jupyter-labs-eda-dataviz.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "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"
        },
        {
            "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": 4,
            "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": 5,
            "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": 5,
                    "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": 6,
            "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": 7,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 1122.38x360 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=3)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Launch Site\",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": 8,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 1122.38x360 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=3)\nplt.xlabel(\"Pay Load Mass (kg)\",fontsize=20)\nplt.ylabel(\"Launch Site\",fontsize=20)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "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"
        },
        {
            "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": 9,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<AxesSubplot:xlabel='Orbit'>"
                    },
                    "execution_count": 9,
                    "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": "# HINT use groupby method on Orbit column and get the mean of Class column\ndf.groupby('Orbit')['Class'].mean().plot.bar()"
        },
        {
            "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": 10,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 1122.38x360 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=3)\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": 11,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 1122.38x360 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=3)\nplt.xlabel(\"Pay Load Mass (kg)\",fontsize=20)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "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"
        },
        {
            "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": 12,
            "metadata": {},
            "outputs": [],
            "source": "# A function to Extract years from the date \nyear=[]\ndef Extract_year(date):\n    for i in date:\n        year.append(i.split(\"-\")[0])\n    return year\n    \n    "
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<AxesSubplot:xlabel='Year'>"
                    },
                    "execution_count": 13,
                    "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\ntempdf = df.copy()\ntempdf['Year'] = Extract_year(df['Date'])\ntempdf.groupby('Year')['Class'].mean().plot()"
        },
        {
            "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": 21,
            "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": 21,
                    "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": 30,
            "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>Outcome</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>Block</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>2010-06-04</td>\n      <td>Falcon 9</td>\n      <td>6104.959412</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</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>2012-05-22</td>\n      <td>Falcon 9</td>\n      <td>525.000000</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</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>2013-03-01</td>\n      <td>Falcon 9</td>\n      <td>677.000000</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</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>2013-09-29</td>\n      <td>Falcon 9</td>\n      <td>500.000000</td>\n      <td>False Ocean</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</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>2013-12-03</td>\n      <td>Falcon 9</td>\n      <td>3170.000000</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</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 82 columns</p>\n</div>",
                        "text/plain": "   FlightNumber        Date BoosterVersion  PayloadMass      Outcome  Flights  \\\n0             1  2010-06-04       Falcon 9  6104.959412    None None        1   \n1             2  2012-05-22       Falcon 9   525.000000    None None        1   \n2             3  2013-03-01       Falcon 9   677.000000    None None        1   \n3             4  2013-09-29       Falcon 9   500.000000  False Ocean        1   \n4             5  2013-12-03       Falcon 9  3170.000000    None None        1   \n\n   GridFins  Reused   Legs  Block  ...  Serial_B1048  Serial_B1049  \\\n0     False   False  False    1.0  ...             0             0   \n1     False   False  False    1.0  ...             0             0   \n2     False   False  False    1.0  ...             0             0   \n3     False   False  False    1.0  ...             0             0   \n4     False   False  False    1.0  ...             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 82 columns]"
                    },
                    "execution_count": 30,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# HINT: Use get_dummies() function on the categorical columns\nfeatures_one_hot=pd.get_dummies(df, columns=['Orbit', 'LaunchSite', 'LandingPad', 'Serial'], drop_first = True)\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": 32,
            "metadata": {},
            "outputs": [],
            "source": "# HINT: use astype function\nfeatures_one_hot=features_one_hot.astype('float64', copy=True, errors='ignore')\nfeatures_one_hot.to_csv('dataset_part_3.csv', index=False)"
        },
        {
            "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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">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"
        },
        {
            "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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">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"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Change Log\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "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"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Copyright \u00a9 2020 IBM Corporation. All rights reserved.\n"
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python",
            "language": "python",
            "name": "conda-env-python-py"
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        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
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            "file_extension": ".py",
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            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
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