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(\"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": 5,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<Figure size 648x432 with 0 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "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\nplt.figure(figsize = (9,6))\nsns.catplot(y=\"LaunchSite\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Flight Number\",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": 6,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "Text(0, 0.5, 'Success rate')"
                    },
                    "execution_count": 6,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 648x432 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\norbit_df = df[[\"Orbit\",\"Class\"]].groupby(by = \"Orbit\", axis = 0).mean().reset_index()\nplt.figure(figsize = (9,6))\nplt.bar(orbit_df[\"Orbit\"],orbit_df[\"Class\"])\nplt.title(\"Relationship between success rate and orbit type\")\nplt.ylabel(\"Success rate\")"
        },
        {
            "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": 7,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<Figure size 648x432 with 0 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "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\nplt.figure(figsize = (9,6))\nsns.catplot(y=\"Orbit\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Orbit\",fontsize=20)\nplt.ylabel(\"Flight Number\",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": 8,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<Figure size 648x432 with 0 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "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 Payload and y axis to be the Orbit, and hue to be the class value\nplt.figure(figsize = (9,6))\nsns.catplot(y=\"Orbit\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Orbit\",fontsize=20)\nplt.ylabel(\"Payload Mass (kg)\",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": 9,
            "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\"])\nyear_df = df[[\"Year\",\"Class\"]].groupby(by = \"Year\", axis = 0).mean().reset_index()"
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "Text(0.5, 1.0, 'The launch success yearly trend')"
                    },
                    "execution_count": 10,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 864x576 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\nplt.figure(figsize = (12,8))\nplt.plot(year_df[\"Year\"],year_df[\"Class\"])\nplt.ylabel(\"Success rate\")\nplt.title(\"The launch success yearly trend\")"
        },
        {
            "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": 11,
            "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": 11,
                    "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": 12,
            "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>Orbit_ES-L1</th>\n      <th>Orbit_GEO</th>\n      <th>Orbit_GTO</th>\n      <th>Orbit_HEO</th>\n      <th>Orbit_ISS</th>\n      <th>Orbit_LEO</th>\n      <th>Orbit_MEO</th>\n      <th>Orbit_PO</th>\n      <th>Orbit_SO</th>\n      <th>Orbit_SSO</th>\n      <th>...</th>\n      <th>Serial_B1048</th>\n      <th>Serial_B1049</th>\n      <th>Serial_B1050</th>\n      <th>Serial_B1051</th>\n      <th>Serial_B1054</th>\n      <th>Serial_B1056</th>\n      <th>Serial_B1058</th>\n      <th>Serial_B1059</th>\n      <th>Serial_B1060</th>\n      <th>Serial_B1062</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\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>1</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>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>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 72 columns</p>\n</div>",
                        "text/plain": "   Orbit_ES-L1  Orbit_GEO  Orbit_GTO  Orbit_HEO  Orbit_ISS  Orbit_LEO  \\\n0            0          0          0          0          0          1   \n1            0          0          0          0          0          1   \n2            0          0          0          0          1          0   \n3            0          0          0          0          0          0   \n4            0          0          1          0          0          0   \n\n   Orbit_MEO  Orbit_PO  Orbit_SO  Orbit_SSO  ...  Serial_B1048  Serial_B1049  \\\n0          0         0         0          0  ...             0             0   \n1          0         0         0          0  ...             0             0   \n2          0         0         0          0  ...             0             0   \n3          0         1         0          0  ...             0             0   \n4          0         0         0          0  ...             0             0   \n\n   Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n0             0             0             0             0             0   \n1             0             0             0             0             0   \n2             0             0             0             0             0   \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 72 columns]"
                    },
                    "execution_count": 12,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# HINT: Use get_dummies() function on the categorical columns\nfeatures_one_hot = pd.get_dummies(features[[\"Orbit\",\"LaunchSite\",\"LandingPad\",\"Serial\"]])\nfeatures_one_hot.head()"
        },
        {
            "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": 13,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 90 entries, 0 to 89\nData columns (total 72 columns):\n #   Column                               Non-Null Count  Dtype  \n---  ------                               --------------  -----  \n 0   Orbit_ES-L1                          90 non-null     float64\n 1   Orbit_GEO                            90 non-null     float64\n 2   Orbit_GTO                            90 non-null     float64\n 3   Orbit_HEO                            90 non-null     float64\n 4   Orbit_ISS                            90 non-null     float64\n 5   Orbit_LEO                            90 non-null     float64\n 6   Orbit_MEO                            90 non-null     float64\n 7   Orbit_PO                             90 non-null     float64\n 8   Orbit_SO                             90 non-null     float64\n 9   Orbit_SSO                            90 non-null     float64\n 10  Orbit_VLEO                           90 non-null     float64\n 11  LaunchSite_CCAFS SLC 40              90 non-null     float64\n 12  LaunchSite_KSC LC 39A                90 non-null     float64\n 13  LaunchSite_VAFB SLC 4E               90 non-null     float64\n 14  LandingPad_5e9e3032383ecb267a34e7c7  90 non-null     float64\n 15  LandingPad_5e9e3032383ecb554034e7c9  90 non-null     float64\n 16  LandingPad_5e9e3032383ecb6bb234e7ca  90 non-null     float64\n 17  LandingPad_5e9e3032383ecb761634e7cb  90 non-null     float64\n 18  LandingPad_5e9e3033383ecbb9e534e7cc  90 non-null     float64\n 19  Serial_B0003                         90 non-null     float64\n 20  Serial_B0005                         90 non-null     float64\n 21  Serial_B0007                         90 non-null     float64\n 22  Serial_B1003                         90 non-null     float64\n 23  Serial_B1004                         90 non-null     float64\n 24  Serial_B1005                         90 non-null     float64\n 25  Serial_B1006                         90 non-null     float64\n 26  Serial_B1007                         90 non-null     float64\n 27  Serial_B1008                         90 non-null     float64\n 28  Serial_B1010                         90 non-null     float64\n 29  Serial_B1011                         90 non-null     float64\n 30  Serial_B1012                         90 non-null     float64\n 31  Serial_B1013                         90 non-null     float64\n 32  Serial_B1015                         90 non-null     float64\n 33  Serial_B1016                         90 non-null     float64\n 34  Serial_B1017                         90 non-null     float64\n 35  Serial_B1018                         90 non-null     float64\n 36  Serial_B1019                         90 non-null     float64\n 37  Serial_B1020                         90 non-null     float64\n 38  Serial_B1021                         90 non-null     float64\n 39  Serial_B1022                         90 non-null     float64\n 40  Serial_B1023                         90 non-null     float64\n 41  Serial_B1025                         90 non-null     float64\n 42  Serial_B1026                         90 non-null     float64\n 43  Serial_B1028                         90 non-null     float64\n 44  Serial_B1029                         90 non-null     float64\n 45  Serial_B1030                         90 non-null     float64\n 46  Serial_B1031                         90 non-null     float64\n 47  Serial_B1032                         90 non-null     float64\n 48  Serial_B1034                         90 non-null     float64\n 49  Serial_B1035                         90 non-null     float64\n 50  Serial_B1036                         90 non-null     float64\n 51  Serial_B1037                         90 non-null     float64\n 52  Serial_B1038                         90 non-null     float64\n 53  Serial_B1039                         90 non-null     float64\n 54  Serial_B1040                         90 non-null     float64\n 55  Serial_B1041                         90 non-null     float64\n 56  Serial_B1042                         90 non-null     float64\n 57  Serial_B1043                         90 non-null     float64\n 58  Serial_B1044                         90 non-null     float64\n 59  Serial_B1045                         90 non-null     float64\n 60  Serial_B1046                         90 non-null     float64\n 61  Serial_B1047                         90 non-null     float64\n 62  Serial_B1048                         90 non-null     float64\n 63  Serial_B1049                         90 non-null     float64\n 64  Serial_B1050                         90 non-null     float64\n 65  Serial_B1051                         90 non-null     float64\n 66  Serial_B1054                         90 non-null     float64\n 67  Serial_B1056                         90 non-null     float64\n 68  Serial_B1058                         90 non-null     float64\n 69  Serial_B1059                         90 non-null     float64\n 70  Serial_B1060                         90 non-null     float64\n 71  Serial_B1062                         90 non-null     float64\ndtypes: float64(72)\nmemory usage: 50.8 KB\n"
                }
            ],
            "source": "# HINT: use astype function\nfeatures_one_hot = features_one_hot.astype(float)\nfeatures_one_hot.info()"
        },
        {
            "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| 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 3.9",
            "language": "python",
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        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
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            "file_extension": ".py",
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