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": 4,
      "outputs": [
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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\nsns.catplot(y=\"LaunchSite\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\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": "<pyolite.display.Image at 0x4ccfb30>",
            "image/png": 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          },
          "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",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "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\nsns.catplot(y=\"LaunchSite\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Pay Load Mass\",fontsize=20)\nplt.ylabel(\"Launch Site\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x4117960>",
            "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\nsns.catplot(x= 'Orbit', y = 'Class', data = df.groupby('Orbit')['Class'].mean().reset_index(), kind = 'bar')\nplt.xlabel('Orbit Type',fontsize=20)\nplt.ylabel('Success Rate',fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x2bc9fa8>",
            "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\nsns.catplot(y=\"Orbit\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Orbit Type\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x38750b0>",
            "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\n",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "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\n\nsns.catplot(y=\"Orbit\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Orbit Type\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x5ba8130>",
            "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": "code",
      "source": "### TASK  6: Visualize the launch success yearly trend\n",
      "metadata": {},
      "execution_count": null,
      "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():\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": 14,
      "outputs": [
        {
          "execution_count": 14,
          "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\nsns.lineplot(data=df, x=\"Date\", y=\"Class\") \nplt.xlabel(\"year\",fontsize=20)\nplt.ylabel(\"Success Rate\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x5b0b9e8>",
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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": {},
      "execution_count": null,
      "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": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "### TASK  7: Create dummy variables to categorical columns\n",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "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`\n",
      "metadata": {},
      "execution_count": null,
      "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": {}
    }
  ]
}