EDA_Visualizations.ipynb

{
    "cells": [
        {
            "metadata": {},
            "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\u00a0Data\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\u00a0Data  Feature Engineering\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "***\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": "# 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",
            "execution_count": 1,
            "outputs": []
        },
        {
            "metadata": {},
            "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": "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)",
            "execution_count": 2,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 2,
                    "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": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "First, let's try to see how the `FlightNumber` (indicating the continuous launch attempts.) and `Payload` variables would affect the launch outcome.\n\nWe can plot out the <code>FlightNumber</code> vs. <code>PayloadMass</code>and overlay the outcome of the launch. We see that as the flight number increases, the first stage is more likely to land successfully. The payload mass is also important; it seems the more massive the payload, the less likely the first stage will return.\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "sns.catplot(y=\"PayloadMass\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Pay load Mass (kg)\",fontsize=20)\nplt.show()",
            "execution_count": 3,
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 2558.88x500 with 1 Axes>",
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                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "We see that different launch sites have different success rates.  <code>CCAFS LC-40</code>, has a success rate of 60 %, while  <code>KSC LC-39A</code> and <code>VAFB SLC 4E</code> has a success rate of 77%.\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Next, let's drill down to each site visualize its detailed launch records.\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### TASK 1: Visualize the relationship between Flight Number and Launch Site\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Use the function <code>catplot</code> to plot <code>FlightNumber</code> vs <code>LaunchSite</code>, set the  parameter <code>x</code>  parameter to <code>FlightNumber</code>,set the  <code>y</code> to <code>Launch Site</code> and set the parameter <code>hue</code> to <code>'class'</code>\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# Plot a scatter point chart with x axis to be Flight Number and y axis to be the launch site, and hue to be the class value\nsns.catplot( x=\"FlightNumber\", y=\"LaunchSite\", hue='Class', data=df, aspect=3 )\nplt.xlabel( 'Flight Number' )\nplt.ylabel( 'Launch Site' )\nplt.title( 'Flight Number and Launch Site Relation' )\nplt.show()",
            "execution_count": 4,
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 1558.88x500 with 1 Axes>",
                        "image/png": 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                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Now try to explain the patterns you found in the Flight Number vs. Launch Site scatter point plots.\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### TASK 2: Visualize the relationship between Payload and Launch Site\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "We also want to observe if there is any relationship between launch sites and their payload mass.\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# Plot a scatter point chart with x axis to be Pay Load Mass (kg) and y axis to be the launch site, and hue to be the class value\nsns.catplot( x='PayloadMass', y='LaunchSite', hue='Class', data=df, aspect=3 )\nplt.xlabel( 'Pay load Mass (kg)' )\nplt.ylabel( 'Launch Site' )\nplt.title( 'Payload per Launch Site' )\nplt.show()",
            "execution_count": 5,
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 1558.88x500 with 1 Axes>",
                        "image/png": 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\n"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Now if you observe Payload Vs. Launch Site scatter point chart you will find for the VAFB-SLC  launchsite there are no  rockets  launched for  heavypayload mass(greater than 10000).\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### TASK  3: Visualize the relationship between success rate of each orbit type\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Next, we want to visually check if there are any relationship between success rate and orbit type.\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Let's create a `bar chart` for the sucess rate of each orbit\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# HINT use groupby method on Orbit column and get the mean of Class column\nax = sns.barplot( x='Orbit', y='Class', data=df.loc[:,['Orbit','Class']].groupby('Orbit').mean('Class').reset_index() )\nax.bar_label( ax.containers[0] )\nplt.xlabel( 'Orbit ID' )\nplt.ylabel( 'Success Rate' )\nplt.title( 'Success Rate per Orbit Identificator' )\nplt.show()",
            "execution_count": 6,
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 640x480 with 1 Axes>",
                        "image/png": 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\n"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Analyze the ploted bar chart try to find which orbits have high sucess rate.\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### TASK  4: Visualize the relationship between FlightNumber and Orbit type\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "For each orbit, we want to see if there is any relationship between FlightNumber and Orbit type.\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# Plot a scatter point chart with x axis to be FlightNumber and y axis to be the Orbit, and hue to be the class value\nsns.catplot( x='FlightNumber', y='Orbit', hue='Class', data=df, aspect=3 )\nplt.xlabel( 'Flight Number' )\nplt.ylabel( 'Orbit ID' )\nplt.title( 'Orbit Identificator and Flight Number' )\nplt.show()",
            "execution_count": 7,
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 1558.88x500 with 1 Axes>",
                        "image/png": 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                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "You should see that in the LEO orbit the Success appears related to the number of flights; on the other hand, there seems to be no relationship between flight number when in GTO orbit.\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### TASK  5: Visualize the relationship between Payload and Orbit type\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Similarly, we can plot the Payload vs. Orbit scatter point charts to reveal the relationship between Payload and Orbit type\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# Plot a scatter point chart with x axis to be Payload and y axis to be the Orbit, and hue to be the class value\nsns.catplot( x='PayloadMass', y='Orbit', hue='Class', data=df, aspect=3 )\nplt.xlabel( 'Pay load Mass (kg)' )\nplt.ylabel( 'Orbit ID' )\nplt.title( 'Orbit Identificator and Pay Load' )\nplt.show()",
            "execution_count": 8,
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 1558.88x500 with 1 Axes>",
                        "image/png": 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\n"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "With heavy payloads the successful landing or positive landing rate are more for Polar,LEO and ISS.\n\nHowever for GTO we cannot distinguish this well as both positive landing rate and negative landing(unsuccessful mission) are both there here.\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### TASK  6: Visualize the launch success yearly trend\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "You can plot a line chart with x axis to be <code>Year</code> and y axis to be average success rate, to get the average launch success trend.\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "The function will help you get the year from the date:\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# A function to Extract years from the date \nyear=[]\ndef Extract_year(date):\n    for i in df[\"Date\"]:\n        year.append(i.split(\"-\")[0])\n    return year",
            "execution_count": 9,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\ndf[\"N_Year\"]= Extract_year( df['Date'] )\nsns.lineplot( x='N_Year', y='Class', data=df.groupby('N_Year').agg({'Class':'mean'}).reset_index() )\nplt.xlabel( 'Year' )\nplt.ylabel( 'Success Rate' )\nplt.title( 'Sucess Rate per Year' )\nplt.show()",
            "execution_count": 10,
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 640x480 with 1 Axes>",
                        "image/png": 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\n"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "you can observe that the sucess rate since 2013 kept increasing till 2020\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Features Engineering\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "By now, you should obtain some preliminary insights about how each important variable would affect the success rate, we will select the features that will be used in success prediction in the future module.\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "features = df[['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights', 'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount', 'Serial']]\nfeatures.head()",
            "execution_count": 11,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 11,
                    "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": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### TASK  7: Create dummy variables to categorical columns\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Use the function <code>get_dummies</code> and <code>features</code> dataframe to apply OneHotEncoder to the column <code>Orbits</code>, <code>LaunchSite</code>, <code>LandingPad</code>, and <code>Serial</code>. Assign the value to the variable <code>features_one_hot</code>, display the results using the method head. Your result dataframe must include all features including the encoded ones.\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# HINT: Use get_dummies() function on the categorical columns\nfeatures_one_hot = pd.get_dummies( features, columns=['Orbit','LaunchSite','LandingPad','Serial'] )\nfeatures_one_hot.head( n=5 )",
            "execution_count": 12,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 12,
                    "data": {
                        "text/plain": "   FlightNumber  PayloadMass  Flights  GridFins  Reused   Legs  Block  \\\n0             1  6104.959412        1     False   False  False    1.0   \n1             2   525.000000        1     False   False  False    1.0   \n2             3   677.000000        1     False   False  False    1.0   \n3             4   500.000000        1     False   False  False    1.0   \n4             5  3170.000000        1     False   False  False    1.0   \n\n   ReusedCount  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]",
                        "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>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "### TASK  8: Cast all numeric columns to `float64`\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Now that our <code>features_one_hot</code> dataframe only contains numbers cast the entire dataframe to variable type <code>float64</code>\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# HINT: use astype function\nfeatures_one_hot = features_one_hot.astype( 'float64' )",
            "execution_count": 13,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "We can now export it to a <b>CSV</b> for the next section,but to make the answers consistent, in the next lab we will provide data in a pre-selected date range.\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "<code>features_one_hot.to_csv('dataset_part\\_3.csv', index=False)</code>\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "features_one_hot.to_csv( 'dataset_part_3.csv', index=False )",
            "execution_count": 14,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Authors\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">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"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">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"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Change Log\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "| Date (YYYY-MM-DD) | Version | Changed By    | Change Description      |\n| ----------------- | ------- | ------------- | ----------------------- |\n| 2021-10-12        | 1.1     | Lakshmi Holla | Modified markdown       |\n| 2020-09-20        | 1.0     | Joseph        | Modified Multiple Areas |\n| 2020-11-10        | 1.1     | Nayef         | updating the input data |\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Copyright \u00a9 2020 IBM Corporation. All rights reserved.\n"
        }
    ],
    "metadata": {
        "kernelspec": {
            "name": "python3",
            "display_name": "Python 3.10",
            "language": "python"
        },
        "language_info": {
            "name": "python",
            "version": "3.10.6",
            "mimetype": "text/x-python",
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "pygments_lexer": "ipython3",
            "nbconvert_exporter": "python",
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