IBM-DS0321EN-SkillsNetwork_labs_module_2_jupyter-labs-eda-dataviz.ipynb.jupyterlite.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": "<p style=\"text-align:center\">\n    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\" target=\"_blank\">\n    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n    </a>\n</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# **SpaceX  Falcon 9 First Stage Landing Prediction**\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Assignment: Exploring and Preparing Data\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Estimated time needed: **70** minutes\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "In this assignment, we will predict if the Falcon 9 first stage will land successfully. SpaceX advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is due to the fact that SpaceX can reuse the first stage.\n\nIn this lab, you will perform Exploratory Data Analysis and Feature Engineering.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Falcon 9 first stage will land successfully\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing_1.gif)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Several examples of an unsuccessful landing are shown here:\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Objectives\n\nPerform exploratory Data Analysis and Feature Engineering using `Pandas` and `Matplotlib`\n\n*   Exploratory Data Analysis\n*   Preparing Data  Feature Engineering\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### Import Libraries and Define Auxiliary Functions\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We will import the following libraries the lab\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import piplite\nawait piplite.install(['numpy'])\nawait piplite.install(['pandas'])\nawait piplite.install(['seaborn'])",
      "metadata": {
        "trusted": true
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "# pandas is a software library written for the Python programming language for data manipulation and analysis.\nimport pandas as pd\n#NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays\nimport numpy as np\n# Matplotlib is a plotting library for python and pyplot gives us a MatLab like plotting framework. We will use this in our plotter function to plot data.\nimport matplotlib.pyplot as plt\n#Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics\nimport seaborn as sns",
      "metadata": {
        "trusted": true
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "## Exploratory Data Analysis\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "First, let's read the SpaceX dataset into a Pandas dataframe and print its summary\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from js import fetch\nimport io\n\nURL = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\"\nresp = await fetch(URL)\ndataset_part_2_csv = io.BytesIO((await resp.arrayBuffer()).to_py())\ndf=pd.read_csv(dataset_part_2_csv)\ndf.head(5)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 4,
      "outputs": [
        {
          "execution_count": 4,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n\n       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n0    None None        1     False   False  False        NaN    1.0   \n1    None None        1     False   False  False        NaN    1.0   \n2    None None        1     False   False  False        NaN    1.0   \n3  False Ocean        1     False   False  False        NaN    1.0   \n4    None None        1     False   False  False        NaN    1.0   \n\n   ReusedCount Serial   Longitude   Latitude  Class  \n0            0  B0003  -80.577366  28.561857      0  \n1            0  B0005  -80.577366  28.561857      0  \n2            0  B0007  -80.577366  28.561857      0  \n3            0  B1003 -120.610829  34.632093      0  \n4            0  B1004  -80.577366  28.561857      0  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>Date</th>\n      <th>BoosterVersion</th>\n      <th>PayloadMass</th>\n      <th>Orbit</th>\n      <th>LaunchSite</th>\n      <th>Outcome</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>LandingPad</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Serial</th>\n      <th>Longitude</th>\n      <th>Latitude</th>\n      <th>Class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2010-06-04</td>\n      <td>Falcon 9</td>\n      <td>6104.959412</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0003</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2012-05-22</td>\n      <td>Falcon 9</td>\n      <td>525.000000</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0005</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>2013-03-01</td>\n      <td>Falcon 9</td>\n      <td>677.000000</td>\n      <td>ISS</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0007</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>2013-09-29</td>\n      <td>Falcon 9</td>\n      <td>500.000000</td>\n      <td>PO</td>\n      <td>VAFB SLC 4E</td>\n      <td>False Ocean</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1003</td>\n      <td>-120.610829</td>\n      <td>34.632093</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>2013-12-03</td>\n      <td>Falcon 9</td>\n      <td>3170.000000</td>\n      <td>GTO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1004</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "First, let's try to see how the `FlightNumber` (indicating the continuous launch attempts.) and `Payload` variables would affect the launch outcome.\n\nWe can plot out the <code>FlightNumber</code> vs. <code>PayloadMass</code>and overlay the outcome of the launch. We see that as the flight number increases, the first stage is more likely to land successfully. The payload mass is also important; it seems the more massive the payload, the less likely the first stage will return.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "sns.catplot(y=\"PayloadMass\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Pay load Mass (kg)\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We see that different launch sites have different success rates.  <code>CCAFS LC-40</code>, has a success rate of 60 %, while  <code>KSC LC-39A</code> and <code>VAFB SLC 4E</code> has a success rate of 77%.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Next, let's drill down to each site visualize its detailed launch records.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "### TASK 1: Visualize the relationship between Flight Number and Launch Site\n",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Use the function <code>catplot</code> to plot <code>FlightNumber</code> vs <code>LaunchSite</code>, set the  parameter <code>x</code>  parameter to <code>FlightNumber</code>,set the  <code>y</code> to <code>Launch Site</code> and set the parameter <code>hue</code> to <code>'class'</code>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Plot a scatter point chart with x axis to be Flight Number and y axis to be the launch site, and hue to be the class value\n\nplt.figure(figsize=(14,8))\nsns.scatterplot(x=\"FlightNumber\", y=\"LaunchSite\", hue=\"Class\", data=df)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Launch Site\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1400x800 with 1 Axes>",
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Now try to explain the patterns you found in the Flight Number vs. Launch Site scatter point plots.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "### TASK 2: Visualize the relationship between Payload and Launch Site\nplt.figure(figsize=(14,8))\nsns.scatterplot(x=\"PayloadMass\",y=\"LaunchSite\", hue =\"Class\",data=df)\nplt.xlabel(\"Pay Load Mass (kg)\",fontsize=20)\nplt.ylabel(\"Launch Site\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1400x800 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We also want to observe if there is any relationship between launch sites and their payload mass.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Plot a scatter point chart with x axis to be Pay Load Mass (kg) and y axis to be the launch site, and hue to be the class value",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Now if you observe Payload Vs. Launch Site scatter point chart you will find for the VAFB-SLC  launchsite there are no  rockets  launched for  heavypayload mass(greater than 10000).\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "### TASK  3: Visualize the relationship between success rate of each orbit type\ndf.groupby(['Orbit']).mean()\nsns.barplot(x=\"Orbit\",y=\"Class\",data=df)\nplt.xlabel(\"Orbit\",fontsize=20)\nplt.ylabel(\"Success rate\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 8,
      "outputs": [
        {
          "name": "stderr",
          "text": "<ipython-input-8-089be334a563>:2: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n  df.groupby(['Orbit']).mean()\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Next, we want to visually check if there are any relationship between success rate and orbit type.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's create a `bar chart` for the sucess rate of each orbit\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# HINT use groupby method on Orbit column and get the mean of Class column",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Analyze the ploted bar chart try to find which orbits have high sucess rate.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "### TASK  4: Visualize the relationship between FlightNumber and Orbit type\nplt.figure(figsize=(14,8))\nsns.scatterplot(x=\"FlightNumber\",y=\"Orbit\",hue=\"Class\",data = df)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1400x800 with 1 Axes>",
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "For each orbit, we want to see if there is any relationship between FlightNumber and Orbit type.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Plot a scatter point chart with x axis to be FlightNumber and y axis to be the Orbit, and hue to be the class value",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "You should see that in the LEO orbit the Success appears related to the number of flights; on the other hand, there seems to be no relationship between flight number when in GTO orbit.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "### TASK  5: Visualize the relationship between Payload and Orbit type\nplt.figure(figsize=(14,8))\nsns.scatterplot(x=\"PayloadMass\",y=\"Orbit\",hue=\"Class\",data = df)\nplt.xlabel(\"PayloadMass\",fontsize=20)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1400x800 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Similarly, we can plot the Payload vs. Orbit scatter point charts to reveal the relationship between Payload and Orbit type\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Plot a scatter point chart with x axis to be Payload and y axis to be the Orbit, and hue to be the class value",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "With heavy payloads the successful landing or positive landing rate are more for Polar,LEO and ISS.\n\nHowever for GTO we cannot distinguish this well as both positive landing rate and negative landing(unsuccessful mission) are both there here.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "### TASK  6: Visualize the launch success yearly trend",
      "metadata": {
        "trusted": true
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "You can plot a line chart with x axis to be <code>Year</code> and y axis to be average success rate, to get the average launch success trend.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "The function will help you get the year from the date:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# A function to Extract years from the date \nyear=[]\ndef Extract_year(date):\n    for i in df[\"Date\"]:\n        year.append(i.split(\"-\")[0])\n    return year\n    \n    ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\ndf['year']=Extract_year(df[\"Date\"])\ndf_year_success=df.groupby(\"year\",as_index=False)[\"Class\"].mean()\n\nplt.figure(figsize=(14,8))\nsns.lineplot(data=df_year_success, x=\"year\", y=\"Class\" )\nplt.xlabel(\"Year\",fontsize=20)\nplt.title('Launch success yearly trend')\nplt.ylabel(\"Success Rate\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1400x800 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "you can observe that the sucess rate since 2013 kept increasing till 2020\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "## Features Engineering\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "By now, you should obtain some preliminary insights about how each important variable would affect the success rate, we will select the features that will be used in success prediction in the future module.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "features = df[['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights', 'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount', 'Serial']]\nfeatures.head()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 18,
      "outputs": [
        {
          "execution_count": 18,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   FlightNumber  PayloadMass Orbit    LaunchSite  Flights  GridFins  Reused  \\\n0             1  6104.959412   LEO  CCAFS SLC 40        1     False   False   \n1             2   525.000000   LEO  CCAFS SLC 40        1     False   False   \n2             3   677.000000   ISS  CCAFS SLC 40        1     False   False   \n3             4   500.000000    PO   VAFB SLC 4E        1     False   False   \n4             5  3170.000000   GTO  CCAFS SLC 40        1     False   False   \n\n    Legs LandingPad  Block  ReusedCount Serial  \n0  False        NaN    1.0            0  B0003  \n1  False        NaN    1.0            0  B0005  \n2  False        NaN    1.0            0  B0007  \n3  False        NaN    1.0            0  B1003  \n4  False        NaN    1.0            0  B1004  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>PayloadMass</th>\n      <th>Orbit</th>\n      <th>LaunchSite</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>LandingPad</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Serial</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>6104.959412</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0003</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>525.000000</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0005</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>677.000000</td>\n      <td>ISS</td>\n      <td>CCAFS SLC 40</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0007</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>500.000000</td>\n      <td>PO</td>\n      <td>VAFB SLC 4E</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1003</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>3170.000000</td>\n      <td>GTO</td>\n      <td>CCAFS SLC 40</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1004</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "### TASK  7: Create dummy variables to categorical columns\nfeatures_one_hot = features\n\nfeatures_one_hot = pd.concat([features_one_hot,pd.get_dummies(df['Orbit'])], axis=1)\nfeatures_one_hot.drop(['Orbit'], axis = 1,inplace=True)\n\nfeatures_one_hot = pd.concat([features_one_hot,pd.get_dummies(df['LaunchSite'])], axis=1)\nfeatures_one_hot.drop(['LaunchSite'], axis = 1,inplace=True)\n\nfeatures_one_hot = pd.concat([features_one_hot,pd.get_dummies(df['LandingPad'])], axis=1)\nfeatures_one_hot.drop(['LandingPad'], axis = 1,inplace=True)\n\nfeatures_one_hot = pd.concat([features_one_hot,pd.get_dummies(df['Serial'])], axis=1)\nfeatures_one_hot.drop(['Serial'], axis = 1,inplace=True)\n\nfeatures_one_hot.head()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 19,
      "outputs": [
        {
          "execution_count": 19,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   FlightNumber  PayloadMass  Flights  GridFins  Reused   Legs  Block  \\\n0             1  6104.959412        1     False   False  False    1.0   \n1             2   525.000000        1     False   False  False    1.0   \n2             3   677.000000        1     False   False  False    1.0   \n3             4   500.000000        1     False   False  False    1.0   \n4             5  3170.000000        1     False   False  False    1.0   \n\n   ReusedCount  ES-L1  GEO  ...  B1048  B1049  B1050  B1051  B1054  B1056  \\\n0            0      0    0  ...      0      0      0      0      0      0   \n1            0      0    0  ...      0      0      0      0      0      0   \n2            0      0    0  ...      0      0      0      0      0      0   \n3            0      0    0  ...      0      0      0      0      0      0   \n4            0      0    0  ...      0      0      0      0      0      0   \n\n   B1058  B1059  B1060  B1062  \n0      0      0      0      0  \n1      0      0      0      0  \n2      0      0      0      0  \n3      0      0      0      0  \n4      0      0      0      0  \n\n[5 rows x 80 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>PayloadMass</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>ES-L1</th>\n      <th>GEO</th>\n      <th>...</th>\n      <th>B1048</th>\n      <th>B1049</th>\n      <th>B1050</th>\n      <th>B1051</th>\n      <th>B1054</th>\n      <th>B1056</th>\n      <th>B1058</th>\n      <th>B1059</th>\n      <th>B1060</th>\n      <th>B1062</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>6104.959412</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>525.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>677.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>500.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>3170.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 80 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Use the function <code>get_dummies</code> and <code>features</code> dataframe to apply OneHotEncoder to the column <code>Orbits</code>, <code>LaunchSite</code>, <code>LandingPad</code>, and <code>Serial</code>. Assign the value to the variable <code>features_one_hot</code>, display the results using the method head. Your result dataframe must include all features including the encoded ones.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# HINT: Use get_dummies() function on the categorical columns",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "### TASK  8: Cast all numeric columns to `float64`\nfeatures_one_hot = features_one_hot.astype(float)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Now that our <code>features_one_hot</code> dataframe only contains numbers cast the entire dataframe to variable type <code>float64</code>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# HINT: use astype function",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "We can now export it to a <b>CSV</b> for the next section,but to make the answers consistent, in the next lab we will provide data in a pre-selected date range.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<code>features_one_hot.to_csv('dataset_part\\_3.csv', index=False)</code>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Authors\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "[Pratiksha Verma](https://www.linkedin.com/in/pratiksha-verma-6487561b1/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2022-01-01)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Change Log\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "| Date (YYYY-MM-DD) | Version | Changed By      | Change Description      |\n| ----------------- | ------- | -------------   | ----------------------- |\n| 2022-11-09        | 1.0     | Pratiksha Verma | Converted initial version to Jupyterlite|\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### <h3 align=\"center\"> IBM Corporation 2022. All rights reserved. <h3/>\n",
      "metadata": {}
    }
  ]
}