jupyter-labs-eda-dataviz-v2.ipynb

{"cells":[{"cell_type":"markdown","id":"d17898c3-7825-475a-9637-548d5a0d3a8b","metadata":{},"source":["<p style=\"text-align:center\">\n","    <a href=\"https://skills.network\" 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","id":"c2b87ee7-4642-4b93-a21f-fb35a7bfee01","metadata":{},"source":["# **SpaceX  Falcon 9 First Stage Landing Prediction**\n"]},{"cell_type":"markdown","id":"1781f707-6c4a-4637-9efd-040f7ea0b9e1","metadata":{},"source":[" ## Assignment: Exploring and Preparing Data\n"]},{"cell_type":"markdown","id":"91a72b3b-5ffc-4bd6-95d9-0f396e0fac76","metadata":{},"source":["Estimated time needed: **70** minutes\n"]},{"cell_type":"markdown","id":"ca017490-937a-4127-8b28-bcff474e1836","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","id":"142a0fac-ef6d-43bf-88d8-91cbcb79e50c","metadata":{},"source":["Falcon 9 first stage will land successfully\n"]},{"cell_type":"markdown","id":"1a9caea9-e8c6-435d-b1cc-91b27e61ae42","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","id":"4a91c272-39e9-4372-b054-1c9ccfc7bd0b","metadata":{},"source":["Several examples of an unsuccessful landing are shown here:\n"]},{"cell_type":"markdown","id":"a4cfbd55-1c3c-45de-a40c-1733ec40a63e","metadata":{},"source":["![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"]},{"cell_type":"markdown","id":"c6e44f32-a62b-448d-b5ca-c00cfc46d818","metadata":{},"source":["Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans. \n"]},{"cell_type":"markdown","id":"1f37b435-5f6b-4ce9-82e6-1ce866947710","metadata":{},"source":["  ## Objectives\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","id":"1d18e402-7b7c-4e39-b9b7-80c4ef7084b2","metadata":{},"source":["----\n"]},{"cell_type":"markdown","id":"4a08f39a-ee8b-4a99-a514-bd677b7fe8ab","metadata":{},"source":["Install the below libraries\n"]},{"cell_type":"code","execution_count":1,"id":"829efb32-43d7-4388-b3ff-5eebecfdb073","metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Defaulting to user installation because normal site-packages is not writeable\n","Requirement already satisfied: pandas in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (2.2.2)\n","Requirement already satisfied: numpy>=1.26.0 in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (from pandas) (1.26.4)\n","Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (from pandas) (2.9.0.post0)\n","Requirement already satisfied: pytz>=2020.1 in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (from pandas) (2024.1)\n","Requirement already satisfied: tzdata>=2022.7 in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (from pandas) (2024.1)\n","Requirement already satisfied: six>=1.5 in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n"]},{"name":"stderr","output_type":"stream","text":["\n","[notice] A new release of pip is available: 24.0 -> 24.2\n","[notice] To update, run: python.exe -m pip install --upgrade pip\n"]},{"name":"stdout","output_type":"stream","text":["Defaulting to user installation because normal site-packages is not writeable\n","Requirement already satisfied: numpy in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (1.26.4)\n"]},{"name":"stderr","output_type":"stream","text":["\n","[notice] A new release of pip is available: 24.0 -> 24.2\n","[notice] To update, run: python.exe -m pip install --upgrade pip\n"]},{"name":"stdout","output_type":"stream","text":["Defaulting to user installation because normal site-packages is not writeable\n","Requirement already satisfied: seaborn in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (0.13.2)\n","Requirement already satisfied: numpy!=1.24.0,>=1.20 in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (from seaborn) (1.26.4)\n","Requirement already satisfied: pandas>=1.2 in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (from seaborn) (2.2.2)\n","Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (from seaborn) (3.9.0)\n","Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.2.1)\n","Requirement already satisfied: cycler>=0.10 in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.53.0)\n","Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n","Requirement already satisfied: packaging>=20.0 in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.2)\n","Requirement already satisfied: pillow>=8 in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.2.0)\n","Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.1.2)\n","Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)\n","Requirement already satisfied: pytz>=2020.1 in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (from pandas>=1.2->seaborn) (2024.1)\n","Requirement already satisfied: tzdata>=2022.7 in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (from pandas>=1.2->seaborn) (2024.1)\n","Requirement already satisfied: six>=1.5 in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n"]},{"name":"stderr","output_type":"stream","text":["\n","[notice] A new release of pip is available: 24.0 -> 24.2\n","[notice] To update, run: python.exe -m pip install --upgrade pip\n"]},{"name":"stdout","output_type":"stream","text":["Defaulting to user installation because normal site-packages is not writeable\n","Requirement already satisfied: matplotlib in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (3.9.0)\n","Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib) (1.2.1)\n","Requirement already satisfied: cycler>=0.10 in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib) (4.53.0)\n","Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib) (1.4.5)\n","Requirement already satisfied: numpy>=1.23 in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib) (1.26.4)\n","Requirement already satisfied: packaging>=20.0 in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib) (23.2)\n","Requirement already satisfied: pillow>=8 in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib) (10.2.0)\n","Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib) (3.1.2)\n","Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib) (2.9.0.post0)\n","Requirement already satisfied: six>=1.5 in c:\\users\\sweha\\appdata\\roaming\\python\\python312\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"]},{"name":"stderr","output_type":"stream","text":["\n","[notice] A new release of pip is available: 24.0 -> 24.2\n","[notice] To update, run: python.exe -m pip install --upgrade pip\n"]}],"source":["!pip install pandas\n","!pip install numpy\n","!pip install seaborn\n","!pip install matplotlib"]},{"cell_type":"markdown","id":"130f174c-a349-4a93-88c7-ce856292310b","metadata":{},"source":["### Import Libraries and Define Auxiliary Functions\n"]},{"cell_type":"markdown","id":"3e7d6a64-980c-4bb6-b1d2-523034f02d29","metadata":{},"source":["We will import the following libraries the lab \n","\n"]},{"cell_type":"code","execution_count":2,"id":"f36001b7-4f9b-458b-925e-65aa74dbf62a","metadata":{},"outputs":[],"source":["# andas 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","id":"8bcf4aaf-f222-4398-b054-f916c722d3c5","metadata":{},"source":["## Exploratory Data Analysis \n"]},{"cell_type":"markdown","id":"b5e2d131-d255-4c2b-8030-923133a69409","metadata":{},"source":["First, let's read the SpaceX dataset into a Pandas dataframe and print its summary\n"]},{"cell_type":"code","execution_count":3,"id":"c92837c0-5bff-44d6-97ba-680b87c3bb55","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","\n","# If you were unable to complete the previous lab correctly you can uncomment and load this csv\n","\n","# df = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/dataset_part_2.csv')\n","\n","df.head(5)"]},{"cell_type":"markdown","id":"ad0f5ac5-1bfb-4448-9b8d-22b454da70a4","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,"id":"da8478a8-70ca-4536-aaa6-c17e50d3f61a","metadata":{},"outputs":[{"data":{"image/png":"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size 2558.88x500 with 1 Axes>"]},"metadata":{},"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.show()"]},{"cell_type":"markdown","id":"29ae2ce3-9499-4d33-a3a8-0da1f71a6886","metadata":{},"source":["Next, let's drill down to each site visualize its detailed launch records.\n"]},{"cell_type":"markdown","id":"475cc0dd-a7fc-44f3-ac77-43125fb2f045","metadata":{},"source":["### TASK 1: Visualize the relationship between Flight Number and Launch Site\n"]},{"cell_type":"markdown","id":"3a2a2148-8ed8-4a80-9631-3de6c1e82ee9","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,"id":"b78088b5-6b5b-4fa0-bef0-b8087c0b16fd","metadata":{},"outputs":[{"data":{"image/png":"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size 1558.88x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Plot FlightNumber vs LaunchSite\n","sns.catplot(x=\"FlightNumber\", y=\"LaunchSite\", hue=\"Class\", data=df, aspect=3)\n","plt.xlabel(\"Flight Number\", fontsize=15)\n","plt.ylabel(\"Launch Site\", fontsize=15)\n","plt.title(\"Flight Number vs Launch Site\", fontsize=18)\n","plt.show()"]},{"cell_type":"markdown","id":"46e2775f-b79f-427d-807e-00bd6c1940fa","metadata":{},"source":["Now try to explain the patterns you found in the Flight Number vs. Launch Site scatter point plots.\n"]},{"cell_type":"markdown","id":"2dc4c1ab-bb89-4a37-bb88-b22cac776e1b","metadata":{},"source":["### TASK 2: Visualize the relationship between Payload and Launch Site\n"]},{"cell_type":"markdown","id":"341019df-f220-4eeb-b067-fd16b7d81ec1","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,"id":"f59db012-3819-40b8-8057-e841d1ec0075","metadata":{},"outputs":[{"data":{"image/png":"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size 1558.88x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Plot PayloadMass vs LaunchSite\n","sns.catplot(x=\"PayloadMass\", y=\"LaunchSite\", hue=\"Class\", data=df, aspect=3)\n","plt.xlabel(\"Payload Mass (kg)\", fontsize=15)\n","plt.ylabel(\"Launch Site\", fontsize=15)\n","plt.title(\"Payload Mass vs Launch Site\", fontsize=18)\n","plt.show()\n"]},{"cell_type":"markdown","id":"f964fb86-7225-4caa-9d54-c256bf930fcc","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","id":"1808de4f-413f-4447-af6f-ecae406cdde2","metadata":{},"source":["### TASK  3: Visualize the relationship between success rate of each orbit type\n"]},{"cell_type":"markdown","id":"782a6e77-31f6-467d-a1ba-3669bac6c91e","metadata":{},"source":["Next, we want to visually check if there are any relationship between success rate and orbit type.\n"]},{"cell_type":"markdown","id":"5abd1233-5df0-44ae-971c-e7c4a929b037","metadata":{},"source":["Let's create a `bar chart` for the sucess rate of each orbit\n"]},{"cell_type":"code","execution_count":7,"id":"edaa707d-d5f7-4525-88d8-391fc4c4f72a","metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Group by Orbit and calculate the mean success rate\n","orbit_success_rate = df.groupby('Orbit')['Class'].mean()\n","\n","# Plotting the success rate for each orbit\n","orbit_success_rate.plot(kind='bar')\n","plt.xlabel('Orbit Type', fontsize=15)\n","plt.ylabel('Success Rate', fontsize=15)\n","plt.title('Success Rate by Orbit Type', fontsize=18)\n","plt.show()\n"]},{"cell_type":"markdown","id":"5788dec1-8bf9-44ec-9323-bd0e2dc95783","metadata":{},"source":["Analyze the ploted bar chart try to find which orbits have high sucess rate.\n"]},{"cell_type":"markdown","id":"d225bb8d-ad05-407a-9e05-e4f84015ee50","metadata":{},"source":["### TASK  4: Visualize the relationship between FlightNumber and Orbit type\n"]},{"cell_type":"markdown","id":"75bb8abc-cd86-4c05-a811-e465dbb54ad5","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,"id":"4c127e85-87d1-49a4-b5bd-1621de3aff35","metadata":{},"outputs":[{"data":{"image/png":"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size 1558.88x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Scatter plot for Flight Number vs Orbit, hue by Class (success/failure)\n","sns.catplot(x='FlightNumber', y='Orbit', hue='Class', data=df, aspect=3)\n","plt.xlabel('Flight Number', fontsize=15)\n","plt.ylabel('Orbit Type', fontsize=15)\n","plt.title('Flight Number vs Orbit Type', fontsize=18)\n","plt.show()\n"]},{"cell_type":"markdown","id":"6d31a91f-131f-48dc-b888-771e94023e7e","metadata":{},"source":["You should see that in the LEO orbit the Success appears related to the number of flights; on the other hand, there seems to be no relationship between flight number when in GTO orbit.\n"]},{"cell_type":"markdown","id":"6bd6c917-48f0-4238-98a6-9978626261e3","metadata":{},"source":["### TASK  5: Visualize the relationship between Payload and Orbit type\n"]},{"cell_type":"markdown","id":"14344404-f5b5-440e-b51f-8f3f896d5f94","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,"id":"92c0575f-2a71-4781-825f-b9e9d14d1914","metadata":{},"outputs":[{"data":{"image/png":"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tYqOjoxUTE6OEhAS1b99evXr1UnBwsDZv3qxZs2YVe415l8/q0qVLsbeJi4tT586d9dtvv2nFihWKjo62zi1fvlxdu3ZVbGysmjVrpqioKCUnJ6tjx47XbPZ4enqqdevWOnTokJKSkoqdCwAAAFDiDAAAAFBGSTIF/Uqbm5trwsPDjSTz7LPPGmOMSUpKMnFxcflid+3aZXx9fY23t7c5d+6cdTwtLc14eHiYatWqmczMTJttcnJyTHBwsHFycjK//PKLdTw2NtZIMsOGDbOJf+ONN4wk07RpU5OSkmIdP3HihGnYsKGRZGbMmGGzTVxcnElKSsqX79y5c40kc/vtt9uMZ2RkmGrVqhlJZu7cuTbPxcSJE63P1QsvvJBvn4WRZJycnIwxxsyZM8dIMvPnz7fOT5482Ugya9asMSdPnjSSTEhIiM0+srKyzGeffWauXLliM37q1CnTunVrI8ls3LjROn7p0iXj6upqvLy88q0/KyvLfPfdd9avk5KSrMf89ddfbWIvXbpkvv/++2KvNSAgwDg7O5uLFy8W+lz8fm2rV682lSpVMr6+vmbTpk02senp6aZKlSpGklm4cKHN3HPPPVfkv8W4cePy/TsCAAAApQ1nLgAAAKDcyMnJ0ZEjR/TAAw8oLi5Orq6uGjFihCSpTp06at++fb5tWrRooZiYGGVkZCg2NtY67uPjo4EDB+r06dNasWKFzTZr165VcnKyunfvrho1ahSZ1zvvvCNJmjZtmgICAqzj1atXt34Sf/r06TbbtG/fXnXq1Mm3rxEjRqhDhw7asGGD0tPTreOffPKJTp8+rc6dO1vXLEkWi0X//Oc/VatWrSLzvJb+/fvL1dXV5tI/CxcuVPXq1dW1a9dCt3N2dlbv3r1VsWJFm/Fq1appypQpkmTz/GZkZCgzM1P16tXLt35nZ2d16tTJ+vXp06clXf039PPzs4l1c3NTeHh4sdZ26tQppaamKigoSO7u7kXGL1myRD169JCnp6c2bNigjh072swvXbpUZ8+eVdeuXTV48GCbueeff14hISHX3H+jRo0kSXv27ClW/gAAAIAjODs6AQAAAOCvKuiyRF5eXpo/f77q1atnHcvJydH69ev1/fff6+TJk8rMzJQkHTlyxObPPKNGjdLcuXM1Z84cDRgwwDqedz38kSNHFplbcnKykpOTVa1aNd1111355u+99175+voqMTFRKSkpCgwMtM6dP39eX375pfbs2aOzZ88qKytLknTy5EkZY/Tjjz+qZcuWkqRNmzZJkgYOHJjvGBUrVlT//v319ttvF5lvYXx9fdW9e3etWLFCKSkpOn78uBISEjR27Fg5OTkVuf2ePXu0du1aHTt2TBcvXpQxxnrpqt8/7/7+/qpVq5b27Nmjp59+WiNHjlTdunUL3GejRo1UqVIlrVq1Sq+//rqio6OL1ez5o1OnTkmSKleuXGTsrFmzFBMTo5CQEK1du1b169fPF7NlyxZJsqmZPM7OzurXr5+mTZtW6DGqVKki6X/NEwAAAKA0orkAAACAMm/YsGGSpAoVKsjb21thYWHq27evzZvFP//8s+69917t3bu30P3kvdmdp02bNmrZsqW++eYbHT16VHXq1FFqaqq+/PJL1apVS1FRUUXmduLECUkq9NPqFotFISEhSktL0y+//GJtLnz77bfWMyeKk29Rx6ldu3aRuRZlyJAhWr58uRYvXqyjR49ax67lypUrGj58uBYtWlRozB+f9/nz52vgwIF69dVX9eqrryokJEQREREaOHCg7r77bmuct7e35syZo5EjR2rChAmaMGGCQkNDdfvtt2vo0KHq0KFDsdaVdwaIl5fXNeN+/vlnPfroo3Jzc1NsbGyhz/XJkyclXb1XRUGCg4OveRxvb29JV29CDgAAAJRWXBYJAAAAZd68efM0b948zZ07V2+//bYefPDBfJ9Cf+ihh7R3717169dP27ZtU1pamnJycmSM0ezZsyVJxph8+x41apSMMfrwww8lXX3jOysrSw888ECxPrFfHH888+L8+fO677779Ouvv+r555/XDz/8oAsXLig3N1fGGA0aNKjQfG+ke+65R76+vvr444+1ZMkSNW7c2HrmRGGmTZumRYsWKSwsTF9//bVSU1N15coVGWOUkJAgKf867rjjDiUmJmrhwoUaOnSocnNz9fHHH+uee+5R//79bWIHDRqkpKQk69klaWlpmj17tjp27Khx48YVa10+Pj6S8jc5/sjf319du3bV5cuX9dRTTyk7O7tY+7dXXrPD19f3huwfAAAAuB5oLgAAAKDcu3DhgtatW6eAgAAtWbJEbdu2lY+PjypUuPrrcFJSUqHbDh48WN7e3vroo4+UnZ2tDz74QBUqVNCDDz5YrGPnXabn2LFjhcbkzdWsWVPS1UscnTlzRv369dM//vEPNW7cWB4eHtYmREH5Vq9e/ZrHudbxi8vV1VUDBgzQ7t27lZqaWuRZC5L02WefSZIWLVqkqKgo+fv7W++/cK3n3dvbW4MHD9bHH3+s5ORkxcXFqVatWvr000/11Vdf2cRWq1ZNDz30kJYuXaqUlBR9/fXX8vb21rRp03TgwIEic/T395cknT179ppxLi4u+vLLL3X77bfrk08+UXR0tHJycvLF5f1bHD9+vMD9FDae57fffrOuCwAAACitaC4AAACg3EtPT1dubq6qV6+e72yDrKws6xvgBalUqZKGDBmiEydOaMKECTpy5IgiIyOLvLRNnuDgYAUHB+v06dNav359vvlVq1bpt99+U/369a2XRMp7c7mgmzAnJiZq165d+cbzbnS8dOnSfHPZ2dn69NNPi5VvUYYOHSo/Pz9VrVpV0dHRRcZfay0F5VqY9u3ba+jQoZKk+Pj4QuMsFouioqLUvXt3SSp2cyEwMFDHjx/XxYsXrxnr7u6ulStXKiIiQkuXLtXQoUPzNRjyLsdU0HOek5Oj5cuXX/MYBw8elCTdeuutReYOAAAAOArNBQAAAJR7/v7+8vHxUXx8vPVmu9LVN3onTpyow4cPX3P7UaNGSZLeeustSdLDDz9s1/H//ve/S5KefPJJm3sopKSkaPz48ZKk0aNHW8dDQ0MlScuXL7eJT0tL04MPPmi9sfPvDRgwQH5+ftqwYYPmz59vHTfG6IUXXlBycrJdORemU6dO+vXXX3X69OlC7znwe3lrmTVrls34J598oo8//jhffHJysubNm5fvTf7Lly8rNjZW0v/uZbB7924tX75cV65csYk9e/astm3bZhNbnHXl5ORo9+7dRcZ6eHho1apV6tSpkxYtWqRhw4YpNzfXOj9gwABVqVJF69at0+LFi222femll6z3qyjM9u3bJUkRERHFyh0AAABwBJoLAAAAKPecnZ01YcIEZWdnKyIiQnfddZcGDhyo+vXra9asWYqJibnm9mFhYbrtttskSYGBgerRo4ddxx87dqzuvvtu7du3Tw0aNFDfvn3Vp08fhYaG6uDBg+rdu7cee+wxa3zr1q115513Kjk5WaGhoerTp4/69OmjOnXq6MSJE+rVq1e+Y3h5eenDDz+Uk5OThg8frvbt22vw4MFq1qyZXn/9dbsbItfLhAkT5OTkpKefflqtW7fW4MGD1aZNGw0YMEBjx47NF3/27FmNGDFC1apVU0REhKKjo9W7d28FBwdr69atat26tfr27Svp6qWe+vXrJ39/f3Xr1k1DhgzRvffeq9q1ayspKUk9evRQeHh4sfLMO9Nhw4YNxYqvVKmSvvrqK3Xo0EELFy7UiBEjrA0GHx8fzZkzR05OTho0aJBuu+02DR48WGFhYXrllVc0cuRISVcvs/RH58+f144dO9SoUSPVrVu3WLkAAAAAjkBzAQAAADeFZ555RvPnz9ctt9yiLVu26JtvvlHz5s2tb1gX5Y477pAkjRgxQs7OznYd28nJSV988YWmT5+uunXras2aNVq7dq0aNmyod999V5988on1/g95VqxYoWeffVbVqlXT119/rZ07d2rgwIHaunVroTf67dWrl2JjY3X77bcrPj5eq1atUvXq1bVx40Zrc6Skde7cWZs3b9Ydd9yhpKQkrVy5Ui4uLvr0008LbOrUq1dPb775prp06aLk5GQtX75cmzdvVkhIiN566y1t3LhRrq6ukq5eKumll15Sq1atlJCQoGXLlmnHjh265ZZbNHfuXLsuBXXffffJx8dH//nPf4q9jaenp77++mvddttt+vjjj/Xggw9aGwx9+/bVN998oy5dumjfvn1atWqVatSooU2bNlkvqeXn55dvn5999pkuX77ssGYQAAAAUFwWY4xxdBIAAABAaWaMUePGjXX48GElJibyifJyauzYsXr77be1Y8cOtWrV6oYdJyoqSmvWrNHWrVvVrl07m7nIyEht3rxZycnJBTYfAAAAgNKCMxcAAACAInzyySdKSEjQPffcQ2OhHJs0aZI8PT01ZcqUv7yvX375RampqTZjubm5euutt7RmzRqFhoaqbdu2NvO7du3S2rVrNW7cOBoLAAAAKPU4cwEAAAAoxEMPPaS0tDStXLlS2dnZ2rZt2w39RDscb/LkyXrxxRe1d+9ehYWF/en9LF68WEOGDFGLFi0UEhKizMxMxcfH66effpKHh4dWr16tTp062WzTu3dvxcXF6ccff5Snp+dfXQoAAABwQ9FcAAAAAAphsVjk7OysBg0aaPLkyerfv7+jU0IZceTIEU2ZMkWbNm1SamqqLl++rMDAQHXp0kVPP/20mjRp4ugUAQAAgL+E5gIAAAAAAAAAALAL91wAAAAAAAAAAAB2obkAAAAAAAAAAADsQnMBAAAAAAAAAADYheYCAAAAAAAAAACwC80FAAAAAAAAAABgF5oLAAAAAAAAAADALjQXAAAAAAAAAACAXWguAAAAAAAAAAAAu9BcAAAAAAAAAAAAdvl/jobiJNqa1isAAAAASUVORK5CYII=","text/plain":["<Figure size 1558.88x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Scatter plot for Payload Mass vs Orbit, hue by Class (success/failure)\n","sns.catplot(x='PayloadMass', y='Orbit', hue='Class', data=df, aspect=3)\n","plt.xlabel('Payload Mass (kg)', fontsize=15)\n","plt.ylabel('Orbit Type', fontsize=15)\n","plt.title('Payload Mass vs Orbit Type', fontsize=18)\n","plt.show()\n"]},{"cell_type":"markdown","id":"b9cbe255-b85e-4dac-82f6-87c76c92fda2","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","\n","\n"]},{"cell_type":"markdown","id":"a74ebebe-1ea8-4a8e-a8c6-392356d2422e","metadata":{},"source":["### TASK  6: Visualize the launch success yearly trend\n"]},{"cell_type":"markdown","id":"1e4289c6-0cb9-475f-8034-5f1e93bee512","metadata":{},"source":["You can plot a line chart with x axis to be <code>Year</code> and y axis to be average success rate, to get the average launch success trend. \n"]},{"cell_type":"markdown","id":"47c2b947-5a40-4a30-a951-4fcb79790a7b","metadata":{},"source":["The function will help you get the year from the date:\n"]},{"cell_type":"code","execution_count":10,"id":"911af26a-466f-43e6-90c2-fd55d09ebb0b","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","    "]},{"cell_type":"code","execution_count":11,"id":"7a99edf4-72c7-4e83-af16-f25c57e86be2","metadata":{},"outputs":[],"source":["# Apply the function and create a new Year column\n","df['Year'] = Extract_year(df)"]},{"cell_type":"code","execution_count":12,"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","      <th>Year</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>2010-06-04</td>\n","      <td>Falcon 9</td>\n","      <td>6104.959412</td>\n","      <td>LEO</td>\n","      <td>CCAFS SLC 40</td>\n","      <td>None None</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>B0003</td>\n","      <td>-80.577366</td>\n","      <td>28.561857</td>\n","      <td>0</td>\n","      <td>2010</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>2012-05-22</td>\n","      <td>Falcon 9</td>\n","      <td>525.000000</td>\n","      <td>LEO</td>\n","      <td>CCAFS SLC 40</td>\n","      <td>None None</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>B0005</td>\n","      <td>-80.577366</td>\n","      <td>28.561857</td>\n","      <td>0</td>\n","      <td>2012</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>2013-03-01</td>\n","      <td>Falcon 9</td>\n","      <td>677.000000</td>\n","      <td>ISS</td>\n","      <td>CCAFS SLC 40</td>\n","      <td>None None</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>B0007</td>\n","      <td>-80.577366</td>\n","      <td>28.561857</td>\n","      <td>0</td>\n","      <td>2013</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>2013-09-29</td>\n","      <td>Falcon 9</td>\n","      <td>500.000000</td>\n","      <td>PO</td>\n","      <td>VAFB SLC 4E</td>\n","      <td>False Ocean</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>B1003</td>\n","      <td>-120.610829</td>\n","      <td>34.632093</td>\n","      <td>0</td>\n","      <td>2013</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>2013-12-03</td>\n","      <td>Falcon 9</td>\n","      <td>3170.000000</td>\n","      <td>GTO</td>\n","      <td>CCAFS SLC 40</td>\n","      <td>None None</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>B1004</td>\n","      <td>-80.577366</td>\n","      <td>28.561857</td>\n","      <td>0</td>\n","      <td>2013</td>\n","    </tr>\n","    <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","    </tr>\n","    <tr>\n","      <th>85</th>\n","      <td>86</td>\n","      <td>2020-09-03</td>\n","      <td>Falcon 9</td>\n","      <td>15400.000000</td>\n","      <td>VLEO</td>\n","      <td>KSC LC 39A</td>\n","      <td>True ASDS</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","      <td>-80.603956</td>\n","      <td>28.608058</td>\n","      <td>1</td>\n","      <td>2020</td>\n","    </tr>\n","    <tr>\n","      <th>86</th>\n","      <td>87</td>\n","      <td>2020-10-06</td>\n","      <td>Falcon 9</td>\n","      <td>15400.000000</td>\n","      <td>VLEO</td>\n","      <td>KSC LC 39A</td>\n","      <td>True ASDS</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","      <td>-80.603956</td>\n","      <td>28.608058</td>\n","      <td>1</td>\n","      <td>2020</td>\n","    </tr>\n","    <tr>\n","      <th>87</th>\n","      <td>88</td>\n","      <td>2020-10-18</td>\n","      <td>Falcon 9</td>\n","      <td>15400.000000</td>\n","      <td>VLEO</td>\n","      <td>KSC LC 39A</td>\n","      <td>True ASDS</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","      <td>-80.603956</td>\n","      <td>28.608058</td>\n","      <td>1</td>\n","      <td>2020</td>\n","    </tr>\n","    <tr>\n","      <th>88</th>\n","      <td>89</td>\n","      <td>2020-10-24</td>\n","      <td>Falcon 9</td>\n","      <td>15400.000000</td>\n","      <td>VLEO</td>\n","      <td>CCAFS SLC 40</td>\n","      <td>True ASDS</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","      <td>-80.577366</td>\n","      <td>28.561857</td>\n","      <td>1</td>\n","      <td>2020</td>\n","    </tr>\n","    <tr>\n","      <th>89</th>\n","      <td>90</td>\n","      <td>2020-11-05</td>\n","      <td>Falcon 9</td>\n","      <td>3681.000000</td>\n","      <td>MEO</td>\n","      <td>CCAFS SLC 40</td>\n","      <td>True ASDS</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","      <td>-80.577366</td>\n","      <td>28.561857</td>\n","      <td>1</td>\n","      <td>2020</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>90 rows × 19 columns</p>\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","85            86  2020-09-03       Falcon 9  15400.000000  VLEO    KSC LC 39A   \n","86            87  2020-10-06       Falcon 9  15400.000000  VLEO    KSC LC 39A   \n","87            88  2020-10-18       Falcon 9  15400.000000  VLEO    KSC LC 39A   \n","88            89  2020-10-24       Falcon 9  15400.000000  VLEO  CCAFS SLC 40   \n","89            90  2020-11-05       Falcon 9   3681.000000   MEO  CCAFS SLC 40   \n","\n","        Outcome  Flights  GridFins  Reused   Legs                LandingPad  \\\n","0     None None        1     False   False  False                       NaN   \n","1     None None        1     False   False  False                       NaN   \n","2     None None        1     False   False  False                       NaN   \n","3   False Ocean        1     False   False  False                       NaN   \n","4     None None        1     False   False  False                       NaN   \n","..          ...      ...       ...     ...    ...                       ...   \n","85    True ASDS        2      True    True   True  5e9e3032383ecb6bb234e7ca   \n","86    True ASDS        3      True    True   True  5e9e3032383ecb6bb234e7ca   \n","87    True ASDS        6      True    True   True  5e9e3032383ecb6bb234e7ca   \n","88    True ASDS        3      True    True   True  5e9e3033383ecbb9e534e7cc   \n","89    True ASDS        1      True   False   True  5e9e3032383ecb6bb234e7ca   \n","\n","    Block  ReusedCount Serial   Longitude   Latitude  Class  Year  \n","0     1.0            0  B0003  -80.577366  28.561857      0  2010  \n","1     1.0            0  B0005  -80.577366  28.561857      0  2012  \n","2     1.0            0  B0007  -80.577366  28.561857      0  2013  \n","3     1.0            0  B1003 -120.610829  34.632093      0  2013  \n","4     1.0            0  B1004  -80.577366  28.561857      0  2013  \n","..    ...          ...    ...         ...        ...    ...   ...  \n","85    5.0            2  B1060  -80.603956  28.608058      1  2020  \n","86    5.0            2  B1058  -80.603956  28.608058      1  2020  \n","87    5.0            5  B1051  -80.603956  28.608058      1  2020  \n","88    5.0            2  B1060  -80.577366  28.561857      1  2020  \n","89    5.0            0  B1062  -80.577366  28.561857      1  2020  \n","\n","[90 rows x 19 columns]"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["df"]},{"cell_type":"markdown","id":"2fc4836a-fa9e-4d94-a6a4-924f3c30a9cb","metadata":{},"source":["You can observe that the success rate since 2013 kept increasing till 2017 (stable in 2014) and after 2015 it started increasing.\n"]},{"cell_type":"markdown","id":"715b375a-b365-4444-8dc1-6ed25caa0e87","metadata":{},"source":["## Features Engineering \n"]},{"cell_type":"markdown","id":"32db0e5c-4f4d-48c9-9e9b-80fc86e69f37","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,"id":"bba9d750-b499-47c6-8735-37e02adb7720","metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>FlightNumber</th>\n","      <th>PayloadMass</th>\n","      <th>Orbit</th>\n","      <th>LaunchSite</th>\n","      <th>Flights</th>\n","      <th>GridFins</th>\n","      <th>Reused</th>\n","      <th>Legs</th>\n","      <th>LandingPad</th>\n","      <th>Block</th>\n","      <th>ReusedCount</th>\n","      <th>Serial</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>6104.959412</td>\n","      <td>LEO</td>\n","      <td>CCAFS SLC 40</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>B0003</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>525.000000</td>\n","      <td>LEO</td>\n","      <td>CCAFS SLC 40</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>B0005</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>677.000000</td>\n","      <td>ISS</td>\n","      <td>CCAFS SLC 40</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>B0007</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>500.000000</td>\n","      <td>PO</td>\n","      <td>VAFB SLC 4E</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>B1003</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>3170.000000</td>\n","      <td>GTO</td>\n","      <td>CCAFS SLC 40</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>B1004</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   FlightNumber  PayloadMass Orbit    LaunchSite  Flights  GridFins  Reused  \\\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","    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  "]},"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.head()"]},{"cell_type":"markdown","id":"6fc3744a-3fea-4307-8e24-e06dabb0de7a","metadata":{},"source":[" ### TASK  7: Create dummy variables to categorical columns\n"]},{"cell_type":"markdown","id":"c1c23e1c-8657-4a89-95f5-d7ac32e6ef9b","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,"id":"433f5a81-13be-40ed-93b9-697e063a01d5","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>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>525.000000</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>677.000000</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>500.000000</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>3170.000000</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</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     False   False  False    1.0   \n","1             2   525.000000        1     False   False  False    1.0   \n","2             3   677.000000        1     False   False  False    1.0   \n","3             4   500.000000        1     False   False  False    1.0   \n","4             5  3170.000000        1     False   False  False    1.0   \n","\n","   ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n","0            0        False      False  ...         False         False   \n","1            0        False      False  ...         False         False   \n","2            0        False      False  ...         False         False   \n","3            0        False      False  ...         False         False   \n","4            0        False      False  ...         False         False   \n","\n","   Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n","0         False         False         False         False         False   \n","1         False         False         False         False         False   \n","2         False         False         False         False         False   \n","3         False         False         False         False         False   \n","4         False         False         False         False         False   \n","\n","   Serial_B1059  Serial_B1060  Serial_B1062  \n","0         False         False         False  \n","1         False         False         False  \n","2         False         False         False  \n","3         False         False         False  \n","4         False         False         False  \n","\n","[5 rows x 80 columns]"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["# Use get_dummies() to create dummy variables for categorical columns\n","features_one_hot = pd.get_dummies(features, columns=['Orbit', 'LaunchSite', 'LandingPad', 'Serial'])\n","\n","# Display the first few rows to verify the one-hot encoding\n","features_one_hot.head()\n"]},{"cell_type":"markdown","id":"7b88ede0-d752-4abd-bc4a-58b0f75c28e5","metadata":{},"source":["### TASK  8: Cast all numeric columns to `float64`\n"]},{"cell_type":"markdown","id":"4bb4ab78-bc17-402f-afe3-c0bf92b85136","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,"id":"58ed548e-327b-4f20-bb95-895b76c7941a","metadata":{},"outputs":[{"data":{"text/plain":["FlightNumber    float64\n","PayloadMass     float64\n","Flights         float64\n","GridFins        float64\n","Reused          float64\n","                 ...   \n","Serial_B1056    float64\n","Serial_B1058    float64\n","Serial_B1059    float64\n","Serial_B1060    float64\n","Serial_B1062    float64\n","Length: 80, dtype: object"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["# Cast all numeric columns to float64\n","features_one_hot = features_one_hot.astype('float64')\n","\n","# Display the first few rows to ensure all columns are now float64\n","features_one_hot.dtypes\n"]},{"cell_type":"markdown","id":"1b3ed396-04e6-4366-bbed-c20404f599ef","metadata":{},"source":["We can now export it to a <b>CSV</b> for the next section,but to make the answers consistent, in the next lab we will provide data in a pre-selected date range. \n"]},{"cell_type":"markdown","id":"af2a807a-5ad6-4c53-b5f5-d6f7007bf01d","metadata":{},"source":["<code>features_one_hot.to_csv('dataset_part_3.csv', index=False)</code>\n"]},{"cell_type":"markdown","id":"b3a4db50-8510-4699-86a4-09e9545526e0","metadata":{},"source":["## Authors\n"]},{"cell_type":"markdown","id":"26398fda-3d43-42ac-8773-7e881ab332de","metadata":{},"source":["<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\">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","id":"59721933-458a-4320-845d-d2496c9b6802","metadata":{},"source":["<a href=\"https://www.linkedin.com/in/nayefaboutayoun/\">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","id":"c36d92e7-3ef2-4071-a90e-5ca2adb428db","metadata":{},"source":["## Change Log\n"]},{"cell_type":"markdown","id":"f6bb3003-5168-45e3-a6d8-6447675fa66a","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","id":"1ec9c0ad-79aa-4c97-9f6c-fe3802370459","metadata":{},"source":["Copyright © 2020 IBM Corporation. All rights reserved.\n"]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","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":"3.12.2"},"prev_pub_hash":"c42aaa426e65a4050d51b17d35d98a541a54b6d7cb6243ed1d264ddc0d0a8ba0"},"nbformat":4,"nbformat_minor":4}