EDA with visualization lab.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": 50,
            "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": 51,
            "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": 51,
                    "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": 52,
            "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": 53,
            "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": "it seems that KSC LC 39A site is new and it starts to be used after the 25 flights approximately and we can see that starting from the 25 flight till the 42 flights are almost on this site. \nthe CCAFS SLC 40 site is extensively used starting from the first flight so maybe it is the oldest site and most of the flights are launched from it. we can see also that between flight numbers 25 and 42 there is a gap and this gap is due to the fact that the KSC LC 39A is used instead.\nthe VAFB SLC 4E site is rarely used compared to the other sites."
        },
        {
            "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": 54,
            "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(x=\"LaunchSite\",y=\"PayloadMass\",hue='Class',data=df,aspect=5)\nplt.ylabel(\"Payload Mass\",fontsize=20)\nplt.xlabel(\" Launch Site\",fontsize=20)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Now try to explain any patterns you found in the Payload Vs. Launch Site scatter point chart.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "CCAFS SLC 40 site is used for low loads and high payloads, and the same can be recognized for the KSC LC 39A sight. whereas the VAFB SLC 4E site is used for mid-payload."
        },
        {
            "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": 55,
            "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</th>\n      <th>Rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>ES-L1</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>GEO</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>GTO</td>\n      <td>0.518519</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>HEO</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>ISS</td>\n      <td>0.619048</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>LEO</td>\n      <td>0.714286</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>MEO</td>\n      <td>0.666667</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>PO</td>\n      <td>0.666667</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>SO</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>SSO</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>VLEO</td>\n      <td>0.857143</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "    Orbit      Rate\n0   ES-L1  1.000000\n1     GEO  1.000000\n2     GTO  0.518519\n3     HEO  1.000000\n4     ISS  0.619048\n5     LEO  0.714286\n6     MEO  0.666667\n7      PO  0.666667\n8      SO  0.000000\n9     SSO  1.000000\n10   VLEO  0.857143"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "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\nbar_data = df.groupby(df['Orbit'])['Class'].mean().reset_index(name=\"Rate\")\n#bar_data.columns=['Orbit','Rate']\ndisplay(bar_data)\nsns.barplot(x='Orbit',y='Rate',data=bar_data)\nplt.ylabel(\"Success Rate\",fontsize=20)\nplt.xlabel(\"Orbit\",fontsize=20)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Analyze the ploted bar chart try to find which orbits have high sucess rate.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK  4: Visualize the relationship between FlightNumber and Orbit type\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "For each orbit, we want to see if there is any relationship between FlightNumber and Orbit type.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 56,
            "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(x=\"FlightNumber\",y=\"Orbit\",hue='Class',data=df,aspect=5)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.xlabel(\" 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": 57,
            "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(x=\"PayloadMass\",y=\"Orbit\",hue='Class',data=df,aspect=5)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.xlabel(\" PayloadMass\",fontsize=20)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "You should observe that Heavy payloads have a negative influence on GTO orbits and positive on GTO and Polar LEO (ISS) orbits.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK  6: Visualize the launch success yearly trend\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "You can plot a line chart with x axis to be <code>Year</code> and y axis to be average success rate, to get the average launch success trend.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "The function will help you get the year from the date:\n"
        },
        {
            "cell_type": "code",
            "execution_count": 58,
            "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\n    "
        },
        {
            "cell_type": "code",
            "execution_count": 59,
            "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      <th>year</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      <td>2010</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      <td>2012</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      <td>2013</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      <td>2013</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      <td>2013</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>86</td>\n      <td>2020-09-03</td>\n      <td>Falcon 9</td>\n      <td>15400.000000</td>\n      <td>VLEO</td>\n      <td>KSC LC 39A</td>\n      <td>True ASDS</td>\n      <td>2</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5e9e3032383ecb6bb234e7ca</td>\n      <td>5.0</td>\n      <td>2</td>\n      <td>B1060</td>\n      <td>-80.603956</td>\n      <td>28.608058</td>\n      <td>1</td>\n      <td>2020</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>87</td>\n      <td>2020-10-06</td>\n      <td>Falcon 9</td>\n      <td>15400.000000</td>\n      <td>VLEO</td>\n      <td>KSC LC 39A</td>\n      <td>True ASDS</td>\n      <td>3</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5e9e3032383ecb6bb234e7ca</td>\n      <td>5.0</td>\n      <td>2</td>\n      <td>B1058</td>\n      <td>-80.603956</td>\n      <td>28.608058</td>\n      <td>1</td>\n      <td>2020</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>88</td>\n      <td>2020-10-18</td>\n      <td>Falcon 9</td>\n      <td>15400.000000</td>\n      <td>VLEO</td>\n      <td>KSC LC 39A</td>\n      <td>True ASDS</td>\n      <td>6</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5e9e3032383ecb6bb234e7ca</td>\n      <td>5.0</td>\n      <td>5</td>\n      <td>B1051</td>\n      <td>-80.603956</td>\n      <td>28.608058</td>\n      <td>1</td>\n      <td>2020</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>89</td>\n      <td>2020-10-24</td>\n      <td>Falcon 9</td>\n      <td>15400.000000</td>\n      <td>VLEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>True ASDS</td>\n      <td>3</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5e9e3033383ecbb9e534e7cc</td>\n      <td>5.0</td>\n      <td>2</td>\n      <td>B1060</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>1</td>\n      <td>2020</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>90</td>\n      <td>2020-11-05</td>\n      <td>Falcon 9</td>\n      <td>3681.000000</td>\n      <td>MEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>True ASDS</td>\n      <td>1</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>5e9e3032383ecb6bb234e7ca</td>\n      <td>5.0</td>\n      <td>0</td>\n      <td>B1062</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>1</td>\n      <td>2020</td>\n    </tr>\n  </tbody>\n</table>\n<p>90 rows \u00d7 19 columns</p>\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..           ...         ...            ...           ...   ...           ...   \n85            86  2020-09-03       Falcon 9  15400.000000  VLEO    KSC LC 39A   \n86            87  2020-10-06       Falcon 9  15400.000000  VLEO    KSC LC 39A   \n87            88  2020-10-18       Falcon 9  15400.000000  VLEO    KSC LC 39A   \n88            89  2020-10-24       Falcon 9  15400.000000  VLEO  CCAFS SLC 40   \n89            90  2020-11-05       Falcon 9   3681.000000   MEO  CCAFS SLC 40   \n\n        Outcome  Flights  GridFins  Reused   Legs                LandingPad  \\\n0     None None        1     False   False  False                       NaN   \n1     None None        1     False   False  False                       NaN   \n2     None None        1     False   False  False                       NaN   \n3   False Ocean        1     False   False  False                       NaN   \n4     None None        1     False   False  False                       NaN   \n..          ...      ...       ...     ...    ...                       ...   \n85    True ASDS        2      True    True   True  5e9e3032383ecb6bb234e7ca   \n86    True ASDS        3      True    True   True  5e9e3032383ecb6bb234e7ca   \n87    True ASDS        6      True    True   True  5e9e3032383ecb6bb234e7ca   \n88    True ASDS        3      True    True   True  5e9e3033383ecbb9e534e7cc   \n89    True ASDS        1      True   False   True  5e9e3032383ecb6bb234e7ca   \n\n    Block  ReusedCount Serial   Longitude   Latitude  Class  year  \n0     1.0            0  B0003  -80.577366  28.561857      0  2010  \n1     1.0            0  B0005  -80.577366  28.561857      0  2012  \n2     1.0            0  B0007  -80.577366  28.561857      0  2013  \n3     1.0            0  B1003 -120.610829  34.632093      0  2013  \n4     1.0            0  B1004  -80.577366  28.561857      0  2013  \n..    ...          ...    ...         ...        ...    ...   ...  \n85    5.0            2  B1060  -80.603956  28.608058      1  2020  \n86    5.0            2  B1058  -80.603956  28.608058      1  2020  \n87    5.0            5  B1051  -80.603956  28.608058      1  2020  \n88    5.0            2  B1060  -80.577366  28.561857      1  2020  \n89    5.0            0  B1062  -80.577366  28.561857      1  2020  \n\n[90 rows x 19 columns]"
                    },
                    "metadata": {},
                    "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\ndf['year']=Extract_year(df)\ndisplay(df)"
        },
        {
            "cell_type": "code",
            "execution_count": 61,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<AxesSubplot:xlabel='year', ylabel='Rate'>"
                    },
                    "execution_count": 61,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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YMKoncbXsEFkkUBGIRJADh0q4/a2l1K8bx7NjB8bML73k5g35/SV9+X5THq/MO3IwXWFRCc/O2sDgzs0Z0Utjpr2gIhCJIH/8ZC1rt+/lqSv7065pfb/jhNUvBiUxum87nvpsPWu3/2sw3evfbgyOme4V1W+ki2QqApEI8U36Tv6+YDM3n9GFs2PwL18z47+vOIkmDepyzzvLOFhcwu6CIv46O4NzerVhSIrGTHtFJ+KKRADnHI/PWEdSswbcH8MjlVs0qsfjY07ixjcW8fTn6wHYf7CYCaNj93sSDioCkQgwc/UOlmfv4Ykx/WL+1MgRvdpyzdBOTJ6bSd06dbh8QBK92jXxO1at5umhITMbbWZpZpZuZg8dZZ2zzGyZma02szle5hGJRMUlpTz52Xq6tUnkikGRfbWxcHn4wt50atEQh+OekT38jlPrebZHYGZxwAvASCAbWGhm05xza8qs0wx4ERjtnNtiZrF3YFRi3tSlW0n/cT+TrhsUM2cJVaZRQjxv3XwyO/YU0rGFxkx7zctDQ0OBdOdcJoCZTQEuBcqOGxwHTHXObQFwzv3oYR6RiHOwuIRnv9hA/+SmjOrbzu84ESWpWQOSmjXwO0ZM8PLQUBKQVeZ2dnBZWT2A5mY228wWm9n1FW3IzMab2SIzW5STk+NRXJHw+8eCLWzdXciEUTo1UvzjZRFU9Kwu/5bBeCAVuBAYBTxiZj87IOicm+ycG+ycG9y6devQJxXxwf6DxbzwVTqndWvJ6d1r9xgJiWxeHhrKBjqWuZ0MbKtgnZ3OuXwg38zmAv2B9R7mEokIr329kdz8IiaM6uV3FIlxXu4RLAS6m1kXM6sHjAWmlVvnn8AZZhZvZg2BYcBaDzOJRIRd+UW8PDeTUX3bMqBjM7/jSIzzbI/AOVdsZrcDM4E44DXn3GozuzV4/yTn3FozmwGsAEqBV5xzq7zKJBIp/jong/yiYu4/T2+UEv95+oYy59x0YHq5ZZPK3X4CeMLLHCKRZPueQt74dhOXD0yme9vGfscR0awhkXCbOGsDzjnuPrf2j5iW6KAiEAmjzJz9vLsom2uHddYbpSRiqAhEwujpz9eTEF+H287u5ncUkZ+oCETCZNXWPXy8Yju/Ob0LrRvXzmsQS3RSEYiEyZOfpdGsYV1uHt7V7ygiR1ARiITBd5m5zE7L4d/OPIEm9ev6HUfkCCoCEY8553h8ZhptmyTwq1NT/I4j8jMqAhGPfbnuRxZv3sWd53Snft3YvuiMRCYVgYiHSksdT8xMI6VlQ64a3LHyLxDxgYpAxEMfrdjGuh37uGdkD+rG6cdNIpOemSIeOVRSytOfr6d3+yZc3K+D33FEjkpFIOKRdxZmsTm3gAdG9aSOLkEpEUxFIOKBwqISJs7awJCU5pzVUxdTksimIhDxwBvfbuLHfQd5YLQuQSmRT0UgEmJ7Cg8xaU4GZ/dszZCUFn7HEamUikAkxCbPzWBP4SHuH6WLzkh0UBGIhNCP+w7w2tebuLh/B/p2aOp3HJEqURGIhNALX6ZzqKSU+0b28DuKSJVVuQjMrIGZaV9X5Ciy8gp46/stXDWkIymtGvkdR6TKqlQEZnYxsAyYEbw9wMymeZhLJOr85fP11DHjzhG6BKVEl6ruEfweGArsBnDOLQNSvAgkEo3Sduzjf5dt5denptCuaX2/44hUS1WLoNg5t8fTJCJR7MnP0kisF8+tZ57gdxSRaqtqEawys3FAnJl1N7PngG89zCUSNZZs2cXna35g/PCuNG9Uz+84ItVW1SK4A+gLHATeAvYAd3kVSiRaOOd4YkYarRLrcePpXfyOI1IjVS2CC51z/985NyT48TBwiZfBRKLB1+k7mZ+Zy21nd6NRQrzfcURqpKpF8NsqLhOJGc45Hp+RRlKzBowb1snvOCI1dsw/YczsfOACIMnMJpa5qwlQ7GUwkUj36aodrNy6hyev7E9CvC5BKdGrsn3ZbcAiAoeBFpdZvg+4x6tQIpGuuKSUJz9Lo3ubRC4fmOR3HJHjcswicM4tB5ab2VvOuUNhyiQS8aYu2UpmTj6TrkslThedkShX1Ve3UszsT0Af4Kd3yzjnunqSSiSCHThUwjNfrKd/x2aM6tvW7zgix62qLxa/DvyVwOsCZwNvAn/3KpRIJPvHd1vYtucAD4zqqYvOSK1Q1SJo4JybBZhzbrNz7vfACO9iiUSm/QeLeeGrdE7r1pLTurXyO45ISFT10NABM6sDbDCz24GtQBvvYolEplfmZZKXX8SEUb38jiISMlXdI7gbaAjcCaQCvwSu9yiTSETKyy/ilXkbGd23HQM6NvM7jkjIVGmPwDm3MPjpfuAGM4sHrga+8yqYSKR58at0CoqKuX+ULjojtcsx9wjMrImZ/dbMnjez8yzgdiAduKqyjZvZaDNLM7N0M3voGOsNMbMSMxtT/f8FEe9t213Imws2c8WgZLq1aex3HJGQqmyP4O/ALmA+cBMwAagHXBa8JsFRmVkc8AIwEsgGFprZNOfcmgrWewyYWZP/AZFwmDhrAzi4+1xddEZqn8qKoKtz7iQAM3sF2Al0cs7tq8K2hwLpzrnM4NdPAS4F1pRb7w7gA2BIdYKLhEtGzn7eXZTF9aekkNy8od9xREKusheLf3o3sXOuBNhYxRIASAKyytzODi77iZklAZcDk461ITMbb2aLzGxRTk5OFR9eJDSe/mw99evGcfuIbn5HEfFEZUXQ38z2Bj/2Af0Of25meyv52oreaePK3X4GeDBYMkflnJvsnBvsnBvcunXrSh5WJHRWbd3DJyu385vTu9AqMcHvOCKeqGzW0PGMVMwGOpa5nUxgiF1Zg4EpwXdntgIuMLNi59yHx/G4IiHz+Mw0mjWsy83DNU1Fai8vr6SxEOhuZl0IvAFtLDCu7ArOuZ8u6WRmbwAfqwQkUizIzGXu+hz+/YJeNKlf1+84Ip7xrAicc8XBU01nAnHAa8651WZ2a/D+Y74uIOKnwEVn1tGuSX2uPyXF7zginvL02nrOuenA9HLLKiwA59yvvcwiUh2z1v7Iki27+e/LT6J+XV10Rmq3qo6YEIkZJaWOJ2amkdKyIVcOTvY7jojnVAQi5UxbvpW0H/Zx73k9qRunHxGp/fQsFymjqLiUpz9fT5/2TbjopPZ+xxEJCxWBSBnvLNxCVl4hE0b1pI4uQSkxQkUgElRQVMzEL9MZmtKCs3rqjYsSO1QEIkFvfLuJnH0HeWC0LkEpsUVFIALsKTjEpNkZjOjVhsEpLfyOIxJWnr6PQKQyJaWOA4eOOWoqLF6ck87eA8Xcf15Pv6OIhJ2KQHxTUuq4cOI81u2o6kBbb13SvwN9OjTxO4ZI2KkIxDefrd7Buh37uP6UziQ3b+BrljpmXDYwqfIVRWohFYH4wjnHpDkZdG7ZkP+4uC9xOlVTxDd6sVh88d3GPJZn7+HmM7qqBER8piIQX0yak0GrxHqMSdUsHxG/qQgk7NZu38vstBx+fWqKJnuKRAAVgYTdy3MzaVgvjutO7ux3FBFBRSBhtnV3IdOWb2PskE40a1jP7zgigopAwuzVeRsB+M0ZXSpZU0TCRUUgYbO7oIgpC7dwSf8OJDXz930DIvIvKgIJm/9ZsJmCohLGn9nV7ygiUoaKQMLiwKES3vh2E2f1bE2vdhrjIBJJVAQSFu8vzmbn/iJuPfMEv6OISDkqAvFcSanj5XmZ9O/YjGFdNOJZJNKoCMRzM1fvYHNuAbcO76oLvohEIBWBeMo5x0tzMujSqhHn9W3ndxwRqYCKQDw1PzNXw+VEIpyKQDz10pxMWiXW44pBmvUvEqlUBOKZtdv3Mmd9Djec1kXD5UQimIpAPDN5biaN6sVx3TANlxOJZCoC8UT2rgKmLd/GNUM70bRhXb/jiMgxqAjEE69+vREDbjxdw+VEIp2KQEJuV34RU77P4pIBHeig4XIiEU9FICH3Pws2U3iohFuGa5yESDRQEUhIHR4uN6JXG3q2a+x3HBGpAhWBhNR7i7PJzS/iluEaNS0SLVQEEjIlpY6X52YyoGMzhmq4nEjU8LQIzGy0maWZWbqZPVTB/dea2Yrgx7dm1t/LPOKtGat2sCWvgFvP1HA5kWjiWRGYWRzwAnA+0Ae4xsz6lFttI3Cmc64f8Cgw2as84i3nHJPmZNC1VSNG9tFwOZFo4uUewVAg3TmX6ZwrAqYAl5ZdwTn3rXNuV/DmAiDZwzziofkZuazcuoebh2u4nEi08bIIkoCsMrezg8uO5jfApxXdYWbjzWyRmS3KyckJYUQJlUlzM2mVmMDlAzVcTiTaeFkEFf1Z6Cpc0exsAkXwYEX3O+cmO+cGO+cGt27dOoQRJRTWbNvL3PU53HBaiobLiUSheA+3nQ10LHM7GdhWfiUz6we8ApzvnMv1MI945KW5GYHhcidruJxINPJyj2Ah0N3MuphZPWAsMK3sCmbWCZgK/NI5t97DLOKRrLwCPl6xnXHDOtG0gYbLiUQjz/YInHPFZnY7MBOIA15zzq02s1uD908Cfge0BF4Mnm5Y7Jwb7FUmCT0NlxOJfl4eGsI5Nx2YXm7ZpDKf3wTc5GUG8c6u/CLeWZjFpQOSaN9Uw+VEopXeWSw19ub84HC5MzVOQiSaqQikRgqLSvjb/E2c06sNPdpquJxINFMRSI28vziLvPwibjlTo6ZFop2KQKqtuKSUl+dtZGCnZgxJae53HBE5TioCqbZPfxoud4KGy4nUAioCqRbnHC/NDQ6X693W7zgiEgIqAqmWbzNyWbV1L+OHd6WOhsuJ1AoqAqmWSXMyaN04gcs0XE6k1lARSJWt2rqHeRt2cuNpXTRcTqQWURFIlU2em0liQjzjhnXyO4qIhJCKQKokK6+AT1ZquJxIbaQikCp59euN1DG48TQNlxOpbVQEUqm8/CKmLNzCZQOSaNe0vt9xRCTEVARSqTfnb+LAoVLGD9dwOZHaSEUgx1RYVMLfvt3Eub3b0F3D5URqJRWBHNN7i7PYVXBIw+VEajEVgRxVcUkpk+dmktq5OUNSWvgdR0Q8oiKQo5q+agfZuwq5Ra8NiNRqKgKpkHOOl+Zk0LV1I87VcDmRWk1FIBX6Jj2X1dv2couGy4nUeioCqdCkORm00XA5kZigIpCfWbV1D1+n7+TG07uQEK/hciK1nYpAfuYlDZcTiSkqAjlCVl4Bn6zYxrXDOtGkvobLicQCFYEc4eV5mcTVMW48XcPlRGKFikB+krv/IO8uyuLygUm0baLhciKxQkUgP3lz/mYNlxOJQSoCAaCgqJg352/i3N5t6dZGw+VEYomKQAB4d2FguNy/naW9AZFYoyIQiktKeXneRgZ3bk5qZw2XE4k1KgLhk5Xb2bq7UKOmRWKUiiDGBYbLZdKtTSLn9GrjdxwR8YGKIMbN27CTNdv3Ml7D5URiloogxr00N4O2TRK4dEAHv6OIiE9UBDFsZfYevknP5cbTNFxOJJZ5WgRmNtrM0sws3cwequB+M7OJwftXmNkgL/PIkV6am0HjhHiu0XA5kZjmWRGYWRzwAnA+0Ae4xsz6lFvtfKB78GM88Fev8siRNufmM33ldq49ubOGy4nEuHgPtz0USHfOZQKY2RTgUmBNmXUuBd50zjlggZk1M7P2zrntoQ4zZ30O//XxmspXjBF7Cg8RX6cON5yW4ncUEfGZl0WQBGSVuZ0NDKvCOknAEUVgZuMJ7DHQqVPNDmMkJsTTvW1ijb62tjqtWysNlxMRT4ugonMRXQ3WwTk3GZgMMHjw4J/dXxWpnZuT2jm1Jl8qIlKreflicTbQscztZGBbDdYREREPeVkEC4HuZtbFzOoBY4Fp5daZBlwfPHvoZGCPF68PiIjI0Xl2aMg5V2xmtwMzgTjgNefcajO7NXj/JGA6cAGQDhQAN3iVR0REKublawQ456YT+GVfdtmkMp874DYvM4iIyLHpncUiIjFORSAiEuNUBCIiMU5FICIS4yzwem30MLMcYHMNv7wVsDOEcWpKOY6kHEeKhByRkAGUo7zjydHZOde6ojuirgiOh5ktcs4NVg7lUI7Iz6Ac4cuhQ0MiIjFORSAiEuNirQgm+x0gSDmOpBxHioQckZABlKM8T3LE1GsEIiLyc7G2RyAiIuWoCEREYlxUF4GZdTSzr8xsrZmtNrO7gstbmNnnZrYh+N/mweUtg+vvN7Pny20r1cxWmlm6mU00s4oumuNpDjNraGafmNm64Hb+7OP3Y4aZLQ9uZ1LwGtRhz1Fmm9PMbJWP34/ZZpZmZsuCH218ylHPzCab2frg8+QX4cxgZo3LfA+WmdlOM3vGp+/FNRb4mV0RfL628inH1cEMq83s8apmqGGOkWa2OPj/vdjMRpTZVo1/h+Gci9oPoD0wKPh5Y2A90Ad4HHgouPwh4LHg542A04FbgefLbet74BQCV037FDg/3DmAhsDZwc/rAfP8yBG8r0nwvwZ8AIz1I0fw/iuAt4BVPj4/ZgODI+B5+gfgv4Kf1wFa+fFvUma7i4HhPvysxAM/Hv7/D379733I0RLYArQO3v4bcI6HOQYCHYKfnwhsLbOtmv8Oq8kTO1I/gH8CI4E0oH2Zb3RaufV+Xe4fsz2wrszta4CXwp2jgu08C9zsZw6gLvARcLUfOYBE4OvgD0e1iiDEOWZTwyIIcY4soJGfGcrc1z2Yx8KdI/i8zAE6E/jFNwkY70OOIcAXZW7/EnjR6xzB5QbkAgkc5++wqD40VJaZpRBoy++Ati54pbPgfyvbjU8icNnMw7KDy8Kdo+x2mgEXA7P8ymFmMwn81bUPeN+nHI8CTxG4cFGNhejf5fXg4ZBHqrXbHaIcwecEwKNmtsTM3jOztuHMUM41wDsu+JsnnDmcc4eAfwNWEri8bR/g1XDnIHBRrV5mlmJm8cBlHHn5XS9z/AJY6pw7yHH+DqsVRWBmiQQOX9ztnNtbk01UsKzaT+4Q5Di8nXjgbWCicy7TrxzOuVEE/tJIAEZUsnrIc5jZAKCbc+5/q/u1ocwRdK1z7iTgjODHL33IEU/gut7fOOcGAfOBJ8OcoayxBJ6n1RaC50ZdAkUwEOgArAB+G+4czrldwRzvEDiUuwko9jqHmfUFHgNuObyoonhVffyoL4LgE+ID4B/OuanBxT+YWfvg/e0J/FV7LNkEfsAOSybwV0a4cxw2GdjgnHumOhk8yIFz7gCBa0tf6kOOU4BUM9tE4PBQDzOb7UMOnHNbg//dR+D1iqE+5MglsGd0uBjfAwaFOcPhbfUH4p1zi6v6+CHOMQDAOZcR3CN5FzjVhxw45z5yzg1zzp1C4JDOBi9zmFkygefA9c65jODi4/odFtVFENw9fxVY65x7usxd04BfBT//FYHjbkcV3PXaZ2YnB7d5fWVf40WO4Lb+C2gK3F3Vxw91DjNLLPMkjCdwXel14c7hnPurc66Dcy6FwAt1651zZ4U7h5nFHz4jJfhDexFQ5TOYQvj9cARerzkruOgcYE04M5RxDTXYGwhhjq1AHzM7PE1zJLDWhxxY8Ayy4Jk9/w94xascwcODnwC/dc59c3jl4/0ddlwvOPn9QeCXgyOwW7gs+HEBgVfyZxFo5llAizJfswnIA/YTaNE+weWDCfxwZwDPU40XwEKVg0CLOwJP6MPbucmHHG2BhcHtrAaeI/DXX9j/Xcrcn0L1zxoK1fejEYGzYw5/P54F4nx6nnYG5ga3NQvo5Me/CZAJ9PL5Z/ZWAj8rKwgUZEufcrxNoJDXUI2z62qSA3gYyC+z7jKgzfH+DtOICRGRGBfVh4ZEROT4qQhERGKcikBEJMapCEREYpyKQEQkxqkIRERinIpAxAdWjbHeIl5TEYhUwswePTwnPnj7j2Z2p5lNMLOFFphF/4cy938YnBW/2szGl1m+38z+08y+IzA+QyQiqAhEKvcqwbf7m1kdAsPWfiAwhnkogbk3qWY2PLj+jc65VALv9LzTzFoGlzci8O7oYc65r8OYX+SY4v0OIBLpnHObzCzXzAYSGL+xlMAc+vOCn0PgugndCYx/uNPMLg8u7xhcnguUEBguJhJRVAQiVfMKgYuStANeIzDw7U/OuZfKrmRmZwHnAqc45wqC01LrB+8+4JwrCVNekSrToSGRqvlfYDSBPYGZwY8bg3PkMbOk4BTKpsCuYAn0Ak72K7BIVWmPQKQKnHNFZvYVsDv4V/1nZtYbmB+8WNl+4DpgBnCrma0gMJt+gV+ZRapK00dFqiD4IvES4ErnXLUuPCIS6XRoSKQSZtaHwLVpZ6kEpDbSHoGISIzTHoGISIxTEYiIxDgVgYhIjFMRiIjEOBWBiEiM+z8AcqKYlEhyIAAAAABJRU5ErkJggg==\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "line_data = df.groupby(df['year'])['Class'].mean().reset_index(name=\"Rate\")\nsns.lineplot(x='year',y='Rate',data=line_data)"
        },
        {
            "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": 62,
            "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": 62,
                    "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": 66,
            "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": 66,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# HINT: Use get_dummies() function on the categorical columns\nfeatures_one_hot=pd.get_dummies(df[[\"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": 70,
            "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      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<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>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>86</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>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>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>87</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>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>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    </tr>\n    <tr>\n      <th>88</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>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>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>89</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>1.0</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>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>90 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..          ...        ...        ...        ...        ...        ...   \n85          0.0        0.0        0.0        0.0        0.0        0.0   \n86          0.0        0.0        0.0        0.0        0.0        0.0   \n87          0.0        0.0        0.0        0.0        0.0        0.0   \n88          0.0        0.0        0.0        0.0        0.0        0.0   \n89          0.0        0.0        0.0        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..        ...       ...       ...        ...  ...           ...           ...   \n85        0.0       0.0       0.0        0.0  ...           0.0           0.0   \n86        0.0       0.0       0.0        0.0  ...           0.0           0.0   \n87        0.0       0.0       0.0        0.0  ...           0.0           0.0   \n88        0.0       0.0       0.0        0.0  ...           0.0           0.0   \n89        1.0       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..           ...           ...           ...           ...           ...   \n85           0.0           0.0           0.0           0.0           0.0   \n86           0.0           0.0           0.0           0.0           1.0   \n87           0.0           1.0           0.0           0.0           0.0   \n88           0.0           0.0           0.0           0.0           0.0   \n89           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..           ...           ...           ...  \n85           0.0           1.0           0.0  \n86           0.0           0.0           0.0  \n87           0.0           0.0           0.0  \n88           0.0           1.0           0.0  \n89           0.0           0.0           1.0  \n\n[90 rows x 72 columns]"
                    },
                    "execution_count": 70,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# HINT: use astype function\nfeatures_one_hot.astype(np.float64)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "We can now export it to a <b>CSV</b> for the next section,but to make the answers consistent, in the next lab we will provide data in a pre-selected date range.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<code>features_one_hot.to_csv('dataset_part\\_3.csv', index=False)</code>\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Authors\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<a href=\"https://www.linkedin.com/in/nayefaboutayoun/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Nayef Abou Tayoun</a> is a Data Scientist at IBM and pursuing a Master of Management in Artificial intelligence degree at Queen's University.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Change Log\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "| Date (YYYY-MM-DD) | Version | Changed By | Change Description      |\n| ----------------- | ------- | ---------- | ----------------------- |\n| 2020-09-20        | 1.0     | Joseph     | Modified Multiple Areas |\n| 2020-11-10        | 1.1     | Nayef      | updating the input data |\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Copyright \u00a9 2020 IBM Corporation. All rights reserved.\n"
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.8",
            "language": "python",
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        },
        "language_info": {
            "codemirror_mode": {
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            "file_extension": ".py",
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