jupyter-labs-eda-dataviz.ipynb

{"cells":[{"attachments":{},"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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2023-01-01\">\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"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["# **SpaceX  Falcon 9 First Stage Landing Prediction**\n"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":[" ## Assignment: Exploring and Preparing Data\n"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["Estimated time needed: **70** minutes\n"]},{"attachments":{},"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"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["Falcon 9 first stage will land successfully\n"]},{"attachments":{},"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"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["Several examples of an unsuccessful landing are shown here:\n"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans. \n"]},{"attachments":{},"cell_type":"markdown","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"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["----\n"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["### Import Libraries and Define Auxiliary Functions\n"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["We will import the following libraries the lab \n","\n"]},{"cell_type":"code","execution_count":1,"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"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## Exploratory Data Analysis \n"]},{"attachments":{},"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":2,"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":2,"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)"]},{"attachments":{},"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":3,"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()"]},{"attachments":{},"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"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["Next, let's drill down to each site visualize its detailed launch records.\n"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["### TASK 1: Visualize the relationship between Flight Number and Launch Site\n"]},{"attachments":{},"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":4,"metadata":{},"outputs":[{"data":{"image/png":"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size 2558.88x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Plot a scatter point chart with x axis to be Flight Number and y axis to be the launch site, and hue to be the class value\n","sns.catplot(x=\"FlightNumber\", y=\"LaunchSite\", hue=\"Class\", data=df, aspect = 5)\n","plt.xlabel(\"Flight Number\",fontsize=20)\n","plt.ylabel(\"Launch Site\",fontsize=20)\n","plt.show()"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["Now try to explain the patterns you found in the Flight Number vs. Launch Site scatter point plots.\n"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["### TASK 2: Visualize the relationship between Payload and Launch Site\n"]},{"attachments":{},"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":9,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Plot a scatter point chart with x axis to be Pay Load Mass (kg) and y axis to be the launch site, and hue to be the class value\n","plt.scatter(x=\"PayloadMass\", y=\"LaunchSite\", data=df)\n","plt.xlabel(\"Payload Mass (kg)\",fontsize=20)\n","plt.ylabel(\"Launch Site\",fontsize=20)\n","plt.show()"]},{"attachments":{},"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"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["### TASK  3: Visualize the relationship between success rate of each orbit type\n"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["Next, we want to visually check if there are any relationship between success rate and orbit type.\n"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["Let's create a `bar chart` for the sucess rate of each orbit\n"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# HINT use groupby method on Orbit column and get the mean of Class column\n","plt.bar(x = df.groupby('Orbit')['Class'].mean().index, height = df.groupby('Orbit')['Class'].mean())\n","plt.xlabel(\"Orbit\",fontsize=20)\n","plt.ylabel(\"Success Rate\",fontsize=20)\n","plt.show()"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["Analyze the ploted bar chart try to find which orbits have high sucess rate.\n"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["### TASK  4: Visualize the relationship between FlightNumber and Orbit type\n"]},{"attachments":{},"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":11,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Plot a scatter point chart with x axis to be FlightNumber and y axis to be the Orbit, and hue to be the class value\n","plt.scatter(x=\"FlightNumber\", y=\"Orbit\", data=df)\n","plt.xlabel(\"Flight Number\",fontsize=20)\n","plt.ylabel(\"Orbit\",fontsize=20)\n","plt.show()\n"]},{"attachments":{},"cell_type":"markdown","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"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["### TASK  5: Visualize the relationship between Payload and Orbit type\n"]},{"attachments":{},"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":12,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Plot a scatter point chart with x axis to be Payload and y axis to be the Orbit, and hue to be the class value\n","plt.scatter(x=\"PayloadMass\", y=\"Orbit\", data=df)\n","plt.xlabel(\"Payload Mass (kg)\",fontsize=20)\n","plt.ylabel(\"Orbit\",fontsize=20)\n","plt.show()"]},{"attachments":{},"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","\n","\n"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["### TASK  6: Visualize the launch success yearly trend\n"]},{"attachments":{},"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"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["The function will help you get the year from the date:\n"]},{"cell_type":"code","execution_count":13,"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":14,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\n","sns.lineplot(x=Extract_year(df[\"Date\"]), y=df[\"Class\"])\n","plt.xlabel(\"Year\",fontsize=20)\n","plt.ylabel(\"Success Rate\",fontsize=20)\n","plt.show()"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["you can observe that the sucess rate since 2013 kept increasing till 2020\n"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## Features Engineering \n"]},{"attachments":{},"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":20,"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":20,"metadata":{},"output_type":"execute_result"}],"source":["features = df[['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights', 'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount', 'Serial']]\n","features.head()"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":[" ### TASK  7: Create dummy variables to categorical columns\n"]},{"attachments":{},"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":16,"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>LaunchSite_CCAFS SLC 40</th>\n","      <th>LaunchSite_KSC LC 39A</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>True</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>True</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>True</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>True</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  LaunchSite_CCAFS SLC 40  LaunchSite_KSC LC 39A  ...   \n","0            0                     True                  False  ...  \\\n","1            0                     True                  False  ...   \n","2            0                     True                  False  ...   \n","3            0                    False                  False  ...   \n","4            0                     True                  False  ...   \n","\n","   Serial_B1048  Serial_B1049  Serial_B1050  Serial_B1051  Serial_B1054   \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_B1056  Serial_B1058  Serial_B1059  Serial_B1060  Serial_B1062  \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","[5 rows x 80 columns]"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["# HINT: Use get_dummies() function on the categorical columns\n","features_one_hot = pd.get_dummies(features, columns=['LaunchSite', 'Orbit', 'LandingPad', 'Serial'])\n","features_one_hot.head()"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["### TASK  8: Cast all numeric columns to `float64`\n"]},{"attachments":{},"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":17,"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>LaunchSite_CCAFS SLC 40</th>\n","      <th>LaunchSite_KSC LC 39A</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>1.0</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>1.0</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>1.0</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>1.0</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>1.0</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>1.0</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>1.0</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>1.0</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>1.0</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  LaunchSite_CCAFS SLC 40  LaunchSite_KSC LC 39A  ...   \n","0           0.0                      1.0                    0.0  ...  \\\n","1           0.0                      1.0                    0.0  ...   \n","2           0.0                      1.0                    0.0  ...   \n","3           0.0                      0.0                    0.0  ...   \n","4           0.0                      1.0                    0.0  ...   \n","..          ...                      ...                    ...  ...   \n","85          2.0                      0.0                    1.0  ...   \n","86          2.0                      0.0                    1.0  ...   \n","87          5.0                      0.0                    1.0  ...   \n","88          2.0                      1.0                    0.0  ...   \n","89          0.0                      1.0                    0.0  ...   \n","\n","    Serial_B1048  Serial_B1049  Serial_B1050  Serial_B1051  Serial_B1054   \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           0.0   \n","87           0.0           0.0           0.0           1.0           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_B1056  Serial_B1058  Serial_B1059  Serial_B1060  Serial_B1062  \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           1.0           0.0  \n","86           0.0           1.0           0.0           0.0           0.0  \n","87           0.0           0.0           0.0           0.0           0.0  \n","88           0.0           0.0           0.0           1.0           0.0  \n","89           0.0           0.0           0.0           0.0           1.0  \n","\n","[90 rows x 80 columns]"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["# HINT: use astype function\n","features_one_hot.astype('float64')"]},{"attachments":{},"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"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["<code>features_one_hot.to_csv('dataset_part_3.csv', index=False)</code>\n"]},{"cell_type":"code","execution_count":18,"metadata":{},"outputs":[],"source":["features_one_hot.to_csv('dataset_part_3.csv', index=False)"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## Authors\n"]},{"attachments":{},"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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2023-01-01\">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"]},{"attachments":{},"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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2023-01-01\">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"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## Change Log\n"]},{"attachments":{},"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"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["Copyright © 2020 IBM Corporation. All rights reserved.\n"]}],"metadata":{"kernelspec":{"display_name":"Python 3","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":"3.11.4"}},"nbformat":4,"nbformat_minor":4}