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": {},
      "execution_count": null,
      "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": 3,
      "outputs": [
        {
          "execution_count": 3,
          "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": "markdown",
      "source": "### TASK 1: Visualize the relationship between Flight Number and Launch Site\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Use the function <code>catplot</code> to plot <code>FlightNumber</code> vs <code>LaunchSite</code>, set the  parameter <code>x</code>  parameter to <code>FlightNumber</code>,set the  <code>y</code> to <code>Launch Site</code> and set the parameter <code>hue</code> to <code>'class'</code>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Plot a scatter point chart with x axis to be Flight Number and y axis to be the launch site, and hue to be the class value\nsns.catplot(y=\"LaunchSite\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Launch Site\",fontsize=20)\n\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 2558.88x500 with 1 Axes>",
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Now try to explain the patterns you found in the Flight Number vs. Launch Site scatter point plots.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### TASK 2: Visualize the relationship between Payload and Launch Site\n",
      "metadata": {}
    },
    {
      "cell_type": "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\nsns.catplot(y=\"LaunchSite\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Pay load Mass (kg)\",fontsize=20)\nplt.ylabel(\"Launch Site\",fontsize=20)\nplt.show()\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 2558.88x500 with 1 Axes>",
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Now if you observe Payload Vs. Launch Site scatter point chart you will find for the VAFB-SLC  launchsite there are no  rockets  launched for  heavypayload mass(greater than 10000).\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### TASK  3: Visualize the relationship between success rate of each orbit type\n",
      "metadata": {}
    },
    {
      "cell_type": "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\npl = df.groupby('Orbit')['Class'].mean()\nax = pl.plot(kind='bar')\nax.set_xlabel(\"Orbit\")\nax.set_ylabel(\"Sucess Rate of each Orbit\")",
      "metadata": {
        "trusted": true
      },
      "execution_count": 12,
      "outputs": [
        {
          "execution_count": 12,
          "output_type": "execute_result",
          "data": {
            "text/plain": "Text(0, 0.5, 'Sucess Rate of each Orbit')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Analyze the ploted bar chart try to find which orbits have high sucess rate.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### TASK  4: Visualize the relationship between FlightNumber and Orbit type\n",
      "metadata": {}
    },
    {
      "cell_type": "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\nsns.catplot(y=\"Orbit\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 2558.88x500 with 1 Axes>",
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "You should see that in the LEO orbit the Success appears related to the number of flights; on the other hand, there seems to be no relationship between flight number when in GTO orbit.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### TASK  5: Visualize the relationship between Payload and Orbit type\n",
      "metadata": {}
    },
    {
      "cell_type": "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\nsns.catplot(y=\"Orbit\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"PayloadMass\",fontsize=20)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 2558.88x500 with 1 Axes>",
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "With heavy payloads the successful landing or positive landing rate are more for Polar,LEO and ISS.\n\nHowever for GTO we cannot distinguish this well as both positive landing rate and negative landing(unsuccessful mission) are both there here.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### TASK  6: Visualize the launch success yearly trend\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "You can plot a line chart with x axis to be <code>Year</code> and y axis to be average success rate, to get the average launch success trend.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "The function will help you get the year from the date:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# A function to Extract years from the date \nyear=[]\ndef Extract_year():\n    for i in df[\"Date\"]:\n        year.append(i.split(\"-\")[0])\n    return year\nExtract_year()\ndf['Date'] = year\ndf.head()\n    ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 15,
      "outputs": [
        {
          "execution_count": 15,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   FlightNumber  Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n0             1  2010       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n1             2  2012       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n2             3  2013       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n3             4  2013       Falcon 9   500.000000    PO   VAFB SLC 4E   \n4             5  2013       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</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</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</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</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</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": "code",
      "source": "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\nyear=[]\ndef Extract_year(date):\n    for i in df[\"Date\"]:\n        year.append(i.split(\"-\")[0])\n    return year\n\ndf['Year'] = Extract_year(df[\"Date\"])\ndf_groupby_year = df.groupby(\"Year\",as_index=False)[\"Class\"].mean()\nsns.lineplot(data = df_groupby_year, x=\"Year\", y=\"Class\")\nplt.xlabel(\"Year\")\nplt.title('Space X Rocket Success Rate')\nplt.ylabel(\"Success Rate\")\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 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": "markdown",
      "source": "## Features Engineering\n",
      "metadata": {}
    },
    {
      "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": "markdown",
      "source": "### TASK  7: Create dummy variables to categorical columns\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Use the function <code>get_dummies</code> and <code>features</code> dataframe to apply OneHotEncoder to the column <code>Orbits</code>, <code>LaunchSite</code>, <code>LandingPad</code>, and <code>Serial</code>. Assign the value to the variable <code>features_one_hot</code>, display the results using the method head. Your result dataframe must include all features including the encoded ones.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# HINT: Use get_dummies() function on the categorical columns\nfeatures_one_hot = features\n\nfeatures_one_hot = pd.concat([features_one_hot, \n                              pd.get_dummies(df['Orbit']),\n                             pd.get_dummies(df['LaunchSite']),\n                             pd.get_dummies(df['LandingPad']),\n                             pd.get_dummies(df['Serial'])], axis=1)\n\nfeatures_one_hot.drop(['Orbit', 'LaunchSite', 'LandingPad', '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": "### TASK  8: Cast all numeric columns to `float64`\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Now that our <code>features_one_hot</code> dataframe only contains numbers cast the entire dataframe to variable type <code>float64</code>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# HINT: use astype function\nfeatures_one_hot = features_one_hot.astype(float)\nfeatures_one_hot\n\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 23,
      "outputs": [
        {
          "execution_count": 23,
          "output_type": "execute_result",
          "data": {
            "text/plain": "    FlightNumber   PayloadMass  Flights  GridFins  Reused  Legs  Block  \\\n0            1.0   6104.959412      1.0       0.0     0.0   0.0    1.0   \n1            2.0    525.000000      1.0       0.0     0.0   0.0    1.0   \n2            3.0    677.000000      1.0       0.0     0.0   0.0    1.0   \n3            4.0    500.000000      1.0       0.0     0.0   0.0    1.0   \n4            5.0   3170.000000      1.0       0.0     0.0   0.0    1.0   \n..           ...           ...      ...       ...     ...   ...    ...   \n85          86.0  15400.000000      2.0       1.0     1.0   1.0    5.0   \n86          87.0  15400.000000      3.0       1.0     1.0   1.0    5.0   \n87          88.0  15400.000000      6.0       1.0     1.0   1.0    5.0   \n88          89.0  15400.000000      3.0       1.0     1.0   1.0    5.0   \n89          90.0   3681.000000      1.0       1.0     0.0   1.0    5.0   \n\n    ReusedCount  ES-L1  GEO  ...  B1048  B1049  B1050  B1051  B1054  B1056  \\\n0           0.0    0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0   \n1           0.0    0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0   \n2           0.0    0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0   \n3           0.0    0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0   \n4           0.0    0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0   \n..          ...    ...  ...  ...    ...    ...    ...    ...    ...    ...   \n85          2.0    0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0   \n86          2.0    0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0   \n87          5.0    0.0  0.0  ...    0.0    0.0    0.0    1.0    0.0    0.0   \n88          2.0    0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0   \n89          0.0    0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0   \n\n    B1058  B1059  B1060  B1062  \n0     0.0    0.0    0.0    0.0  \n1     0.0    0.0    0.0    0.0  \n2     0.0    0.0    0.0    0.0  \n3     0.0    0.0    0.0    0.0  \n4     0.0    0.0    0.0    0.0  \n..    ...    ...    ...    ...  \n85    0.0    0.0    1.0    0.0  \n86    1.0    0.0    0.0    0.0  \n87    0.0    0.0    0.0    0.0  \n88    0.0    0.0    1.0    0.0  \n89    0.0    0.0    0.0    1.0  \n\n[90 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>1.0</td>\n      <td>6104.959412</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2.0</td>\n      <td>525.000000</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3.0</td>\n      <td>677.000000</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.0</td>\n      <td>500.000000</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>3170.000000</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>86.0</td>\n      <td>15400.000000</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>87.0</td>\n      <td>15400.000000</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>88.0</td>\n      <td>15400.000000</td>\n      <td>6.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>89.0</td>\n      <td>15400.000000</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>90.0</td>\n      <td>3681.000000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>90 rows × 80 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "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": {
        "tags": []
      }
    },
    {
      "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": {}
    }
  ]
}