EDA with Visualisation.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": 47,
            "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 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 = 1)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Launch Site\",fontsize=20)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Now try to explain the patterns you found in the Flight Number vs. Launch Site scatter point plots.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK 2: Visualize the relationship between Payload and Launch Site\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "We also want to observe if there is any relationship between launch sites and their payload mass.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": "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\n",
                        "text/plain": "<Figure size 1842.38x360 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot a scatter point chart with x axis to be Pay Load Mass (kg) and y axis to be the launch site, and hue to be the class value\nsns.catplot(y=\"LaunchSite\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Payload 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": 39,
            "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"
                },
                {
                    "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>Class</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     Class\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"
                    },
                    "execution_count": 39,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# HINT use groupby method on Orbit column and get the mean of Class column\nsns.barplot(y=\"Class\", x=\"Orbit\", data=df)\nplt.xlabel(\"Orbit\",fontsize=20)\nplt.ylabel(\"Success rate\",fontsize=20)\nplt.show()\n\ndf_test = df[[\"Orbit\", \"Class\"]]\ndf_grp = df_test.groupby([\"Orbit\"], as_index=False).mean()\ndf_grp"
        },
        {
            "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": {
                        "text/plain": "<AxesSubplot:xlabel='FlightNumber', ylabel='Orbit'>"
                    },
                    "execution_count": 18,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": "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\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot a scatter point chart with x axis to be FlightNumber and y axis to be the Orbit, and hue to be the class value\nsns.scatterplot(x=\"FlightNumber\", y=\"Orbit\", hue=\"Class\", data=df)"
        },
        {
            "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": 20,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<AxesSubplot:xlabel='PayloadMass', ylabel='Orbit'>"
                    },
                    "execution_count": 20,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": "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\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot a scatter point chart with x axis to be Payload and y axis to be the Orbit, and hue to be the class value\nsns.scatterplot(x=\"PayloadMass\", y=\"Orbit\", hue=\"Class\", data=df)"
        },
        {
            "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": 23,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "[]"
                    },
                    "execution_count": 23,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "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\nyear"
        },
        {
            "cell_type": "code",
            "execution_count": 24,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<AxesSubplot:ylabel='Class'>"
                    },
                    "execution_count": 24,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": "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\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\nsns.lineplot(x=Extract_year(\"Date\"), y=\"Class\", data=df)"
        },
        {
            "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": 25,
            "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": 25,
                    "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": 44,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>PayloadMass</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Orbit_ES-L1</th>\n      <th>Orbit_GEO</th>\n      <th>...</th>\n      <th>Serial_B1048</th>\n      <th>Serial_B1049</th>\n      <th>Serial_B1050</th>\n      <th>Serial_B1051</th>\n      <th>Serial_B1054</th>\n      <th>Serial_B1056</th>\n      <th>Serial_B1058</th>\n      <th>Serial_B1059</th>\n      <th>Serial_B1060</th>\n      <th>Serial_B1062</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>6104.959412</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>525.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>677.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>500.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>3170.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 80 columns</p>\n</div>",
                        "text/plain": "   FlightNumber  PayloadMass  Flights  GridFins  Reused   Legs  Block  \\\n0             1  6104.959412        1     False   False  False    1.0   \n1             2   525.000000        1     False   False  False    1.0   \n2             3   677.000000        1     False   False  False    1.0   \n3             4   500.000000        1     False   False  False    1.0   \n4             5  3170.000000        1     False   False  False    1.0   \n\n   ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n0            0            0          0  ...             0             0   \n1            0            0          0  ...             0             0   \n2            0            0          0  ...             0             0   \n3            0            0          0  ...             0             0   \n4            0            0          0  ...             0             0   \n\n   Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n0             0             0             0             0             0   \n1             0             0             0             0             0   \n2             0             0             0             0             0   \n3             0             0             0             0             0   \n4             0             0             0             0             0   \n\n   Serial_B1059  Serial_B1060  Serial_B1062  \n0             0             0             0  \n1             0             0             0  \n2             0             0             0  \n3             0             0             0  \n4             0             0             0  \n\n[5 rows x 80 columns]"
                    },
                    "execution_count": 44,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# HINT: Use get_dummies() function on the categorical columns\nfeatures_one_hot = pd.get_dummies(features, columns=['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": 45,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>PayloadMass</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Orbit_ES-L1</th>\n      <th>Orbit_GEO</th>\n      <th>...</th>\n      <th>Serial_B1048</th>\n      <th>Serial_B1049</th>\n      <th>Serial_B1050</th>\n      <th>Serial_B1051</th>\n      <th>Serial_B1054</th>\n      <th>Serial_B1056</th>\n      <th>Serial_B1058</th>\n      <th>Serial_B1059</th>\n      <th>Serial_B1060</th>\n      <th>Serial_B1062</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>6104.959412</td>\n      <td>1.0</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>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2.0</td>\n      <td>525.000000</td>\n      <td>1.0</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>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3.0</td>\n      <td>677.000000</td>\n      <td>1.0</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>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.0</td>\n      <td>500.000000</td>\n      <td>1.0</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>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>3170.000000</td>\n      <td>1.0</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>0.0</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      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>86.0</td>\n      <td>15400.000000</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>2.0</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>86</th>\n      <td>87.0</td>\n      <td>15400.000000</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>2.0</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>88.0</td>\n      <td>15400.000000</td>\n      <td>6.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>5.0</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>89.0</td>\n      <td>15400.000000</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>2.0</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>90.0</td>\n      <td>3681.000000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>5.0</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 80 columns</p>\n</div>",
                        "text/plain": "    FlightNumber   PayloadMass  Flights  GridFins  Reused  Legs  Block  \\\n0            1.0   6104.959412      1.0       0.0     0.0   0.0    1.0   \n1            2.0    525.000000      1.0       0.0     0.0   0.0    1.0   \n2            3.0    677.000000      1.0       0.0     0.0   0.0    1.0   \n3            4.0    500.000000      1.0       0.0     0.0   0.0    1.0   \n4            5.0   3170.000000      1.0       0.0     0.0   0.0    1.0   \n..           ...           ...      ...       ...     ...   ...    ...   \n85          86.0  15400.000000      2.0       1.0     1.0   1.0    5.0   \n86          87.0  15400.000000      3.0       1.0     1.0   1.0    5.0   \n87          88.0  15400.000000      6.0       1.0     1.0   1.0    5.0   \n88          89.0  15400.000000      3.0       1.0     1.0   1.0    5.0   \n89          90.0   3681.000000      1.0       1.0     0.0   1.0    5.0   \n\n    ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\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          2.0          0.0        0.0  ...           0.0           0.0   \n86          2.0          0.0        0.0  ...           0.0           0.0   \n87          5.0          0.0        0.0  ...           0.0           0.0   \n88          2.0          0.0        0.0  ...           0.0           0.0   \n89          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 80 columns]"
                    },
                    "execution_count": 45,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# HINT: use astype function\nfeatures_one_hot.astype('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": "code",
            "execution_count": 46,
            "metadata": {},
            "outputs": [],
            "source": "features_one_hot.to_csv('dataset_part_3.csv', index=False)"
        },
        {
            "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
}