Exploratory Analysis Using Pandas and Matplotlib.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['Date'] = pd.to_datetime(df['Date'])\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": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 1842.38x360 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot a scatter point chart with x axis to be Pay Load Mass (kg) and y axis to be the launch site, and hue to be the class value\nsns.catplot(y=\"LaunchSite\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"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": 6,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<AxesSubplot:xlabel='Orbit'>"
                    },
                    "execution_count": 6,
                    "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\nmean = df.groupby('Orbit').mean()\n# mean.head()\nmean.plot.bar(y='Class')"
        },
        {
            "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": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 1842.38x360 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot a scatter point chart with x axis to be FlightNumber and y axis to be the Orbit, and hue to be the class value\nsns.catplot(y=\"Orbit\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "You should see that in the LEO orbit the Success appears related to the number of flights; on the other hand, there seems to be no relationship between flight number when in GTO orbit.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK  5: Visualize the relationship between Payload and Orbit type\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Similarly, we can plot the Payload vs. Orbit scatter point charts to reveal the relationship between Payload and Orbit type\n"
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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                        "text/plain": "<Figure size 1842.38x360 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot a scatter point chart with x axis to be Payload and y axis to be the Orbit, and hue to be the class value\nsns.catplot(y=\"Orbit\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"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": 9,
            "metadata": {},
            "outputs": [],
            "source": "# # A function to Extract years from the date \n# year=[]\n# def Extract_year(date):\n#     for i in df[\"Date\"]:\n#         year.append(i.split(\"-\")[0])\n#     return year\ndf['Year'] = df['Date'].dt.year    "
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\nsns.lineplot(y=\"Class\", x='Year', data=df)\nplt.xlabel(\"Year\",fontsize=20)\nplt.ylabel(\"Class\",fontsize=20)\nplt.show()"
        },
        {
            "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": [
                {
                    "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>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \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        0.0        0.0        0.0        1.0   \n1          0.0        0.0        0.0        0.0        0.0        1.0   \n2          0.0        0.0        0.0        0.0        1.0        0.0   \n3          0.0        0.0        0.0        0.0        0.0        0.0   \n4          0.0        0.0        1.0        0.0        0.0        0.0   \n\n   Orbit_MEO  Orbit_PO  Orbit_SO  Orbit_SSO  ...  Serial_B1048  Serial_B1049  \\\n0        0.0       0.0       0.0        0.0  ...           0.0           0.0   \n1        0.0       0.0       0.0        0.0  ...           0.0           0.0   \n2        0.0       0.0       0.0        0.0  ...           0.0           0.0   \n3        0.0       1.0       0.0        0.0  ...           0.0           0.0   \n4        0.0       0.0       0.0        0.0  ...           0.0           0.0   \n\n   Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n0           0.0           0.0           0.0           0.0           0.0   \n1           0.0           0.0           0.0           0.0           0.0   \n2           0.0           0.0           0.0           0.0           0.0   \n3           0.0           0.0           0.0           0.0           0.0   \n4           0.0           0.0           0.0           0.0           0.0   \n\n   Serial_B1059  Serial_B1060  Serial_B1062  \n0           0.0           0.0           0.0  \n1           0.0           0.0           0.0  \n2           0.0           0.0           0.0  \n3           0.0           0.0           0.0  \n4           0.0           0.0           0.0  \n\n[5 rows x 72 columns]"
                    },
                    "execution_count": 13,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# HINT: use astype function\nfeatures_one_hot = features_one_hot.astype('float64')\nfeatures_one_hot.head()"
        },
        {
            "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": "code",
            "execution_count": 14,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>PayloadMass</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Orbit_ES-L1</th>\n      <th>Orbit_GEO</th>\n      <th>...</th>\n      <th>Serial_B1048</th>\n      <th>Serial_B1049</th>\n      <th>Serial_B1050</th>\n      <th>Serial_B1051</th>\n      <th>Serial_B1054</th>\n      <th>Serial_B1056</th>\n      <th>Serial_B1058</th>\n      <th>Serial_B1059</th>\n      <th>Serial_B1060</th>\n      <th>Serial_B1062</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>6104.959412</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>525.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>677.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>500.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>3170.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 80 columns</p>\n</div>",
                        "text/plain": "   FlightNumber  PayloadMass  Flights  GridFins  Reused   Legs  Block  \\\n0             1  6104.959412        1     False   False  False    1.0   \n1             2   525.000000        1     False   False  False    1.0   \n2             3   677.000000        1     False   False  False    1.0   \n3             4   500.000000        1     False   False  False    1.0   \n4             5  3170.000000        1     False   False  False    1.0   \n\n   ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n0            0            0          0  ...             0             0   \n1            0            0          0  ...             0             0   \n2            0            0          0  ...             0             0   \n3            0            0          0  ...             0             0   \n4            0            0          0  ...             0             0   \n\n   Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n0             0             0             0             0             0   \n1             0             0             0             0             0   \n2             0             0             0             0             0   \n3             0             0             0             0             0   \n4             0             0             0             0             0   \n\n   Serial_B1059  Serial_B1060  Serial_B1062  \n0             0             0             0  \n1             0             0             0  \n2             0             0             0  \n3             0             0             0  \n4             0             0             0  \n\n[5 rows x 80 columns]"
                    },
                    "execution_count": 14,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# Needed for quiz\npd.get_dummies(features).head()"
        },
        {
            "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 3.8",
            "language": "python",
            "name": "python3"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "file_extension": ".py",
            "mimetype": "text/x-python",
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
            "version": "3.8.12"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 4
}