04-spacex-Exploring-Preparing-Data.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'])\nprint(\"Done\")",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "Done\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 1
    },
    {
      "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
      },
      "outputs": [],
      "execution_count": 2
    },
    {
      "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
      },
      "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": {}
        }
      ],
      "execution_count": 3
    },
    {
      "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
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        }
      ],
      "execution_count": 4
    },
    {
      "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": "code",
      "source": "sns.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
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 2558.88x500 with 1 Axes>",
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "execution_count": 5
    },
    {
      "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": "markdown",
      "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": {}
    },
    {
      "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": "code",
      "source": "sns.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()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 2558.88x500 with 1 Axes>",
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "execution_count": 6
    },
    {
      "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": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "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": "code",
      "source": "data_barchart =  df.groupby([\"Orbit\"])['Class'].mean().reset_index()\nprint(data_barchart.head())\n\nplt.figure(figsize=(10, 6))\nsns.barplot(data=data_barchart, x=\"Orbit\", y=\"Class\")\nplt.xlabel('Orbit Type')\nplt.ylabel('Average Success Rate')\nplt.title('Sucess rate of each orbit')\n\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "   Orbit     Class\n0  ES-L1  1.000000\n1    GEO  1.000000\n2    GTO  0.518519\n3    HEO  1.000000\n4    ISS  0.619048\n",
          "output_type": "stream"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1000x600 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 7
    },
    {
      "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": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "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": "code",
      "source": "sns.catplot(y=\"Orbit\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect=5)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Orbits\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 2558.88x500 with 1 Axes>",
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "execution_count": 8
    },
    {
      "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": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "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": "code",
      "source": "sns.catplot(y=\"Orbit\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect=5)\nplt.xlabel(\"Pay load Mass (kg)\", fontsize=20)\nplt.ylabel(\"Orbit Type\", fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 2558.88x500 with 1 Axes>",
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "execution_count": 10
    },
    {
      "cell_type": "markdown",
      "source": "Similarly, we can plot the Payload vs. Orbit scatter point charts to reveal the relationship between Payload and Orbit type\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Plot a scatter point chart with x axis to be Payload and y axis to be the Orbit, and hue to be the class value",
      "metadata": {},
      "outputs": [],
      "execution_count": null
    },
    {
      "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": "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
      },
      "outputs": [
        {
          "execution_count": 11,
          "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": {}
        }
      ],
      "execution_count": 11
    },
    {
      "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(x=\"Date\", y=\"Outcome\", data=df)\n\n# plt.figure(figsize=(10,9))\nplt.title('The Launch Success Yearly Trend')\nplt.xlabel('Year')\nplt.ylabel('Success Rate')\nplt.show()\n",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 20
    },
    {
      "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
      },
      "outputs": [
        {
          "execution_count": 27,
          "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": {}
        }
      ],
      "execution_count": 27
    },
    {
      "cell_type": "markdown",
      "source": "### TASK  7: Create dummy variables to categorical columns\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.preprocessing import OneHotEncoder\n\n#Intitialize encoder\nencoder = OneHotEncoder()\n\ncategories = features[['Orbit', 'LaunchSite', 'LandingPad', 'Serial']]\nencoded_data = encoder.fit_transform(categories)\n\n#New Dataframe with encoded data\nfeatures_one_hot =  pd.DataFrame(encoded_data.toarray(), columns=encoder.get_feature_names_out())\n\nfeatures_one_hot.head()\n",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 35,
          "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        0.0        0.0        0.0        1.0   \n1          0.0        0.0        0.0        0.0        0.0        1.0   \n2          0.0        0.0        0.0        0.0        1.0        0.0   \n3          0.0        0.0        0.0        0.0        0.0        0.0   \n4          0.0        0.0        1.0        0.0        0.0        0.0   \n\n   Orbit_MEO  Orbit_PO  Orbit_SO  Orbit_SSO  ...  Serial_B1048  Serial_B1049  \\\n0        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   \n2        0.0       0.0       0.0        0.0  ...           0.0           0.0   \n3        0.0       1.0       0.0        0.0  ...           0.0           0.0   \n4        0.0       0.0       0.0        0.0  ...           0.0           0.0   \n\n   Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n0           0.0           0.0           0.0           0.0           0.0   \n1           0.0           0.0           0.0           0.0           0.0   \n2           0.0           0.0           0.0           0.0           0.0   \n3           0.0           0.0           0.0           0.0           0.0   \n4           0.0           0.0           0.0           0.0           0.0   \n\n   Serial_B1059  Serial_B1060  Serial_B1062  \n0           0.0           0.0           0.0  \n1           0.0           0.0           0.0  \n2           0.0           0.0           0.0  \n3           0.0           0.0           0.0  \n4           0.0           0.0           0.0  \n\n[5 rows x 73 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>0.0</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  </tbody>\n</table>\n<p>5 rows × 73 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 35
    },
    {
      "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": {},
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": "### TASK  8: Cast all numeric columns to `float64`\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "features_one_hot = features_one_hot.astype('float64')\nfeatures_one_hot.head()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 36,
          "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        0.0        0.0        0.0        1.0   \n1          0.0        0.0        0.0        0.0        0.0        1.0   \n2          0.0        0.0        0.0        0.0        1.0        0.0   \n3          0.0        0.0        0.0        0.0        0.0        0.0   \n4          0.0        0.0        1.0        0.0        0.0        0.0   \n\n   Orbit_MEO  Orbit_PO  Orbit_SO  Orbit_SSO  ...  Serial_B1048  Serial_B1049  \\\n0        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   \n2        0.0       0.0       0.0        0.0  ...           0.0           0.0   \n3        0.0       1.0       0.0        0.0  ...           0.0           0.0   \n4        0.0       0.0       0.0        0.0  ...           0.0           0.0   \n\n   Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n0           0.0           0.0           0.0           0.0           0.0   \n1           0.0           0.0           0.0           0.0           0.0   \n2           0.0           0.0           0.0           0.0           0.0   \n3           0.0           0.0           0.0           0.0           0.0   \n4           0.0           0.0           0.0           0.0           0.0   \n\n   Serial_B1059  Serial_B1060  Serial_B1062  \n0           0.0           0.0           0.0  \n1           0.0           0.0           0.0  \n2           0.0           0.0           0.0  \n3           0.0           0.0           0.0  \n4           0.0           0.0           0.0  \n\n[5 rows x 73 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>0.0</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  </tbody>\n</table>\n<p>5 rows × 73 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 36
    },
    {
      "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": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "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": {}
    }
  ]
}