Spacex Assessment(Data Visualization).ipynb

{"cells":[{"cell_type":"markdown","metadata":{},"source":["<p style=\"text-align:center\">\n","    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\" target=\"_blank\">\n","    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n","    </a>\n","</p>\n"]},{"cell_type":"markdown","metadata":{},"source":["# **SpaceX  Falcon 9 First Stage Landing Prediction**\n"]},{"cell_type":"markdown","metadata":{},"source":["## Assignment: Exploring and Preparing Data\n"]},{"cell_type":"markdown","metadata":{},"source":["Estimated time needed: **70** minutes\n"]},{"cell_type":"markdown","metadata":{},"source":["In this assignment, we will predict if the Falcon 9 first stage will land successfully. SpaceX advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is due to the fact that SpaceX can reuse the first stage.\n","\n","In this lab, you will perform Exploratory Data Analysis and Feature Engineering.\n"]},{"cell_type":"markdown","metadata":{},"source":["Falcon 9 first stage will land successfully\n"]},{"cell_type":"markdown","metadata":{},"source":["![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing\\_1.gif)\n"]},{"cell_type":"markdown","metadata":{},"source":["Several examples of an unsuccessful landing are shown here:\n"]},{"cell_type":"markdown","metadata":{},"source":["![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"]},{"cell_type":"markdown","metadata":{},"source":["Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans.\n"]},{"cell_type":"markdown","metadata":{},"source":["## Objectives\n","\n","Perform exploratory Data Analysis and Feature Engineering using `Pandas` and `Matplotlib`\n","\n","*   Exploratory Data Analysis\n","*   Preparing Data  Feature Engineering\n"]},{"cell_type":"markdown","metadata":{},"source":["***\n"]},{"cell_type":"markdown","metadata":{},"source":["### Import Libraries and Define Auxiliary Functions\n"]},{"cell_type":"markdown","metadata":{},"source":["We will import the following libraries the lab\n"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[],"source":["# Pandas is a software library written for the Python programming language for data manipulation and analysis.\n","import pandas as pd\n","#NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays\n","import numpy as np\n","# Matplotlib is a plotting library for python and pyplot gives us a MatLab like plotting framework. We will use this in our plotter function to plot data.\n","import matplotlib.pyplot as plt\n","#Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics\n","import seaborn as sns"]},{"cell_type":"markdown","metadata":{},"source":["## Exploratory Data Analysis\n"]},{"cell_type":"markdown","metadata":{},"source":["First, let's read the SpaceX dataset into a Pandas dataframe and print its summary\n"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>FlightNumber</th>\n","      <th>Date</th>\n","      <th>BoosterVersion</th>\n","      <th>PayloadMass</th>\n","      <th>Orbit</th>\n","      <th>LaunchSite</th>\n","      <th>Outcome</th>\n","      <th>Flights</th>\n","      <th>GridFins</th>\n","      <th>Reused</th>\n","      <th>Legs</th>\n","      <th>LandingPad</th>\n","      <th>Block</th>\n","      <th>ReusedCount</th>\n","      <th>Serial</th>\n","      <th>Longitude</th>\n","      <th>Latitude</th>\n","      <th>Class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>2010-06-04</td>\n","      <td>Falcon 9</td>\n","      <td>6104.959412</td>\n","      <td>LEO</td>\n","      <td>CCAFS SLC 40</td>\n","      <td>None None</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>B0003</td>\n","      <td>-80.577366</td>\n","      <td>28.561857</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>2012-05-22</td>\n","      <td>Falcon 9</td>\n","      <td>525.000000</td>\n","      <td>LEO</td>\n","      <td>CCAFS SLC 40</td>\n","      <td>None None</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>B0005</td>\n","      <td>-80.577366</td>\n","      <td>28.561857</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>2013-03-01</td>\n","      <td>Falcon 9</td>\n","      <td>677.000000</td>\n","      <td>ISS</td>\n","      <td>CCAFS SLC 40</td>\n","      <td>None None</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>B0007</td>\n","      <td>-80.577366</td>\n","      <td>28.561857</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>2013-09-29</td>\n","      <td>Falcon 9</td>\n","      <td>500.000000</td>\n","      <td>PO</td>\n","      <td>VAFB SLC 4E</td>\n","      <td>False Ocean</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>B1003</td>\n","      <td>-120.610829</td>\n","      <td>34.632093</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>2013-12-03</td>\n","      <td>Falcon 9</td>\n","      <td>3170.000000</td>\n","      <td>GTO</td>\n","      <td>CCAFS SLC 40</td>\n","      <td>None None</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>B1004</td>\n","      <td>-80.577366</td>\n","      <td>28.561857</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n","0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n","1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n","2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n","3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n","4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n","\n","       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n","0    None None        1     False   False  False        NaN    1.0   \n","1    None None        1     False   False  False        NaN    1.0   \n","2    None None        1     False   False  False        NaN    1.0   \n","3  False Ocean        1     False   False  False        NaN    1.0   \n","4    None None        1     False   False  False        NaN    1.0   \n","\n","   ReusedCount Serial   Longitude   Latitude  Class  \n","0            0  B0003  -80.577366  28.561857      0  \n","1            0  B0005  -80.577366  28.561857      0  \n","2            0  B0007  -80.577366  28.561857      0  \n","3            0  B1003 -120.610829  34.632093      0  \n","4            0  B1004  -80.577366  28.561857      0  "]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["df=pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\")\n","df.head(5)"]},{"cell_type":"markdown","metadata":{},"source":["First, let's try to see how the `FlightNumber` (indicating the continuous launch attempts.) and `Payload` variables would affect the launch outcome.\n","\n","We can plot out the <code>FlightNumber</code> vs. <code>PayloadMass</code>and overlay the outcome of the launch. We see that as the flight number increases, the first stage is more likely to land successfully. The payload mass is also important; it seems the more massive the payload, the less likely the first stage will return.\n"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1842.38x360 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["sns.catplot(y=\"PayloadMass\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect=5)\n","plt.xlabel(\"Flight Number\", fontsize=20)\n","plt.ylabel(\"Pay load Mass (kg)\", fontsize=20)\n","plt.title('FlightNumber vs. PayloadMass', fontsize=20)\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["We see that different launch sites have different success rates.  <code>CCAFS LC-40</code>, has a success rate of 60 %, while  <code>KSC LC-39A</code> and <code>VAFB SLC 4E</code> has a success rate of 77%.\n"]},{"cell_type":"markdown","metadata":{},"source":["Next, let's drill down to each site visualize its detailed launch records.\n"]},{"cell_type":"markdown","metadata":{},"source":["### TASK 1: Visualize the relationship between Flight Number and Launch Site\n"]},{"cell_type":"markdown","metadata":{},"source":["Use the function <code>catplot</code> to plot <code>FlightNumber</code> vs <code>LaunchSite</code>, set the  parameter <code>x</code>  parameter to <code>FlightNumber</code>,set the  <code>y</code> to <code>Launch Site</code> and set the parameter <code>hue</code> to <code>'class'</code>\n"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAACgcAAAIWCAYAAAB56M9AAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAACwbElEQVR4nOzdeViU5f7H8c8AAgICKiLihvu+a+5r5r6Vmkumrb8Wy1Nalh3LNK1OptlJ03LNUnMp97Xc9y1R3MXdFHcBZZFlfn/MYZQYEGSYgeH9uq65Lrjv53nu7yCDMPOZ720wGo1GAQAAAAAAAAAAAAAAAAAAh+Fk7wIAAAAAAAAAAAAAAAAAAIB1EQ4EAAAAAAAAAAAAAAAAAMDBEA4EAAAAAAAAAAAAAAAAAMDBEA4EAAAAAAAAAAAAAAAAAMDBEA4EAAAAAAAAAAAAAAAAAMDBEA4EAAAAAAAAAAAAAAAAAMDBEA4EAAAAAAAAAAAAAAAAAMDBEA4EAAAAAAAAAAAAAAAAAMDBEA4EAAAAAAAAAAAAAAAAAMDBEA4EAAAAAADIhmbNmiWDwSCDwaBz586lmH/hhRdkMBgUFBRktxqQNfi6I7do0aKFDAaDWrRoYe9SeNwBAAAAAACHRDgQAAAAAAAgC2zatMkcNEnPbdasWfYuOVtICj0m3VavXv3Ic5KOfeGFF7K+QOR6n376qfl7btOmTfYuBw/ZtGmTXnrpJVWuXFne3t5ycXGRt7e3KlasqC5dumj06NHatWuXEhMT7V0qAAAAAACATRAOBAAAAAAAQJbKTJfDESNGWL8gAA7l7t27evrpp9WyZUvNnDlTx44dU2RkpBISEhQZGakTJ05o+fLl+vjjj9WwYUOtW7cuw2vYolsrAAAAAACAtbnYuwAAAAAAAABH98Ybb+jNN99M85hixYpl6JqzZs3KFd0G9+7dq2XLlqlLly72LgVANtWjRw+tXbtWklS2bFm9+uqrqlevnvLnz6979+7p1KlT2r59u5YtW6Zr165ZvMYLL7xA91EAAAAAAOBwCAcCAAAAAABkMX9/f1WtWtXeZeQ4fn5+unHjhkaMGKHOnTvLYDDYuyQA2czKlSvNwcC2bdtq6dKlcnNzS3ZM48aN9cILL2jKlClasmSJihcvbo9SAQAAAAAAbI5thQEAAAAAAJAtDR06VJIUHBys33//3c7VAMiOli5dav543LhxKYKBD3N2dlb37t1VpUoVW5QGAAAAAABgd4QDAQAAAAAAcqAXXnhBBoNBQUFBaR63fPlytWvXToUKFZKHh4fKly+v999/X2FhYZKkoKAgGQyGdG2nmZiYqB9//FGNGjVS/vz55enpqerVq2vMmDGKiopKcfynn34qg8Ggn376SZJ0/vx5GQyGFLfUDBw4UIULF5YkjRgxQomJiY+s8Z82bdpkXmfTpk1pHpt03KeffprqfUmqNyIiQp9++qmqVasmLy8v+fv7q0OHDtqxY0ey865du6bhw4erSpUq8vT0VMGCBdW1a1cdOHAg3fchNjZWX3/9tWrXri0fHx95e3urfv36+v7775WQkPDI8xMSEvTTTz+pU6dOCgwMlJubmwoWLKgmTZpo/Pjxio6OTvXcFi1ayGAwqEWLFpKkU6dO6a233lK5cuXk4eEhg8Ggc+fOpet+lClTRgaDQY0bN37ksX///becnZ1lMBjMIdGHLV68WN26dVOxYsXk5uamfPnyqXTp0mratKk+/vhj7dmzJ1012cOVK1f0/fffq0ePHipXrpw8PT3l5uamokWLqmvXrpo/f36a3+uzZs0yfy+m9bU/d+6c+ThLW5D/82fInTt39Mknn5i/V319fdWsWTPNmTMnXfcrMjJS48aNU6tWrRQQECBXV1d5e3urVq1aevvtt7V9+/ZHXuPvv//W4MGDVbZsWeXNm1cFCxZU27ZttXr16lTPuXDhgvnjsmXLpqtWS1L7umbm51hMTIwmTpyoJ5980vw18ff3V+vWrTV9+nTFx8c/dr0AAAAAAADpwbbCAAAAAAAADmrgwIH6/vvvk42dOnVKX3/9tX755RetWrUq3deKiopSmzZttH79+mTjISEhCgkJ0bJly7RhwwZ5enpapXZJ8vDw0Icffqh3331XR44c0fz589WnTx+rXf9xXbx4Ua1bt9bJkyfNY/fu3dPq1au1bt06zZs3Tz179tShQ4fUoUMH/f333+bjoqKitGzZMq1du1arV69Wy5Yt01zr9u3b6tGjh/bv359sfM+ePdqzZ4/mz5+vlStXysvLy+L5Fy5cUJcuXXTw4MFk47du3dL27du1fft2TZ48WStXrlT58uXTrGXp0qV67rnndO/evTSPS03fvn01evRo7dy5U+fOnUsz2Dpv3jxzQO65554zjyckJKhPnz5auHBhsuPv37+vu3fv6uzZs9q2bZtWr16tffv2PVadWSkhIUHFihWzGP67fPmyli1bpmXLlmn69On6/fffU/13tbYTJ06oXbt2KcKGW7du1datW7Vz505NnDgx1fP//PNP9enTRzdu3Eg2HhcXp+DgYAUHB2vixIkyGo2pXmP79u3q1q1bsmvExMRo3bp1WrduncaOHav33nsvxXmurq7mj48dO6aaNWs+4t7axsGDB9W1a1edP38+2fj169e1fv16rV+/Xj/88IOWL19uDkEDAAAAAABYG50DAQAAAAAAHNBXX31lDgYWL15ckyZN0u7du7Vlyxb9+9//Vnh4uHr06GGx458lr776qjZu3KgBAwZo5cqV2r9/vxYvXqyGDRtKMoXVRo8eneycN998UyEhIerataskKTAw0BwmfPiWltdff12BgYGSpJEjR6arU15W69mzpy5duqRhw4Zp8+bN2rt3r7755ht5e3srISFBL7/8ss6ePatOnTopOjpaY8aM0bZt27R7926NHDlSrq6uio2N1QsvvKD79++nudZrr72m/fv3q1evXlq1apX27dunuXPnql69epKkLVu26Pnnn7d47s2bN9WkSRMdPHhQbm5ueuutt7Rw4ULt3btXGzdu1LBhw+Th4aHQ0FC1b99e4eHhqdZx4cIF9evXTx4eHvryyy+1fft27dq1S9999126A2xJIT+j0ai5c+emeWzSfJUqVVSjRg3z+OTJk83BwCZNmmjWrFnaunWr/vrrL/3xxx8aN26cnnrqKTk7O6erJltLCse1atVKY8eO1Zo1a7R//35t2rRJM2bMMD+e/vjjDw0cONAmNUVFRalz5866efOmhg8frk2bNmnfvn2aOnWqihUrJkmaNGmS1q5da/H8jRs3qn379rpx44acnZ31wgsvaPHixdq/f7+2b9+uqVOn6plnnlGePHlSreHKlSvq1q2bnJyc9OWXX2rbtm3as2ePxo8fL19fX0nSsGHDdOTIkRTn1q5d2/zxW2+9pevXr2fiq5HS4/wcCw0NVfPmzXX+/Hl5e3tr2LBhWrx4sfbt26e1a9dq4MCBcnFx0d69e9W1a1fFxcVZtWYAAAAAAIAkdA4EAAAAAADIYteuXdPhw4dTnff395e/v7/V1gsLC9OIESMkmbbZ3Llzp/z8/MzzTZs2VYcOHdSyZctHhtOS7NixQz///LP69etnHqtdu7bat2+vunXr6vDhw5o6dao+++wzubi4JLtfSeGePHnyqGrVqhm6L+7u7vr3v/+tgQMH6sSJE5ozZ4769++foWtYW3BwsDZv3qz69eubx+rWraty5cqpU6dOioyMVP369WU0GrVnzx6VKVPGfNwTTzwhPz8/DRw4UBcuXNDKlSv19NNPp7rW3r179fnnn2vYsGHmsTp16qhnz57q1KmT1q5dqyVLlmjVqlXq0KFDsnMHDRqkixcvqmTJktq4caNKlSqVbL5Fixbq2bOnmjZtqjNnzuirr77SmDFjLNZx9uxZBQYGaufOnSpRooR5/OGvwaNUrFhRtWvX1l9//aW5c+fqo48+snjc8ePHzdsuP9w1UJIWLFhgXnfjxo3m77UkrVu31uDBg3Xr1q1012VLzs7OOnHihMXtb5s3b64XX3xRI0aM0KhRo/Tzzz9r+PDhKleuXJbWdP36dd2/f187d+5UlSpVzON16tRRixYtVK1aNcXExOj7779X27Ztk50bExOjfv36KT4+Xh4eHlq5cqV5C+okjRo10iuvvKKLFy+mWsPJkydVsmRJbd++XUWLFjWP16tXT/Xq1VOzZs0UHx+vH3/8Ud9++22yc19++WWNHTtWUVFR2r59u0qWLKmOHTuqefPmql+/vmrUqJGsu2BGPc7PsQEDBig8PFy1atXSunXrkv38laQ2bdqoU6dO6tixo3bv3q1Zs2bp1VdffewaAQAAAAAAUkPnQAAAAAAAgCw2efJkVatWLdXbP7f+zayffvpJMTExkqQJEyakCKZIpsBORjqTPfPMM8mCgUmSOtJJpk51R48efcyqU/fKK6+YA2mjRo1SfHy81dfIiHfeecdiKK5jx44qWbKkJFPg6rPPPksWDEzy4osvyt3dXZJp29a0VK9eXR9++GGKcRcXF02bNs3cje2f30Pnzp3T/PnzJUkTJ05MEQxMUqtWLfP3waxZs9Ks5csvv0wWDHwcSWG/I0eOpNjqOMmcOXMkSQaDQX379k02FxYWJsn0/fvPYODDChQokKk6s4rBYLAYDHzYJ598Ij8/PxmNRi1btswmdX322WfJgoFJypYtq27dukmStm3blmJ+9uzZunz5siTp888/TxEMfFjx4sXTrOG7775LFgxM0qRJE/PjzdLjpXjx4po/f765g2V0dLQWLVqkt99+W0888YS8vb3VrFkzffPNNzYJjW7dulU7duyQZPpZbOnnryS1a9dOPXr0kPToxx4AAAAAAMDjIhwIAAA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size 2558.88x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.catplot(y=\"LaunchSite\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect=5)\n","plt.title('FlightNumber vs. LaunchSite', fontsize=20)\n","plt.ylabel('Launch Site', fontsize=20)\n","plt.xlabel('Flight Number', fontsize=20)\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["As the flight number increases, the first stage is more likely to land successfully."]},{"cell_type":"markdown","metadata":{},"source":["### TASK 2: Visualize the relationship between Payload and Launch Site\n"]},{"cell_type":"markdown","metadata":{},"source":["We also want to observe if there is any relationship between launch sites and their payload mass.\n"]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[{"data":{"image/png":"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size 2558.88x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.catplot(x=\"PayloadMass\", y=\"LaunchSite\", hue=\"Class\", data=df, aspect=5)\n","plt.title('PayloadMass vs. LaunchSite', fontsize=20)\n","plt.xlabel('PayloadMass (kg)', fontsize=20)\n","plt.ylabel('LaunchSite', fontsize=20)\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["Now if you observe Payload Vs. Launch Site scatter point chart you will find for the VAFB-SLC  launchsite there are no  rockets  launched for  heavypayload mass(greater than 10000).\n"]},{"cell_type":"markdown","metadata":{},"source":["### TASK  3: Visualize the relationship between success rate of each orbit type\n"]},{"cell_type":"markdown","metadata":{},"source":["Next, we want to visually check if there are any relationship between success rate and orbit type.\n"]},{"cell_type":"markdown","metadata":{},"source":["Let's create a `bar chart` for the sucess rate of each orbit\n"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"data":{"image/png":"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size 2000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>FlightNumber</th>\n","      <th>PayloadMass</th>\n","      <th>Flights</th>\n","      <th>GridFins</th>\n","      <th>Reused</th>\n","      <th>Legs</th>\n","      <th>Block</th>\n","      <th>ReusedCount</th>\n","      <th>Longitude</th>\n","      <th>Latitude</th>\n","      <th>Class</th>\n","    </tr>\n","    <tr>\n","      <th>Orbit</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>ES-L1</th>\n","      <td>13.000000</td>\n","      <td>570.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>-80.577366</td>\n","      <td>28.561857</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>GEO</th>\n","      <td>83.000000</td>\n","      <td>6104.959412</td>\n","      <td>2.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>5.000000</td>\n","      <td>2.000000</td>\n","      <td>-80.577366</td>\n","      <td>28.561857</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>HEO</th>\n","      <td>49.000000</td>\n","      <td>350.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>4.000000</td>\n","      <td>1.000000</td>\n","      <td>-80.577366</td>\n","      <td>28.561857</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>SSO</th>\n","      <td>60.800000</td>\n","      <td>2060.000000</td>\n","      <td>2.400000</td>\n","      <td>1.000000</td>\n","      <td>0.800000</td>\n","      <td>1.000000</td>\n","      <td>4.600000</td>\n","      <td>3.200000</td>\n","      <td>-112.604136</td>\n","      <td>33.418046</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>VLEO</th>\n","      <td>78.928571</td>\n","      <td>15315.714286</td>\n","      <td>3.928571</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>5.000000</td>\n","      <td>3.928571</td>\n","      <td>-80.586862</td>\n","      <td>28.578358</td>\n","      <td>0.857143</td>\n","    </tr>\n","    <tr>\n","      <th>LEO</th>\n","      <td>20.000000</td>\n","      <td>3882.839748</td>\n","      <td>1.000000</td>\n","      <td>0.571429</td>\n","      <td>0.000000</td>\n","      <td>0.714286</td>\n","      <td>2.142857</td>\n","      <td>0.428571</td>\n","      <td>-80.584963</td>\n","      <td>28.575058</td>\n","      <td>0.714286</td>\n","    </tr>\n","    <tr>\n","      <th>MEO</th>\n","      <td>77.666667</td>\n","      <td>3987.000000</td>\n","      <td>1.000000</td>\n","      <td>0.666667</td>\n","      <td>0.000000</td>\n","      <td>0.666667</td>\n","      <td>5.000000</td>\n","      <td>0.666667</td>\n","      <td>-80.577366</td>\n","      <td>28.561857</td>\n","      <td>0.666667</td>\n","    </tr>\n","    <tr>\n","      <th>PO</th>\n","      <td>36.333333</td>\n","      <td>7583.666667</td>\n","      <td>1.333333</td>\n","      <td>0.888889</td>\n","      <td>0.333333</td>\n","      <td>0.777778</td>\n","      <td>3.222222</td>\n","      <td>1.555556</td>\n","      <td>-120.610829</td>\n","      <td>34.632093</td>\n","      <td>0.666667</td>\n","    </tr>\n","    <tr>\n","      <th>ISS</th>\n","      <td>39.142857</td>\n","      <td>3279.938095</td>\n","      <td>1.238095</td>\n","      <td>0.809524</td>\n","      <td>0.238095</td>\n","      <td>0.857143</td>\n","      <td>3.142857</td>\n","      <td>1.285714</td>\n","      <td>-80.583697</td>\n","      <td>28.572857</td>\n","      <td>0.619048</td>\n","    </tr>\n","    <tr>\n","      <th>GTO</th>\n","      <td>35.037037</td>\n","      <td>5011.994444</td>\n","      <td>1.407407</td>\n","      <td>0.629630</td>\n","      <td>0.333333</td>\n","      <td>0.629630</td>\n","      <td>3.037037</td>\n","      <td>0.962963</td>\n","      <td>-80.586229</td>\n","      <td>28.577258</td>\n","      <td>0.518519</td>\n","    </tr>\n","    <tr>\n","      <th>SO</th>\n","      <td>73.000000</td>\n","      <td>6104.959412</td>\n","      <td>4.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>5.000000</td>\n","      <td>3.000000</td>\n","      <td>-80.603956</td>\n","      <td>28.608058</td>\n","      <td>0.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       FlightNumber   PayloadMass   Flights  GridFins    Reused      Legs  \\\n","Orbit                                                                       \n","ES-L1     13.000000    570.000000  1.000000  1.000000  0.000000  1.000000   \n","GEO       83.000000   6104.959412  2.000000  1.000000  1.000000  1.000000   \n","HEO       49.000000    350.000000  1.000000  1.000000  0.000000  1.000000   \n","SSO       60.800000   2060.000000  2.400000  1.000000  0.800000  1.000000   \n","VLEO      78.928571  15315.714286  3.928571  1.000000  1.000000  1.000000   \n","LEO       20.000000   3882.839748  1.000000  0.571429  0.000000  0.714286   \n","MEO       77.666667   3987.000000  1.000000  0.666667  0.000000  0.666667   \n","PO        36.333333   7583.666667  1.333333  0.888889  0.333333  0.777778   \n","ISS       39.142857   3279.938095  1.238095  0.809524  0.238095  0.857143   \n","GTO       35.037037   5011.994444  1.407407  0.629630  0.333333  0.629630   \n","SO        73.000000   6104.959412  4.000000  0.000000  1.000000  0.000000   \n","\n","          Block  ReusedCount   Longitude   Latitude     Class  \n","Orbit                                                          \n","ES-L1  1.000000     0.000000  -80.577366  28.561857  1.000000  \n","GEO    5.000000     2.000000  -80.577366  28.561857  1.000000  \n","HEO    4.000000     1.000000  -80.577366  28.561857  1.000000  \n","SSO    4.600000     3.200000 -112.604136  33.418046  1.000000  \n","VLEO   5.000000     3.928571  -80.586862  28.578358  0.857143  \n","LEO    2.142857     0.428571  -80.584963  28.575058  0.714286  \n","MEO    5.000000     0.666667  -80.577366  28.561857  0.666667  \n","PO     3.222222     1.555556 -120.610829  34.632093  0.666667  \n","ISS    3.142857     1.285714  -80.583697  28.572857  0.619048  \n","GTO    3.037037     0.962963  -80.586229  28.577258  0.518519  \n","SO     5.000000     3.000000  -80.603956  28.608058  0.000000  "]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["orbit_mean = df.groupby(\"Orbit\").mean().sort_values(by=\"Class\", ascending=False)\n","\n","sns.set(rc={'figure.figsize':(20,6)})\n","sns.barplot(y='Class', x=orbit_mean.index, data=orbit_mean, ci='sd', palette='Set3', orient='v')\n","plt.title('Success Rate of Each Orbit')\n","plt.ylabel('Success Rate')\n","plt.xlabel('Orbit')\n","plt.show()\n","\n","orbit_mean"]},{"cell_type":"markdown","metadata":{},"source":["ES-L1, GEO, HEO, AND SSO have a 100% success rate.\n","GTO has a 50% success rate.\n","SO has a 0% success rate."]},{"cell_type":"markdown","metadata":{},"source":["### TASK  4: Visualize the relationship between FlightNumber and Orbit type\n"]},{"cell_type":"markdown","metadata":{},"source":["For each orbit, we want to see if there is any relationship between FlightNumber and Orbit type.\n"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"data":{"image/png":"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size 2563.5x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.catplot(x=\"FlightNumber\", y=\"Orbit\", hue=\"Class\", data=df, aspect=5)\n","plt.title('Flight Number vs. Orbit type', fontsize=20)\n","plt.xlabel('FlightNumber', fontsize=20)\n","plt.ylabel('Orbit', fontsize=20)\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["For the LEO orbit the Success appears related to the number of flights; on the other hand, there seems to be no relationship between flight number when in GTO orbit.\n"]},{"cell_type":"markdown","metadata":{},"source":["### TASK  5: Visualize the relationship between Payload and Orbit type\n"]},{"cell_type":"markdown","metadata":{},"source":["Similarly, we can plot the Payload vs. Orbit scatter point charts to reveal the relationship between Payload and Orbit type\n"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"data":{"image/png":"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BAAAAAAAAAACgiAgGAgAAAAAAAAAAQBERDAQAAAAAAAAAAIAiIhgIAAAAAAAAAAAARUQwEAAAAAAAAAAAAIqIYCAAAAAAAAAAAAAUkf8fLbys5Nv2rjUAAAAASUVORK5CYII=","text/plain":["<Figure size 2563.5x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.catplot(x=\"PayloadMass\", y=\"Orbit\", hue=\"Class\", data=df, aspect=5)\n","plt.title('PayloadMass vs. Orbit', fontsize=20)\n","plt.xlabel(\"PayloadMass (kg)\", fontsize=20)\n","plt.ylabel(\"Orbit\", fontsize=20)\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["With heavy payloads the successful landing or positive landing rate are more for Polar,LEO and ISS.\n","\n","However for GTO we cannot distinguish this well as both positive landing rate and negative landing(unsuccessful mission) are both there here.\n"]},{"cell_type":"markdown","metadata":{},"source":["### TASK  6: Visualize the launch success yearly trend\n"]},{"cell_type":"markdown","metadata":{},"source":["You can plot a line chart with x axis to be <code>Year</code> and y axis to be average success rate, to get the average launch success trend.\n"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[],"source":["# A function to Extract years from the date \n","year=[]\n","def Extract_year(date):\n","    for i in df[\"Date\"]:\n","        year.append(i.split(\"-\")[0])\n","    return year\n","\n","year = Extract_year(df['Date'])"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[],"source":["# A function to calculate the success rate per year\n","successratelist = []\n","successrate = 0.00\n","records = 1\n","data = 0\n","\n","for x in df['Class']:\n","    data = x + data\n","    successrate = data/records\n","    successratelist.append(successrate)\n","    records= records +1\n","    \n","successratelist = np.array(successratelist)\n","d = {'successrate':successratelist, 'year':year}"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[{"data":{"image/png":"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size 2000x800 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Plotting the line graph of success rate per year\n","sns.set(rc={'figure.figsize':(20,8)})\n","sns.lineplot(data=d, x=\"year\", y=\"successrate\" )\n","\n","plt.xlabel(\"Year\",fontsize=20)\n","plt.title('Average Launch Success Tend', fontsize=20)\n","plt.ylabel(\"Success Rate\",fontsize=20)\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["The sucess rate since 2013 has kept increasing till 2020\n"]},{"cell_type":"markdown","metadata":{},"source":["## Features Engineering\n"]},{"cell_type":"markdown","metadata":{},"source":["By now, you should obtain some preliminary insights about how each important variable would affect the success rate, we will select the features that will be used in success prediction in the future module.\n"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>FlightNumber</th>\n","      <th>PayloadMass</th>\n","      <th>Orbit</th>\n","      <th>LaunchSite</th>\n","      <th>Flights</th>\n","      <th>GridFins</th>\n","      <th>Reused</th>\n","      <th>Legs</th>\n","      <th>LandingPad</th>\n","      <th>Block</th>\n","      <th>ReusedCount</th>\n","      <th>Serial</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>6104.959412</td>\n","      <td>LEO</td>\n","      <td>CCAFS SLC 40</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>B0003</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>525.000000</td>\n","      <td>LEO</td>\n","      <td>CCAFS SLC 40</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>B0005</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>677.000000</td>\n","      <td>ISS</td>\n","      <td>CCAFS SLC 40</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>B0007</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>500.000000</td>\n","      <td>PO</td>\n","      <td>VAFB SLC 4E</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>B1003</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>3170.000000</td>\n","      <td>GTO</td>\n","      <td>CCAFS SLC 40</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>B1004</td>\n","    </tr>\n","    <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","    </tr>\n","    <tr>\n","      <th>85</th>\n","      <td>86</td>\n","      <td>15400.000000</td>\n","      <td>VLEO</td>\n","      <td>KSC LC 39A</td>\n","      <td>2</td>\n","      <td>True</td>\n","      <td>True</td>\n","      <td>True</td>\n","      <td>5e9e3032383ecb6bb234e7ca</td>\n","      <td>5.0</td>\n","      <td>2</td>\n","      <td>B1060</td>\n","    </tr>\n","    <tr>\n","      <th>86</th>\n","      <td>87</td>\n","      <td>15400.000000</td>\n","      <td>VLEO</td>\n","      <td>KSC LC 39A</td>\n","      <td>3</td>\n","      <td>True</td>\n","      <td>True</td>\n","      <td>True</td>\n","      <td>5e9e3032383ecb6bb234e7ca</td>\n","      <td>5.0</td>\n","      <td>2</td>\n","      <td>B1058</td>\n","    </tr>\n","    <tr>\n","      <th>87</th>\n","      <td>88</td>\n","      <td>15400.000000</td>\n","      <td>VLEO</td>\n","      <td>KSC LC 39A</td>\n","      <td>6</td>\n","      <td>True</td>\n","      <td>True</td>\n","      <td>True</td>\n","      <td>5e9e3032383ecb6bb234e7ca</td>\n","      <td>5.0</td>\n","      <td>5</td>\n","      <td>B1051</td>\n","    </tr>\n","    <tr>\n","      <th>88</th>\n","      <td>89</td>\n","      <td>15400.000000</td>\n","      <td>VLEO</td>\n","      <td>CCAFS SLC 40</td>\n","      <td>3</td>\n","      <td>True</td>\n","      <td>True</td>\n","      <td>True</td>\n","      <td>5e9e3033383ecbb9e534e7cc</td>\n","      <td>5.0</td>\n","      <td>2</td>\n","      <td>B1060</td>\n","    </tr>\n","    <tr>\n","      <th>89</th>\n","      <td>90</td>\n","      <td>3681.000000</td>\n","      <td>MEO</td>\n","      <td>CCAFS SLC 40</td>\n","      <td>1</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>5e9e3032383ecb6bb234e7ca</td>\n","      <td>5.0</td>\n","      <td>0</td>\n","      <td>B1062</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>90 rows × 12 columns</p>\n","</div>"],"text/plain":["    FlightNumber   PayloadMass Orbit    LaunchSite  Flights  GridFins  Reused  \\\n","0              1   6104.959412   LEO  CCAFS SLC 40        1     False   False   \n","1              2    525.000000   LEO  CCAFS SLC 40        1     False   False   \n","2              3    677.000000   ISS  CCAFS SLC 40        1     False   False   \n","3              4    500.000000    PO   VAFB SLC 4E        1     False   False   \n","4              5   3170.000000   GTO  CCAFS SLC 40        1     False   False   \n","..           ...           ...   ...           ...      ...       ...     ...   \n","85            86  15400.000000  VLEO    KSC LC 39A        2      True    True   \n","86            87  15400.000000  VLEO    KSC LC 39A        3      True    True   \n","87            88  15400.000000  VLEO    KSC LC 39A        6      True    True   \n","88            89  15400.000000  VLEO  CCAFS SLC 40        3      True    True   \n","89            90   3681.000000   MEO  CCAFS SLC 40        1      True   False   \n","\n","     Legs                LandingPad  Block  ReusedCount Serial  \n","0   False                       NaN    1.0            0  B0003  \n","1   False                       NaN    1.0            0  B0005  \n","2   False                       NaN    1.0            0  B0007  \n","3   False                       NaN    1.0            0  B1003  \n","4   False                       NaN    1.0            0  B1004  \n","..    ...                       ...    ...          ...    ...  \n","85   True  5e9e3032383ecb6bb234e7ca    5.0            2  B1060  \n","86   True  5e9e3032383ecb6bb234e7ca    5.0            2  B1058  \n","87   True  5e9e3032383ecb6bb234e7ca    5.0            5  B1051  \n","88   True  5e9e3033383ecbb9e534e7cc    5.0            2  B1060  \n","89   True  5e9e3032383ecb6bb234e7ca    5.0            0  B1062  \n","\n","[90 rows x 12 columns]"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["features = df[['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights', 'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount', 'Serial']]\n","features"]},{"cell_type":"markdown","metadata":{},"source":["### TASK  7: Create dummy variables to categorical columns\n"]},{"cell_type":"markdown","metadata":{},"source":["Use the function <code>get_dummies</code> and <code>features</code> dataframe to apply OneHotEncoder to the column <code>Orbits</code>, <code>LaunchSite</code>, <code>LandingPad</code>, and <code>Serial</code>. Assign the value to the variable <code>features_one_hot</code>, display the results using the method head. Your result dataframe must include all features including the encoded ones.\n"]},{"cell_type":"code","execution_count":14,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>FlightNumber</th>\n","      <th>PayloadMass</th>\n","      <th>Flights</th>\n","      <th>GridFins</th>\n","      <th>Reused</th>\n","      <th>Legs</th>\n","      <th>Block</th>\n","      <th>ReusedCount</th>\n","      <th>Orbit_ES-L1</th>\n","      <th>Orbit_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>0</td>\n","      <td>0</td>\n","      <td>0</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>0</td>\n","      <td>0</td>\n","      <td>0</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>0</td>\n","      <td>0</td>\n","      <td>0</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>0</td>\n","      <td>0</td>\n","      <td>0</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>0</td>\n","      <td>0</td>\n","      <td>0</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>"],"text/plain":["   FlightNumber  PayloadMass  Flights  GridFins  Reused  Legs  Block  \\\n","0             1  6104.959412        1         0       0     0    1.0   \n","1             2   525.000000        1         0       0     0    1.0   \n","2             3   677.000000        1         0       0     0    1.0   \n","3             4   500.000000        1         0       0     0    1.0   \n","4             5  3170.000000        1         0       0     0    1.0   \n","\n","   ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n","0            0            0          0  ...             0             0   \n","1            0            0          0  ...             0             0   \n","2            0            0          0  ...             0             0   \n","3            0            0          0  ...             0             0   \n","4            0            0          0  ...             0             0   \n","\n","   Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n","0             0             0             0             0             0   \n","1             0             0             0             0             0   \n","2             0             0             0             0             0   \n","3             0             0             0             0             0   \n","4             0             0             0             0             0   \n","\n","   Serial_B1059  Serial_B1060  Serial_B1062  \n","0             0             0             0  \n","1             0             0             0  \n","2             0             0             0  \n","3             0             0             0  \n","4             0             0             0  \n","\n","[5 rows x 80 columns]"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["# Using the SKLEARN library to encode the categorical data\n","from sklearn.preprocessing import LabelEncoder\n","le = LabelEncoder()\n","\n","dfle = features.set_index('FlightNumber')\n","\n","# Get dummy variables for orbit, launchsite, landingpad and series\n","dummies = pd.get_dummies(dfle[['Orbit', 'LaunchSite', 'LandingPad', 'Serial']])\n","\n","# convert remaining data in table to binary form\n","dfle['GridFins']=le.fit_transform(dfle['GridFins'])\n","dfle['Reused']=le.fit_transform(dfle['Reused'])\n","dfle['Legs']=le.fit_transform(dfle['Legs'])\n","\n","# encode variables into a new table\n","features_one_hot = pd.concat([dfle, dummies], axis=1)\n","features_one_hot = features_one_hot.drop(['Orbit', 'LaunchSite', 'LandingPad', 'Serial'], axis=1).reset_index()\n","features_one_hot.head()"]},{"cell_type":"markdown","metadata":{},"source":["### TASK  8: Cast all numeric columns to `float64`\n"]},{"cell_type":"markdown","metadata":{},"source":["Now that our <code>features_one_hot</code> dataframe only contains numbers cast the entire dataframe to variable type <code>float64</code>\n"]},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>FlightNumber</th>\n","      <th>PayloadMass</th>\n","      <th>Flights</th>\n","      <th>GridFins</th>\n","      <th>Reused</th>\n","      <th>Legs</th>\n","      <th>Block</th>\n","      <th>ReusedCount</th>\n","      <th>Orbit_ES-L1</th>\n","      <th>Orbit_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>1.0</td>\n","      <td>6104.959412</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2.0</td>\n","      <td>525.000000</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3.0</td>\n","      <td>677.000000</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.0</td>\n","      <td>500.000000</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5.0</td>\n","      <td>3170.000000</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</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>86.0</td>\n","      <td>15400.000000</td>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>5.0</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>86</th>\n","      <td>87.0</td>\n","      <td>15400.000000</td>\n","      <td>3.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>5.0</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>87</th>\n","      <td>88.0</td>\n","      <td>15400.000000</td>\n","      <td>6.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>5.0</td>\n","      <td>5.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>88</th>\n","      <td>89.0</td>\n","      <td>15400.000000</td>\n","      <td>3.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>5.0</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>89</th>\n","      <td>90.0</td>\n","      <td>3681.000000</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>5.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>90 rows × 80 columns</p>\n","</div>"],"text/plain":["    FlightNumber   PayloadMass  Flights  GridFins  Reused  Legs  Block  \\\n","0            1.0   6104.959412      1.0       0.0     0.0   0.0    1.0   \n","1            2.0    525.000000      1.0       0.0     0.0   0.0    1.0   \n","2            3.0    677.000000      1.0       0.0     0.0   0.0    1.0   \n","3            4.0    500.000000      1.0       0.0     0.0   0.0    1.0   \n","4            5.0   3170.000000      1.0       0.0     0.0   0.0    1.0   \n","..           ...           ...      ...       ...     ...   ...    ...   \n","85          86.0  15400.000000      2.0       1.0     1.0   1.0    5.0   \n","86          87.0  15400.000000      3.0       1.0     1.0   1.0    5.0   \n","87          88.0  15400.000000      6.0       1.0     1.0   1.0    5.0   \n","88          89.0  15400.000000      3.0       1.0     1.0   1.0    5.0   \n","89          90.0   3681.000000      1.0       1.0     0.0   1.0    5.0   \n","\n","    ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n","0           0.0          0.0        0.0  ...           0.0           0.0   \n","1           0.0          0.0        0.0  ...           0.0           0.0   \n","2           0.0          0.0        0.0  ...           0.0           0.0   \n","3           0.0          0.0        0.0  ...           0.0           0.0   \n","4           0.0          0.0        0.0  ...           0.0           0.0   \n","..          ...          ...        ...  ...           ...           ...   \n","85          2.0          0.0        0.0  ...           0.0           0.0   \n","86          2.0          0.0        0.0  ...           0.0           0.0   \n","87          5.0          0.0        0.0  ...           0.0           0.0   \n","88          2.0          0.0        0.0  ...           0.0           0.0   \n","89          0.0          0.0        0.0  ...           0.0           0.0   \n","\n","    Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n","0            0.0           0.0           0.0           0.0           0.0   \n","1            0.0           0.0           0.0           0.0           0.0   \n","2            0.0           0.0           0.0           0.0           0.0   \n","3            0.0           0.0           0.0           0.0           0.0   \n","4            0.0           0.0           0.0           0.0           0.0   \n","..           ...           ...           ...           ...           ...   \n","85           0.0           0.0           0.0           0.0           0.0   \n","86           0.0           0.0           0.0           0.0           1.0   \n","87           0.0           1.0           0.0           0.0           0.0   \n","88           0.0           0.0           0.0           0.0           0.0   \n","89           0.0           0.0           0.0           0.0           0.0   \n","\n","    Serial_B1059  Serial_B1060  Serial_B1062  \n","0            0.0           0.0           0.0  \n","1            0.0           0.0           0.0  \n","2            0.0           0.0           0.0  \n","3            0.0           0.0           0.0  \n","4            0.0           0.0           0.0  \n","..           ...           ...           ...  \n","85           0.0           1.0           0.0  \n","86           0.0           0.0           0.0  \n","87           0.0           0.0           0.0  \n","88           0.0           1.0           0.0  \n","89           0.0           0.0           1.0  \n","\n","[90 rows x 80 columns]"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["features_one_hot.astype(float)"]},{"cell_type":"markdown","metadata":{},"source":["We can now export it to a <b>CSV</b> for the next section,but to make the answers consistent, in the next lab we will provide data in a pre-selected date range.\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["features_one_hot.to_csv('dataset_part\\_3.csv', index=False)"]},{"cell_type":"markdown","metadata":{},"source":["## Authors\n"]},{"cell_type":"markdown","metadata":{},"source":["<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n"]},{"cell_type":"markdown","metadata":{},"source":["<a href=\"https://www.linkedin.com/in/nayefaboutayoun/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">Nayef Abou Tayoun</a> is a Data Scientist at IBM and pursuing a Master of Management in Artificial intelligence degree at Queen's University.\n"]},{"cell_type":"markdown","metadata":{},"source":["## Change Log\n"]},{"cell_type":"markdown","metadata":{},"source":["| Date (YYYY-MM-DD) | Version | Changed By    | Change Description      |\n","| ----------------- | ------- | ------------- | ----------------------- |\n","| 2021-10-12        | 1.1     | Lakshmi Holla | Modified markdown       |\n","| 2020-09-20        | 1.0     | Joseph        | Modified Multiple Areas |\n","| 2020-11-10        | 1.1     | Nayef         | updating the input data |\n"]},{"cell_type":"markdown","metadata":{},"source":["Copyright © 2020 IBM Corporation. All rights reserved.\n"]}],"metadata":{"kernelspec":{"display_name":"Python 3.10.5 64-bit (windows store)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.5"},"vscode":{"interpreter":{"hash":"2faf70c136f86ac887b7e298222016f48a7af03b1a5108908d67115325e0bfdc"}}},"nbformat":4,"nbformat_minor":4}