2-2 jupyter-labs-eda-dataviz.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": "markdown",
      "source": "## Exploratory Data Analysis\n",
      "metadata": {}
    },
    {
      "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(\"Launch Site\",fontsize=20)\nplt.ylabel(\"Pay load Mass (kg)\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_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",
      "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', data=df, hue='Class', aspect=4)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Launch Site\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 2058.88x500 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": "+ With increase in flight number, Success rate increases for all launch sites.\n+ KSC LC 39A has minimum Failure rate and maximum Success rate.",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### TASK 2: Visualize the relationship between Payload and Launch Site",
      "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(x = 'PayloadMass', y = 'LaunchSite', data = df, hue = 'Class', aspect=4)\nplt.xlabel(\"Pay Load Mass (kg)\",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 2058.88x500 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",
      "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": "s_rate = df.groupby('Orbit')['Class'].mean().reset_index()\ns_rate['Class'] = s_rate['Class'] * 100\nsns.barplot(x = 'Orbit', y = 'Class', data = s_rate)\nplt.xlabel(\"Orbit\",fontsize=15)\nplt.ylabel(\"Success Rate (%age)\",fontsize=15)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 7,
      "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": "Analyze the ploted bar chart try to find which orbits have high sucess rate.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "+ Orbits ES-L1, GEO, HEO and SSO have a highest success rate of 100% ",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### TASK  4: Visualize the relationship between FlightNumber and Orbit type",
      "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\", data=df, aspect = 5)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 2500x500 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(\"Pay Load\",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 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(3)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 10,
      "outputs": [
        {
          "execution_count": 10,
          "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   \n\n     Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  ReusedCount  \\\n0  None None        1     False   False  False        NaN    1.0            0   \n1  None None        1     False   False  False        NaN    1.0            0   \n2  None None        1     False   False  False        NaN    1.0            0   \n\n  Serial  Longitude   Latitude  Class  \n0  B0003 -80.577366  28.561857      0  \n1  B0005 -80.577366  28.561857      0  \n2  B0007 -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  </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\nys_rate = df.groupby('Date')['Class'].mean().reset_index()\nys_rate['Class'] = ys_rate['Class'] * 100\nsns.lineplot(x = 'Date', y = 'Class', data = ys_rate)\nplt.xlabel(\"Year\",fontsize=15)\nplt.ylabel(\"Success Rate (%age)\",fontsize=15)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 11,
      "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": 12,
      "outputs": [
        {
          "execution_count": 12,
          "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 = pd.get_dummies(features[['Orbit', 'LaunchSite', 'LandingPad', 'Serial']])\nfeatures_one_hot.head(3)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 13,
      "outputs": [
        {
          "execution_count": 13,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   Orbit_ES-L1  Orbit_GEO  Orbit_GTO  Orbit_HEO  Orbit_ISS  Orbit_LEO  \\\n0            0          0          0          0          0          1   \n1            0          0          0          0          0          1   \n2            0          0          0          0          1          0   \n\n   Orbit_MEO  Orbit_PO  Orbit_SO  Orbit_SSO  ...  Serial_B1048  Serial_B1049  \\\n0          0         0         0          0  ...             0             0   \n1          0         0         0          0  ...             0             0   \n2          0         0         0          0  ...             0             0   \n\n   Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n0             0             0             0             0             0   \n1             0             0             0             0             0   \n2             0             0             0             0             0   \n\n   Serial_B1059  Serial_B1060  Serial_B1062  \n0             0             0             0  \n1             0             0             0  \n2             0             0             0  \n\n[3 rows x 72 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>Orbit_ES-L1</th>\n      <th>Orbit_GEO</th>\n      <th>Orbit_GTO</th>\n      <th>Orbit_HEO</th>\n      <th>Orbit_ISS</th>\n      <th>Orbit_LEO</th>\n      <th>Orbit_MEO</th>\n      <th>Orbit_PO</th>\n      <th>Orbit_SO</th>\n      <th>Orbit_SSO</th>\n      <th>...</th>\n      <th>Serial_B1048</th>\n      <th>Serial_B1049</th>\n      <th>Serial_B1050</th>\n      <th>Serial_B1051</th>\n      <th>Serial_B1054</th>\n      <th>Serial_B1056</th>\n      <th>Serial_B1058</th>\n      <th>Serial_B1059</th>\n      <th>Serial_B1060</th>\n      <th>Serial_B1062</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>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>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>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>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>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>3 rows × 72 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('float64')\nfeatures_one_hot",
      "metadata": {
        "trusted": true
      },
      "execution_count": 14,
      "outputs": [
        {
          "execution_count": 14,
          "output_type": "execute_result",
          "data": {
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            "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>Orbit_ES-L1</th>\n      <th>Orbit_GEO</th>\n      <th>Orbit_GTO</th>\n      <th>Orbit_HEO</th>\n      <th>Orbit_ISS</th>\n      <th>Orbit_LEO</th>\n      <th>Orbit_MEO</th>\n      <th>Orbit_PO</th>\n      <th>Orbit_SO</th>\n      <th>Orbit_SSO</th>\n      <th>...</th>\n      <th>Serial_B1048</th>\n      <th>Serial_B1049</th>\n      <th>Serial_B1050</th>\n      <th>Serial_B1051</th>\n      <th>Serial_B1054</th>\n      <th>Serial_B1056</th>\n      <th>Serial_B1058</th>\n      <th>Serial_B1059</th>\n      <th>Serial_B1060</th>\n      <th>Serial_B1062</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>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      <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>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      <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>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      <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>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      <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>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      <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>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      <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>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      <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>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      <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>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      <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>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      <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 × 72 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": {}
    },
    {
      "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": {}
    }
  ]
}