EDA with Data Visualization.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<center>\n    <img src=\"https://gitlab.com/ibm/skills-network/courses/placeholder101/-/raw/master/labs/module%201/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n</center>\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# **SpaceX  Falcon 9 First Stage Landing Prediction**\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Assignment: Exploring and Preparing\u00a0Data\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Estimated time needed: **70** minutes\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "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"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Falcon 9 first stage will land successfully\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing\\_1.gif)\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Several examples of an unsuccessful landing are shown here:\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Objectives\n\nPerform exploratory Data Analysis and Feature Engineering using `Pandas` and `Matplotlib`\n\n*   Exploratory Data Analysis\n*   Preparing\u00a0Data  Feature Engineering\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "***\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Import Libraries and Define Auxiliary Functions\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "We will import the following libraries the lab\n"
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [],
            "source": "# andas 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"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Exploratory Data Analysis\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "First, let's read the SpaceX dataset into a Pandas dataframe and print its summary\n"
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "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>",
                        "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  "
                    },
                    "execution_count": 2,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df=pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\")\n\n# If you were unable to complete the previous lab correctly you can uncomment and load this csv\n\n# df = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/dataset_part_2.csv')\n\ndf.head(5)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "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"
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [
                {
                    "data": {
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\n",
                        "text/plain": "<Figure size 1842.38x360 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "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()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "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"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Next, let's drill down to each site visualize its detailed launch records.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK 1: Visualize the relationship between Flight Number and Launch Site\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "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"
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 1842.38x360 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot a scatter point chart with x axis to be Flight Number and y axis to be the launch site, and hue to be the class value\nsns.catplot(y=\"LaunchSite\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"LaunchSite\",fontsize=20)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Now try to explain the patterns you found in the Flight Number vs. Launch Site scatter point plots.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK 2: Visualize the relationship between Payload and Launch Site\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "We also want to observe if there is any relationship between launch sites and their payload mass.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 1842.38x360 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot a scatter point chart with x axis to be Pay Load Mass (kg) and y axis to be the launch site, and hue to be the class value\nsns.catplot(y=\"LaunchSite\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"PayloadMass\",fontsize=20)\nplt.ylabel(\"LaunchSite\",fontsize=20)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Now try to explain any patterns you found in the Payload Vs. Launch Site scatter point chart.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "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    <tr>\n      <th>5</th>\n      <td>6</td>\n      <td>2014-01-06</td>\n      <td>Falcon 9</td>\n      <td>3325.000000</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>B1005</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>7</td>\n      <td>2014-04-18</td>\n      <td>Falcon 9</td>\n      <td>2296.000000</td>\n      <td>ISS</td>\n      <td>CCAFS SLC 40</td>\n      <td>True Ocean</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1006</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>8</td>\n      <td>2014-07-14</td>\n      <td>Falcon 9</td>\n      <td>1316.000000</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>True Ocean</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1007</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>9</td>\n      <td>2014-08-05</td>\n      <td>Falcon 9</td>\n      <td>4535.000000</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>B1008</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>10</td>\n      <td>2014-09-07</td>\n      <td>Falcon 9</td>\n      <td>4428.000000</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>B1011</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>11</td>\n      <td>2014-09-21</td>\n      <td>Falcon 9</td>\n      <td>2216.000000</td>\n      <td>ISS</td>\n      <td>CCAFS SLC 40</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>B1010</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>12</td>\n      <td>2015-01-10</td>\n      <td>Falcon 9</td>\n      <td>2395.000000</td>\n      <td>ISS</td>\n      <td>CCAFS SLC 40</td>\n      <td>False ASDS</td>\n      <td>1</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>5e9e3032383ecb761634e7cb</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1012</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>13</td>\n      <td>2015-02-11</td>\n      <td>Falcon 9</td>\n      <td>570.000000</td>\n      <td>ES-L1</td>\n      <td>CCAFS SLC 40</td>\n      <td>True Ocean</td>\n      <td>1</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1013</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>14</td>\n      <td>2015-04-14</td>\n      <td>Falcon 9</td>\n      <td>1898.000000</td>\n      <td>ISS</td>\n      <td>CCAFS SLC 40</td>\n      <td>False ASDS</td>\n      <td>1</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>5e9e3032383ecb761634e7cb</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1015</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>15</td>\n      <td>2015-04-27</td>\n      <td>Falcon 9</td>\n      <td>4707.000000</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>B1016</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>17</td>\n      <td>2015-12-22</td>\n      <td>Falcon 9</td>\n      <td>2034.000000</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>True RTLS</td>\n      <td>1</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>5e9e3032383ecb267a34e7c7</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1019</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>18</td>\n      <td>2016-01-17</td>\n      <td>Falcon 9</td>\n      <td>553.000000</td>\n      <td>PO</td>\n      <td>VAFB SLC 4E</td>\n      <td>False ASDS</td>\n      <td>1</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>5e9e3033383ecbb9e534e7cc</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1017</td>\n      <td>-120.610829</td>\n      <td>34.632093</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>26</td>\n      <td>2017-01-14</td>\n      <td>Falcon 9</td>\n      <td>9600.000000</td>\n      <td>PO</td>\n      <td>VAFB SLC 4E</td>\n      <td>True ASDS</td>\n      <td>1</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>5e9e3033383ecbb9e534e7cc</td>\n      <td>3.0</td>\n      <td>1</td>\n      <td>B1029</td>\n      <td>-120.610829</td>\n      <td>34.632093</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>30</td>\n      <td>2017-05-01</td>\n      <td>Falcon 9</td>\n      <td>6104.959412</td>\n      <td>LEO</td>\n      <td>KSC LC 39A</td>\n      <td>True RTLS</td>\n      <td>1</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>5e9e3032383ecb267a34e7c7</td>\n      <td>3.0</td>\n      <td>1</td>\n      <td>B1032</td>\n      <td>-80.603956</td>\n      <td>28.608058</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>34</td>\n      <td>2017-06-25</td>\n      <td>Falcon 9</td>\n      <td>9600.000000</td>\n      <td>PO</td>\n      <td>VAFB SLC 4E</td>\n      <td>True ASDS</td>\n      <td>1</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>5e9e3033383ecbb9e534e7cc</td>\n      <td>3.0</td>\n      <td>1</td>\n      <td>B1036</td>\n      <td>-120.610829</td>\n      <td>34.632093</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>37</td>\n      <td>2017-08-24</td>\n      <td>Falcon 9</td>\n      <td>475.000000</td>\n      <td>SSO</td>\n      <td>VAFB SLC 4E</td>\n      <td>True ASDS</td>\n      <td>1</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>5e9e3033383ecbb9e534e7cc</td>\n      <td>3.0</td>\n      <td>1</td>\n      <td>B1038</td>\n      <td>-120.610829</td>\n      <td>34.632093</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>39</td>\n      <td>2017-10-09</td>\n      <td>Falcon 9</td>\n      <td>9600.000000</td>\n      <td>PO</td>\n      <td>VAFB SLC 4E</td>\n      <td>True ASDS</td>\n      <td>1</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>5e9e3033383ecbb9e534e7cc</td>\n      <td>4.0</td>\n      <td>1</td>\n      <td>B1041</td>\n      <td>-120.610829</td>\n      <td>34.632093</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>49</td>\n      <td>2018-04-18</td>\n      <td>Falcon 9</td>\n      <td>350.000000</td>\n      <td>HEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>True ASDS</td>\n      <td>1</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>5e9e3032383ecb6bb234e7ca</td>\n      <td>4.0</td>\n      <td>1</td>\n      <td>B1045</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>57</td>\n      <td>2018-10-08</td>\n      <td>Falcon 9</td>\n      <td>2800.000000</td>\n      <td>SSO</td>\n      <td>VAFB SLC 4E</td>\n      <td>True RTLS</td>\n      <td>2</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5e9e3032383ecb554034e7c9</td>\n      <td>5.0</td>\n      <td>4</td>\n      <td>B1048</td>\n      <td>-120.610829</td>\n      <td>34.632093</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>59</td>\n      <td>2018-12-03</td>\n      <td>Falcon 9</td>\n      <td>4000.000000</td>\n      <td>SSO</td>\n      <td>VAFB SLC 4E</td>\n      <td>True ASDS</td>\n      <td>3</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5e9e3033383ecbb9e534e7cc</td>\n      <td>5.0</td>\n      <td>3</td>\n      <td>B1046</td>\n      <td>-120.610829</td>\n      <td>34.632093</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>61</td>\n      <td>2018-12-23</td>\n      <td>Falcon 9</td>\n      <td>4400.000000</td>\n      <td>MEO</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>5.0</td>\n      <td>0</td>\n      <td>B1054</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>64</th>\n      <td>65</td>\n      <td>2019-05-24</td>\n      <td>Falcon 9</td>\n      <td>13620.000000</td>\n      <td>VLEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>True ASDS</td>\n      <td>3</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5e9e3032383ecb6bb234e7ca</td>\n      <td>5.0</td>\n      <td>5</td>\n      <td>B1049</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>66</td>\n      <td>2019-06-12</td>\n      <td>Falcon 9</td>\n      <td>1425.000000</td>\n      <td>SSO</td>\n      <td>VAFB SLC 4E</td>\n      <td>True RTLS</td>\n      <td>2</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5e9e3032383ecb554034e7c9</td>\n      <td>5.0</td>\n      <td>5</td>\n      <td>B1051</td>\n      <td>-120.610829</td>\n      <td>34.632093</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>68</th>\n      <td>69</td>\n      <td>2019-11-11</td>\n      <td>Falcon 9</td>\n      <td>15600.000000</td>\n      <td>VLEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>True ASDS</td>\n      <td>4</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5e9e3032383ecb6bb234e7ca</td>\n      <td>5.0</td>\n      <td>4</td>\n      <td>B1048</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>72</td>\n      <td>2020-01-07</td>\n      <td>Falcon 9</td>\n      <td>15400.000000</td>\n      <td>VLEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>True ASDS</td>\n      <td>4</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5e9e3032383ecb6bb234e7ca</td>\n      <td>5.0</td>\n      <td>5</td>\n      <td>B1049</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>72</th>\n      <td>73</td>\n      <td>2020-01-19</td>\n      <td>Falcon 9</td>\n      <td>6104.959412</td>\n      <td>SO</td>\n      <td>KSC LC 39A</td>\n      <td>None None</td>\n      <td>4</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>5.0</td>\n      <td>3</td>\n      <td>B1046</td>\n      <td>-80.603956</td>\n      <td>28.608058</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>74</td>\n      <td>2020-01-29</td>\n      <td>Falcon 9</td>\n      <td>15600.000000</td>\n      <td>VLEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>True ASDS</td>\n      <td>3</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5e9e3032383ecb6bb234e7ca</td>\n      <td>5.0</td>\n      <td>5</td>\n      <td>B1051</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>75</td>\n      <td>2020-02-17</td>\n      <td>Falcon 9</td>\n      <td>15400.000000</td>\n      <td>VLEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>False ASDS</td>\n      <td>4</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5e9e3032383ecb6bb234e7ca</td>\n      <td>5.0</td>\n      <td>3</td>\n      <td>B1056</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>81</th>\n      <td>82</td>\n      <td>2020-06-30</td>\n      <td>Falcon 9</td>\n      <td>3880.000000</td>\n      <td>MEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>True ASDS</td>\n      <td>1</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>5e9e3033383ecbb9e534e7cc</td>\n      <td>5.0</td>\n      <td>2</td>\n      <td>B1060</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>82</th>\n      <td>83</td>\n      <td>2020-07-20</td>\n      <td>Falcon 9</td>\n      <td>6104.959412</td>\n      <td>GEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>True ASDS</td>\n      <td>2</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5e9e3033383ecbb9e534e7cc</td>\n      <td>5.0</td>\n      <td>2</td>\n      <td>B1058</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>85</td>\n      <td>2020-08-30</td>\n      <td>Falcon 9</td>\n      <td>1600.000000</td>\n      <td>SSO</td>\n      <td>CCAFS SLC 40</td>\n      <td>True RTLS</td>\n      <td>4</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5e9e3032383ecb267a34e7c7</td>\n      <td>5.0</td>\n      <td>3</td>\n      <td>B1059</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>90</td>\n      <td>2020-11-05</td>\n      <td>Falcon 9</td>\n      <td>3681.000000</td>\n      <td>MEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>True ASDS</td>\n      <td>1</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>5e9e3032383ecb6bb234e7ca</td>\n      <td>5.0</td>\n      <td>0</td>\n      <td>B1062</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "    FlightNumber        Date BoosterVersion   PayloadMass  Orbit  \\\n0              1  2010-06-04       Falcon 9   6104.959412    LEO   \n1              2  2012-05-22       Falcon 9    525.000000    LEO   \n2              3  2013-03-01       Falcon 9    677.000000    ISS   \n3              4  2013-09-29       Falcon 9    500.000000     PO   \n4              5  2013-12-03       Falcon 9   3170.000000    GTO   \n5              6  2014-01-06       Falcon 9   3325.000000    GTO   \n6              7  2014-04-18       Falcon 9   2296.000000    ISS   \n7              8  2014-07-14       Falcon 9   1316.000000    LEO   \n8              9  2014-08-05       Falcon 9   4535.000000    GTO   \n9             10  2014-09-07       Falcon 9   4428.000000    GTO   \n10            11  2014-09-21       Falcon 9   2216.000000    ISS   \n11            12  2015-01-10       Falcon 9   2395.000000    ISS   \n12            13  2015-02-11       Falcon 9    570.000000  ES-L1   \n13            14  2015-04-14       Falcon 9   1898.000000    ISS   \n14            15  2015-04-27       Falcon 9   4707.000000    GTO   \n16            17  2015-12-22       Falcon 9   2034.000000    LEO   \n17            18  2016-01-17       Falcon 9    553.000000     PO   \n25            26  2017-01-14       Falcon 9   9600.000000     PO   \n29            30  2017-05-01       Falcon 9   6104.959412    LEO   \n33            34  2017-06-25       Falcon 9   9600.000000     PO   \n36            37  2017-08-24       Falcon 9    475.000000    SSO   \n38            39  2017-10-09       Falcon 9   9600.000000     PO   \n48            49  2018-04-18       Falcon 9    350.000000    HEO   \n56            57  2018-10-08       Falcon 9   2800.000000    SSO   \n58            59  2018-12-03       Falcon 9   4000.000000    SSO   \n60            61  2018-12-23       Falcon 9   4400.000000    MEO   \n64            65  2019-05-24       Falcon 9  13620.000000   VLEO   \n65            66  2019-06-12       Falcon 9   1425.000000    SSO   \n68            69  2019-11-11       Falcon 9  15600.000000   VLEO   \n71            72  2020-01-07       Falcon 9  15400.000000   VLEO   \n72            73  2020-01-19       Falcon 9   6104.959412     SO   \n73            74  2020-01-29       Falcon 9  15600.000000   VLEO   \n74            75  2020-02-17       Falcon 9  15400.000000   VLEO   \n81            82  2020-06-30       Falcon 9   3880.000000    MEO   \n82            83  2020-07-20       Falcon 9   6104.959412    GEO   \n84            85  2020-08-30       Falcon 9   1600.000000    SSO   \n89            90  2020-11-05       Falcon 9   3681.000000    MEO   \n\n      LaunchSite      Outcome  Flights  GridFins  Reused   Legs  \\\n0   CCAFS SLC 40    None None        1     False   False  False   \n1   CCAFS SLC 40    None None        1     False   False  False   \n2   CCAFS SLC 40    None None        1     False   False  False   \n3    VAFB SLC 4E  False Ocean        1     False   False  False   \n4   CCAFS SLC 40    None None        1     False   False  False   \n5   CCAFS SLC 40    None None        1     False   False  False   \n6   CCAFS SLC 40   True Ocean        1     False   False   True   \n7   CCAFS SLC 40   True Ocean        1     False   False   True   \n8   CCAFS SLC 40    None None        1     False   False  False   \n9   CCAFS SLC 40    None None        1     False   False  False   \n10  CCAFS SLC 40  False Ocean        1     False   False  False   \n11  CCAFS SLC 40   False ASDS        1      True   False   True   \n12  CCAFS SLC 40   True Ocean        1      True   False   True   \n13  CCAFS SLC 40   False ASDS        1      True   False   True   \n14  CCAFS SLC 40    None None        1     False   False  False   \n16  CCAFS SLC 40    True RTLS        1      True   False   True   \n17   VAFB SLC 4E   False ASDS        1      True   False   True   \n25   VAFB SLC 4E    True ASDS        1      True   False   True   \n29    KSC LC 39A    True RTLS        1      True   False   True   \n33   VAFB SLC 4E    True ASDS        1      True   False   True   \n36   VAFB SLC 4E    True ASDS        1      True   False   True   \n38   VAFB SLC 4E    True ASDS        1      True   False   True   \n48  CCAFS SLC 40    True ASDS        1      True   False   True   \n56   VAFB SLC 4E    True RTLS        2      True    True   True   \n58   VAFB SLC 4E    True ASDS        3      True    True   True   \n60  CCAFS SLC 40    None None        1     False   False  False   \n64  CCAFS SLC 40    True ASDS        3      True    True   True   \n65   VAFB SLC 4E    True RTLS        2      True    True   True   \n68  CCAFS SLC 40    True ASDS        4      True    True   True   \n71  CCAFS SLC 40    True ASDS        4      True    True   True   \n72    KSC LC 39A    None None        4     False    True  False   \n73  CCAFS SLC 40    True ASDS        3      True    True   True   \n74  CCAFS SLC 40   False ASDS        4      True    True   True   \n81  CCAFS SLC 40    True ASDS        1      True   False   True   \n82  CCAFS SLC 40    True ASDS        2      True    True   True   \n84  CCAFS SLC 40    True RTLS        4      True    True   True   \n89  CCAFS SLC 40    True ASDS        1      True   False   True   \n\n                  LandingPad  Block  ReusedCount Serial   Longitude  \\\n0                        NaN    1.0            0  B0003  -80.577366   \n1                        NaN    1.0            0  B0005  -80.577366   \n2                        NaN    1.0            0  B0007  -80.577366   \n3                        NaN    1.0            0  B1003 -120.610829   \n4                        NaN    1.0            0  B1004  -80.577366   \n5                        NaN    1.0            0  B1005  -80.577366   \n6                        NaN    1.0            0  B1006  -80.577366   \n7                        NaN    1.0            0  B1007  -80.577366   \n8                        NaN    1.0            0  B1008  -80.577366   \n9                        NaN    1.0            0  B1011  -80.577366   \n10                       NaN    1.0            0  B1010  -80.577366   \n11  5e9e3032383ecb761634e7cb    1.0            0  B1012  -80.577366   \n12                       NaN    1.0            0  B1013  -80.577366   \n13  5e9e3032383ecb761634e7cb    1.0            0  B1015  -80.577366   \n14                       NaN    1.0            0  B1016  -80.577366   \n16  5e9e3032383ecb267a34e7c7    1.0            0  B1019  -80.577366   \n17  5e9e3033383ecbb9e534e7cc    1.0            0  B1017 -120.610829   \n25  5e9e3033383ecbb9e534e7cc    3.0            1  B1029 -120.610829   \n29  5e9e3032383ecb267a34e7c7    3.0            1  B1032  -80.603956   \n33  5e9e3033383ecbb9e534e7cc    3.0            1  B1036 -120.610829   \n36  5e9e3033383ecbb9e534e7cc    3.0            1  B1038 -120.610829   \n38  5e9e3033383ecbb9e534e7cc    4.0            1  B1041 -120.610829   \n48  5e9e3032383ecb6bb234e7ca    4.0            1  B1045  -80.577366   \n56  5e9e3032383ecb554034e7c9    5.0            4  B1048 -120.610829   \n58  5e9e3033383ecbb9e534e7cc    5.0            3  B1046 -120.610829   \n60                       NaN    5.0            0  B1054  -80.577366   \n64  5e9e3032383ecb6bb234e7ca    5.0            5  B1049  -80.577366   \n65  5e9e3032383ecb554034e7c9    5.0            5  B1051 -120.610829   \n68  5e9e3032383ecb6bb234e7ca    5.0            4  B1048  -80.577366   \n71  5e9e3032383ecb6bb234e7ca    5.0            5  B1049  -80.577366   \n72                       NaN    5.0            3  B1046  -80.603956   \n73  5e9e3032383ecb6bb234e7ca    5.0            5  B1051  -80.577366   \n74  5e9e3032383ecb6bb234e7ca    5.0            3  B1056  -80.577366   \n81  5e9e3033383ecbb9e534e7cc    5.0            2  B1060  -80.577366   \n82  5e9e3033383ecbb9e534e7cc    5.0            2  B1058  -80.577366   \n84  5e9e3032383ecb267a34e7c7    5.0            3  B1059  -80.577366   \n89  5e9e3032383ecb6bb234e7ca    5.0            0  B1062  -80.577366   \n\n     Latitude  Class  \n0   28.561857      0  \n1   28.561857      0  \n2   28.561857      0  \n3   34.632093      0  \n4   28.561857      0  \n5   28.561857      0  \n6   28.561857      1  \n7   28.561857      1  \n8   28.561857      0  \n9   28.561857      0  \n10  28.561857      0  \n11  28.561857      0  \n12  28.561857      1  \n13  28.561857      0  \n14  28.561857      0  \n16  28.561857      1  \n17  34.632093      0  \n25  34.632093      1  \n29  28.608058      1  \n33  34.632093      1  \n36  34.632093      1  \n38  34.632093      1  \n48  28.561857      1  \n56  34.632093      1  \n58  34.632093      1  \n60  28.561857      0  \n64  28.561857      1  \n65  34.632093      1  \n68  28.561857      1  \n71  28.561857      1  \n72  28.608058      0  \n73  28.561857      1  \n74  28.561857      0  \n81  28.561857      1  \n82  28.561857      1  \n84  28.561857      1  \n89  28.561857      1  "
                    },
                    "execution_count": 13,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "grouped = df.groupby(by=[\"Orbit\"])"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK  3: Visualize the relationship between success rate of each orbit type\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Next, we want to visually check if there are any relationship between success rate and orbit type.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Let's create a `bar chart` for the sucess rate of each orbit\n"
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": "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\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# HINT use groupby method on Orbit column and get the mean of Class column\nsns.barplot(y=\"Orbit\", x=\"Class\", data=df)\nplt.xlabel(\"Class\",fontsize=20)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Analyze the ploted bar chart try to find which orbits have high sucess rate.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK  4: Visualize the relationship between FlightNumber and Orbit type\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "For each orbit, we want to see if there is any relationship between FlightNumber and Orbit type.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 1842.38x360 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot a scatter point chart with x axis to be FlightNumber and y axis to be the Orbit, and hue to be the class value\nsns.catplot(y=\"Orbit\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "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"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK  5: Visualize the relationship between Payload and Orbit type\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Similarly, we can plot the Payload vs. Orbit scatter point charts to reveal the relationship between Payload and Orbit type\n"
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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                        "text/plain": "<Figure size 1842.38x360 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot a scatter point chart with x axis to be Payload and y axis to be the Orbit, and hue to be the class value\nsns.catplot(y=\"Orbit\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"PayloadMass\",fontsize=20)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "You should observe that Heavy payloads have a negative influence on GTO orbits and positive on GTO and Polar LEO (ISS) orbits.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK  6: Visualize the launch success yearly trend\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "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"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "The function will help you get the year from the date:\n"
        },
        {
            "cell_type": "code",
            "execution_count": 23,
            "metadata": {},
            "outputs": [],
            "source": "# A function to Extract years from the date \nyear=[]\ndef Extract_year(date):\n    for i in df[\"Date\"]:\n        year.append(i.split(\"-\")[0])\n    return year\ndf[\"year\"] = Extract_year(df[\"Date\"])"
        },
        {
            "cell_type": "code",
            "execution_count": 24,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "0     2010\n1     2012\n2     2013\n3     2013\n4     2013\n      ... \n85    2020\n86    2020\n87    2020\n88    2020\n89    2020\nName: year, Length: 90, dtype: object"
                    },
                    "execution_count": 24,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df[\"year\"]"
        },
        {
            "cell_type": "code",
            "execution_count": 26,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<AxesSubplot:xlabel='year', ylabel='Class'>"
                    },
                    "execution_count": 26,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\nsns.lineplot(x=\"year\",y=\"Class\",data=df)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "you can observe that the sucess rate since 2013 kept increasing till 2020\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Features Engineering\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "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"
        },
        {
            "cell_type": "code",
            "execution_count": 27,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "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>",
                        "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  "
                    },
                    "execution_count": 27,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "features = df[['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights', 'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount', 'Serial']]\nfeatures.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK  7: Create dummy variables to categorical columns\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "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"
        },
        {
            "cell_type": "code",
            "execution_count": 34,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>PayloadMass</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Orbit_ES-L1</th>\n      <th>Orbit_GEO</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>1</td>\n      <td>6104.959412</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>525.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>677.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>500.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>3170.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 80 columns</p>\n</div>",
                        "text/plain": "   FlightNumber  PayloadMass  Flights  GridFins  Reused   Legs  Block  \\\n0             1  6104.959412        1     False   False  False    1.0   \n1             2   525.000000        1     False   False  False    1.0   \n2             3   677.000000        1     False   False  False    1.0   \n3             4   500.000000        1     False   False  False    1.0   \n4             5  3170.000000        1     False   False  False    1.0   \n\n   ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n0            0            0          0  ...             0             0   \n1            0            0          0  ...             0             0   \n2            0            0          0  ...             0             0   \n3            0            0          0  ...             0             0   \n4            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   \n3             0             0             0             0             0   \n4             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  \n3             0             0             0  \n4             0             0             0  \n\n[5 rows x 80 columns]"
                    },
                    "execution_count": 34,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# HINT: Use get_dummies() function on the categorical columns\nfeatures_one_hot = pd.get_dummies(data=features,columns=[\"Orbit\",\"LaunchSite\",\"LandingPad\",\"Serial\"])\nfeatures_one_hot.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK  8: Cast all numeric columns to `float64`\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Now that our <code>features_one_hot</code> dataframe only contains numbers cast the entire dataframe to variable type <code>float64</code>\n"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": "# HINT: use astype function\nfeatures_one_hot.astype(float64)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "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"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<code>features_one_hot.to_csv('dataset_part\\_3.csv', index=False)</code>\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Authors\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<a href=\"https://www.linkedin.com/in/nayefaboutayoun/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Nayef Abou Tayoun</a> is a Data Scientist at IBM and pursuing a Master of Management in Artificial intelligence degree at Queen's University.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Change Log\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "| Date (YYYY-MM-DD) | Version | Changed By | Change Description      |\n| ----------------- | ------- | ---------- | ----------------------- |\n| 2020-09-20        | 1.0     | Joseph     | Modified Multiple Areas |\n| 2020-11-10        | 1.1     | Nayef      | updating the input data |\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Copyright \u00a9 2020 IBM Corporation. All rights reserved.\n"
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.8",
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        "language_info": {
            "codemirror_mode": {
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