sarashahin/jupyter-labs-eda-dataviz.ipynb

## jupyter-labs-eda-dataviz.ipynb
{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<center>\n    <img src=\"https://gitlab.com/ibm/skills-network/courses/placeholder101/-/raw/master/labs/module%201/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n</center>\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# **SpaceX  Falcon 9 First Stage Landing Prediction**\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Assignment: Exploring and Preparing\u00a0Data\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Estimated time needed: **70** minutes\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "In this assignment, we will predict if the Falcon 9 first stage will land successfully. SpaceX advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is due to the fact that SpaceX can reuse the first stage.\n\nIn this lab, you will perform Exploratory Data Analysis and Feature Engineering.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Falcon 9 first stage will land successfully\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing\\_1.gif)\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Several examples of an unsuccessful landing are shown here:\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Objectives\n\nPerform exploratory Data Analysis and Feature Engineering using `Pandas` and `Matplotlib`\n\n*   Exploratory Data Analysis\n*   Preparing\u00a0Data  Feature Engineering\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "***\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Import Libraries and Define Auxiliary Functions\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "We will import the following libraries the lab\n"
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [],
            "source": "# andas is a software library written for the Python programming language for data manipulation and analysis.\nimport pandas as pd\n#NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays\nimport numpy as np\n# Matplotlib is a plotting library for python and pyplot gives us a MatLab like plotting framework. We will use this in our plotter function to plot data.\nimport matplotlib.pyplot as plt\n#Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics\nimport seaborn as sns"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Exploratory Data Analysis\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "First, let's read the SpaceX dataset into a Pandas dataframe and print its summary\n"
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>Date</th>\n      <th>BoosterVersion</th>\n      <th>PayloadMass</th>\n      <th>Orbit</th>\n      <th>LaunchSite</th>\n      <th>Outcome</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>LandingPad</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Serial</th>\n      <th>Longitude</th>\n      <th>Latitude</th>\n      <th>Class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2010-06-04</td>\n      <td>Falcon 9</td>\n      <td>6104.959412</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0003</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2012-05-22</td>\n      <td>Falcon 9</td>\n      <td>525.000000</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0005</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>2013-03-01</td>\n      <td>Falcon 9</td>\n      <td>677.000000</td>\n      <td>ISS</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0007</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>2013-09-29</td>\n      <td>Falcon 9</td>\n      <td>500.000000</td>\n      <td>PO</td>\n      <td>VAFB SLC 4E</td>\n      <td>False Ocean</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1003</td>\n      <td>-120.610829</td>\n      <td>34.632093</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>2013-12-03</td>\n      <td>Falcon 9</td>\n      <td>3170.000000</td>\n      <td>GTO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1004</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n\n       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n0    None None        1     False   False  False        NaN    1.0   \n1    None None        1     False   False  False        NaN    1.0   \n2    None None        1     False   False  False        NaN    1.0   \n3  False Ocean        1     False   False  False        NaN    1.0   \n4    None None        1     False   False  False        NaN    1.0   \n\n   ReusedCount Serial   Longitude   Latitude  Class  \n0            0  B0003  -80.577366  28.561857      0  \n1            0  B0005  -80.577366  28.561857      0  \n2            0  B0007  -80.577366  28.561857      0  \n3            0  B1003 -120.610829  34.632093      0  \n4            0  B1004  -80.577366  28.561857      0  "
                    },
                    "execution_count": 2,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df=pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\")\n\n# If you were unable to complete the previous lab correctly you can uncomment and load this csv\n\n# df = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/dataset_part_2.csv')\n\ndf.head(5)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "First, let's try to see how the `FlightNumber` (indicating the continuous launch attempts.) and `Payload` variables would affect the launch outcome.\n\nWe can plot out the <code>FlightNumber</code> vs. <code>PayloadMass</code>and overlay the outcome of the launch. We see that as the flight number increases, the first stage is more likely to land successfully. The payload mass is also important; it seems the more massive the payload, the less likely the first stage will return.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "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": "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": 15,
            "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(\"Pay load Mass (kg)\",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": 16,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": "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\n",
                        "text/plain": "<Figure size 402.375x360 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\n# 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)\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": 17,
            "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": "# HINT use groupby method on Orbit column and get the mean of Class column\nt = df.groupby(['Orbit', 'Class'])['Class'].agg(['mean']).reset_index()\nsns.barplot(y=\"Class\", x=\"Orbit\", data=t)\n\nplt.xlabel(\"Orbit\",fontsize=20)\nplt.ylabel(\"Class\",fontsize=20)\nplt.show()\n"
        },
        {
            "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": 18,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": "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\n",
                        "text/plain": "<Figure size 402.375x360 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)\nplt.xlabel(\"FlightNumber\",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": 23,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": "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\n",
                        "text/plain": "<Figure size 402.375x360 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)\nplt.xlabel(\"Payload\",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": 26,
            "metadata": {},
            "outputs": [],
            "source": "# A function to Extract years from the date \ndef Extract_year():\n    for i in df[\"Date\"]:\n        year.append(i.split(\"-\")[0])\n    return year\n    "
        },
        {
            "cell_type": "code",
            "execution_count": 27,
            "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</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</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</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</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</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       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n1             2  2012       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n2             3  2013       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n3             4  2013       Falcon 9   500.000000    PO   VAFB SLC 4E   \n4             5  2013       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": 27,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\nyear=[]\ndf1 = df.copy()\nyear = Extract_year()\ndf1[\"Date\"] = year\ndf1.head()"
        },
        {
            "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": 28,
            "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": 28,
                    "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": [],
            "source": "# HINT: Use get_dummies() function on the categorical columns\n"
        },
        {
            "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": [],
            "source": "# HINT: use astype function\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "We can now export it to a <b>CSV</b> for the next section,but to make the answers consistent, in the next lab we will provide data in a pre-selected date range.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<code>features_one_hot.to_csv('dataset_part\\_3.csv', index=False)</code>\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Authors\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<a href=\"https://www.linkedin.com/in/nayefaboutayoun/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Nayef Abou Tayoun</a> is a Data Scientist at IBM and pursuing a Master of Management in Artificial intelligence degree at Queen's University.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Change Log\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "| Date (YYYY-MM-DD) | Version | Changed By    | Change Description      |\n| ----------------- | ------- | ------------- | ----------------------- |\n| 2021-10-12        | 1.1     | Lakshmi Holla | Modified markdown       |\n| 2020-09-20        | 1.0     | Joseph        | Modified Multiple Areas |\n| 2020-11-10        | 1.1     | Nayef         | updating the input data |\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Copyright \u00a9 2020 IBM Corporation. All rights reserved.\n"
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.8",
            "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
}