IBM-DS0321EN-SkillsNetwork_labs_module_2_jupyter-labs-eda-dataviz.ipynb.jupyterlite.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<p style=\"text-align:center\">\n",
        "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\" target=\"_blank\">\n",
        "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
        "    </a>\n",
        "</p>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# **SpaceX  Falcon 9 First Stage Landing Prediction**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Assignment: Exploring and Preparing Data\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",
        "\n",
        "In 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",
        "\n",
        "Perform exploratory Data Analysis and Feature Engineering using `Pandas` and `Matplotlib`\n",
        "\n",
        "*   Exploratory Data Analysis\n",
        "*   Preparing Data  Feature Engineering\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": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "# import piplite\n",
        "# await piplite.install(['numpy'])\n",
        "# await piplite.install(['pandas'])\n",
        "# await piplite.install(['seaborn'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "# pandas is a software library written for the Python programming language for data manipulation and analysis.\n",
        "import 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\n",
        "import 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.\n",
        "import 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\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "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": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n",
            "0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n",
            "1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n",
            "2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n",
            "3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n",
            "4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n",
            "\n",
            "       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n",
            "0    None None        1     False   False  False        NaN    1.0   \n",
            "1    None None        1     False   False  False        NaN    1.0   \n",
            "2    None None        1     False   False  False        NaN    1.0   \n",
            "3  False Ocean        1     False   False  False        NaN    1.0   \n",
            "4    None None        1     False   False  False        NaN    1.0   \n",
            "\n",
            "   ReusedCount Serial   Longitude   Latitude  Class  \n",
            "0            0  B0003  -80.577366  28.561857      0  \n",
            "1            0  B0005  -80.577366  28.561857      0  \n",
            "2            0  B0007  -80.577366  28.561857      0  \n",
            "3            0  B1003 -120.610829  34.632093      0  \n",
            "4            0  B1004  -80.577366  28.561857      0  \n"
          ]
        }
      ],
      "source": [
        "import requests\n",
        "from io import BytesIO\n",
        "\n",
        "URL = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\"\n",
        "\n",
        "# Make a GET request to fetch the raw CSV data\n",
        "response = requests.get(URL)\n",
        "\n",
        "# Ensure the request was successful\n",
        "if response.status_code == 200:\n",
        "    # Use BytesIO to handle the binary stream returned by requests\n",
        "    dataset_part_2_csv = BytesIO(response.content)\n",
        "\n",
        "    # Read the CSV data into a DataFrame\n",
        "    df = pd.read_csv(dataset_part_2_csv)\n",
        "\n",
        "    # Display the first 5 rows of the DataFrame\n",
        "    print(df.head(5))\n",
        "else:\n",
        "    print(\"Failed to retrieve data\")"
      ]
    },
    {
      "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",
        "\n",
        "We 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": [
        {
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            "text/plain": [
              "<Figure size 2558.88x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.catplot(y=\"PayloadMass\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\n",
        "plt.xlabel(\"Flight Number\",fontsize=20)\n",
        "plt.ylabel(\"Pay load Mass (kg)\",fontsize=20)\n",
        "plt.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": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "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": 5,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x13edbf650>"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2558.88x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "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\n",
        "sns.catplot(y=\"LaunchSite\", x=\"FlightNumber\", hue = \"Class\",data=df,aspect=5)"
      ]
    },
    {
      "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": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [],
      "source": [
        "### TASK 2: Visualize the relationship between Payload and Launch Site  \n",
        "# The majority of the flights come from CCAFS SLC 40 launch site.\n",
        "# Early on nearly all flights came from that site, and near the end still the majority came from there. \n",
        "# VAFB SLC 4E is the least used launch site with no launches happening after flight 70."
      ]
    },
    {
      "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": 8,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x13e9c5a90>"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2558.88x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "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",
        "sns.catplot(y=\"LaunchSite\", x=\"PayloadMass\", hue = \"Class\",data=df,aspect=5)"
      ]
    },
    {
      "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": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "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": 9,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# HINT use groupby method on Orbit column and get the mean of Class column\n",
        "\n",
        "# Group by the 'Orbit' column and calculate the mean of the 'Class' column\n",
        "success_rate = df.groupby('Orbit')['Class'].mean()\n",
        "\n",
        "# Create a bar chart\n",
        "success_rate.plot(kind='bar', color='skyblue', figsize=(10, 6))\n",
        "\n",
        "# Adding titles and labels\n",
        "plt.title('Success Rate by Orbit Type')\n",
        "plt.xlabel('Orbit Type')\n",
        "plt.ylabel('Success Rate')\n",
        "plt.xticks(rotation=45)  # Rotates the Orbit labels to make them readable\n",
        "plt.grid(axis='y')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Analyze the ploted bar chart try to find which orbits have high sucess rate.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "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": 10,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x13ef00ad0>"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2558.88x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "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\n",
        "\n",
        "sns.catplot(y=\"Orbit\", x=\"FlightNumber\", hue = \"Class\",data=df,aspect=5)"
      ]
    },
    {
      "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": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "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": 12,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x13ea63fd0>"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2558.88x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "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\n",
        "\n",
        "sns.catplot(y=\"Orbit\", x=\"PayloadMass\", hue = \"Class\",data=df,aspect=5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "With heavy payloads the successful landing or positive landing rate are more for Polar,LEO and ISS.\n",
        "\n",
        "However for GTO we cannot distinguish this well as both positive landing rate and negative landing(unsuccessful mission) are both there here.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "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": 14,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>FlightNumber</th>\n",
              "      <th>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  \\\n",
              "0             1  2010       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n",
              "1             2  2012       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n",
              "2             3  2013       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n",
              "3             4  2013       Falcon 9   500.000000    PO   VAFB SLC 4E   \n",
              "4             5  2013       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n",
              "\n",
              "       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n",
              "0    None None        1     False   False  False        NaN    1.0   \n",
              "1    None None        1     False   False  False        NaN    1.0   \n",
              "2    None None        1     False   False  False        NaN    1.0   \n",
              "3  False Ocean        1     False   False  False        NaN    1.0   \n",
              "4    None None        1     False   False  False        NaN    1.0   \n",
              "\n",
              "   ReusedCount Serial   Longitude   Latitude  Class  \n",
              "0            0  B0003  -80.577366  28.561857      0  \n",
              "1            0  B0005  -80.577366  28.561857      0  \n",
              "2            0  B0007  -80.577366  28.561857      0  \n",
              "3            0  B1003 -120.610829  34.632093      0  \n",
              "4            0  B1004  -80.577366  28.561857      0  "
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# A function to Extract years from the date \n",
        "year=[]\n",
        "def Extract_year():\n",
        "    for i in df[\"Date\"]:\n",
        "        year.append(i.split(\"-\")[0])\n",
        "    return year\n",
        "Extract_year()\n",
        "df['Date'] = year\n",
        "df.head()\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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FCsuyLGvu3LlWpUqVrJCQEOv666+3xo4da82ZMyddJ7nU1/jiiy9a0dHRVt68ea369etbmzdvvmJXvQ0bNmSaJ/Xrpnr77bet2267zQoNDbXy589vVa9ePd17kNn3xLKsDO9tZvbu3WsNHjzYuvPOO63ixYtbQUFBVoECBazbb7/deuWVV9J1ebMs0+mtbt26Vr58+aySJUtaw4YNs2bPnp3hvchObssynRrr169v5cuXzwoLC7OqVKlijR8/Pt0x2Rlf/fv3t2rVqmUVLFgw7fvUq1cv6+jRo5ZlWdahQ4esp59+2qpcubKVL18+K3/+/Fa1atWsqVOnZniNV3Olrnr33XdfpscfOXLE6t69u1WuXDkrODjYKlSokFWzZk1r0KBB1pkzZyzLuvqYVyadGmfPnm1VqFDBypMnj1WxYkVr7ty52fpeA0BOOSzrX3cuBAD4rC1btuiWW27Rq6++mtY4AAAAXDsKJwDwA3/99Zf+/vtvDRw4UHv27NGff/6ZYT0LAADIOdqRA4AfGDVqlGJiYnTmzBl99NFHFE0AALgYM04AAAAAkAVmnAAAAAAgCxROAAAAAJAFCicAAAAAyEKuuwFuSkqKDhw4oAIFCmR6h3YAAAAAuYNlWTp9+rRKlCihgICrzynlusLpwIEDio6OtjsGAAAAAC+xd+9elSpV6qrH5LrCqUCBApLMmxMeHm5zGikpKUnLli1T48aNFRwcbHcc+DnGGzyNMQdPYrzB0xhzvu/UqVOKjo5OqxGuJtcVTqmX54WHh3tN4RQWFqbw8HD+g4PbMd7gaYw5eBLjDZ7GmPMf2VnCQ3MIAAAAAMgChRMAAAAAZIHCCQAAAACyQOEEAAAAAFmgcAIAAACALFA4AQAAAEAWKJwAAAAAIAsUTgAAAACQBQonAAAAAMgChRMAAAAAZIHCCQAAAACyQOEEAAAAAFmgcAIAAACALFA4AQAAAE5KTpZWrnRo1aqSWrnSoeRkuxPB3SicAAAAACcsXiyVLSvFxARpypRaiokJUtmyZj/8F4UTAAAAkE2LF0uPPirt25d+//79Zj/Fk/+icAIAAACyITlZ6tFDsqyMj6Xu69lTXLbnpyicAAAAgGxYvTrjTNPlLEvau9ccB/9D4QQAAABkQ3x89o6bPfvqBRZ8E4UTAAAAkA1RUdk77r33pOho6a67pJkzpSNH3JsLnkHhBAAAAGRDvXpSyZJXftzhkAoWlO6803y+erXUrZspuJo0kebPl06c8ERSuAOFEwAAAJANgYFSnTqZP+ZwmH9nz5a++07as0eaNEmqWdM0i1i2TGrfXoqMlJo3lxYulM6e9Vh0uACFEwAAAJANu3dLX35ptgsXTv9YqVLSxx9LLVqYz6OjpT59pB9/lH7/XRo1SqpSRUpMlD77THr8calYMal1a+nzz6WEBI++FOQAhRMAAACQDf36SRcuSA0bSgcPSnFxF9W794+Ki7uoXbsuFU3/VqGCNHiw9Msv0pYt0sCBUrly0rlz0gcfSA89JBUvLnXsKMXFSRcvevZ1IXsonAAAAIAsrFhhZpQCAqRp06SgIKl+fUt33bVf9etbCgzM+hwOh3TzzdLLL0t//SV9/73Uq5dUooRZ+zR3rtS4sVlH9fzz5pK/lBS3vzRkE4UTAAAAcBUXL5ob30pSly6m+LlWDodUu7Y0ZYpZD/Xtt+bchQtLhw9Lr75qmlGULWtmujZuzPzGu/AcCicAAADgKt58U9q61XTMGznS9ecPDJTq1zety+Pjpa++kp56SgoPNzfUnTRJqlVLqlRJGjpU2rbN9RmQNQonAAAA4AqOHzfrkyRTNP27KYSrBQdL995rWpcfOiQtXiy1bCnlzSv98YdpMnHTTVK1atLYsdLOne7Ng0sonAAAAIArGDbMFE9Vq5pL6TwpNFR6+GHTuvzQIXNj3QceMMXV1q2myUT58tIdd0ixsdKBA57Nl9tQOAEAAACZ+OUXc/mcZAqToCD7shQoILVpY1qXHzxo7hd1zz2mWUVqk4lSpUzHv9dfl44etS+rv6JwAgAAAP7FsqSePc3Nax9+WLr7brsTXVKo0KXW5fv3S6+8ItWtazKnNpmIipKaNZPefls6dcruxP6BwgkAAAD4l08/lb75RgoJkSZPtjvNlRUvblqXr1ljbtA7YYJUvbrpBJjaZKJYMemRR6SPPjL3jkLOUDgBAAAAl7lwQerTx2z37WtuVusLypQxrct/+kn67TdpxAipcmUpIeFSk4nISOnJJ6Uvv5QSE+1O7FsonAAAAIDLTJki7dplbkzbv7/daXLm8tblmzeb11G2rHTmzKUmE8WLS888Y2bWkpPtTuz9KJwAAACA/7d/vzRmjNmeMEHKn9/ePNfK4ZBuueVS6/J168zNfIsXl/7551KTiZIlpe7dpbVrpZQUu1N7JwonAAAA4P/17y+dPWuaLbRpY3ca13I4LrUu37dPWr5cevZZ02zi0CHTZOLOO82liS+9JG3aZBpOwKBwAgAAAGRmY9591xQY06aZf/1VYOCl1uXx8dJ//yu1bWtm2PbsMbNtNWpIN94oDR9u1kzldhROAAAAyPVSUsylapLUvr1Uq5a9eTwpT55LrcsPH5Y+/lh69FFzA94dO0yTiRtvNN36xo833ftyIwonAAAA5HpvvSX9+KO50WzqGqfcKG/eS63LDx2S3nlHuu8+c/Pf1CYT5cqZSxmnTzezVbkFhRMAAABytVOnpAEDzPbQoaZlN6Tw8Eutyw8elN54Q2rUyFzCmNpkolQpc3PgN9+Ujh2zO7F7UTgBAAAgVxs1ysyuVKx46XI9pFe48KXW5fv3mzVgdeqYSxxTm0wULy7df79ZJ3b6dObnSU6Wvv1WWrDA/OtLbdApnAAAAJBr/f67KQIkaepUs94HVxcVdal1+a5d0rhx0q23ShcvXmoyUayY9Nhj0qJF0vnz5nmLF5t7STVsaDoWNmxoPl+82MYX4wQKJwAAAORavXtLSUlS06amQQKcU7bspdbl27ZJw4aZmbsLFy41mYiMlBo0MGun9u1L//z9+80xvlA8UTgBAAAgV/rqKzNDEhRkZptwbS5vXf7TT9KLL0qlS5vL9lauzPw5qfeJ6tnT+y/bo3ACAAA+LzlZWrnSoVWrSmrlSofX/wIG+yUmSr16me0ePaRKlezN408cjkuty3ftMjfWvRrLkvbulVav9ky+nKJwAgAAPi113URMTJCmTKmlmJggn1o3AXvMmGHuUVSsmDRkiN1p/FdAgGkskR3e3tqcwgkAAPisxYvN+ghfXjcBzzt0yNzUVTL3bIqIsDePv4uKcu1xdqFwAgAAPik52VxilbpG4nK+tG4Cnjd4sLl3U82aUvv2dqfxf/Xqmfs9ORyZP+5wSNHR5jhvRuEEAAB80urVGWeaLucr6ybgWRs3SnPmmO1p08ylZHCvwMBLLd//XTylfh4ba47zZgwVAADgk7K7HsLb103Acyzr0ixlmzbSnXfanSj3aNHCtCcvWTL9/lKlzP4WLezJ5YwguwMAAADkhL+sm4DnfPCBtGaNFBZmOr7Bs1q0kB56yMwCx8eb/zbr1fP+maZUFE4AAMAn1atnOqIdPnzlY3xh3QQ84+xZqV8/sz1ggJnpgOcFBpqb4foiLtUDAAA+6dixrBs/NGvmO3/NhnuNH2+6LZYtK/XpY3ca+CIKJwAA4HOSkqTHHjPFU1SUVKJE+sdT20vPny9t2uTxePAyu3dLEyea7UmTpLx5bY0DH0XhBAAAfE7v3tKqVVKBAtI330h79khxcRfVu/ePiou7qCNHpPvvlxISpJYtTetp5F59+0oXLkgNG/pGEwJ4JwonAADgU+bOlWbMMNvvvivdeKO5HK9+fUt33bVf9etbCg6W3npLKl1a+vNPqVOnzO/3BP+3YoW0aJFpOz5t2pXvJQRkhcIJAAD4jPXrpa5dzfbIkdKDD1752EKFpIULpaAg6aOPpNde80xGeI+LF037cUnq0kW6+WZ788C3UTgBAACfEB9vLrNKTJQeflgaNCjr59xxx6W1Lb16ST/+6N6M8C5vvCFt3SoVLGgKbeBaUDgBAACvl5AgPfKIKZ6qVDGX4QVk87eYHj1MoZWUZNY7nTjh1qjwEsePS0OGmO1Ro6TChe3NA99H4QQAALyaZUnPPy+tWyddd5302WemKUR2ORxmXVS5ctKuXVKHDqx3yg2GDTPFU9WqUufOdqeBP6BwAgAAXu3116XZs00BtGCBdMMNzp/juuukDz+U8uSRPvnENAmA//rlF2nmTLM9bZpZ5wZcK9sLp9dee03lypVTaGioatasqdWrV1/1+Pfee0+33HKLwsLCFBUVpfbt2+vYsWMeSgsAADxp9WrphRfM9tix0r335vxctWpJU6aY7X79TKMJ+B/LMpdnJiebNXGNGtmdCP7C1sJp4cKF6tmzpwYNGqRNmzapXr16atq0qfbs2ZPp8d99953atWunjh076tdff9VHH32kDRs2qFOnTh5ODgAA3G3fPunRR01ntFatpBdfvPZzdutmbpybes7jx6/9nPAun34qLV8uhYSYm90CrmLrxOWUKVPUsWPHtMInNjZWX3/9tWbOnKmxY8dmOH79+vUqW7asunfvLkkqV66cOnfurAkTJlzxayQkJCghISHt81P/fwe8pKQkJSUlufLl5EhqBm/IAv/HeIOnMeaQUxcuSM2bB+rw4QBVq2Zp1qyLunjx6s/J7nibOVPatClIf/7pUNu2KVq8ODnbjSbg3S5ckPr0CZLkUK9eySpVKkXu/PHDzzjf58z3zmFZ9iyPTExMVFhYmD766CM9/PDDaft79OihzZs3a+XKlRmes3btWjVs2FCffPKJmjZtqsOHD6tly5a68cYbNWvWrEy/zvDhwzVixIgM+99//32FhYW57gUBAACXsCxp+vTqWrGitAoUSNCkSSsVGXnepV9j585wvfTSXUpKCtRTT/2qhx/+06Xnhz0++qiC3nuvigoXPq9XX/1GoaHJdkeClzt37pzatGmjkydPKjw8/KrH2lY4HThwQCVLltSaNWtUt27dtP1jxozRW2+9pR07dmT6vI8//ljt27fXhQsXdPHiRT344IP6+OOPFRwcnOnxmc04RUdH6+jRo1m+OZ6QlJSkuLg4xcTEXPE1AK7CeIOnMeaQE6+8EqA+fQIVGGjpv/9NVqNG2ftVxdnxNnu2Q926BSkw0NL//pesO++k1Z4v279fqlo1SGfPOjR//kW1aeP+7yc/43zfqVOnVKRIkWwVTrb3GHE4HOk+tywrw75U27ZtU/fu3TV06FA1adJE8fHx6tevn7p06aI5c+Zk+pyQkBCFhIRk2B8cHOxVA9zb8sC/Md7gaYw5ZNfy5ZfWMk2a5FCTJs7/qpLd8dali/Tdd9L77zv05JNB2rRJKlrU6S8HLzF4sHT2rFS3rtSuXZCu8OukW/Azznc5832zrXAqUqSIAgMDdfDgwXT7Dx8+rMjIyEyfM3bsWN15553q16+fJKlatWrKly+f6tWrp9GjRysqKsrtuQEAgHvs3m1uUJucLLVtazqjuZPDYVqdb9wo7dhhvuaSJdm/sS68x9q10nvvme/p9OnyaNGE3MO2Hw158uRRzZo1FRcXl25/XFxcukv3Lnfu3DkF/OunWWBgoCQzUwUAAHzTuXNS8+bSsWNSzZqmoPHEL7/580sffSTlzSt9/bU0bpz7vyZcKyXlUpHdvr0ZP4A72Po3ld69e2v27NmaO3eutm/frl69emnPnj3q0qWLJGnAgAFq165d2vEPPPCAFi9erJkzZ2rnzp1as2aNunfvrtq1a6tEiRJ2vQwAAHANLEvq2FH6+Wdzqdwnn5hCxlNuvlmaMcNsDxkiffut5742rt38+dKPP0rh4dKYMXangT+zdY1Tq1atdOzYMY0cOVLx8fGqWrWqlixZojJlykiS4uPj093T6emnn9bp06c1Y8YM9enTR9ddd50aNWqk8ePH2/USAADANZo0SfrgAykoSFq0SIqO9nyG9u2lVaukt96SWreWNm+WrrByAF7k1ClpwACzPXQo3zO4l+3NIbp166Zu3bpl+tj8+fMz7HvhhRf0QuotxAEAgE/7+mupf3+zPX26VK+ePTkcDunVV6UNG6Rt26QnnjDZ/n9FALzUqFHS4cNSxYoSvx7C3Vj+CAAAbPHnn9Ljj5s1Kp06mS53dsqXz6x3CguTvvlGGj3a3jy4ut9/l6ZNM9tTp0p58tibB/6PwgkAAHjc6dOmGcSJE9Idd5g1Rt7QCa1KFWnWLLM9YoT0v//ZmwdX1ru3lJQkNWtmPgB3o3ACAAAelZIiPfWU9OuvUlSUWdeUyS0XbdO2rZkBsyxzyV58vN2J8G9ffSX9979mXdyUKXanQW5B4QQAADxqzBjTOS9PHmnxYskbG+NOny5Vq2bWz7RuLV28aHcipEpMlHr2NNs9ekiVKtkaB7kIhRMAAPCYL74w3c8k04zhjjvszXMlefOa9U7580srV0rDh9udCKlmzDDrm4oVM+3jAU+hcAIAAB7x22/Sk0+aS+C6dTOXw3mzihWlN9802y+/LC1dam8eSIcOmbVnkpm5jIiwNw9yFwonAADgdidPmmYQp05Jd90lxcbanSh7Hn9c6trVbD/5pLRvn715crvBg80YqlnT3HsL8CQKJwAA4FYpKabo2LFDKlXKXAIXHGx3quybMkWqXl06dswUUklJdifKnTZulObMMdvTpkkB/BYLD2PIAQAAtxo2TPrySyk0VPr0U7M2xZeEhppiLzxcWrPGzHrAsyzLNIKwLKlNG+nOO+1OhNyIwgkAALjNokWXbiT7xhvmEitfVL68NHeu2Z4wwRSC8JwPPjBFa1iYNH683WmQW1E4AQAAt/jlF3O/Jknq1cvcH8mXPfKI1L272W7XTvr7b3vz5BZnz0r9+pntAQPM5Z6AHSicAACAyx0/Lj30kPmlt1EjM0vjDyZOlG67TfrnH6lVK3NPIbjXuHHS/v1S2bJSnz52p0FuRuEEAABcKjnZ3DR2507zy+7ChVJQkN2pXCNPHunDD6XrrpO+/17q39/uRP5t925TrErS5Mnm/lqAXSicAACASw0cKC1bZtajfPqpVKSI3Ylcq2xZaf58sz11qvTJJ3am8W99+0oJCVLDhtLDD9udBrkdhRMAAHCZDz64dFnevHnSLbfYm8ddHnro0mVj7dub2TW41ooVprlIQIBpP+5w2J0IuR2FEwAAcInNm6UOHcx2//5Sy5a2xnG7sWOlOnXMzX1btjQzI3CNixdN+3HJ3ID45pvtzQNIFE4AAMAFjhyRmjeXzp+X7r33UgtyfxYcbNZvFSpkbs7at6/difzHG29IW7dKBQtKI0bYnQYwKJwAAMA1SUoyHeb+/lu64Qbp/felwEC7U3lGdLT0zjtme8YM0zgC1+b4cWnIELM9apRUuLC9eYBUFE4AAOCa9Otn1qPkz2+aQRQsaHciz2rW7FJ3vU6dpD/+sDePrxs2zBRPVatKnTvbnQa4hMIJAADk2FtvmYX7kvT229JNN9mbxy6jRkn16kmnT5v1Thcu2J3IN/3yizRzptmeNs1/2tjDP1A4AQCAHNmw4dKMwNChubtddFCQtGCBVLSoaZLRs6fdiXyPZZmGEMnJUosW5sbJgDehcAIAAE47dMgUSgkJ0oMPmsurcruSJaV33zVts19/3az1QvZ9+qm0fLkUEiJNmmR3GiAjCicAAOCUxETp0Uel/fulypVNc4QAfqOQJDVuLA0ebLaffVb67Td78/iKCxcu3Rerb1+pXDl78wCZ4cccAABwSs+e0nffSeHh0mefmX9xybBhUsOG0tmz0mOPSefO2Z3I+02eLO3aZWbtBgywOw2QOQonAACQbW++aRbvOxzmUrSKFe1O5H0CA817Exlpmh288ILdibzb/v3SmDFme8IEKV8+e/MAV0LhBAAAsmXtWum558z26NHSfffZm8ebFS9uiqeAAGnuXNN9EJl76SUzK1e3rtS6td1pgCujcAIAAFnav1965BFzs9tHH+Vyquxo1EgaPtxsd+0q/fqrrXG80tq10nvvmRnM6dPNv4C3onACAABXdeGCaQ998KC5Kem8efyCm10DB0oxMdL582a905kzdifyHikpUvfuZrtDB6lmTXvzAFmhcAIAAFdkWebyvB9+kAoWNC2j8+e3O5XvCAw0LcpLlJC2b5e6dTPvKaT586WNG01zkZdftjsNkDUKJwAAcEWvvWbW6AQESAsXSuXL253I9xQrZm6OGxBgWrfPmWN3IvudPHnpcs+hQ00jDcDbUTgBAIBMrVxpWo9LpttZTIytcXzaXXddmlV54QVpyxZ789ht9Gjp8GHTlZGug/AVFE4AACCDPXvMmpyLF6U2baTeve1O5PtefFFq1sysGXvsMen0absT2eP336Vp08z21KlSnjz25gGyi8IJAACkc+6c9PDD0pEjUvXq5t5NNIO4dgEBpi15qVKmeHj22dy53ql3b9OdsVkz8wH4CgonAACQxrLML/Q//SQVKSJ98okUFmZ3Kv9RpIhZKxYUJH3wgTRrlt2JPOurr6T//te8/ilT7E4DOIfCCQAApJk61dxXJzBQ+ugjqUwZuxP5n7p1pXHjzHbPnqZIzQ0SEy+tmevRQ6pUydY4gNMonAAAgCTpf/+T+vUz21OnSg0a2BrHr/XuLT34oCkmHnvMdJnzdzNmmEsUixWThgyxOw3gPAonAACgnTulVq3MTUnbt5eef97uRP7N4TD3MSpTxrz3HTv693qnQ4ekESPM9pgxUkSEvXmAnKBwAgAglzt7VmreXDp+XKpd29y7iWYQ7lewoPThh1JwsLRokfTKK3Yncp9Bg6RTp6SaNU1hDvgiCicAAHIxyzK/yG7dam5CunixFBpqd6rco3ZtadIks923r/TDD/bmcYeNG81NlCVp+nTTXRDwRQxdAABysfHjTROI1FmPkiXtTpT7vPCC9MgjpkV3y5bSP//Ynch1LMs0grAscz+wunXtTgTkHIUTAAC51JIl0sCBZnvGDOnOO+3Nk1s5HNKcOdL110t//y09/bT/rHdasEBas8a0tB8/3u40wLWhcAIAIBf6/XczA2BZUufO5t5NsE9EhJn5y5NH+vxz/7jH0dmz0osvmu2BA82NfwFfRuEEAEAuc+qUaQZx8qSZZZo+3e5EkKQaNUwbeEnq319at87ePNdq3Dhp/36pbFnTfh3wdRROAADkIikpUrt20vbtZj3Txx+bWQ54h65dTVv4ixfNv8eO2Z0oZ3btkiZONNuTJ0t589qbB3AFCicAAHKRUaOkzz6TQkKkTz6Rihe3OxEu53BIb7whVagg7d1rityUFLtTOa9fPykhQWrUSHr4YbvTAK5B4QQAQC7x2WfS8OFme9Ys6bbbbI2DKwgPN+udQkNNA48JE+xO5JwVK0yHxoAAKTaWe4LBf1A4AQCQC2zbJj35pNnu3t10boP3uuWWS2vPBg+WVq+2N092XbxoxpdkLju8+WZ78wCuROEEAICfO3HCNIM4c0Zq0ODSDVfh3Tp1MsVucrL0+OPS4cN2J8raG29Iv/wiFSokjRxpdxrAtSicAADwY8nJpu34H39IpUtLH35obnYL7+dwSDNnSpUrSwcOSG3bmu+ntzp+XBoyxGyPHGmKJ8CfUDgBAODHhgyRvvrKdDX79FOpaFG7E8EZ+fOb9U5580rLlkljxtid6MqGDjXF0803m3uDAf6GwgkAAD/14YfS2LFme84cqXp1e/MgZ6pWlV57zWwPH26aL3ibrVvN7JhkGkIEBdkaB3ALCicAAPzQli1S+/Zmu18/qXVre/Pg2jz9tPl+pqSY7+XBg3YnusSypJ49TbYWLUwLcsAfUTgBAOBnjh0zzSDOnZMaN7406wTfNmOGmX06dMisW/OW9U6ffCItX27uDUbjEfgzCicAAPzIxYumA9uuXdL110sLFkiBgXangiuEhZn1Tvnymcv1RoywO5F04YLUp4/Z7ttXKlfO3jyAO1E4AQDgR/r3l/73P/PL9aef0tnM31SuLL3+utkePdo0jLDT5MnS7t1SyZLSgAH2ZgHcjcIJAAA/8d575hdZSXrrLW4+6q+eeEJ69lmztujJJ02rcjvs23epy9+ECaZYB/wZhRMAAH5g40Zzw1RJGjRIeuQRe/PAvWJjpVtukY4cMZdmXrzo+Qz9+5t1dHXr0nwEuQOFEwAAPu7wYenhh816k/vu8461L3CvvHnNeqcCBaTVq809lDxp7Vozw+lwSNOnm38Bf0fhBACAD0tKkh57TNq7V6pY0fwySzOI3KFCBWn2bLM9dqy0ZIlnvm5KitS9u9nu0EGqWdMzXxewG4UTAAA+rHdvadUqM/Pw2WdSRITdieBJLVtKzz1nttu2NQW0u82fby4NDQ+XXn7Z/V8P8BYUTgAA+Ki5c829fSQz01S5sr15YI/Jk82sz/HjUqtWZhbSXU6evNQ9b+hQKTLSfV8L8DYUTgAA+KDvv5e6djXbI0dKDzxgbx7YJyRE+vBDM9u4bp00cKD7vtbo0WZNXcWK0gsvuO/rAN6IwgkAAB8THy+1aCElJpqmEIMG2Z0Idrv+emnePLM9aZL0+eeu/xq//y5Nm2a2p06V8uRx/dcAvBmFEwAAPiQhwbQaP3BAqlLF3K8pgP+bQ6aI7tnTbD/1lLkxrSv16mUuA2zWzHwAuQ0/agEA8BGWZS6PWrdOuu460wyiQAG7U8GbjB8v1a4tnThh1jslJrrmvEuWmI+gIDPbBORGFE4AAPiI11+X3nzTzDAtWCDdcIPdieBt8uQx650KFpR++EF68cVrP2diopltkqQePcz6JiA3onACAMAHrF59aTH+2LHSvffamwfeq0wZcwmnZNYkLVp0bed75RWzvqlYMWnIkGvPB/gqCicAALzcvn3So49KFy+ay6/69bM7EbzdAw9cGicdOkh//ZWz8xw6ZLo2SqZg5z5hyM0onAAA8GIXLphF/4cPS7fcIs2ZIzkcdqeCL3j5ZaluXenUKXOj3AsXnD/HoEHm+TVrSk8/7fKIgE+hcAIAwIskJ0vffmvWMK1YIT37rPTjj1LhwtKnn0r58tmdEL4iOFhauNCMnZ9+kvr0ce75GzeamyxL0vTpdG8EguwOAAAAjMWLzeL7ffvS7w8IML8Aly1rSyz4sFKlpHfeMe3DX3tNuusuc7lnVixL6t7d/PvEE2bmCsjt+NsBAABeYPFis47p30WTJKWkSCdPej4T/EPTptLAgWa7UyfT6CErCxZIa9dKYWHSuHHuzQf4CgonAABslpxsZposK/PHHQ5zY9PkZI/Ggh8ZMUKqX186c0Z67DHp/PkrH3v27KU25gMHmlkrABROAADYbvXqzGeaUlmWtHevOQ7IiaAg6f33paJFpS1bTKF+JePGSfv3m0tDnV0XBfgzCicAAGwWH+/a44DMlChhiieHw9xI+d13Mx6za5c0caLZnjxZCg31bEbAm1E4AQBgs6go1x4HXMk990hDh5rtzp2l7dvTP963r5SQIDVqZNrgA7iEwgkAAJvVq3f1dSQOhxQdbY4DrtWQIdLdd0vnzpn1TqdOmRb4gwebJiUOhzRtGvcLA/6NwgkAAJsFBpq/9Gcm9ZfX2FhzHHCtAgOl996TiheXfv3VzGQ2bGhumCuZTnrZ6bwH5DYUTgAA2MyypK++Mtv/XlNSqpT08cdSixaezwX/FRkpde1qts+dS//YuXOmNf7ixZ7PBXgzboALAIDNliyRvv5aCg6WNm82TSDi481MQL16zDTB9ZKTTYOIzFjWpRb4Dz3E+ANSUTgBAGCjxESpd2+z3bOnVKmS+QDcyZkW+A0aeCwW4NVydKne6tWr9eSTT6pOnTrav3+/JOmdd97Rd99959JwAAD4uxkzzHqSYsXM4nzAE2iBDzjP6cJp0aJFatKkifLmzatNmzYpISFBknT69GmNGTPG6QCvvfaaypUrp9DQUNWsWVOrs7i7X0JCggYNGqQyZcooJCRE5cuX19y5c53+ugAA2O3IEWnkSLM9ZowUHm5vHuQetMAHnOd04TR69GjNmjVLb775poKDg9P2161bVz/99JNT51q4cKF69uypQYMGadOmTapXr56aNm2qPXv2XPE5LVu21DfffKM5c+Zox44dWrBggSpXruzsywAAwHZDhkgnT0rVq0tPP213GuQmqS3wr9RynBb4QEZOr3HasWOH7rrrrgz7w8PDdeLECafONWXKFHXs2FGdOnWSJMXGxurrr7/WzJkzNXbs2AzHL126VCtXrtTOnTtVqFAhSVLZsmWdfQkAANhuy5ZLi/OnTWMBPjwrMNCMu0cfNUWSZV16jBb4QOacLpyioqL0559/ZihYvvvuO11//fXZPk9iYqI2btyo/v37p9vfuHFjrV27NtPnfP7556pVq5YmTJigd955R/ny5dODDz6oUaNGKW/evJk+JyEhIe1yQkk6deqUJCkpKUlJSUnZzusuqRm8IQv8H+MNnsaYy5xlST16BColJUCPPJKiO+5IFm/RtWO8OeeBB6QPPnCod+9A7d9/aeqpZElLkycn64EHLMZlFhhzvs+Z753ThVPnzp3Vo0cPzZ07Vw6HQwcOHNC6devUt29fDR06NNvnOXr0qJKTkxUZGZluf2RkpA4ePJjpc3bu3KnvvvtOoaGh+uSTT3T06FF169ZNx48fv+I6p7Fjx2rEiBEZ9i9btkxhYWHZzutucXFxdkdALsJ4g6cx5tJbty5K335bW3nyJKtx42+0ZMl5uyP5FcZb9oWESNOnS9u2FdY//4SqYMELqlLlmAIDTZt8ZA9jzned+/eNzK7C6cLpxRdf1MmTJ9WwYUNduHBBd911l0JCQtS3b189//zzzp5Ojn9dXGtZVoZ9qVJSUuRwOPTee+8pIiJCkrnc79FHH9Wrr76a6azTgAED1Du1z6vMjFN0dLQaN26scC9YhZuUlKS4uDjFxMSkWzMGuAPjDZ7GmMvowgWpVy/zv98+faT27RvanMh/MN5y7oEH7E7gmxhzvi/1arTsyNF9nF5++WUNGjRI27ZtU0pKiqpUqaL8+fM7dY4iRYooMDAww+zS4cOHM8xCpYqKilLJkiXTiiZJuvHGG2VZlvbt26cKFSpkeE5ISIhCQkIy7A8ODvaqAe5teeDfGG/wNMbcJZMnS7t2SSVKSAMHBio4mEUkrsZ4g6cx5nyXM983p7vqdejQQadPn1ZYWJhq1aql2rVrK3/+/Dp79qw6dOiQ7fPkyZNHNWvWzDC1GRcXp7p162b6nDvvvFMHDhzQmTNn0vb9/vvvCggIUKlSpZx9KQAAeFR8vPTyy2Z73DjJyb85AgBs5HTh9NZbb+n8+YzXYp8/f15vv/22U+fq3bu3Zs+erblz52r79u3q1auX9uzZoy5dukgyl9m1a9cu7fg2bdqocOHCat++vbZt26ZVq1apX79+6tChwxWbQwAA4C0GDpTOnJFuv1164gm70wAAnJHtS/VOnToly7JkWZZOnz6t0NDQtMeSk5O1ZMkSFStWzKkv3qpVKx07dkwjR45UfHy8qlatqiVLlqhMmTKSpPj4+HT3dMqfP7/i4uL0wgsvqFatWipcuLBatmyp0aNHO/V1AQDwtB9/lObPN9vTpkkBTv/pEgBgp2wXTtddd50cDoccDocqVqyY4XGHw5Fp97qsdOvWTd26dcv0sfmp/4e5TOXKlelcAgDwKZYl9exptp980sw4AQB8S7YLpxUrVsiyLDVq1EiLFi1KuwGtZNYrlSlTRiVKlHBLSAAAfNnChdKaNVJYmFnbBADwPdkunOrXry9J2rVrl6KjoxXANQYAAGTp3DmpXz+z3b+/VLKkvXkAADnjdDvy1PVH586d0549e5SYmJju8WrVqrkmGQAAfmDiRGnfPql0aalvX7vTAAByyunC6ciRI2rfvr2++uqrTB9PTk6+5lAAAPiDvXul8ePN9sSJEg1gAcB3OX29Xc+ePfXPP/9o/fr1yps3r5YuXaq33npLFSpU0Oeff+6OjAAA+KT+/aXz56V69aTHHrM7DQDgWjg947R8+XJ99tlnuu222xQQEKAyZcooJiZG4eHhGjt2rO677z535AQAwKesXSu9/77kcEixseZfAIDvcnrG6ezZs2n3aypUqJCOHDkiSbr55pv1008/uTYdAAA+KCVF6tHDbHfoINWoYW8eAMC1c7pwqlSpknbs2CFJuvXWW/X6669r//79mjVrlqKiolweEAAAX/POO+aGtwUKSC+/bHcaAIArOH2pXs+ePRUfHy9JGjZsmJo0aaL33ntPefLkyfSGtQAA5CZnzkgDBpjtwYOlyEh78wAAXMPpwumJJ55I265evbp2796t3377TaVLl1aRIkVcGg4AAF8zdqwUHy+VL3/pcj0AgO+75rvYhoWFqUaNGsqfP78mTZrkikwAAPikXbukyZPN9uTJUkiIvXkAAK7jVOF09OhR/fe//9WyZcvS7teUlJSkadOmqWzZsho3bpxbQgIA4Av69ZMSEqS775YefNDuNAAAV8r2pXpr167Vfffdp5MnT8rhcKhWrVqaN2+emjdvrpSUFA0ePFgdOnRwZ1YAALzWypXSokVSQIA0dSrtxwHA32R7xmnIkCFq0qSJtmzZoh49emjDhg26//77NXjwYP3xxx96/vnnFRYW5s6sAAB4peTkS+uZOneWbr7Z3jwAANfLduH0888/a8iQIapatapGjx4th8Oh8ePHq127dnLwZzUAQC42Z47088/SdddJI0fanQYA4A7ZLpyOHz+uokWLSjINIcLCwlS9enW3BQMAwBecPGnajkvS8OESDWYBwD9le42Tw+HQ6dOnFRoaKsuy5HA4dO7cOZ06dSrdceHh4S4PCQCAtxo1SjpyRKpcWerWze40AAB3yXbhZFmWKlasmO7zy2ecUoup1G57AAD4uz/+kKZPN9tTpkjBwfbmAQC4T7YLpxUrVrgzBwAAPqdPHykpSWra1HwAAPxXtgun+vXruzMHAAA+Zdky6YsvpKAgM9sEAPBvTt0AFwAASBcvSr16me3nnzfrmwAA/o3CCQAAJ82aJW3bJhUuLA0dancaAIAnUDgBAOCEY8cuFUujRkkFC9qbBwDgGRROAAA4Yfhw6Z9/pJtvlp55xu40AABPuebC6dSpU/r000+1fft2V+QBAMBr/fqrNHOm2Y6NNY0hAAC5g9OFU8uWLTVjxgxJ0vnz51WrVi21bNlS1apV06JFi1weEAAAb2BZUu/eUnKy1Ly51KiR3YkAAJ7kdOG0atUq1atXT5L0ySefyLIsnThxQtOnT9fo0aNdHhAAAG/w3/+aFuR58kiTJtmdBgDgaU4XTidPnlShQoUkSUuXLtUjjzyisLAw3Xffffrjjz9cHhAAALslJprZJsm0IS9f3t48AADPc7pwio6O1rp163T27FktXbpUjRs3liT9888/Cg0NdXlAAADs9sor0h9/SJGR0qBBdqcBANjB6WWtPXv21BNPPKH8+fOrTJkyatCggSRzCd/NN9/s6nwAANjq8GFp5EizPWaMVKCAvXkAAPZwunDq1q2bateurb179yomJkYBAWbS6vrrr2eNEwDA7wwZIp06JdWoIT39tN1pAAB2yVEj1Vq1aqlWrVqSpOTkZG3dulV169ZVQe4CCADwI5s3S2++abZjY6UA7n4IALmW0/8L6Nmzp+bMmSPJFE3169dXjRo1FB0drW+//dbV+QAAsIVlST17mn9btZL+v6EsACCXcrpw+vjjj3XLLbdIkr744gvt2rVLv/32m3r27KlBrJgFAPiJxYullSul0FBpwgS70wAA7OZ04XT06FEVL15ckrRkyRI99thjqlixojp27KitW7e6PCAAAJ524YLUt6/Z7tdPKl3a3jwAAPs5XThFRkZq27ZtSk5O1tKlS3XPPfdIks6dO6fAwECXBwQAwNOmTpV275ZKlpReesnuNAAAb+B0c4j27durZcuWioqKksPhUExMjCTp+++/V+XKlV0eEAAATzpwQHr5ZbM9fryUL5+9eQAA3sHpwmn48OGqWrWq9u7dq8cee0whISGSpMDAQPXv39/lAQEA8KSBA6WzZ6U77pDatLE7DQDAW+SoHfmjjz4qSbpw4ULavqeeeso1iQAAsMmGDdJbb5ntadMkh8PePAAA7+H0Gqfk5GSNGjVKJUuWVP78+bVz505J0pAhQ9LalAMA4GssS+rRw2y3bSvVrm1vHgCAd3G6cHr55Zc1f/58TZgwQXny5Enbf/PNN2v27NkuDQcAgKcsWCCtW2fWNI0da3caAIC3cbpwevvtt/XGG2/oiSeeSNdFr1q1avrtt99cGg4AAE84e/ZS97wBA0w3PQAALud04bR//37dcMMNGfanpKQoKSnJJaEAAPCkiROlffukMmWk3r3tTgMA8EZOF0433XSTVq9enWH/Rx99pOrVq7skFAAAnrJ3rzRhgtmeOFHKm9fePAAA7+R0V71hw4apbdu22r9/v1JSUrR48WLt2LFDb7/9tr788kt3ZAQAwG1eekk6f1666y7p/5vGAgCQgdMzTg888IAWLlyoJUuWyOFwaOjQodq+fbu++OKLtJvhAgDgC9asMU0hHA4pNpb24wCAK8vRfZyaNGmiJk2auDoLAAAek5Jyqf14x44SV5sDAK7G6RmnDRs26Pvvv8+w//vvv9ePP/7oklAAALjb229LGzdKBQpIo0fbnQYA4O2cLpyee+457d27N8P+/fv367nnnnNJKAAA3On0adN2XJKGDJEiI+3NAwDwfk4XTtu2bVONGjUy7K9evbq2bdvmklAAALjTmDHSwYPSDTdI3bvbnQYA4AucLpxCQkJ06NChDPvj4+MVFJSjJVMAAHjMzp3SlClme/JkKSTE3jwAAN/gdOEUExOjAQMG6OTJk2n7Tpw4oYEDB9JVDwDg9fr1kxITpXvukR54wO40AABf4fQU0eTJk3XXXXepTJkyaTe83bx5syIjI/XOO++4PCAAAK6yYoW0eLEUECBNnUr7cQBA9jldOJUsWVJbtmzRe++9p59//ll58+ZV+/bt1bp1awUHB7sjIwAA1yw5WerZ02x37SpVrWprHACAj8nRoqR8+fLp2WefdXUWAADcZvZsacsWqWBBacQIu9MAAHyN02ucxo4dq7lz52bYP3fuXI0fP94loQAAcKUTJ6TBg8328OFS4cJ2pgEA+CKnC6fXX39dlStXzrD/pptu0qxZs1wSCgAAVxo1Sjp6VLrxRnOZHgAAznK6cDp48KCioqIy7C9atKji4+NdEgoAAFfZsUOaPt1sT50qsRwXAJATThdO0dHRWrNmTYb9a9asUYkSJVwSCgAAV+nTR7p4UbrvPqlJE7vTAAB8ldPNITp16qSePXsqKSlJjRo1kiR98803evHFF9WnTx+XBwQAIKe+/lr673+loCBzs1sAAHLK6cLpxRdf1PHjx9WtWzclJiZKkkJDQ/XSSy9pwIABLg8IAEBOJCVJvXqZ7RdekCpVsjcPAMC3OV04ORwOjR8/XkOGDNH27duVN29eVahQQSEhIe7IBwBAjsyaJW3fLhUpIg0dancaAICvy9F9nCQpf/78uu2221yZBQAAlzh2TBo2zGyPHi1dd52tcQAAfsDpwqlhw4ZyOBxXfHz58uXXFAgAgGs1bJj0zz9StWpSp052pwEA+AOnC6dbb7013edJSUnavHmzfvnlFz311FOuygUAQI78+qu5TE+SYmOlwEBb4wAA/ITThdPUqVMz3T98+HCdOXPmmgMBAJBTlmUaQiQnSw8/LDVsaHciAIC/cPo+Tlfy5JNPau7cua46HQAATvviCykuTsqTR5o0ye40AAB/4rLCad26dQoNDXXV6QAAcEpCgrnZrST17i1df729eQAA/sXpS/VatGiR7nPLshQfH68ff/xRQ4YMcVkwAACc8cor0p9/SsWLSwMH2p0GAOBvnC6cIiIi0n0eEBCgSpUqaeTIkWrcuLHLggEAkF2HD0ujRpntMWOkAgXszQMA8D9OF07z5s1zRw4AAHJs8GDp1CmpZk2JBq8AAHfI8Q1wJenChQtauHChzp49q5iYGFWoUMFVuQAAyJZNm6TZs832tGlSgMtW7wIAcEm2C6d+/fopMTFR06ZNkyQlJibqjjvu0LZt2xQWFqYXX3xRcXFxqlOnjtvCAgBwOcuSevY0/z7+uHTnnXYnAgD4q2z/Xe6rr77S3Xffnfb5e++9pz179uiPP/7QP//8o8cee0yjR492S0gAADKzaJG0apWUN680frzdaQAA/izbhdOePXtUpUqVtM+XLVumRx99VGXKlJHD4VCPHj20adMmt4QEAODfzp+X+vUz2/36SaVL25sHAODfsl04BQQEyLKstM/Xr1+vO+64I+3z6667Tv/8849r0wEAcAVTpki7d0ulSkkvvmh3GgCAv8t24VS5cmV98cUXkqRff/1Ve/bsUcOGDdMe//vvvxUZGen6hAAA/MuBA9LYsWZ7/HgpXz578wAA/J9TzSFat26t//73v/r111/VrFkzlStXLu3xJUuWqHbt2m4JCQDA5QYMkM6elerUkVq3tjsNACA3yPaM0yOPPKIlS5aoWrVq6tWrlxYuXJju8bCwMHXr1s3lAQEAuNwPP0hvv222p02THA578wAAcgen7uN0zz336J577sn0sWHDhrkkEAAAV2JZUo8eZvupp6TbbrM3DwAg9+A2gQAAn/H++9L69WZN05gxdqcBAOQmFE4AAJ9w9qz00ktme+BAqUQJe/MAAHIX2wun1157TeXKlVNoaKhq1qyp1atXZ+t5a9asUVBQkG699Vb3BgQAeIUJE6T9+6WyZaXeve1OAwDIbWwtnBYuXKiePXtq0KBB2rRpk+rVq6emTZtqz549V33eyZMn1a5dO919990eSgoAsNOePaZwkqRJk6TQUHvzAABynxwVThcvXtT//vc/vf766zp9+rQk6cCBAzpz5oxT55kyZYo6duyoTp066cYbb1RsbKyio6M1c+bMqz6vc+fOatOmjerUqZOT+AAAH/Pii9KFC1L9+lKLFnanAQDkRk511ZPMjW7vvfde7dmzRwkJCYqJiVGBAgU0YcIEXbhwQbNmzcrWeRITE7Vx40b1798/3f7GjRtr7dq1V3zevHnz9Ndff+ndd9/V6NGjs/w6CQkJSkhISPv81KlTkqSkpCQlJSVlK6s7pWbwhizwf4w3eJorxtyaNQ4tXBgkh8PSxIkXdfGiq9LB3/AzDp7GmPN9znzvnC6cevTooVq1aunnn39W4cKF0/Y//PDD6tSpU7bPc/ToUSUnJysyMjLd/sjISB08eDDT5/zxxx/q37+/Vq9eraCg7EUfO3asRowYkWH/smXLFBYWlu287hYXF2d3BOQijDd4Wk7HXEqK1K9ffUnX6Z57/taBAz/rwAHXZoP/4WccPI0x57vOnTuX7WOdLpy+++47rVmzRnny5Em3v0yZMtq/f7+zp5PjX3cutCwrwz5JSk5OVps2bTRixAhVrFgx2+cfMGCAel+2ivjUqVOKjo5W48aNFR4e7nReV0tKSlJcXJxiYmIUHBxsdxz4OcYbPO1ax9xbbzn0119BCg+3NG9eSRUrVtINKeEv+BkHT2PM+b7Uq9Gyw+nCKSUlRcnJyRn279u3TwUKFMj2eYoUKaLAwMAMs0uHDx/OMAslSadPn9aPP/6oTZs26fnnn0/LYlmWgoKCtGzZMjVq1CjD80JCQhQSEpJhf3BwsFcNcG/LA//GeIOn5WTMnTolDR5stocOdahkScYssoefcfA0xpzvcub75nRziJiYGMXGxqZ97nA4dObMGQ0bNkzNmjXL9nny5MmjmjVrZpjajIuLU926dTMcHx4erq1bt2rz5s1pH126dFGlSpW0efNm3X777c6+FACAFxszRjp0SKpQQXrhBbvTAAByO6dnnKZOnaqGDRuqSpUqunDhgtq0aaM//vhDRYoU0YIFC5w6V+/evdW2bVvVqlVLderU0RtvvKE9e/aoS5cuksxldvv379fbb7+tgIAAVa1aNd3zixUrptDQ0Az7AQC+7a+/pKlTzfbkydK/rg4HAMDjnC6cSpQooc2bN2vBggX66aeflJKSoo4dO+qJJ55Q3rx5nTpXq1atdOzYMY0cOVLx8fGqWrWqlixZojJlykiS4uPjs7ynEwDA//TrJyUmSjEx0v33250GAIAcFE6SlDdvXnXo0EEdOnS45gDdunVTt27dMn1s/vz5V33u8OHDNXz48GvOAADwHsuXS598IgUGmlmnTPoFAQDgcU4XTp9//nmm+x0Oh0JDQ3XDDTeoXLly1xwMAJD7XLwo9exptrt2lW66ydY4AACkcbpwat68uRwOhyzLSrc/dZ/D4dB//vMfffrppypYsKDLggIA/N/s2dLWrVLBghIXFAAAvInTXfXi4uJ02223KS4uTidPntTJkycVFxen2rVr68svv9SqVat07Ngx9e3b1x15AQB+6sQJacgQsz1ihHTZPdYBALCd0zNOPXr00BtvvJGuZfjdd9+t0NBQPfvss/r1118VGxvrkvVPAIDcY+RI6ehRqUoV6f+bqwIA4DWcnnH666+/FB4enmF/eHi4du7cKUmqUKGCjh49eu3pAAC5wm+/Sa+8YranTpW4jyQAwNs4XTjVrFlT/fr105EjR9L2HTlyRC+++KJuu+02SdIff/yhUqVKuS4lAMCv9eljGkPcf7/UuLHdaQAAyMjpS/XmzJmjhx56SKVKlVJ0dLQcDof27Nmj66+/Xp999pkk6cyZMxqSeqE6AABXsXSptGSJmWWaPNnuNAAAZM7pwqlSpUravn27vv76a/3++++yLEuVK1dWTEyMAgLMBFbz5s1dnRMA4IeSkqRevcx29+5SxYr25gEA4EpydANch8Ohe++9V/fee6+r8wAAcpHXXjPrm4oWvdRRDwAAb5Sjwuns2bNauXKl9uzZo8TExHSPde/e3SXBAAD+7ejRS/dqGj1aioiwNQ4AAFfldOG0adMmNWvWTOfOndPZs2dVqFAhHT16VGFhYSpWrBiFEwAgW4YNM/duuuUWqWNHu9MAAHB1TnfV69Wrlx544AEdP35cefPm1fr16/X333+rZs2amjRpkjsyAgD8zNat0qxZZjs2VgoMtDUOAABZcrpw2rx5s/r06aPAwEAFBgYqISFB0dHRmjBhggYOHOiOjAAAP2JZpiFESor0yCNSgwZ2JwIAIGtOF07BwcFyOBySpMjISO3Zs0eSFBERkbYNAMCVfP659M03UkiINHGi3WkAAMgep9c4Va9eXT/++KMqVqyohg0baujQoTp69Kjeeecd3Xzzze7ICADwEwkJ5ma3ktS7t1SunL15AADILqdnnMaMGaOoqChJ0qhRo1S4cGF17dpVhw8f1htvvOHygAAA/zF9uvTXX1Lx4tKAAXanAQAg+5yacbIsS0WLFtVNN90kSSpatKiWLFnilmAAAP9y6JA0apTZHjdOKlDA3jwAADjDqRkny7JUoUIF7du3z115AAB+atAg6fRp6bbbpLZt7U4DAIBznCqcAgICVKFCBR07dsxdeQAAfmjTJmnuXLMdGysFOH2hOAAA9nL6f10TJkxQv3799Msvv7gjDwB4leRk6dtvpQULzL/JyXYn8h3JydLKlQ6tWlVSHTsGyrKk1q2lunXtTgYAgPOc7qr35JNP6ty5c7rllluUJ08e5c2bN93jx48fd1k4ALDT4sVSjx7S5VcnlyolTZsmtWhhXy5fcOm9C5JUK20/92wCAPgqpwun2NhYN8QAAO+yeLH06KPmZq2X27/f7P/4Y4qnK7nSeydJXbpIRYrw3gEAfI/ThdNTTz3ljhwA4DWSk81sSWa/+FuW5HBIPXtKDz0kBQZ6PJ5Xu9p7l4r3DgDgi5wunCTpr7/+0rx58/TXX39p2rRpKlasmJYuXaro6Oi0VuUA4KtWr05/ed6/WZa0d69UsqQUGuq5XL7gwgXTdvxKUt+71au5bA8A4FucLpxWrlyppk2b6s4779SqVav08ssvq1ixYtqyZYtmz56tjz/+2B05AcBj4uOzd9zVCgRcXXbfYwAAvIXThVP//v01evRo9e7dWwUuu3thw4YNNW3aNJeGAwA7REVl77iZM6WaNd2bxdds3Ch17Zr1cdl9jwEA8BZOF05bt27V+++/n2F/0aJFub8TAL9Qr57pnrd/f+ZrdRwO8/gzz7BO599q1JBefjnr965ePc9nAwDgWjh9H6frrrtO8ZlcY7Fp0yaVLFnSJaEAwE6Bgabl+JV+8ZfMTVwpmjJKfe+kS+9VKt47AIAvc7pwatOmjV566SUdPHhQDodDKSkpWrNmjfr27at27dq5IyMAeNyDD0qRkRn3lypFK/KstGhh3qN//y2N9w4A4MucvlTv5Zdf1tNPP62SJUvKsixVqVJFycnJatOmjQYPHuyOjADgce++a5o/FC4svfOOdOKEWZdTrx6zJdnRooVpOb5ixUV99dVmNW16qxo2DOK9AwD4LKcLp+DgYL333nsaOXKkNm3apJSUFFWvXl0VKlRwRz4A8LikJGnUKLP90ktS06b25vFVgYFS/fqWzp7dr/r1b6FoAgD4tBy1I69fv77Kly+v8uXLuyMTANjqnXeknTulYsWkbt3sTgMAALyB02ucYmJiVLp0afXv31+//PKLOzIBgG2SkqTRo832iy9K+fLZmwcAAHgHpwunAwcO6MUXX9Tq1atVrVo1VatWTRMmTNC+ffvckQ8APOqtt6Rdu8xsU5cudqcBAADewunCqUiRInr++ee1Zs0a/fXXX2rVqpXefvttlS1bVo0aNXJHRgDwiMREcw8iyaxtYrYJAACkcrpwuly5cuXUv39/jRs3TjfffLNWrlzpqlwA4HFvvSXt3m3akDPbBAAALpfjwmnNmjXq1q2boqKi1KZNG91000368ssvXZkNADwmMfHS2qb+/aWwMHvzAAAA7+J0V72BAwdqwYIFOnDggO655x7FxsaqefPmCuO3DAA+bN48ac8ec6+mzp3tTgMAALyN04XTt99+q759+6pVq1YqUqRIusc2b96sW2+91VXZAMAjEhIurW3q31/Km9fePAAAwPs4XTitXbs23ecnT57Ue++9p9mzZ+vnn39WcnKyy8IBgCfMnSvt3Wtmm555xu40AADAG+V4jdPy5cv15JNPKioqSq+88oqaNWumH3/80ZXZAMDtEhKkMWPM9oABzDYBAIDMOTXjtG/fPs2fP19z587V2bNn1bJlSyUlJWnRokWqUqWKuzICgNvMmSPt2yeVLMlsEwAAuLJszzg1a9ZMVapU0bZt2/TKK6/owIEDeuWVV9yZDQDc6sKF9LNNoaH25gEAAN4r2zNOy5YtU/fu3dW1a1dVqFDBnZkAwCNmz5b275dKlZI6dbI7DQAA8GbZnnFavXq1Tp8+rVq1aun222/XjBkzdOTIEXdmAwC3uXBBGjvWbA8cKIWE2JsHAAB4t2wXTnXq1NGbb76p+Ph4de7cWR988IFKliyplJQUxcXF6fTp0+7MCQAu9eab0oEDUnS01KGD3WkAAIC3c7qrXlhYmDp06KDvvvtOW7duVZ8+fTRu3DgVK1ZMDz74oDsyAoBLnT/PbBMAAHBOjtuRS1KlSpU0YcIE7du3TwsWLHBVJgBwqzfekOLjpdKlmW0CAADZc02FU6rAwEA1b95cn3/+uStOBwBuc/68NG6c2R40SMqTx948AADAN7ikcAIAXzFrlnTwoFSmjPT003anAQAAvoLCCUCuce6cNH682R48mNkmAACQfRROAHKNWbOkQ4eksmWlp56yOw0AAPAlFE4AcoWzZ9PPNgUH25sHAAD4FgonALnCzJnS4cPS9ddL7drZnQYAAPgaCicAfu/sWWnCBLPNbBMAAMgJCicAfu/VV6UjR6Ty5aW2be1OAwAAfBGFEwC/duaMNHGi2R4yRAoKsjcPAADwTRROAPzaq69KR49KN9wgPfGE3WkAAICvonAC4LdOn2a2CQAAuAaFEwC/NWOGdOyYVKGC1KaN3WkAAIAvo3AC4JdOnZImTTLbQ4cy2wQAAK4NhRMAv/TKK9Lx41KlSlLr1nanAQAAvo7CCYDfOXlSmjzZbA8dKgUG2psHAAD4PgonAH7nlVekf/6RKleWWrWyOw0AAPAHFE4A/AqzTQAAwB0onAD4lWnTpBMnpBtvlFq2tDsNAADwFxROAPzGiRPSlClme9gwZpsAAIDrUDgB8BuxseZSvZtukh57zO40AADAn1A4AfAL//wjTZ1qtocNkwL46QYAAFyIXy0A+IXYWHPT26pVpUcesTsNAADwNxROAHzeP/+YwklitgkAALgHv14A8HlTppjZpmrVpBYt7E4DAAD8EYUTAJ92/LhpQS4x2wQAANyHXzEA+LTJk6XTp6VbbpGaN7c7DQAA8FcUTgB81tGj0vTpZnv4cGabAACA+/BrBgCfNWWKdOaMdOut0kMP2Z0GAAD4MwonAD7p6FHplVfM9vDhksNhaxwAAODnKJwA+KRJk8xsU40a0oMP2p0GAAD4OwonAD7nyBFpxgyzzWwTAADwBAonAD5n4kTp7FmpVi3p/vvtTgMAAHIDCicAPuXwYenVV802s00AAMBTKJwA+JSJE6Vz56TbbpOaNbM7DQAAyC0onAD4jEOHmG0CAAD2sL1weu2111SuXDmFhoaqZs2aWr169RWPXbx4sWJiYlS0aFGFh4erTp06+vrrrz2YFoCdJkyQzp+Xbr9datrU7jQAACA3sbVwWrhwoXr27KlBgwZp06ZNqlevnpo2bao9e/ZkevyqVasUExOjJUuWaOPGjWrYsKEeeOABbdq0ycPJAXjawYPSzJlmm9kmAADgaUF2fvEpU6aoY8eO6tSpkyQpNjZWX3/9tWbOnKmxY8dmOD42Njbd52PGjNFnn32mL774QtWrV8/0ayQkJCghISHt81OnTkmSkpKSlJSU5KJXknOpGbwhC/yfL4+3sWMDdP58oG6/PUWNGiXLB19CruTLYw6+h/EGT2PM+T5nvne2FU6JiYnauHGj+vfvn25/48aNtXbt2mydIyUlRadPn1ahQoWueMzYsWM1YsSIDPuXLVumsLAw50K7UVxcnN0RkIv42ng7fjxEs2bFSJLuvXe9vvrqiM2J4CxfG3PwbYw3eBpjznedO3cu28faVjgdPXpUycnJioyMTLc/MjJSBw8ezNY5Jk+erLNnz6ply5ZXPGbAgAHq3bt32uenTp1SdHS0GjdurPDw8JyFd6GkpCTFxcUpJiZGwcHBdseBn/PV8danT4ASEwN1xx0pGjjwNi7T8yG+Oubgmxhv8DTGnO9LvRotO2y9VE+SHP/6DciyrAz7MrNgwQINHz5cn332mYoVK3bF40JCQhQSEpJhf3BwsFcNcG/LA//mS+PtwAHpjTfM9siRAcqTx/aeNsgBXxpz8H2MN3gaY853OfN9s61wKlKkiAIDAzPMLh0+fDjDLNS/LVy4UB07dtRHH32ke+65x50xAdhs3DgpIUG6806J/9wBAIBdbPvTbZ48eVSzZs0M14TGxcWpbt26V3zeggUL9PTTT+v999/Xfffd5+6YAGy0f/+l2aYRI+ikBwAA7GPrpXq9e/dW27ZtVatWLdWpU0dvvPGG9uzZoy5dukgy65P279+vt99+W5Ipmtq1a6dp06bpjjvuSJutyps3ryIiImx7HQDcY+xYM9tUr57UqJHdaQAAQG5ma+HUqlUrHTt2TCNHjlR8fLyqVq2qJUuWqEyZMpKk+Pj4dPd0ev3113Xx4kU999xzeu6559L2P/XUU5o/f76n4wNwo337pDffNNvMNgEAALvZ3hyiW7du6tatW6aP/bsY+vbbb90fCIBXGDtWSkyU7rpLatDA7jQAACC3oz0VAK+zd680e7bZZrYJAAB4AwonAF5nzBgz29SgAbNNAADAO1A4AfAqf/8tzZljtkeMsDcLAABAKgonAF5lzBgpKcl00bvrLrvTAAAAGBROALzG339Lc+ea7eHDbY0CAACQDoUTAK/x8svSxYvS3XebezcBAAB4CwonAF5h1y5p3jyzzdomAADgbSicAHiF1NmmmBjpzjvtTgMAAJAehRMA2+3cKaXe75rZJgAA4I0onADYbvRoKTlZatJEqlPH7jQAAAAZUTgBsNVff0lvv2226aQHAAC8FYUTAFulzjbde690xx12pwEAAMgchRMA2/z5p/TOO2abtU0AAMCbUTgBsM2oUWa2qVkzqXZtu9MAAABcGYUTAFv8/rv07rtmm7VNAADA21E4AbDFqFFSSop0//3SbbfZnQYAAODqKJwAeNyOHdL775ttZpsAAIAvoHAC4HGps00PPCDVrGl3GgAAgKxROAHwqN9+kxYsMNvMNgEAAF9B4QTAo0aONLNNDz0k1ahhdxoAAIDsoXAC4DHbtkkffGC2mW0CAAC+hMIJgMeMHClZlvTww9Ktt9qdBgAAIPsonAB4xK+/Sh9+aLaHDbM3CwAAgLMonAB4ROpsU4sW0i232J0GAADAORROANzul1+kjz4y28w2AQAAX0ThBMDtRowws02PPipVq2Z3GgAAAOdROAFwqy1bpI8/lhwOZpsAAIDvonAC4FYjRph/H3tMqlrV3iwAAAA5ReEEwG1+/llavNjMNg0dancaAACAnKNwAuA2qbNNLVtKN91kbxYAAIBrQeEEwC02b5Y++YTZJgAA4B8onAC4xfDh5t/HH5eqVLE1CgAAwDWjcALgcj/9JH32mRQQwGwTAADwDxROAFwudW1T69ZS5cr2ZgEAAHAFCicALrVxo/T552a2acgQu9MAAAC4BoUTAJdKXdvUpo1UqZKtUQAAAFyGwgmAy2zYIH35JbNNAADA/1A4AXCZ1NmmJ5+UKla0NQoAAIBLUTgBcInvv5eWLJECA5ltAgAA/ofCCYBLpHbSa9tWuuEGe7MAAAC4GoUTgGu2fr301VdmtmnwYLvTAAAAuB6FE4Brlrq2qV07qXx5W6MAAAC4BYUTgGuybp309ddSUBCzTQAAwH9ROAG4JsOGmX+fekq6/np7swAAALgLhROAHFuzRoqLY7YJAAD4PwonADmWurapfXupbFk7kwAAALgXhROAHPnuO+l//zOzTYMG2Z0GAADAvSicAORI6tqmDh2kMmXszQIAAOBuFE4AnLZqlbR8uRQczGwTAADIHSicADgtdbapY0epdGl7swAAAHgChRMAp3z7rfnIk0caONDuNAAAAJ5B4QTAKamd9Dp1kqKjbY0CAADgMRROALJtxQpp5Uoz2zRggN1pAAAAPIfCCUC2WNaltU3PPCOVKmVvHgAAAE+icAKQLcuXS6tXSyEhzDYBAIDch8IJQJYun2169lmpZEl78wAAAHgahROALP3vf9KaNVJoqNS/v91pAAAAPI/CCcBVWdalTnqdO0slStgaBwAAwBYUTgCuKi5OWrvWzDa99JLdaQAAAOxB4QTgii5f29SlixQVZW8eAAAAu1A4Abiir7+W1q+X8uZltgkAAORuFE4AMnX5bFPXrlLx4vbmAQAAsBOFE4BMffWV9MMPZrbpxRftTgMAAGAvCicAGVzeSe+556TISFvjAAAA2I7CCUAGS5ZIGzZIYWFSv352pwEAALAfhROAdP4921SsmK1xAAAAvAKFE4B0vvxS+vFHKV8+ZpsAAABSUTgBSHP5bNPzz0tFi9oaBwAAwGtQOAFI88UX0k8/SfnzS3372p0GAADAe1A4AZCUfrbphRekIkVsjQMAAOBVKJwASJI++0zatMnMNvXpY3caAAAA70LhBEApKZdmm7p3lwoXtjUOAACA16FwAqBPP5V+/lkqUIDZJgAAgMxQOAG53OWzTT16SIUK2RoHAADAK1E4AbncJ59IW7dK4eFS7952pwEAAPBOFE5ALnb5bFPPnlLBgnamAQAA8F4UTkAutmiR9MsvUkSE1KuX3WkAAAC8F4UTkEulpEgjRpjtnj2l666zMw0AAIB3o3ACcqmPPpJ+/dXMNvXsaXcaAAAA70bhBORCycmXZpt692a2CQAAICsUTkAu9PHHDm3fbgqmHj3sTgMAAOD9guwOkJslJ0srVzq0alVJ5cvnUMOGUmCg3am8X3KytHq1FB8vRUVJ9erxvmVH6nj79tuSWrTIvGF9+phL9QAAAHB1ts84vfbaaypXrpxCQ0NVs2ZNrV69+qrHr1y5UjVr1lRoaKiuv/56zZo1y0NJXWvxYqlsWSkmJkhTptRSTEyQypY1+3Flqe9bw4ZSmzbmX963rF0+3mJja2nvXoccDrMPAAAAWbO1cFq4cKF69uypQYMGadOmTapXr56aNm2qPXv2ZHr8rl271KxZM9WrV0+bNm3SwIED1b17dy1atMjDya/N4sXSo49K+/al379/v9lPEZA53recudL7ZllSu3a8bwAAANlh66V6U6ZMUceOHdWpUydJUmxsrL7++mvNnDlTY8eOzXD8rFmzVLp0acXGxkqSbrzxRv3444+aNGmSHnnkEU9Gz7HkZLOmxLIyPpa679lnzXFcfnZJcrLUtSvvm7Ou9r6l6tlTeugh3jcAAICrsa1wSkxM1MaNG9W/f/90+xs3bqy1a9dm+px169apcePG6fY1adJEc+bMUVJSkoKDgzM8JyEhQQkJCWmfnzp1SpKUlJSkpKSka30ZTlu50qF9+67+th87JrVs6aFAfoT3zXmWJe3dK61YcVH161+lugJyKPXnrB0/b5H7MN7gaYw53+fM9862wuno0aNKTk5WZGRkuv2RkZE6ePBgps85ePBgpsdfvHhRR48eVVRUVIbnjB07ViNS+y5fZtmyZQoLC7uGV5Azq1aVlFQry+NKlDitiIhE9wfyESdP5tGBAwWyPI73Lb3svm9ffbVZZ8/u90Ai5FZxcXF2R0AuwniDpzHmfNe5c+eyfaztXfUcDke6zy3LyrAvq+Mz259qwIAB6t27d9rnp06dUnR0tBo3bqzw8PCcxs6xfPkcmjIl6+Peeiuv6tcPdX8gH7FypUMxMVkfx/uWXnbft6ZNb1X9+re4PxBynaSkJMXFxSkmJibTqwIAV2K8wdMYc74v9Wq07LCtcCpSpIgCAwMzzC4dPnw4w6xSquLFi2d6fFBQkAoXLpzpc0JCQhQSEpJhf3BwsC0DvGFDqVQp09Ags3UnDod5vGHDINacXIb3LWd43+At7PqZi9yJ8QZPY8z5Lme+b7Z11cuTJ49q1qyZYWozLi5OdevWzfQ5derUyXD8smXLVKtWLZ8ZrIGB0rRpZvvfk2Spn8fGslD/33jfcob3DQAAwDVsbUfeu3dvzZ49W3PnztX27dvVq1cv7dmzR126dJFkLrNr165d2vFdunTR33//rd69e2v79u2aO3eu5syZo759+9r1EnKkRQvp44+lkiXT7y9Vyuxv0cKeXN6O9y1neN8AAACuna1rnFq1aqVjx45p5MiRio+PV9WqVbVkyRKVKVNGkhQfH5/unk7lypXTkiVL1KtXL7366qsqUaKEpk+f7jOtyC/XooVpAb1ixUV99dVmNW16K5dLZUPq+7Z6tRQfL0VFSfXqMWOSFcYbAADAtbG9OUS3bt3UrVu3TB+bP39+hn3169fXTz/95OZUnhEYKNWvb+ns2f2qX/8WfonNpsBAqUEDu1P4HsYbAABAztl6qR4AAAAA+AIKJwAAAADIAoUTAAAAAGSBwgkAAAAAskDhBAAAAABZoHACAAAAgCxQOAEAAABAFiicAAAAACALFE4AAAAAkAUKJwAAAADIAoUTAAAAAGSBwgkAAAAAskDhBAAAAABZCLI7gKdZliVJOnXqlM1JjKSkJJ07d06nTp1ScHCw3XHg5xhv8DTGHDyJ8QZPY8z5vtSaILVGuJpcVzidPn1akhQdHW1zEgAAAADe4PTp04qIiLjqMQ4rO+WVH0lJSdGBAwdUoEABORwOu+Po1KlTio6O1t69exUeHm53HPg5xhs8jTEHT2K8wdMYc77PsiydPn1aJUqUUEDA1Vcx5boZp4CAAJUqVcruGBmEh4fzHxw8hvEGT2PMwZMYb/A0xpxvy2qmKRXNIQAAAAAgCxROAAAAAJAFCiebhYSEaNiwYQoJCbE7CnIBxhs8jTEHT2K8wdMYc7lLrmsOAQAAAADOYsYJAAAAALJA4QQAAAAAWaBwAgAAAIAsUDgBAAAAQBYonK7R2LFjddttt6lAgQIqVqyYmjdvrh07dqQ7xrIsDR8+XCVKlFDevHnVoEED/frrr+mOeeONN9SgQQOFh4fL4XDoxIkTGb7WP//8o7Zt2yoiIkIRERFq27ZtpsfBf3lyvD344IMqXbq0QkNDFRUVpbZt2+rAgQPufHnwQp4cc2XLlpXD4Uj30b9/f3e+PHghT425b7/9NsN4S/3YsGGDu18mvIQnf8b99NNPiomJ0XXXXafChQvr2Wef1ZkzZ9z58uBiFE7XaOXKlXruuee0fv16xcXF6eLFi2rcuLHOnj2bdsyECRM0ZcoUzZgxQxs2bFDx4sUVExOj06dPpx1z7tw53XvvvRo4cOAVv1abNm20efNmLV26VEuXLtXmzZvVtm1bt74+eBdPjreGDRvqww8/1I4dO7Ro0SL99ddfevTRR936+uB9PDnmJGnkyJGKj49P+xg8eLDbXhu8k6fGXN26ddONtfj4eHXq1Elly5ZVrVq13P464R08Nd4OHDige+65RzfccIO+//57LV26VL/++quefvppd79EuJIFlzp8+LAlyVq5cqVlWZaVkpJiFS9e3Bo3blzaMRcuXLAiIiKsWbNmZXj+ihUrLEnWP//8k27/tm3bLEnW+vXr0/atW7fOkmT99ttv7nkx8HruGm+Z+eyzzyyHw2ElJia6LD98jzvHXJkyZaypU6e6Kzp8lKd+ziUmJlrFihWzRo4c6dL88C3uGm+vv/66VaxYMSs5OTlt36ZNmyxJ1h9//OGeFwOXY8bJxU6ePClJKlSokCRp165dOnjwoBo3bpx2TEhIiOrXr6+1a9dm+7zr1q1TRESEbr/99rR9d9xxhyIiIpw6D/yLu8bbvx0/flzvvfee6tatq+Dg4GsLDZ/m7jE3fvx4FS5cWLfeeqtefvllJSYmuiY4fJanfs59/vnnOnr0KDMAuZy7xltCQoLy5MmjgIBLv3rnzZtXkvTdd9+5Ijo8gMLJhSzLUu/evfWf//xHVatWlSQdPHhQkhQZGZnu2MjIyLTHsuPgwYMqVqxYhv3FihVz6jzwH+4cb6leeukl5cuXT4ULF9aePXv02WefXXtw+Cx3j7kePXrogw8+0IoVK/T8888rNjZW3bp1c014+CRP/JxLNWfOHDVp0kTR0dE5Dwyf5s7x1qhRIx08eFATJ05UYmKi/vnnn7TL+uLj4130CuBuFE4u9Pzzz2vLli1asGBBhsccDke6zy3LyrAvK5kdn5PzwD+4e7xJUr9+/bRp0yYtW7ZMgYGBateunSzLynFm+DZ3j7levXqpfv36qlatmjp16qRZs2Zpzpw5Onbs2DXlhu/yxM85Sdq3b5++/vprdezYMUfPh39w53i76aab9NZbb2ny5MkKCwtT8eLFdf311ysyMlKBgYHXnB2eQeHkIi+88II+//xzrVixQqVKlUrbX7x4cUnK8FeJw4cPZ/jrxdUUL15chw4dyrD/yJEjTp0H/sHd4y1VkSJFVLFiRcXExOiDDz7QkiVLtH79+msLD5/kqTF3uTvuuEOS9Oeff17TeeCbPDnm5s2bp8KFC+vBBx/MeWD4NE+MtzZt2ujgwYPav3+/jh07puHDh+vIkSMqV67ctb8AeASF0zWyLEvPP/+8Fi9erOXLl2cY/OXKlVPx4sUVFxeXti8xMVErV65U3bp1s/116tSpo5MnT+qHH35I2/f999/r5MmTTp0Hvs1T4+1KX1sy12kj97BzzG3atEmSFBUVdU3ngW/x9JizLEvz5s1Tu3btWMOZC9nxMy4yMlL58+fXwoULFRoaqpiYmGt6DfCcILsD+LrnnntO77//vj777DMVKFAg7S8SERERyps3rxwOh3r27KkxY8aoQoUKqlChgsaMGaOwsDC1adMm7TwHDx7UwYMH0/6yunXrVhUoUEClS5dWoUKFdOONN+ree+/VM888o9dff12S9Oyzz+r+++9XpUqVPP/CYQtPjbcffvhBP/zwg/7zn/+oYMGC2rlzp4YOHary5curTp06trx22MNTY27dunVav369GjZsqIiICG3YsEG9evVKu58Ycg9PjblUy5cv165du7hML5fy5HibMWOG6tatq/z58ysuLk79+vXTuHHjdN1113n8dSOHPNzFz+9IyvRj3rx5acekpKRYw4YNs4oXL26FhIRYd911l7V169Z05xk2bFiW5zl27Jj1xBNPWAUKFLAKFChgPfHEE9lqIw3/4anxtmXLFqthw4ZWoUKFrJCQEKts2bJWly5drH379nnw1cIbeGrMbdy40br99tutiIgIKzQ01KpUqZI1bNgw6+zZsx58tfAGnvz/qmVZVuvWra26det64JXBG3lyvLVt29YqVKiQlSdPHqtatWrW22+/7aFXCVdxWBYrvQEAAADgaljjBAAAAABZoHACAAAAgCxQOAEAAABAFiicAAAAACALFE4AAAAAkAUKJwAAAADIAoUTAAAAAGSBwgkAAAAAskDhBAAAAABZoHACAPg0y7J0zz33qEmTJhkee+211xQREaE9e/bYkAwA4E8onAAAPs3hcGjevHn6/vvv9frrr6ft37Vrl1566SVNmzZNpUuXdunXTEpKcun5AADej8IJAODzoqOjNW3aNPXt21e7du2SZVnq2LGj7r77btWuXVvNmjVT/vz5FRkZqbZt2+ro0aNpz126dKn+85//6LrrrlPhwoV1//3366+//kp7fPfu3XI4HPrwww/VoEEDhYaG6t1337XjZQIAbOSwLMuyOwQAAK7QvHlznThxQo888ohGjRqlDRs2qFatWnrmmWfUrl07nT9/Xi+99JIuXryo5cuXS5IWLVokh8Ohm2++WWfPntXQoUO1e/dubd68WQEBAdq9e7fKlSunsmXLavLkyapevbpCQkJUokQJm18tAMCTKJwAAH7j8OHDqlq1qo4dO6aPP/5YmzZt0vfff6+vv/467Zh9+/YpOjpaO3bsUMWKFTOc48iRIypWrJi2bt2qqlWrphVOsbGx6tGjhydfDgDAi3CpHgDAbxQrVkzPPvusbrzxRj388MPauHGjVqxYofz586d9VK5cWZLSLsf766+/1KZNG11//fUKDw9XuXLlJClDQ4latWp59sUAALxKkN0BAABwpaCgIAUFmf+9paSk6IEHHtD48eMzHBcVFSVJeuCBBxQdHa0333xTJUqUUEpKiqpWrarExMR0x+fLl8/94QEAXovCCQDgt2rUqKFFixapbNmyacXU5Y4dO6bt27fr9ddfV7169SRJ3333nadjAgB8AJfqAQD81nPPPafjx4+rdevW+uGHH7Rz504tW7ZMHTp0UHJysgoWLKjChQvrjTfe0J9//qnly5erd+/edscGAHghCicAgN8qUaKE1qxZo+TkZDVp0kRVq1ZVjx49FBERoYCAAAUEBOiDDz7Qxo0bVbVqVfXq1UsTJ060OzYAwAvRVQ8AAAAAssCMEwAAAABkgcIJAAAAALJA4QQAAAAAWaBwAgAAAIAsUDgBAAAAQBYonAAAAAAgCxROAAAAAJAFCicAAAAAyAKFEwAAAABkgcIJAAAAALJA4QQAAAAAWfg/aAl/xDBe0t0AAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "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\n",
        "\n",
        "\n",
        "# Group by the 'Date' (which is now the year) and calculate the mean of the 'Class' column\n",
        "yearly_success_rate = df.groupby('Date')['Class'].mean()\n",
        "\n",
        "# Plot the line chart\n",
        "yearly_success_rate.plot(kind='line', color='blue', marker='o', figsize=(10, 6))\n",
        "\n",
        "# Adding titles and labels\n",
        "plt.title('Yearly Launch Success Trend')\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Average Success Rate')\n",
        "plt.grid(True)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "you can observe that the sucess rate since 2013 kept increasing till 2020\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "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": 16,
      "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  \\\n",
              "0             1  6104.959412   LEO  CCAFS SLC 40        1     False   False   \n",
              "1             2   525.000000   LEO  CCAFS SLC 40        1     False   False   \n",
              "2             3   677.000000   ISS  CCAFS SLC 40        1     False   False   \n",
              "3             4   500.000000    PO   VAFB SLC 4E        1     False   False   \n",
              "4             5  3170.000000   GTO  CCAFS SLC 40        1     False   False   \n",
              "\n",
              "    Legs LandingPad  Block  ReusedCount Serial  \n",
              "0  False        NaN    1.0            0  B0003  \n",
              "1  False        NaN    1.0            0  B0005  \n",
              "2  False        NaN    1.0            0  B0007  \n",
              "3  False        NaN    1.0            0  B1003  \n",
              "4  False        NaN    1.0            0  B1004  "
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "features = df[['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights', 'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount', 'Serial']]\n",
        "features.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "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": 17,
      "metadata": {},
      "outputs": [
        {
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              "      <td>4</td>\n",
              "      <td>500.000000</td>\n",
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              "      <td>False</td>\n",
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              "<p>5 rows × 80 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   FlightNumber  PayloadMass  Flights  GridFins  Reused   Legs  Block  \\\n",
              "0             1  6104.959412        1     False   False  False    1.0   \n",
              "1             2   525.000000        1     False   False  False    1.0   \n",
              "2             3   677.000000        1     False   False  False    1.0   \n",
              "3             4   500.000000        1     False   False  False    1.0   \n",
              "4             5  3170.000000        1     False   False  False    1.0   \n",
              "\n",
              "   ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n",
              "0            0            0          0  ...             0             0   \n",
              "1            0            0          0  ...             0             0   \n",
              "2            0            0          0  ...             0             0   \n",
              "3            0            0          0  ...             0             0   \n",
              "4            0            0          0  ...             0             0   \n",
              "\n",
              "   Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n",
              "0             0             0             0             0             0   \n",
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              "2             0             0             0             0             0   \n",
              "3             0             0             0             0             0   \n",
              "4             0             0             0             0             0   \n",
              "\n",
              "   Serial_B1059  Serial_B1060  Serial_B1062  \n",
              "0             0             0             0  \n",
              "1             0             0             0  \n",
              "2             0             0             0  \n",
              "3             0             0             0  \n",
              "4             0             0             0  \n",
              "\n",
              "[5 rows x 80 columns]"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# HINT: Use get_dummies() function on the categorical columns\n",
        "\n",
        "# Selecting the columns for one-hot encoding\n",
        "categorical_cols = ['Orbit', 'LaunchSite', 'LandingPad', 'Serial']\n",
        "\n",
        "# Applying get_dummies to these columns\n",
        "features_one_hot = pd.get_dummies(features, columns=categorical_cols)\n",
        "\n",
        "# Display the first few rows of the modified DataFrame\n",
        "features_one_hot.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "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": 18,
      "metadata": {},
      "outputs": [
        {
          "data": {
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              "      <th>4</th>\n",
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            ],
            "text/plain": [
              "   FlightNumber  PayloadMass  Flights  GridFins  Reused  Legs  Block  \\\n",
              "0           1.0  6104.959412      1.0       0.0     0.0   0.0    1.0   \n",
              "1           2.0   525.000000      1.0       0.0     0.0   0.0    1.0   \n",
              "2           3.0   677.000000      1.0       0.0     0.0   0.0    1.0   \n",
              "3           4.0   500.000000      1.0       0.0     0.0   0.0    1.0   \n",
              "4           5.0  3170.000000      1.0       0.0     0.0   0.0    1.0   \n",
              "\n",
              "   ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n",
              "0          0.0          0.0        0.0  ...           0.0           0.0   \n",
              "1          0.0          0.0        0.0  ...           0.0           0.0   \n",
              "2          0.0          0.0        0.0  ...           0.0           0.0   \n",
              "3          0.0          0.0        0.0  ...           0.0           0.0   \n",
              "4          0.0          0.0        0.0  ...           0.0           0.0   \n",
              "\n",
              "   Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n",
              "0           0.0           0.0           0.0           0.0           0.0   \n",
              "1           0.0           0.0           0.0           0.0           0.0   \n",
              "2           0.0           0.0           0.0           0.0           0.0   \n",
              "3           0.0           0.0           0.0           0.0           0.0   \n",
              "4           0.0           0.0           0.0           0.0           0.0   \n",
              "\n",
              "   Serial_B1059  Serial_B1060  Serial_B1062  \n",
              "0           0.0           0.0           0.0  \n",
              "1           0.0           0.0           0.0  \n",
              "2           0.0           0.0           0.0  \n",
              "3           0.0           0.0           0.0  \n",
              "4           0.0           0.0           0.0  \n",
              "\n",
              "[5 rows x 80 columns]"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# HINT: use astype function\n",
        "# Cast the entire features_one_hot DataFrame to float64\n",
        "features_one_hot = features_one_hot.astype('float64')\n",
        "\n",
        "# Display the first few rows to verify the change\n",
        "features_one_hot.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We can now export it to a <b>CSV</b> for the next section,but to make the answers consistent, in the next lab we will provide data in a pre-selected date range.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<code>features_one_hot.to_csv('dataset_part\\_3.csv', index=False)</code>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Authors\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "[Pratiksha Verma](https://www.linkedin.com/in/pratiksha-verma-6487561b1/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2022-01-01)\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",
        "| 2022-11-09        | 1.0     | Pratiksha Verma | Converted initial version to Jupyterlite|\n"
      ]
    },
    {
      "cell_type": "markdown",
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