,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<seaborn.axisgrid.FacetGrid at 0x9d69fd8>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 2558.88x500 with 1 Axes>",
            "image/png": 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          },
          "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",
      "metadata": {
        "trusted": true
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "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": "code",
      "source": "sns.catplot(y=\"Orbit\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 32,
      "outputs": [
        {
          "execution_count": 32,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<seaborn.axisgrid.FacetGrid at 0x45b5690>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 2558.88x500 with 1 Axes>",
            "image/png": 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          },
          "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",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "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['Date'] = year\ndf.head()\n    \nsns.lineplot(data=df, x=\"Date\", y=df[\"Class\"] * 100)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 19,
      "outputs": [
        {
          "execution_count": 19,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<AxesSubplot:xlabel='Date', ylabel='Class'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "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",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "you can observe that the sucess rate since 2013 kept increasing till 2020\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "## Features Engineering\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "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": 21,
      "outputs": [
        {
          "execution_count": 21,
          "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": "one_hot_orbit = pd.get_dummies(features[\"Orbit\"])\none_hot_launch = pd.get_dummies(features[\"LaunchSite\"])\none_hot_landing = pd.get_dummies(features[\"LandingPad\"])\none_hot_serial = pd.get_dummies(features[\"Serial\"])\n\nremainder = features[[\"FlightNumber\",\"PayloadMass\", \"Flights\", \"GridFins\", \"Reused\", \"Legs\", \"Block\",\"ReusedCount\"]]\nfeatures_one_hot = pd.concat([one_hot_launch, one_hot_landing, one_hot_serial, one_hot_orbit], axis=1)\nfeatures_one_hot.head()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 36,
      "outputs": [
        {
          "execution_count": 36,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   CCAFS SLC 40  KSC LC 39A  VAFB SLC 4E  5e9e3032383ecb267a34e7c7  \\\n0             1           0            0                         0   \n1             1           0            0                         0   \n2             1           0            0                         0   \n3             0           0            1                         0   \n4             1           0            0                         0   \n\n   5e9e3032383ecb554034e7c9  5e9e3032383ecb6bb234e7ca  \\\n0                         0                         0   \n1                         0                         0   \n2                         0                         0   \n3                         0                         0   \n4                         0                         0   \n\n   5e9e3032383ecb761634e7cb  5e9e3033383ecbb9e534e7cc  B0003  B0005  ...  GEO  \\\n0                         0                         0      1      0  ...    0   \n1                         0                         0      0      1  ...    0   \n2                         0                         0      0      0  ...    0   \n3                         0                         0      0      0  ...    0   \n4                         0                         0      0      0  ...    0   \n\n   GTO  HEO  ISS  LEO  MEO  PO  SO  SSO  VLEO  \n0    0    0    0    1    0   0   0    0     0  \n1    0    0    0    1    0   0   0    0     0  \n2    0    0    1    0    0   0   0    0     0  \n3    0    0    0    0    0   1   0    0     0  \n4    1    0    0    0    0   0   0    0     0  \n\n[5 rows x 72 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>CCAFS SLC 40</th>\n      <th>KSC LC 39A</th>\n      <th>VAFB SLC 4E</th>\n      <th>5e9e3032383ecb267a34e7c7</th>\n      <th>5e9e3032383ecb554034e7c9</th>\n      <th>5e9e3032383ecb6bb234e7ca</th>\n      <th>5e9e3032383ecb761634e7cb</th>\n      <th>5e9e3033383ecbb9e534e7cc</th>\n      <th>B0003</th>\n      <th>B0005</th>\n      <th>...</th>\n      <th>GEO</th>\n      <th>GTO</th>\n      <th>HEO</th>\n      <th>ISS</th>\n      <th>LEO</th>\n      <th>MEO</th>\n      <th>PO</th>\n      <th>SO</th>\n      <th>SSO</th>\n      <th>VLEO</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\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      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\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      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\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      <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>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>3</th>\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      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\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      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</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      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 72 columns</p>\n</div>"
          },
          "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",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "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": {}
    }
  ]
}