AI_dengan_Python(pengenalan).ipynb

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  "metadata": {
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      "name": "AI_dengan_Python(pengenalan).ipynb",
      "provenance": [],
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      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/riskydav/Belajar-Python/blob/main/AI_dengan_Python(pengenalan).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F9RmP8oJEr-8"
      },
      "source": [
        "# Pengenalan AI dengan Python\n",
        "Sebelum lanjut, harap:\n",
        "1. Jika menggunakan jupyter, silahkan pilih \"File\" --> \"Download\" --> \"Download .ipynb\"\n",
        "2. Jika menggunakan colab, silahkan pilih \"File\" --> \"Save a copy in drive\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ttcCioV2TWeK"
      },
      "source": [
        "Materi : \n",
        "1. Reading & Writing File\n",
        "2. Operasi Matematika & Numpy\n",
        "3. Pandas & Statistik Deskriptif\n",
        "4. Library untuk Visualisasi"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WZgEMeZ7JLNm"
      },
      "source": [
        "## Reading & Writing File"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fnd29cqZ4uTK"
      },
      "source": [
        "### Fungsi 'open'\n",
        "1. Struktur menggunakan \"with\"\n",
        "<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Images/ReadWith.png\" width=\"500\" />\n",
        "\n",
        "2. Struktur menggunakan \"file object\"\n",
        "<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Images/ReadOpen.png\" width=\"500\" />\n",
        "\n",
        "Keterangan mode:\n",
        "*   Read (kode: **r**) – membaca sebuah file (**r+** artinya sekaligus menulis)\n",
        "*   Write (kode: **w**) – menulis sebuah file (**w+** artinya sekaligus membaca)\n",
        "*   Append (kode: **a**) – menambah isi sebuah file (**a+** artinya sekaligus membaca)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O1tkv-JEnLlu"
      },
      "source": [
        "import os\n",
        "#Menambah folder 'latihan' \n",
        "os.makedirs('latihan')"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lSms7K9V95BU"
      },
      "source": [
        "#Menulis file baru\n",
        "with open('latihan/contoh.txt', 'w') as c:\n",
        "  c.write('saya keren.')                    #hanya bisa menulis karena modenya \"w\" bukan \"w+\""
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "XOHOWBbZ93KA",
        "outputId": "1f213296-60e4-4b9f-b6ce-418f0192d9fc"
      },
      "source": [
        "#Membaca file dengan \"with\"\n",
        "with open('latihan/contoh.txt', 'r') as c:\n",
        "    print(c.read())\n",
        "\n",
        "#Membaca file dengan \"file object\"\n",
        "file = open('latihan/contoh.txt', 'r')\n",
        "file.read()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saya keren.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'saya keren.'"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YfCzYjMsHsPQ"
      },
      "source": [
        "### Membedakan \"write\" dan \"append\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "CyviqdwBFkP-",
        "outputId": "b28666bd-ca1d-4b0d-cc8c-76107cd5f349"
      },
      "source": [
        "#Append\n",
        "file = open('latihan/contoh.txt', 'a+')\n",
        "file.write(' belajar AI menyenangkan.')\n",
        "file.seek(0)                          #seek(0) artinya mulai dari huruf yang pertama (index 0)\n",
        "file.read()                           #bisa sekaligus read karena modenya \"a+\""
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'saya keren. belajar AI menyenangkan.'"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "gOFk_bC0IA3F",
        "outputId": "ca1c762e-ea6b-44d6-9eef-e6483d8265c0"
      },
      "source": [
        "#Write\n",
        "file = open('latihan/contoh.txt', 'w+')\n",
        "file.write(' belajar AI menyenangkan.')\n",
        "file.seek(0)                          #seek(0) artinya mulai dari huruf yang pertama (index 0)\n",
        "file.read()                           #bisa sekaligus read karena modenya \"w+\""
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "' belajar AI menyenangkan.'"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZmKKOMhFIIR_"
      },
      "source": [
        "Bisa kah kamu melihat perbedaannya?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7dOhVBlg-acT",
        "outputId": "d099c09d-65bb-479f-9e01-14ef6b2b49db"
      },
      "source": [
        "#Mengetahui nama file dan lokasinya\n",
        "print(file.name)\n",
        "\n",
        "#Mengetahui mode dari file\n",
        "print(file.mode)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "latihan/contoh.txt\n",
            "w+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oREZu1YaJe4l"
      },
      "source": [
        "### Latihan 1 (menulis file)\n",
        "\n",
        "Buat file baru di folder \"latihan\", namanya \"kata bung karno.txt\". Isinya : \"Bermimpilah setinggi langit. Jika engkau jatuh, kau kan jatuh di antara bintang-bintang. \""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uxPw2hOfLyN4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5258543d-ecde-44a7-b565-c69081d7e37a"
      },
      "source": [
        "#Tulis jawabanmu disini\n",
        "import os\n",
        "\n",
        "#Menambah folder 'latihan' \n",
        "os.makedirs('Latihan')\n",
        "\n",
        "#Menulis file baru\n",
        "with open('Latihan/kata bung karno.txt', 'w') as c:\n",
        "  c.write('Bermimpilah setinggi langit. Jika engkau jatuh, kau kan jatuh di antara bintang-bintang.')\n",
        "\n",
        "#Membaca file dengan \"with\"\n",
        "with open('Latihan/kata bung karno.txt', 'r') as c:\n",
        "    print(c.read())"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bermimpilah setinggi langit. Jika engkau jatuh, kau kan jatuh di antara bintang-bintang.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-RX-DtsEFC5r"
      },
      "source": [
        "## Operasi Matematika & Numpy\n",
        "Numpy → library untuk membantu operasi matematika di python.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DBVuNosPErdc"
      },
      "source": [
        "import numpy as np"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GWGWrUU_QOR6"
      },
      "source": [
        "### Pengenalan array"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rojHc0kGGJVw",
        "outputId": "9e15b3fb-b0b4-4477-e166-0097fb87787a"
      },
      "source": [
        "#1D array\n",
        "a = np.array([3, 1, 5, 6])\n",
        "print('banyaknya entry = ', a.size)\n",
        "print('dimensi array = ', a.ndim)\n",
        "print('ukuran array = ', a.shape)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "banyaknya entry =  4\n",
            "dimensi array =  1\n",
            "ukuran array =  (4,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ay-3fo-sIOqV",
        "outputId": "aa216f45-db7d-48ec-f50e-b0db5048a464"
      },
      "source": [
        "#Slicing\n",
        "a[1:]"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 5, 6])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kfeTP-8qHBVx",
        "outputId": "50638468-5b78-4383-835a-763047f2c480"
      },
      "source": [
        "print('a[1:3] = ', a[1:3])\n",
        "print('a[:3] = ', a[:3])"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "a[1:3] =  [1 5]\n",
            "a[:3] =  [3 1 5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "myrQM61bHn2t",
        "outputId": "6a399253-3018-4cb9-aac1-9f6890b03e7a"
      },
      "source": [
        "#2D Array (Matriks)\n",
        "A = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n",
        "print(A)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1 2 3]\n",
            " [4 5 6]\n",
            " [7 8 9]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7LxTP-IdLaSa"
      },
      "source": [
        "###Latihan-2 (2D array)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ncKwccePLyf",
        "outputId": "00af46d2-a464-41c6-807f-4d42118e6bfd"
      },
      "source": [
        "#Lengkapi kodingan berikut\n",
        "print('banyaknya entry = ', a.size )\n",
        "print('dimensi array = ', a.ndim )\n",
        "print('ukuran array = ', a.shape)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "banyaknya entry =  4\n",
            "dimensi array =  1\n",
            "ukuran array =  (4,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LAmNOJZYGrNY",
        "outputId": "2ad5c2ad-b022-48af-d067-de264a68daff"
      },
      "source": [
        "#Slicing\n",
        "print('baris ke-2 = ', A[1])\n",
        "print('entry ke-2 & 3 baris ke-2 = ', A[1, 1:])\n",
        "print('entry pertama & 2 kolom ke-2 = ', A[:2, 1])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "baris ke-2 =  [4 5 6]\n",
            "entry ke-2 & 3 baris ke-2 =  [5 6]\n",
            "entry pertama & 2 kolom ke-2 =  [2 5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dabJyOB3L6Kb"
      },
      "source": [
        "###Latihan-3 (Slicing)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zm20sFkSNbds",
        "outputId": "a1ce8a03-598f-4a10-fcf7-788b115c4069"
      },
      "source": [
        "#Lengkapi kodingan berikut\n",
        "print('baris ke-3 = ', A[2])\n",
        "print('entry ke-1 & 2 baris ke-3 = ', A[2, :2])\n",
        "print('entry ke-2 & 3 kolom ke-3 = ', A[1:, 2])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "baris ke-3 =  [7 8 9]\n",
            "entry ke-1 & 2 baris ke-3 =  [7 8]\n",
            "entry ke-2 & 3 kolom ke-3 =  [6 9]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u69Wt0HHOdDh"
      },
      "source": [
        "output jawaban:\n",
        "baris ke-3 =  [7 8 9]\n",
        "\n",
        "entry ke-1 & 2 baris ke-3 =  [7 8]\n",
        "\n",
        "entry ke-2 & 3 kolom ke-3 =  [6 9]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bQ9RSERjQYu-"
      },
      "source": [
        "### Operasi Matematika di Numpy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A7ITbltnVKCK",
        "outputId": "edd00493-09f5-4e93-c7ea-53fd4d9d8cd1"
      },
      "source": [
        "C = A*A\n",
        "print('perkalian antar entry = ')\n",
        "print(C)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "perkalian antar entry = \n",
            "[[ 1  4  9]\n",
            " [16 25 36]\n",
            " [49 64 81]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qs72ZsAJQERO",
        "outputId": "b399015c-6495-4488-b8a5-6b265498ef53"
      },
      "source": [
        "print('perkalian matriks = ')\n",
        "print(np.matmul(A,A))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "perkalian matriks = \n",
            "[[ 30  36  42]\n",
            " [ 66  81  96]\n",
            " [102 126 150]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DqYW2wD_8HZT"
      },
      "source": [
        "Bisa dilihat perbedaan perkalian antar entry dan perkalian matriks biasa?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_esB0rtlQkOp",
        "outputId": "7dc0f749-05b4-4df8-dbaa-3b402486ae9c"
      },
      "source": [
        "print('determinan matriks A = ', np.linalg.det(A))\n",
        "print('determinan matriks C = ', np.linalg.det(C))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "determinan matriks A =  0.0\n",
            "determinan matriks C =  -216.00000000000006\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Joz3Z61TQd6V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "97f91d59-da4a-45ce-8c2d-3aee4e720dab"
      },
      "source": [
        "print('nilai dan vektor eigen matriks A = ', np.linalg.eig(A))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nilai dan vektor eigen matriks A =  (array([ 1.61168440e+01, -1.11684397e+00, -9.75918483e-16]), array([[-0.23197069, -0.78583024,  0.40824829],\n",
            "       [-0.52532209, -0.08675134, -0.81649658],\n",
            "       [-0.8186735 ,  0.61232756,  0.40824829]]))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YIXOE54OQWWd",
        "outputId": "ea54de9a-1887-45c4-f795-8a59fef643d4"
      },
      "source": [
        "print('nilai eigen matriks A = ', np.linalg.eig(A)[0])"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nilai eigen matriks A =  [ 1.61168440e+01 -1.11684397e+00 -9.75918483e-16]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FjdAWfgKQaf1",
        "outputId": "ae02d439-a8bb-4f39-fad8-8a78d27a2495"
      },
      "source": [
        "print('vektor eigen matriks A = ', np.linalg.eig(A)[1])"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "vektor eigen matriks A =  [[-0.23197069 -0.78583024  0.40824829]\n",
            " [-0.52532209 -0.08675134 -0.81649658]\n",
            " [-0.8186735   0.61232756  0.40824829]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kjS8YZLAMXB_"
      },
      "source": [
        "### Latihan-4 (Operasi matriks)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RIH4ROFmGuip"
      },
      "source": [
        "A = np.array([[1, 2], [7,8]])\n",
        "B = np.array([[3, 5], [1,6]])"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dj6lY65jLBqx"
      },
      "source": [
        "Hitunglah:\n",
        "*   2*A\n",
        "*   2+A\n",
        "*   A+B\n",
        "*   A-B\n",
        "*   determinan A\n",
        "*   invers dari B, simpan jadi matriks D\n",
        "*   transpose D\n",
        "*   nilai eigen dan vektor eigen dari A dan B\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zkywTKVELriJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d2dc9c1-9d79-4d08-d249-df7304e5e8c5"
      },
      "source": [
        "#Ketik Kode disini\n",
        "import numpy as np\n",
        "\n",
        "A = np.array([[1, 2], [7,8]])\n",
        "B = np.array([[3, 5], [1,6]])\n",
        "2 * A"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 2,  4],\n",
              "       [14, 16]])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wB30wRmWdHp0",
        "outputId": "c381fc62-2d55-4de1-9ced-e2cde4196d7c"
      },
      "source": [
        "2 + A"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 3,  4],\n",
              "       [ 9, 10]])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SzBuwM_SdKoA",
        "outputId": "83562189-c0e0-4b45-8851-a172584d63c6"
      },
      "source": [
        "A + B"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 4,  7],\n",
              "       [ 8, 14]])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i_rkRgnhdNwa",
        "outputId": "f44e2612-153c-49b8-b652-4a71f22bbbd0"
      },
      "source": [
        "A - B"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-2, -3],\n",
              "       [ 6,  2]])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zcV-OdhvdQ6w",
        "outputId": "81a04ddc-1ef3-4cc1-c4ef-bb0d94ef3536"
      },
      "source": [
        "# Determinan A\n",
        "print(np.linalg.det(A))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-6.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-TTPm3midXeG",
        "outputId": "c116c690-5c63-4def-df32-cc8214cfb32d"
      },
      "source": [
        "# invers dari B, simpan jadi matriks D\n",
        "D = np.linalg.inv(B)\n",
        "print(D)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.46153846 -0.38461538]\n",
            " [-0.07692308  0.23076923]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aZ-Zs-lcdbzT",
        "outputId": "657b4d30-ab24-4496-a977-36aba511f7ed"
      },
      "source": [
        "# transpose D\n",
        "D.transpose()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.46153846, -0.07692308],\n",
              "       [-0.38461538,  0.23076923]])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5rtOKoBddfN3",
        "outputId": "5c43a128-eb01-413d-9c76-60256467e283"
      },
      "source": [
        "# nilai eigen dan vektor eigen dari A dan B\n",
        "w, v = np.linalg.eig(A)\n",
        "\n",
        "# printing eigen values\n",
        "print(\"Printing the Eigen values of the given square array:\\n\",\n",
        "      w)\n",
        "  \n",
        "# printing eigen vectors\n",
        "print(\"Printing Right eigenvectors of the given square array:\\n\",\n",
        "      v)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Printing the Eigen values of the given square array:\n",
            " [-0.62347538  9.62347538]\n",
            "Printing Right eigenvectors of the given square array:\n",
            " [[-0.77640358 -0.22592838]\n",
            " [ 0.63023605 -0.97414391]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zBLTEATZdiyP",
        "outputId": "5aa9f97d-513a-42d3-8ab3-e09844f741ce"
      },
      "source": [
        "np.linalg.eig(B)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([1.8074176, 7.1925824]), array([[-0.97271381, -0.76626443],\n",
              "        [ 0.23200828, -0.64252535]]))"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7NwMsWC-MQgG"
      },
      "source": [
        "Apa perbedaan dari B*D (perkalian antar entry di B dan D) dan BD (perkalian matriks B dan D)? "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0_PrzTpUMgQ8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f0079098-d4c8-4d78-8e28-e39654539888"
      },
      "source": [
        "#Tunjukkan bedanya di kodingan di bawah ini\n",
        "B * D"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.38461538, -1.92307692],\n",
              "       [-0.07692308,  1.38461538]])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "crFOqdC0dpvq",
        "outputId": "d48ce2fc-5d5e-4f09-a854-d31a20251e27"
      },
      "source": [
        "B @ D"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.00000000e+00, -1.11022302e-16],\n",
              "       [ 1.11022302e-16,  1.00000000e+00]])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "633JPFjMTk3Y"
      },
      "source": [
        "output jawaban: \n",
        "\n",
        "perkalian entry = [[ 1.38461538, -1.92307692], [-0.07692308,  1.38461538]]\n",
        "\n",
        "perkalian matriks = [[ 1.00000000e+00, -1.11022302e-16],\n",
        "       [ 1.11022302e-16,  1.00000000e+00]]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "emE1XJjgyTML"
      },
      "source": [
        "## Pandas dan Statistik Deskriptif"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LbUkdam20Luk"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WA8wMETa0VCO"
      },
      "source": [
        "### Mengenal DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "zeMEfrs6yOj_",
        "outputId": "df9ed119-49cb-415f-fdf7-9c8c5bed51f1"
      },
      "source": [
        "#Dari Array\n",
        "df1 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), columns=['a', 'b', 'c'])\n",
        "df1"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   a  b  c\n",
              "0  1  2  3\n",
              "1  4  5  6\n",
              "2  7  8  9"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "vOxPCBpazyKO",
        "outputId": "f3876172-d3a1-4503-b0b4-d4ec98bbd75a"
      },
      "source": [
        "#Dari dictionary\n",
        "df2=pd.DataFrame({'a': [1,2,3], 'b': [4,5,6], 'c': [7, 8, 9]})\n",
        "df2"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   a  b  c\n",
              "0  1  4  7\n",
              "1  2  5  8\n",
              "2  3  6  9"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "SNZmiYTfz_2r",
        "outputId": "e6691898-5a12-47b4-8cd9-409ff48aafc4"
      },
      "source": [
        "#Menambahkan kolom baru\n",
        "df1['d'] = [3, 5, 6]\n",
        "df1"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "      <th>d</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   a  b  c  d\n",
              "0  1  2  3  3\n",
              "1  4  5  6  5\n",
              "2  7  8  9  6"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KXyPuM_OOHFa"
      },
      "source": [
        "#Menyimpan dataframe ke csv file\n",
        "df1.to_csv('latihan/df1.csv')"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "zV0QN-471Jri",
        "outputId": "e72f10bb-0572-4a04-8104-3a17cb607b80"
      },
      "source": [
        "#Mengambil kolom tertentu: \n",
        "df3 = df1[['a','d']]\n",
        "df3"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>a</th>\n",
              "      <th>d</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   a  d\n",
              "0  1  3\n",
              "1  4  5\n",
              "2  7  6"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "w_AovvMd1gHV",
        "outputId": "d901423d-0e4a-4a61-ca17-421faaca5c9a"
      },
      "source": [
        "#Mengambil sebagian data berdasarkan entrynya\n",
        "df4a = df1[df1['a']>2] #df1 yang df1['a']>2\n",
        "df4a"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "      <th>d</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   a  b  c  d\n",
              "1  4  5  6  5\n",
              "2  7  8  9  6"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "J6REVPcjO3XR",
        "outputId": "70cd44ea-d660-499b-eb57-25dae1ef809e"
      },
      "source": [
        "df4b = df1[df1['a']==4] #df1 yang df1['a']=4\n",
        "df4b"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "      <th>d</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   a  b  c  d\n",
              "1  4  5  6  5"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "eQZK_ddv2LnV",
        "outputId": "8c6e4506-89d3-4b6c-fd33-b69a7eb6048c"
      },
      "source": [
        "#Mengambil sebagian data berdasarkan indexnya\n",
        "df5 = df1.iloc[0:2, 0:3]\n",
        "df5"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   a  b  c\n",
              "0  1  2  3\n",
              "1  4  5  6"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oQtNPAVe4cVH"
      },
      "source": [
        "### Mengimport data ke DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "O507C0Zg2f3t",
        "outputId": "59d8f5fe-58e2-40bf-b47b-1a79732b1928"
      },
      "source": [
        "#Contoh mengimport dari web\n",
        "url = 'https://people.sc.fsu.edu/~jburkardt/data/csv/freshman_kgs.csv'\n",
        "df6 = pd.read_csv(url)\n",
        "\n",
        "df6"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Sex</th>\n",
              "      <th>\"Weight (Sep)\"</th>\n",
              "      <th>\"Weight (Apr)\"</th>\n",
              "      <th>\"BMI (Sep)\"</th>\n",
              "      <th>\"BMI (Apr)\"</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>M</td>\n",
              "      <td>72</td>\n",
              "      <td>59</td>\n",
              "      <td>22.02</td>\n",
              "      <td>18.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>M</td>\n",
              "      <td>97</td>\n",
              "      <td>86</td>\n",
              "      <td>19.70</td>\n",
              "      <td>17.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M</td>\n",
              "      <td>74</td>\n",
              "      <td>69</td>\n",
              "      <td>24.09</td>\n",
              "      <td>22.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>M</td>\n",
              "      <td>93</td>\n",
              "      <td>88</td>\n",
              "      <td>26.97</td>\n",
              "      <td>25.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>F</td>\n",
              "      <td>68</td>\n",
              "      <td>64</td>\n",
              "      <td>21.51</td>\n",
              "      <td>20.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>M</td>\n",
              "      <td>65</td>\n",
              "      <td>71</td>\n",
              "      <td>22.51</td>\n",
              "      <td>24.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>M</td>\n",
              "      <td>75</td>\n",
              "      <td>82</td>\n",
              "      <td>23.69</td>\n",
              "      <td>25.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>F</td>\n",
              "      <td>42</td>\n",
              "      <td>49</td>\n",
              "      <td>15.08</td>\n",
              "      <td>17.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>M</td>\n",
              "      <td>74</td>\n",
              "      <td>82</td>\n",
              "      <td>22.64</td>\n",
              "      <td>25.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>M</td>\n",
              "      <td>94</td>\n",
              "      <td>105</td>\n",
              "      <td>36.57</td>\n",
              "      <td>40.86</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>67 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Sex   \"Weight (Sep)\"   \"Weight (Apr)\"   \"BMI (Sep)\"   \"BMI (Apr)\"\n",
              "0    M               72               59         22.02         18.14\n",
              "1    M               97               86         19.70         17.44\n",
              "2    M               74               69         24.09         22.43\n",
              "3    M               93               88         26.97         25.57\n",
              "4    F               68               64         21.51         20.10\n",
              "..  ..              ...              ...           ...           ...\n",
              "62   M               65               71         22.51         24.45\n",
              "63   M               75               82         23.69         25.80\n",
              "64   F               42               49         15.08         17.74\n",
              "65   M               74               82         22.64         25.33\n",
              "66   M               94              105         36.57         40.86\n",
              "\n",
              "[67 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "_vANo60lC4Nn",
        "outputId": "e8f9a97d-df49-4785-a1a0-2d1eadf4cf37"
      },
      "source": [
        "#5 data teratas\n",
        "df6.head()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Sex</th>\n",
              "      <th>\"Weight (Sep)\"</th>\n",
              "      <th>\"Weight (Apr)\"</th>\n",
              "      <th>\"BMI (Sep)\"</th>\n",
              "      <th>\"BMI (Apr)\"</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>M</td>\n",
              "      <td>72</td>\n",
              "      <td>59</td>\n",
              "      <td>22.02</td>\n",
              "      <td>18.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>M</td>\n",
              "      <td>97</td>\n",
              "      <td>86</td>\n",
              "      <td>19.70</td>\n",
              "      <td>17.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M</td>\n",
              "      <td>74</td>\n",
              "      <td>69</td>\n",
              "      <td>24.09</td>\n",
              "      <td>22.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>M</td>\n",
              "      <td>93</td>\n",
              "      <td>88</td>\n",
              "      <td>26.97</td>\n",
              "      <td>25.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>F</td>\n",
              "      <td>68</td>\n",
              "      <td>64</td>\n",
              "      <td>21.51</td>\n",
              "      <td>20.10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Sex   \"Weight (Sep)\"   \"Weight (Apr)\"   \"BMI (Sep)\"   \"BMI (Apr)\"\n",
              "0   M               72               59         22.02         18.14\n",
              "1   M               97               86         19.70         17.44\n",
              "2   M               74               69         24.09         22.43\n",
              "3   M               93               88         26.97         25.57\n",
              "4   F               68               64         21.51         20.10"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VncYiRCo8f3m"
      },
      "source": [
        "### Membuat deskriptif"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "cRvrFniE8etF",
        "outputId": "5aa8eac1-f6e7-4099-b476-12f99421f527"
      },
      "source": [
        "#DataNumerik\n",
        "df6.describe()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>\"Weight (Sep)\"</th>\n",
              "      <th>\"Weight (Apr)\"</th>\n",
              "      <th>\"BMI (Sep)\"</th>\n",
              "      <th>\"BMI (Apr)\"</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>67.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>67.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>65.059701</td>\n",
              "      <td>66.238806</td>\n",
              "      <td>22.030000</td>\n",
              "      <td>22.484179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>11.285390</td>\n",
              "      <td>11.284327</td>\n",
              "      <td>3.308901</td>\n",
              "      <td>3.602527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>42.000000</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>15.080000</td>\n",
              "      <td>16.890000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>56.500000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>19.965000</td>\n",
              "      <td>20.230000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>64.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>21.730000</td>\n",
              "      <td>22.310000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>70.500000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>23.160000</td>\n",
              "      <td>23.860000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>97.000000</td>\n",
              "      <td>105.000000</td>\n",
              "      <td>36.570000</td>\n",
              "      <td>40.860000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        \"Weight (Sep)\"   \"Weight (Apr)\"   \"BMI (Sep)\"   \"BMI (Apr)\"\n",
              "count        67.000000        67.000000     67.000000     67.000000\n",
              "mean         65.059701        66.238806     22.030000     22.484179\n",
              "std          11.285390        11.284327      3.308901      3.602527\n",
              "min          42.000000        47.000000     15.080000     16.890000\n",
              "25%          56.500000        58.000000     19.965000     20.230000\n",
              "50%          64.000000        66.000000     21.730000     22.310000\n",
              "75%          70.500000        70.000000     23.160000     23.860000\n",
              "max          97.000000       105.000000     36.570000     40.860000"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "zmdLGchV8uuU",
        "outputId": "5dcb8624-eecb-4124-ce4c-49a6efd41352"
      },
      "source": [
        "df6.hist()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f9382ee3950>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9382ebcd10>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f9382e813d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9382e35a50>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "t4AclPYb81C3",
        "outputId": "911efeaa-4733-4d08-f2a4-4fe17b079f66"
      },
      "source": [
        "#data non-numerik\n",
        "df6.describe(include=[\"object\", \"bool\"]) "
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Sex</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Sex\n",
              "count   67\n",
              "unique   2\n",
              "top      F\n",
              "freq    35"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3pHZDA8d6o-r",
        "outputId": "ab897c49-49c8-4262-fa08-f5e86a841acb"
      },
      "source": [
        "#List nama kolom\n",
        "df6.columns"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Sex', ' \"Weight (Sep)\"', ' \"Weight (Apr)\"', ' \"BMI (Sep)\"',\n",
              "       ' \"BMI (Apr)\"'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W_fSI7xFDNmo"
      },
      "source": [
        "Untuk memudahkan saat kita butuh menuliskan nama kolomnya, bisa langsung copas agar sesuai formatnya (misalnya huruf besar/kecil, dll)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vdWlI9_l59k-"
      },
      "source": [
        "### Latihan-5 (mengambil sebagian data)\n",
        "Buat kodingan untuk memilih data yang perempuan saja"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "b669Ar295lNf",
        "outputId": "81f9d1e9-39b4-4059-c82d-8c5f5b1b1a0b"
      },
      "source": [
        "#Pilih data yang perempuan saja \n",
        "\n",
        "df6.loc[df6['Sex']=='F']"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Sex</th>\n",
              "      <th>\"Weight (Sep)\"</th>\n",
              "      <th>\"Weight (Apr)\"</th>\n",
              "      <th>\"BMI (Sep)\"</th>\n",
              "      <th>\"BMI (Apr)\"</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>F</td>\n",
              "      <td>68</td>\n",
              "      <td>64</td>\n",
              "      <td>21.51</td>\n",
              "      <td>20.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>F</td>\n",
              "      <td>64</td>\n",
              "      <td>60</td>\n",
              "      <td>24.24</td>\n",
              "      <td>22.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>F</td>\n",
              "      <td>56</td>\n",
              "      <td>53</td>\n",
              "      <td>21.23</td>\n",
              "      <td>20.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>F</td>\n",
              "      <td>70</td>\n",
              "      <td>68</td>\n",
              "      <td>30.26</td>\n",
              "      <td>29.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>F</td>\n",
              "      <td>58</td>\n",
              "      <td>56</td>\n",
              "      <td>21.88</td>\n",
              "      <td>21.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>F</td>\n",
              "      <td>50</td>\n",
              "      <td>47</td>\n",
              "      <td>17.63</td>\n",
              "      <td>16.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>F</td>\n",
              "      <td>56</td>\n",
              "      <td>55</td>\n",
              "      <td>20.97</td>\n",
              "      <td>20.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>F</td>\n",
              "      <td>70</td>\n",
              "      <td>68</td>\n",
              "      <td>27.30</td>\n",
              "      <td>26.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>F</td>\n",
              "      <td>61</td>\n",
              "      <td>60</td>\n",
              "      <td>23.30</td>\n",
              "      <td>22.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>F</td>\n",
              "      <td>53</td>\n",
              "      <td>52</td>\n",
              "      <td>19.48</td>\n",
              "      <td>19.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>F</td>\n",
              "      <td>57</td>\n",
              "      <td>58</td>\n",
              "      <td>20.69</td>\n",
              "      <td>20.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>F</td>\n",
              "      <td>58</td>\n",
              "      <td>58</td>\n",
              "      <td>21.09</td>\n",
              "      <td>21.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>F</td>\n",
              "      <td>49</td>\n",
              "      <td>50</td>\n",
              "      <td>18.37</td>\n",
              "      <td>18.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>F</td>\n",
              "      <td>69</td>\n",
              "      <td>69</td>\n",
              "      <td>28.17</td>\n",
              "      <td>28.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>F</td>\n",
              "      <td>52</td>\n",
              "      <td>53</td>\n",
              "      <td>19.31</td>\n",
              "      <td>19.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>F</td>\n",
              "      <td>63</td>\n",
              "      <td>64</td>\n",
              "      <td>21.78</td>\n",
              "      <td>22.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>F</td>\n",
              "      <td>56</td>\n",
              "      <td>57</td>\n",
              "      <td>19.78</td>\n",
              "      <td>20.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>F</td>\n",
              "      <td>54</td>\n",
              "      <td>56</td>\n",
              "      <td>20.15</td>\n",
              "      <td>20.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>F</td>\n",
              "      <td>57</td>\n",
              "      <td>59</td>\n",
              "      <td>22.15</td>\n",
              "      <td>22.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>F</td>\n",
              "      <td>63</td>\n",
              "      <td>65</td>\n",
              "      <td>23.87</td>\n",
              "      <td>24.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>F</td>\n",
              "      <td>54</td>\n",
              "      <td>56</td>\n",
              "      <td>18.61</td>\n",
              "      <td>19.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>F</td>\n",
              "      <td>56</td>\n",
              "      <td>58</td>\n",
              "      <td>21.73</td>\n",
              "      <td>22.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>F</td>\n",
              "      <td>63</td>\n",
              "      <td>66</td>\n",
              "      <td>21.89</td>\n",
              "      <td>22.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>F</td>\n",
              "      <td>51</td>\n",
              "      <td>54</td>\n",
              "      <td>18.31</td>\n",
              "      <td>19.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>F</td>\n",
              "      <td>59</td>\n",
              "      <td>62</td>\n",
              "      <td>19.64</td>\n",
              "      <td>20.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>F</td>\n",
              "      <td>65</td>\n",
              "      <td>68</td>\n",
              "      <td>23.02</td>\n",
              "      <td>24.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>F</td>\n",
              "      <td>53</td>\n",
              "      <td>56</td>\n",
              "      <td>20.63</td>\n",
              "      <td>21.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>F</td>\n",
              "      <td>62</td>\n",
              "      <td>65</td>\n",
              "      <td>22.61</td>\n",
              "      <td>23.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>F</td>\n",
              "      <td>55</td>\n",
              "      <td>58</td>\n",
              "      <td>22.03</td>\n",
              "      <td>23.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>F</td>\n",
              "      <td>57</td>\n",
              "      <td>61</td>\n",
              "      <td>23.47</td>\n",
              "      <td>25.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>F</td>\n",
              "      <td>64</td>\n",
              "      <td>68</td>\n",
              "      <td>22.84</td>\n",
              "      <td>24.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>F</td>\n",
              "      <td>60</td>\n",
              "      <td>64</td>\n",
              "      <td>19.50</td>\n",
              "      <td>20.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>F</td>\n",
              "      <td>52</td>\n",
              "      <td>57</td>\n",
              "      <td>17.72</td>\n",
              "      <td>19.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>F</td>\n",
              "      <td>55</td>\n",
              "      <td>60</td>\n",
              "      <td>21.64</td>\n",
              "      <td>23.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>F</td>\n",
              "      <td>42</td>\n",
              "      <td>49</td>\n",
              "      <td>15.08</td>\n",
              "      <td>17.74</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Sex   \"Weight (Sep)\"   \"Weight (Apr)\"   \"BMI (Sep)\"   \"BMI (Apr)\"\n",
              "4    F               68               64         21.51         20.10\n",
              "6    F               64               60         24.24         22.88\n",
              "7    F               56               53         21.23         20.23\n",
              "8    F               70               68         30.26         29.24\n",
              "9    F               58               56         21.88         21.02\n",
              "10   F               50               47         17.63         16.89\n",
              "13   F               56               55         20.97         20.36\n",
              "14   F               70               68         27.30         26.73\n",
              "15   F               61               60         23.30         22.88\n",
              "16   F               53               52         19.48         19.24\n",
              "18   F               57               58         20.69         20.79\n",
              "20   F               58               58         21.09         21.24\n",
              "21   F               49               50         18.37         18.53\n",
              "23   F               69               69         28.17         28.43\n",
              "27   F               52               53         19.31         19.75\n",
              "29   F               63               64         21.78         22.22\n",
              "30   F               56               57         19.78         20.23\n",
              "33   F               54               56         20.15         20.69\n",
              "36   F               57               59         22.15         22.93\n",
              "37   F               63               65         23.87         24.67\n",
              "38   F               54               56         18.61         19.34\n",
              "39   F               56               58         21.73         22.58\n",
              "43   F               63               66         21.89         22.79\n",
              "44   F               51               54         18.31         19.28\n",
              "45   F               59               62         19.64         20.63\n",
              "46   F               65               68         23.02         24.10\n",
              "47   F               53               56         20.63         21.91\n",
              "48   F               62               65         22.61         23.81\n",
              "49   F               55               58         22.03         23.42\n",
              "54   F               57               61         23.47         25.11\n",
              "55   F               64               68         22.84         24.29\n",
              "56   F               60               64         19.50         20.90\n",
              "59   F               52               57         17.72         19.42\n",
              "61   F               55               60         21.64         23.81\n",
              "64   F               42               49         15.08         17.74"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vXplXWBV-mA8"
      },
      "source": [
        "## Visualisasi Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6NobwuVw-v3k"
      },
      "source": [
        "### Dengan Matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aiQmw5bZDG4b"
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "_G4GenFmDHph",
        "outputId": "8854ccf6-7e97-4998-a711-bfcda371079a"
      },
      "source": [
        "#Line Chart\n",
        "df6[[' \"Weight (Sep)\"', ' \"BMI (Sep)\"']].plot(kind='line', \n",
        "             figsize=(10, 5))\n",
        "\n",
        "plt.title('Perbandingan berat dan BMI bulan september', fontsize = 20)\n",
        "plt.ylabel('nilai', fontsize = 10)\n",
        "plt.xlabel('peserta', fontsize = 10)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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eQctfuA5tWHZDCvYRid8C7wbequ/7UX3f1+r7XsZ4iAYpZUAI8SM0CYJ9elttaMPSK9BGKF00je01eBVNx2enEOIJxnXIyoDPhybySimfFkLcBnwLOCyEeBxNT6wILVl8M9oAhrfGs+NEjoGUclgI8SJwgRDi92gaQ2PAo1LKiAMH9PU+gqY/9k8hRKgO2Sq0a/nCSav9D5oW1Z+EEPejJTCv0n/fH9GutZmgKWRAjkC7z96GJhlzDG2EdVSklC4m6lJNF3H3CVLKF4UQxnF/SQjxDJohczlafxXOcxaOdJ6ntwDfEUJsR7sWu9HOy5Vo93tQxy+J+6cDLQcutF+5Du3Y/o+U8rmQfUS9P1J9DyfB34DvCSHehqaZZuiQeYAPTUr4/wZaLum/Apfr10k72sCw09CeI19Gq1xxapLuYZ6n+gfC65BFWb4auAt4Dc3wGkYbuXI/mlZRbsiydxBhOLo+v16fvwVtdIvhfXChdcKXRljvJjRjcAStXNFDaIKtYfdHFKkIIusR2dDero+hJS83oz2w8sJtL8X7zge+jtbBhe67Tl/+4UnL56Llv+xHGynUiSb0uijcPog9pH2ryWtC6uvMQ/OEdaN1iLuA90RZ73y0B91JtLzAHv28fp+pGl/NQHOUbZk6Bvo6S9GMeAfaQ08CN8X5m9+C9sBx6dfsI2g1YCPtayOaqG0/mgjr82hhqCZ9+TviPQeMyxTE21bj2pz8GUF7iN3JJC2p0DYAS+PYR6plL7Zgrk8oQwuhdqPdM68BH4l0rWfaedJ/6/fRRmv2MH7f3w9sTNX9g+Z5/G80Q8QLHEDzYosw2495fyTShgi/JdpxnHKuQs8H2ojPp9Dyb4fQBmNsiLAtgab1+DTjGont+nn+ErAg3LUYz7U7Gz5C/+EKhSIGQoi3oHU2d0kpv5ju9igUiuxA6OXqpJT16W2JIpNROWQKxSQi5GRUonkmYWL+jEKhUCgUSaNyyBSKqXxfCLEGTcSxBy2X5G1o+VA/lVLGEtpVKBQKhcIUyiBTKKbyIONJyWVo+Vivo9Vb+2Ua26VQKBSKWYrKIVMoFAqFQqFIMyqHTKFQKBQKhSLNTFvIUgjxK+AdaPXvVunTKtD0g+rRhuC+U0rZrwuL/hB4O9rw6puklLvCbTeUOXPmyPr6+mlpv0KhUCgUCkUq2blzZ6+UsircvOnMIdsC/JiJxW9vA56WUt6li9rdBnwBLWH6NP1zLvC/+ndU6uvreeWVV1LcbIVCoVAoFIrUI4Q4EWnetIUspaY63Ddp8pXAb/S/f8N4fbArgd9KjR1AmRCidrraplAoFAqFQpFJzHQO2Vw5Xny4k/E6YXVAa8hybfq0KQghPiKEeEUI8UpPT8/0tVShUCgUCoVihkhbUr/UhneaHuIppfyZlHK9lHJ9VVXYMKxCoVAoFApFVjHTOmRdQohaKWWHHpLs1qe3M7EA7Xx9mkKhyEB8Ph9tbW14PJ50N0WRAPn5+cyfPx+r1ZrupigUCp2ZNsgeBW5EK0FzI1rhWmP6x4UQf0BL5h8MCW0qFIoMo62tjeLiYurr69EGSSuyBSklDoeDtrY2Fi9enO7mKBQKnWkLWQoh7gW2A8uEEG1CiH9BM8TeIoQ4DLyZ8dqAjwPHgCPAz4GPTVe7FApF8ng8HiorK5UxloUIIaisrFTeTYUiw5g2D5mU8oYIsy4Os6wEbpmutigUitSjjLHsRZ07hSLzUEr9CoUi6zEEoo3vq6++mocffjg4f9myZdx5553B/6+99loefPDBiNu7+eab2b9/f9R93nTTTdx///1Tpjc3N3PPPfdEXK+jo4N3vOMdALhcLt773vdy5plnsmrVKs4//3yGh4ej7jcSn/3sZ3nmmWeC/zc1NdHc3IwSz1YosgNlkCkUilnHpk2b2LZtGwAOh4PCwkK2b98enL99+3Y2btwYcf1f/OIXrFy5MqF9xzLIvv/97/PhD38YgB/+8IfMnTuXffv28dprr/HLX/4y4UT7T3ziE9x1112xF1QoFBmJMsiSoHPQw4EOZ7qboVCc8hgSOMb3xo0bgwbZtm3buPzyy+np6UFKyfHjx7Hb7dTU1PDEE0/Q2NjIWWedxfXXXx/0TjU1NQWrgPzyl7/k9NNP55xzzuHDH/4wH//4x4P7fe6559i4cSNLliwJestuu+02/vnPf7J27Vp+8IMfTGnrAw88wFvf+lZA85bV1Y1LLi5btoy8vDwAfve733HOOeewdu1aPvrRjzI2NgZAUVERt956K2eccQYXX3wxhh7jokWLcDgcdHZ2AlBRUUFOTg5KHkihyA6UQZYE3/n7G9z8G1W6SaFINy+//PKE77PPPpvXXnuN0dFRtm3bRmNjI8uWLePAgQNs27aNjRs30tvby5133slTTz3Frl27WL9+Pd///vcnbPfkyZN84xvfYMeOHbzwwgscPHhwwvyOjg6ef/55/vznP3PbbbcBcNddd3HBBRewZ88ebr311gnLHz9+nPLy8qDR9aEPfYhvf/vbNDY2cvvtt3P48GEADhw4wH333ccLL7zAnj17yMnJ4fe//z0AIyMjrF+/ntdff53Nmzfzta99Lbj9s846ixdeeAGABx98kAULFgSPiUKhyGxmWvZiVtE+4OLkoBvfWABrjrJtFacmX3vsdfafTK2neOW8Er56+RkJr5+Xl8cZZ5zBrl272LFjB5///Oc5duwY27ZtY/fu3WzatIkdO3awf/9+Nm3aBMDo6CiNjY0TtvPSSy+xefNmKioqALj++us5dOhQcP5VV12FxWJh5cqVdHV1xWxXR0fHBI/V2rVrOXbsGE888QRPPfUUGzZsYPv27Tz99NPs3LmTDRs2AOB2u6murgbAYrHwrne9C4D3ve99XHPNNcHtVVdXc/LkyUQOmUKhSDPKIEuCLqcXKaF7yEtdmT3dzVEoFCFs2rSJ5557jqGhIcrLyznvvPP48Y9/zO7du/noRz/KiRMneMtb3sK9996b8D4MTxdo+l6xsNvtU+QmioqKuOaaa7jmmmuwWCw8/vjj2Gw2brzxRr71rW/F3GboiEmPx4PdrvoihSIbUQZZgkgp6XJqHWvnoFsZZIpTlmQ8WdPJxo0b+cxnPkNTUxMAq1evZseOHXR1dbFq1Spqa2u55ZZbOHLkCEuXLmVkZIT29nZOP/304DY2bNjApz71Kfr7+ykuLuaBBx7gzDPPjLrf4uJihoaGws47/fTTaW5uDv7/wgsvsHLlSsrLyxkdHWX//v00NTWxcuVKrrzySm699Vaqq6vp6+tjaGiIRYsWEQgEuP/++3n3u9/NPffcw/nnnx/c3qFDh7j++usTP2gKhSJtqDhbggx7/bhGtSTbkwNKYFGhyDQ2btzIsWPHgmHI3NxcqqurWb9+PRaLhaqqKrZs2cINN9zA6tWraWxsnJIjVldXx5e+9CXOOeccNm3aRH19PaWlpVH3u3r1anJyclizZs2UpP7CwkIaGho4cuQIAEePHmXz5s2ceeaZrFu3jvXr13PttdeycuVK7rzzTi655BJWr17NW97yFjo6OoLbeOmll1i1ahXPPPMM//7v/w5o5ayOHDnC+vXrU3L8FArFzCLicbNnKuvXr5fGSKiZ5kj3MG/+/rMAfPntK/jwhUvS0g6FIh0cOHCAFStWpLsZM8Lw8DBFRUX4/X6uvvpqPvShD3H11VcnvL2HHnqInTt3TtBFM0NRUVFYrbKHHnqIXbt28Y1vfCOu7ZxK51ChyBSEEDullGHfmpSHLEG6neNesY5B5SFTKGYrd9xxB2vXrmXVqlUsXryYq666KqntXX311dMi1ur3+/nMZz6T8u0qFIqZQeWQJUjXkGaE2XItdAy609wahUIxXXz3u99N+TZvvvnmhNeNpOSvcscUiuxGecgSpMvpBeCMeSXKQ6ZQKBQKRRbzwM42jvYkVrYsVSiDLEG6nB6K8nJZWlVEpzLIFAqFQqHISgbdPj7zp708fSC2luB0ogyyBOl2eqkuyaO2NJ/uIQ/+sUC6m6RQKBQKhcIkbf0uAOaXF6S1HcogS5Aup4e5xfnUlNoJ6OKwZpBSsrulPy4xSYVCoVAoFNNDa5+WB75AGWTZSdeQh7kledSW5QPmR1q+eLyPq/9nG7ta+qejeQrFKYUxatH4bm5uxm63s3btWtasWcPGjRt54403ANi6dStCCH7xi18E19+zZw9CiGAC/0033RQsFj6ZT33qUzz33HMA/PnPf2bdunWsWbOGlStX8tOf/jSh9vf09AQLjhttvOmmm9iyZQt33HFHQttUKBTxMe4hS6/AuzLIEkBT6fcytySf2lLDIDM30vJIt5Y82NLnSnn7FAoFNDQ0sGfPHvbu3cuNN97IN7/5zeC8VatW8cc//jH4/7333suaNWtibtPhcLBjxw4uvPBCfD4fH/nIR3jsscfYu3cvu3fvDlYFMEtVVRW1tbXBwuAKhWLmaOt3U5SXS1mBNa3tUAZZAgy4fIz6A5pBVqJZ1GYT+1t1Q8wYranIbPpGRoNvUYrMwyjYHVq4OxSn00l5eXnw/0WLFuHxeOjq6kJKyd/+9jfe9ra3xdzPAw88EPRkDQ0N4ff7qaysBLS6lsuWLQM0j9e1117Lhg0b2LBhQ9DQuuOOO3j/+99PY2Mjp512Gj//+c+D277qqqv4/e9/D4DNZqO0tBS73U5RUZHZw6FQKEzQ1u9ifrl9Ql3YdKB0yBLA0CCbW5JPiT2XAluO6fJJJxyGQaZGaGYD33r8APvaB/nbpy5Md1MUYXj55ZcnfINWlmjt2rUMDQ3hcrl48cUXJ6xz3XXX8ac//Yl169Zx1llnTSgUHokXXniB6667DoCKigquuOIKFi1axMUXX8w73vEObrjhBiwWC5/85Ce59dZbOf/882lpaeHSSy/lwIEDALz66qvs2LGDkZER1q1bx2WXXca8efNYv349t99+O6CVfdq4cWNKjo1CoYhOa5+bBRXpzR8DZZAlhOHVmluShxCCmtJ8Op3mQpZGqLJbeciygk6nR+nNReKvt0HnvtRus+ZMeNtdSW3CCFkC3HfffXzkIx/hb3/7W3D+O9/5Tt71rndx8OBBbrjhBrZt2xZzmx0dHRO8cL/4xS/Yt28fTz31FN/97nd58skn2bJlC0899RT79+8PLud0OoOCrldeeSV2ux273c5FF13ESy+9xFVXXUV1dTUnT55M6jcrFApzSClp7XexcWllupuiQpaJYHi15pZo+WPzSu2mHtZSypCQpXrIZwNOtw+nx8dYQI2KzUauuOKKYCK+QU1NDVarlSeffJKLL744ru3Y7XY8non37Jlnnsmtt97Kk08+yQMPPABAIBBgx44d7Nmzhz179tDe3h4MPU4Oixj/ezwe7Pb0JhUrFKca/S4frtGxtEtegPKQJYRRx7KqWAtx1JTm8/zh3rjX73f5GPL6gfHwpyKzGXT7kFIzzMoLbeluTmaRpCdrJnj++edpaGiYMv3rX/863d3d5OTkxLWdFStWcOTIEZqamhgeHuaVV14JJvLv2bOHRYsWAXDJJZdw991387nPfS44b+3atQA88sgjfPGLX2RkZIStW7dy113a8Tt06BCrVq1K9qcqFAoTGM6RBWkeYQnKIEuILqeXsgIr+VatEw8Vh83Nie10NMKVDVWFtPa7kVKmPZlQEZ1Btw+AfteoMsiyBCOHTEqJzWabIHNhYDZP67LLLuOnP/0pN998M1JK/vM//5OPfvSj2O12CgsL2bJlCwA/+tGPuOWWW1i9ejV+v58LL7yQn/zkJwCsXr2aiy66iN7eXr7yla8wb948AP7xj39w2WWXJfejFQqFKVr1wVoqhyxLMURhDWp1cdieYS+1pbGtbMMg21BfwdGeVgbdPsoK1EM+U5FS4vRoHs0B3TBTZDb19fW43eHzOpuamsLKU4TqfRmG1WQuuOACvvjFLzIwMEBZWRmPP/542OXmzJnDfffdF3be6tWr+e1vfztl+qOPPsojjzwSdh2FQjE9tPVr/US6NchA5ZAlRNeQVjbJwNAii3ekZYtjBICzF2nD8JX0RWYzMjoWzB0bcI2muTWKdPO9732PlpaWlG6zp6eHT3/60xOkORQKxfTT2ueirEaCmcIAACAASURBVMBKcX56NchAecgSotvp4bTqOcH/a3SDLF4tspY+F9XFedTPKdTWc3pYVlOc+oYqUsJgiFdswKU8ZKc65557bsLrRlLdr6qq4qqrrkp4uwqFIjHa+t0Z4R0D5SEzTSAg6R7yMjfEQzZPD1PGq9bf0udiYUUBNfooTTXSMrMZDDHC+pVBplAoFLOG1n5X2mtYGiiDzCS9I17GAjIoeQFQYs/Fbs2JW/qixaEZZMYozW5lkGU0Tk+oh0yFLA2kVBIg2Yo6dwqF5mBp688MUVhQBplpuoOisOMGmRCC2tL8uEKWXv8YHU4PCysLyLfmUFZgVTlkGY4KWU4lPz8fh8OhHuxZiJQSh8NBfn5+7IUVillM77CXUX8gY0KWKofMJJNFYQ1qy/I5GUfIsr3fjZSwULfI5xbnq5BlhmMYZLZcC/3KQwbA/PnzaWtro6enJ91NUSRAfn4+8+fPT3czFIq0EpS8yJCQpTLITBJaNimUmhI7247GFoc9oUteGAZZdUkeXUPKQ5bJOHWDbGFFgfKQ6VitVhYvXpzuZigUCkXCtPZpTpQFFZnhIVMhS5N0OT0IAXOKJhpk88ry6R7y4h8LRF3fUAVeWKl7yEryVQ5ZhuN0+xBC06kZcCsPmUKhUMwG2nQPWV1ZZnjIlEFmku4hD5WFeVgnKfLXlOYzFpD0DEf3drU4XORbLVTpBt3ckjy6h7wEVI3EjGXQ7aM4L5eKAhv9I7PLQ+YY9jKil/FSKBSKU4nWPjdzivKw2+IrnTbdKIPMJF1O75RwJYyLw8YaaXlCl7wwSiXNLdEMOceI8rxkKoNuH6UFVsoKbLNulOX7fvkSn/zDnnQ3Q6FQKGactgFXxiT0gzLITNPl9ExJ6AeCJZNijbRs7XOxsKIw+H91sdIiy3ScHj+ldivlBVZGRscY9UcPS2cLvrEAh7uGePpgF829I+lujkKhUMworX2ZI3kByiAzTSwP2cmByCMtpZRBUVgDY1vKIMtcBt0+SvKtlBVopTVmSx7ZyQE3/oBESvjt9hPpbo5CoVDMGGMByckBNwuUhyw78Y0FcIx4g16tUErtVvKtlqgest7hUVyjYywMGdFhlF1SWmSZy6DbR6ndGiwAP1tGWh7XvWKLKgv4085WlUumUChOGTqdHvwByfwMkbwAZZCZonfYi5RTNchAE4edV2qnI4qnq0UfYbmocjxkOacoDyGUhyyTceoGWfksM8hOOLTr8YtvW86Qx89Du9vT3CKFQqGYGQzFg0yRvABlkJnC8H6FC1mC5u3qiBKyHL8Axi1ya46FysI8uoeUQZapjHvItJDlbBGHbXaMUGjL4dIzalhVV8Jvtzcr5X2FQnFKYDyPlYcsS+kKUzYplJoY5ZNOOFxBPatQ5pbkqZBlhuLxjeH1BygJMcgGZ5GHbFFlIUIIbmys51DXMNuPOdLdLIVCoZh22vrdCKFpiGYKyiAzgeHFimSQzSu10zWkFR8PR0ufi5qSfPKtEzVP5pao8kmZiqHSXxISspw1HrLeEernaG+Hl6+ZR3mBld9sa05voxQKhWIGaO3Xnsd5uZmhQQbKIDNFl9NDjkVQWWgLOz8oDhuhFFJrnyvsEFvlIctcnB7NICu1Wymw5WDNEfTPAg+ZfyxAa78rmM+Yb83h3ecs5Mn9XUH1aoVCoZittPW7M0qDDJRBZooup5fq4jwsFhF2vuH67IhQZPxE38gEyQuD6uJ8HCNefDHKLilmHqOweKndihCCsgIbg7NA9qJj0INvTFJfOX49vu+8RQD8bkdLupqlUCgUM0JbnytjioobKIPMBF1OD9URwpWgFRiH8Gr9Ht8YXU4vi8J6yPKRUhvFqcgsDIOsJD8XgPIC66won9Ts0CQv6kNG/NaV2blkZQ33vdyCxzeWrqYpFArFtDLqD9Dh9DA/g0RhQRlkpuh2eplbHH6EJUQvn2SEgRZWhg9ZgtIiy0RCPWQAZXbbrMghM5T56+cUTpj+gY2L6Hf5eHTvyXQ0S6FQKKadjkE3Uk4dYJdulEFmgq6h8GWTDMoKDHHYqSFLQ/MpfA6ZKp+UqTjdmlhq0CArsAaNtGymWS9yXz3pBaNxSSXL5hbzm21KAkOhUMxOWvu0Z7QKWWYpHt8YAy5fRA0y0MRha0vtnAzjIQuKwiqDLKsYDBllCZpBNhs8ZCccI9TrkhehCCH4wMZFvH7Syc4T/WlqnUKhUEwfrf2GBpnykGUlxsjJaDlkADUl4bXIWvpcFNpyqAgzQrOy0EaORSiDLAMZdPv00ZXarVJeYKPf5ct671GzwzUhfyyUq9fVUZyfy29UfUuFQjELaet3kWMRwTSjTEEZZHHS6YyuQWZQWxbBIHNokheTPRIAFougulhJX2QiRtkkg7ICG6P+AB5f9o6IHQtIWhwuFs0J764vsOXyzvUL+Ou+DvWSoFAoZh2tfW7mleWTm5NZJlBmtSaDMR5MNbEMstJ8Op2eKeKwLX0uFoVJ6DeoVuKwGcngFIMs+8sndQy6GR0LRPSQAXygcRFjUvL7F5UEhkKhmF209buYX5ZZ+WOgDLK4GS+bFDmHDKCm1M5YQE6QsJBS0tLnCqtBZjC3OI9u5SHLOAbdPkryxw2yct0gy+YC48YAk2gvCIsqC7loWTX3vNjCqD97vYEKhUIxmdZ+d0YVFTdIi0EmhLhVCPG6EOI1IcS9Qoh8IcRiIcSLQogjQoj7hBDh5fDTRLfTgy3XMsFbEo55ekz6ZEiR8e4hL15/ILpBVpJPlyownnEMun3BhH7QQpYAA1nsITM0yBbPiewhA3h/4yJ6h708d6hnJpqlUCgU047HN0bPkDejioobzLhBJoSoA/4NWC+lXAXkAO8Gvg38QEq5FOgH/mWm2xaNLqeHuSV5YXPAQqnRDbLQPDJjhOXCKCGiuSV5DLh8SpAzwxjy+COELLPbQ5aXa2FucfTw+1kLywE4rmuWKRQKRbbT1q9LXigPWZBcwC6EyAUKgA7gTcD9+vzfAFelqW1h6XJ6Yz7AQCswDhPFYVv0EFE0D5kxejNSHUxFepicQ2YUGB/I4vJJx3tHWFRZELEEmEGp3UpxXi7tA+FLgZ2qjPoDBALZPcpWoThVMSQvMk2DDNJgkEkp24HvAi1ohtggsBMYkFL69cXagLqZbls0YonCGpQVWMnLtUyoZ3miz4UQWmmaSCgtsszDPxZg2OunxJ4bnGYYZ9mdQzYSLCoei7pye/CNUgGBgOTC//wH/7dDSYIoFNlIW5+hQaYMMoQQ5cCVwGJgHlAIvNXE+h8RQrwihHilp2fmclu6nV6qYyT0gyEOmz/BQ9ba52JeqR1bbuTDrconZR5Oz0SVfoB8aw52aw79I9npIQsEJCccrpj5YwZ1ZXblIQuh3zVKp9PDy8196W6KQqFIgLZ+N7acqVVKMoF0hCzfDByXUvZIKX3Ag8AmoEwPYQLMB9rDrSyl/JmUcr2Ucn1VVdWMNHjY62fY64/LQwZQW2qfkkMWLVwJ43IancpDljE4J9WxNCgvsDKQpeWTuoY8eP2BqCMsQ9E8ZK5pblX20K2nFBzpHk5zSxQKRSK09ruoK7fHTNlIB+kwyFqA84QQBULLkL8Y2A/8A7hOX+ZG4JE0tC0s3UFR2Pgs6skeshOO2AZZqd2KLdcS3Jci/QTLJuVPNMhKC2xZO8rSSNCPpkEWSl2ZnSGPH6cnOw3QVGMYZMd6R6ZoDSoUisynrd+dcSWTDNKRQ/YiWvL+LmCf3oafAV8APi2EOAJUAr+c6bZFIqhBFkdSP2gjLbt0cVjXqJ/eYS8LY3gkhBDMLclTOWQZhGGQlRZM9ZBl6yjLeDTIQqnTO652lUcGjL+cjfoDynOoUGQhrX1a1ZxMJDf2IqlHSvlV4KuTJh8DzklDc2JiGEmx6lga1JbZ8QckjmFv8MEdy0MGmsGncsgyB8MrNDVkaeNgpzMdTUqaZscItlxLcDRwLIyBKO39blbUlkxn07KC7pBR0Ed7huMeHKFQKNLPsNdPv8unPGTZTLBsUpyFSGt1w+3koIcTughnXAaZEofNKAYj5JCVFlizdpTliV4tfB5v/kTQQ6YS+wFNlsam179TeWQKRXbRlsGSF6AMsrjocnoptOVQlBefQ3FcHNY9Lgobh0FWXaLKJ2USkXLIjKR+KbMvh6jZMUJ9nOFKgDmFedhyLcog0+ke8rCgws6cIpsyyBSKLKO1zxCFVQZZ1hKvBpnBPD3Mc3LAQ2ufi+L83KDCezTmluQHR3Qq0s+g24ctx0K+deJtUl5gYywgGcqy8ySlpNmEBhmAxSI06QuVQwbo8jfF+TRUFZ1yBpkaxKDIdlqDGmQqZJm1dDs9cWmQGZTr4rCdTg8ndMmLWCWXYHwUpxppmRk43X5K7NYp5y4oDjuSXWHL7iEvHl/AlIcMtDyyNuUhA7RjWF2Sx9LqIo72jGSllzQR/uupQ2y66xn8Y6rQvCJ7aet3Y7fmUFmYUaWygyiDLA66nF5THrJQcdh4NMgMjFGcKrE/M3C6fRNU+g2ytXxSsyF5EacorIHykGlIKeke8lBdrBlkg24fvcPZdQ0kwv6TTu5+5gidTk+wML1CkY209ruYX26Py0GSDpRBFgMppV5YPH6DDLQ8svZ+F2197piSFwbGKM5uldifEUyuY2lQXpidBcaNh2m8GmQGdeV2eoe9p3zhe6fHj8cXoLo4n6XVRcDsT+wfC0hue/BVcvVBIPs7htLcIoUicdr63RmbPwbKIIuJ0+3H6w+YLrNQW2rn9ZNORscCcXvIjMEASossM3B6whtkpXbdQ5Zl4rDNDhfWHM17a4a6YE7kqe0l6xky5G/yaKjSDbKe2W2Q/fqF47zaNsi3rjmTXIvgYEd2yr0oFFJK2vpcLMjQ/DFQBllMDBkKsx6y2tJ8vH4t3yJeg6woL5dCWw6dgypkmQlE9JAVZGeB8ROOERaUF5CbY+62V9IXGsYI6KriPGpL8ym05XB0FnvIWvtcfO+JQ7xpeTVXr6ujoaqIA8ogU2Qpg24fQ15/RhYVN1AGWQy6nIkbZAaLKuIPESktssxh0O2bInkB40n9/dnmIet1mc4fg4nisKcyhihsdXE+Qggaqos4Oks9ZFJKbn/4NSwCvnHVKoQQrKgt5oAKWSqylLZ+Q/JCeciylmDZJBOjLAFqdCX0HIugtix+Y07TIlMGWboJBCTOCB6y3BwLxfm5WeUhG5e8MP92WFOaj0UoD1n30MSatktnsfTFo3tP8uyhHj576bKgQb6itoROp4f+kex6EVHMDgZco5z3zad59lBPQuuPS14oD1nWkqyHbF5ZPlYTIaK5Jap8UiYwMuonIKeq9BuUZ1mB8Z5hL67RMdMJ/QDWHAu1pWqkZbfTi906LhDdUF1Ex6Bn1ukG9o+M8vXH9rN2QRkfaKwPTl+ul846kKVlwxTZzfajDjqdHvafTOz6a81wlX5QBllMupweSu1W8q05ptYzDDIz4UowDDLPKaNvlKlEKptkUJZlBcbNFhWfjNIiG9cgM4bMG4n9sy2P7M6/HGDQ7eOua88kJ6TE1oraYgAOnkJhy0BAMuoPRPwosdyZY9tRB5B4qkhbv5vi/FxK4xBpTxdpKS6eTWiSF+bClQAVhTbs1hzTD8Dq4jy8/gBOtz+jL5zZTrBsUhgdMoCyLPOQGRpkixPIIQMtsf+l432pbFLWYWiQGRjSF0d7hlmzoCxdzUop/zzcwwO72rjlogaW10wsJl9VlEdloe2USez3jwVo+u7WYO5ROAptOTz56c3B6iyK6WP7Mc0g60swZN7W7w6G3zMVZZDFwKworIEQgp9/YD2Lq8x7yEAb3akMsvQxbpBFCllag4Xjs4Fmxwi5ehmkRKgrs9Pp9OAfC5gepTlb6B7ysiLESFlUWUCuRcyaPDL36Bhffug1Fs8p5BNvOm3KfC2xv+SUCVl2DHpo63dz2Zm1rJxXMmX+sNfP/249ynOHenj3OQvT0MJTh+4hT/A+SzSHsXfYG9T6zFSUQRaDbqeHhqo5Ca17/mnm1wsaZE4Pp88tTmi/iuRxurW8oIghS7s1q5Kbmx2aQnWixlRduZ2xgKTT6cnopNjppMfp5cLTxj1k1hwL9XMKZ41B9l9PHaKlz8W9Hz4vYorGitpifrP9xClhmLfoSeDvPW8hGxum9uVSSu7f2ca2ow5lkE0zO45p3vnyAmvCIcu+kdFgmkGmMrvvqCQJBCTdQ96EQpaJYuxLJfanF6fhIQsjewFayNLp8WdNDskJk0XFJ3OqS1+4Rv0Mef1Tato2VBXOCnHY9gE3v3j+OO9av4DGhsqIyy2vKWHUH+B4b/Z4hxPFMMgi3TdCCBqXVLL9mEPl/E4z2486KM7LpbGhMuHc3b6R0WDZu0xFGWRR6HON4g/IhEKWiRLqIVOkj2BSf4SwsSEOayyXyUgpOdHrSjh/DJQ4rCEKW108sS9YWl1Ei8OFL8uLbh/scDIWkLzrnAVRl1sRHGk5+xP7T+iVLWqi9P+NDZX0DHk52jP7DdR0sv1oL+csrqCqKC+hHDKPbwzX6BiVRcogy1rGJS9mzkOWb82h1G5VBlmacXp8WAQU2SIn9UN2iMM6RkYZ8voTHmEJykM2Lgo7sS9YWl2EPyCzKp8wHEFvUIyqIg3VheRaxCmR2N/a52J+ecGEkaaT2ah7E42Ec0Xq6Rh00+xw0dhQSVmBjUG3D7/JFyCjn1Yesiwm+FY8w4mAc0vylEGWZgbdPkrsViwROuOyLCqfdCLBouKh5FtzmFNkO3U9ZCF1LENZWqXleWZ7HtkJh4tCWw4VhdEfWHm5OSytPjVKKLX0uWKWvVtYUcC80ny2H+2doVademzX5S4aGyqD16fZyIRjWDPIKgoze6CcMsiiML/czi0XNcR8a0w1Shw2/UQqm2RgvGllg/RFc29yGmQGdWX2U9cgixCyXKKPos52g6y1z8WCioKgxlo0VtSWnBJaZCccIzENMiEE5zVUsuNYH4EsySfNNrYfdVBWYGVFTQnlhYlFJozlKwpnLtqVCMogi8Jpc4v53KXLqSya2ZNYXZyvyielmUiFxQ0MD1k2iMOecIyQYxFJj46sKz911fq7h7xYc0Qwd9CgMC+XeaX5WW+QtfS54jbYl9cUz/oSSoMuH06PP6ZBBtC4pJK+kVEOdc9+IzUdbDvq4NzFFVgsggr9RbhvxFy/a+SdKQ+ZwjRzS/LoHvLOyjeu2x/ex6fv25Pxo5Ii1bE0KMsiD9lxh4u6Mju23ORud8NDlunnbjrQRGHzw3qQtCLj2ZtDFgjIuMJzBitOgRJKRk7dwjiMVGNUqhFaO5XpHxnlTd/dyqttAynZXmufi/YBN41LtGNsvAibTewfN8iUh0xhkrkl+fgDkr4seNib5blDvTy4u53H93WmuylR0XLIIsv0FeflYhHZk0OWbLgSNIPM6w/QOzz7rstY9Ax5qSoO35kvrS7iaM9w1r5A9Qx78foDcRtky/USSgdmcdjyRJ9mYMdzTOaXF7CwokAZZMD+DifHekd4/khqcuqMY7pxqaYDV5FoyHJkFCEi60pmCsogy0DGtchmV9gyEJB0DGohr68++jqDGWzMDLr9UW9ei0VQVmDL+FGWUkqO944kldBvUKeHPE/FPLJup3fKCEuDpdVFuEbH6MjS+3XcGxTfNVJdnM+cotldQsk4JgviNFIbl1Sy45gja3QJpwvjuKUqhL/9mIM5RTZO08uUlSc4ut0xMkqZ3Rp1xGwmoAyyDMQY1dmdpsT+z/1pLz986nDKt9s95MU3JrnhnIX0u0b55uMHUrr97/z9IF988NWUbMvp8UUsm2RQVmBlIMN1yAZcPoY8fuqT0CAzSKX0xYjXz6U/eI7nD2fH6LTuIc+UEZYGhvp3tuaRGYXn4/WQgZ7YP4tDlq19LuYU2SjKi6+YTWNDJU6Pf1YbqfFgXEtHU3AvSCnZftTBuUsqg6kCdlsOdmuO6fzFftdozBHEmYAyyDKQdIrDjgUkj716kkf2tqd82+0D2s16ycq53HzBYu57pTWlbv7H93XywM52Rrz+pLbj8Y0x6g/EdG+X2a0Zn0PWHJS8SEHIMigO60p6W4e6hnija4hfvXA86W1NN17/GP0u35QRlgbBIuNZapC19LmwCEzVOV1eU8yhrmHTelDZwgmHK27vGKg8MoNW3UN2tGck6VzT470jdDo9wfwxg4pCm+mkfsewMsgUCWKERtIhfXHCMYLHF+BYz0jKVejbdM9KXbmdT118OgsrCvjSQ/vw+MaS3rZr1E+zY4TRsUDSneJgjLJJBuUFNvpNdgwzjWGQJVM2yaDUbqU4LzclHjLjTfrZQz30Dme2xEtPBFFYg8pCG2UF1qwtodTa56K21NygjxW1s7uEkplBDqC9RC+pKjzlBWKNkOWw15/088s4lhsnlfIqS6CepfKQKRLGmmNhTpGNrqGZ95CFJuruaxtM6baN3KO6Mjt2Ww7fvPpMjveOcPczyYdH3+gcwngh23qoO6ltBcsmxQxZ2jK2dNKoP8Dj+zr41fPNWAQsqIjf+xGNuvLUaJEZD/KxgOSxvSeT3t50ElTpjxCyFEKwtKooi0OWsfW2JmOMtNw/C0N0vrEAJwfcpvUnG5dU8tLxvlnrNYyHE44RGlKkzbf9qIO5JXlTSr5VFJrP3e0b8SmDTJE41cX5dA2mwyBzYuQ97mntT+m22/rdlBdYKdTzMs4/bQ7XnjWfnz57LOncC8OQXF5TzNY3epJylzvjNsjMv6lNN0d7hvnm4wdo/NbTfOz3u+gd9nLnVWeSl5uTku3PL7cHPZ3JcMIxQl2ZnTPmlfDQ7tSHx1NJJFHYUBqqirI4ZOk2bZA1VBVhzREcnIU1Ldv73QRk/An9Bo0NlQx7/exrT+2LbLZgaLe9aXk1AEeS0GWTUrLjWB+NIfljBlpkIv5+NxCQ9Lsyv7A4KIMsY5lbkpcWD9nBTicNVUUsqSpkT2uKPWT97mAeksHtl62g1G7ltgf3JTVC6WCnk+K8XN577kLa+t0cSyKUEq+HrLzAimt0DK8/+ZBrMnh8Yzy4q413/mQ7F3/vWX71/HHW15fz6w9u4PkvvIn3nLswZftKlVp/s0MTIr16XR2vtg0m1XlPNz1G2aQIIUvQ8sgcI6NZJ5bqGvXTO+yNS28rFFuuhYaq2VlCKTjq1KRBdt6SU7uupXHczl5UTnF+blIh/CPdw/QOe4O5eaFoOWTx32dDHj9jAZkVHrL4hpAoZpy5Jfm82jaIlDKuciap4kDHEGctKseaI3juUG9K998+4A66sw3KC238++Ur+eQf9vDb7c18cNPihLZ9oMPJ8tpimpZVA6/z7Bs9wdFvZgnmkMURsgTtzbC6JDUeKLN0Oz28/UfP0zvspb6ygC+8dTnXnl0X1ZuTDHXldoY8fm0Uaowcu2iccIzwtjNruWLtPL75+AEe3NXO59+6PCVt9PrHuOZ/tkU1HCsKbDx0y6a4dIm6h7xYBFErdgQT+3uGWV9YYb7RaSJR4wO0sOW2WVjDMVho3WTe5ZyiPJbNLWb7UQcfa1o6HU3LaMa12wo1bb7uxF+Ktxn1K5fMmTKvrMCK0+PHPxYgNye2T6kvWDYp8w0y5SHLUM6oK8UxMpqUp8csg24f7QNuVtQWs3ZBGb3DXk6mKGwqpdQ8ZGVTO/4r1sxj8+lVfOfvbyTkfZFScrBjiOU1JSyoKGBJVSFbD/Uk3Nb4c8jSXz5pV0s/vcNefvjutfzjs038v6aGaTPGgOD5Syaxf9Dlo9/lo76ygOrifC44rYpH9pxMmbDqgY4hXj/pZEN9BVeumTfls2npHI71jsStJt7t9DKnKC+qhpFhkGVbHllLApIXBitqi+lyek2rpmc6LX0ubLmWqB7RSDQ2VPJKcz+j/lMvjyy0usHSqqKkPGTbjzqoK7OHzX01DKt4JYf6RrwT1stklEGWoTSdXgXAs28kbliY5aAeflhRW8Ka+WUA7G1NTQmMfpcPt29sSsgStKToO69ahZTwlYdfM53/1dbvZsjrDyYaN51ezYvHHAmP3nS6NdmMkvzoDuRMKDBujFZsWlY9I55U4/wlk0c2eeTnNWfV0T7g5sXjfck3kPFr9mtXnMHXrlw15XPnlasA4g63RdMgM6grs5OXa8k+g6wv8cLzxv12cJaFLVscLhaU27EkICJ63pJK3L4x9qaodFA20drnorJQ025rqC6iZ8ib0KCnQECy47iDxoap+WMQIg4b54uAIZGhDDJFwqTC02MW4wG1oqaEFbUl2HIs7EmRQWZ4VCJpHS2oKOAzl5zOMwe7eeaguVGSRruNki6bl1Xh9QcSzuUYdPsotOXEdIfH6yE73jvCO+7+JyenQeG+pc9FWYF1xkqCjIvDJq5FNq6Nphlkl6ysodCWw0O725JvILCndYCq4jxqS8N7CssLbdSU5Mdd+qd7yBvT62ixCJYk6RVIBy19LorzcxO6fpbXGDUtMzf/LxG0QuuJycSct6QCIU5NPbJQ7balSYglH+wcYsDlm6I/ZmAYVvF6Zg0PmUrqVyRFsp4esxzsHKK8wMrckjxsuRZWzitJnUGmi4nOD+MhM7hpYz3Febk8bdIgO9g5hBCwbK5mkJ27uIK8XEvC3sXBGIXFDeItMP74vg5ea3fyt9dSX7/TrF5SsswpspGXa0kqsd/w6hleGbsth7edWctf93Wm5Frf2zrAmvllUT2GK2qLTXjIIpdNCsWoaZlNGNdPIt7VquI85hTlzarEfinNFVqfTFmBjZW1JaekQaYZsrpBloRYspGXGC6hH0JfhM15yCqLlEGmSIJkPT1mOdDhZHlNSbBzXrugjH1tgynR1WmL4SEDyM2xsG5ROTubzcltHOhwsqiiICinkW/NobGhkmcT9C7GUzYJtFGWEDuXYYd+/qbD29naZ05RPFmEEEmPtGzuHaG2NJ98omTbbwAAIABJREFU6/hAiGvW1THk9fPk/q6k2jfo8nGsd4R1C8uiLre8toSjPcMxc33GAhLHcJwGWVURbf3uGXuBSgWhD9FEMGPYZgP9Lh/DXn9S91Tjkkp2tvRn1XWQLIZ2m2HILqgowJZrSegFZccxB/WVBcyL8KwY95DFFw7td42Sl2vBbk3PwCszKIMsgzl3cQX51sQ9PWYYC0je6BoK5oWAZpC5fWMcTkFeTPuAmwJbTvDtJhLrF5XzRteQqcLjBzsnthtg8+lVHO8d4YTD/KCIeD1kdmsOthxL1De1UX+Al5v7yLGIlHs7/WMB2vrNC1gmS125Pamk/mbHyBQj4LwlldSW5vPgruTClkbujpEDGYkVtSX4xmTMkIpj2EtAQlVJ7IESS6uLkJKs8ZKNBSRtfe6kjI8VtSUcnkUllII5dckYZA2VjPoD7GpJrY7jTOAfC/C9J97gY7/faSqXd7J2W45FsGROoemQ5VhA8uLxvojeMTBfYNwxPEploW1G1QoSRRlkGUy+NYfzliTu6TFDs14yaYWehwWwZkHqEvu1EZb2mDfF+vpygLg7M6Nk0mSDTJO/IKFj53TH5yETQmgFxqO8qe1pHcDjC3DDOQtS7u3sGPTgD8gZDVlC8lpkJxyuYP6YgcUiuHJtHc8d7g2WKkoE41pdvaA06nIrarTrPFaB7C5n9LJJoWTbSMsup4fRsUBS18+K2mJGxwIzOhp8OjFe4MzqsoWyYXEFFgE7sixs6Rj2cuOvX+LuZ47w+L7OYIWKeAhnyDYkkFP5+slBhjz+oKZbOPKtORTY4i8w3u8apTwLEvpBGWQZT1MSnh4zHNQTnEMNm/rKAkrt1pSMGGofmCoKG461C8rIsQheORHfiLuDesmk5TXFE6bXVxawsKIgIe9ivB4y0N7WBtyRO4btRx0IAf/2ptNS7u1sTUJDKhnqyuz0Do8m5O1zenw4RkapnzM1afqas+qSLqW0t22AhqrCmBppi+cUYsu1xAy3dcchCmtQP6cAi8ieIuPjD9HE65wGE/tnSdjSuKcWlCd+T5XkWzlzfllWCcTubR3g8ruf5+Xmft53niYkbaYsVqjkhUFDdRGtfS5T/cT2oP5YZIMMtH63L+4csuyoYwnKIMt4Nifh6THDgQ4nORYRfMsHzQO0ZkEZu1tSZJBFyR8zKLDlcsa8El6JM48snCEJWtubllWx7aj5MKHThEFWWmCNOspy+7FeVtaWUF2ST2OKvZ0nwnSCM4FhWCfiJTvRq7W5PkybT59bnFQpJSkle1oHgp7daOTmWFg2tzhm6Z/xOpaxQ5Z5uTksrCjgaE92eIuS0SAzMEooxTtiNRG+/beD/PCp5OvdxkNLn4vq4jzstuTyjRqXVLKndQDXqD9FLZs+/vBSC9f/ZDtCCB7414187hJNoPmgiXNqaLfNDRmNvLS6iIDEVAH6bUcdLK0uinm/lRdaTcheKINMkSIWzylkUWVinh4zHOhw0lBVOCHRGmDt/FIOdQ0l1bGMeP0MuHxxechAK72xp3UgLnHFAx1ayaRwozc3n16F2zcWt3EHWnLqyOiYCQ+ZNWK+m8c3xq6WgeDbXjJ5beFo6XORaxHUlqamcHi8jEtfmDfIJmuQTebqdXXsa0+slFL7gJve4VHWxWGQgeZVjekh00OWVVFU+kNZWp09RcZb+lzkWAS1ZYkLCU93CaWTA25+9twx7n2pZVq2P5kTjtSMWm5sqMQ3Jk31PTONxzfGF+5/ldse3Me5Syr48yfO58z5pZQWWKkrs5s6p+G02wzpi3hzKn1jWr5tLO8Y6PUs48wz7h/JjjqWoAyyrGDz6Yl5esxwsHMoGH4IZe3CMgIS9rUlXtfS8KTE4yED2FBfgdcf4PWTsfd5sFMrmRQuN62xoRJbjoWtb8Qvo2EUFo8lCmugdQzh39R2ndAUu40E1WTy2sLR0udifrk9qoL8dJCUhyxokIV/6F2xdh45FsGDu8x7yfbqtVfj8ZCB5lXtHR4NhiXD0T3koaLQhi03vq6yobqI470jWZHk3tLnoq7MjjWO8jPRWFlbEjMXL1HuebGFsYCk0+mhM0VVQ6LRmiIZmfWLysm1iIwNW7YPuHnnT7dz3yut3HJRA1s+eM6EPKvlNcWmzmk4qZAlVYUIEX9O5attg7hGx6Im9BtUFEbud0Px+scY8vqVh0yROpqWmfP0dAy6eedPtsf9ZjLoMkomTTXIVhuK/UnkkRmelGgaZKGsX6Ql9u88Ef33GiWTwrUbtPDnOYsrTBlATo/mCSyNMRrUoLTAyoDLF3ZE0vZjDnIsgnMWa7UN61Ps7WxxuFiYoIBlMtSU5JNjEQl6yFzMLcmjwBbe4NVKKc3h4d3tpksp7Wntx5ZrCftiEQ5DSDhaaCZeDTKDhqoiRscCwXByJnMiRcbHitqSaSmh5PGNce9LLcHyOanSRIyE1z9Gh9OTkhSAwrxc1i4oY8sLzWz+zj/Cfpq+8w8e2ZNYeP7+nW3ccs8uxhIoN9bcO8Lldz/P8Z4Rfvb+s/ncpcunvNStqC3haM9IXE4AQ7ttstc735rD/HJ73AaZIQ8ULaHfoLwgvgLjA67sUekHZZBlBectMefp+dHTh3mpuY8/vtIa1/LGm9Dy2uIp8+YU5TG/3B70PiRCW9BDFl9HV12Sz4IKOy83R0/sN0omRXsANy2r4nD3cNzenHjrWBqUF9gYHQvgDtNxbT/qYFVdKcUhCeZNKfR2am+lMxuuBC3/qqYkPyEPWXPvSEwV9KvX1XFy0MOO4+a8C3tbBzljXknc3qwVcSSkdw95qTJhkJ23WHuYPPF6cnpqM0GqNOzGDdvUesn+8moHjpFR7rj8DKw5YtrLEbX1u5EydYNkPvnm07j0jLmsW1AW9jPqD/C/W4+a3q6UkrufOcxfXu3g1y8cN73ulx7ah88f4OGPb+KSM2rCLreitoSxQGxZGIiu3ba0Kv4Q/rajvSyvKY7LeCovsDHk8eOL4Yk2jDZlkClSRoEtl3OXxOfpaXG4+NMrbQgBf3+tMy4tGeOBtDKCp2ntgrKk3k7b+91Yc4QpT8OGRRXsPNEftf3BUk9hDEmDzSZrgg4GQ5bx55DB1PJJrlE/e1oHpuRDbDbp7YzYTpePQbdvxkdYGtSVJaZF1uxwhU3oD+WSlTUU5eXykImwpX8swL72wZj6Y6EYJZSiJfb3OD2mirUvrCzg7EXlPLirzXRN1plkyOOjb2Q0KVFYA8NDbWZUXjz8dnszDVWFvGl5NStqS9iTgsFF0WhJ8ajlC06r4r/evS7i56ObGzjYOWQ6/25XywAnHFrdyO89cSg4MjQe/rSzjW1HHdz29uU0VBVFXM4wsuNpW7TjtlQP4cfy5Hn9Wp8YT7gSoKJQF+WOkUemDDLFtLD59Pg8PT965jAWi+ATbzqNZoeLQ12x304OdAxRUWiLaDCtXVBG+4A7aq5NNNr6XcwrM1es9+z6cnqHR4NldsJxoEMvmVQT2SBbWl1EXZk9bu+iWQ9ZqT18oduXm/vxB+SUDua8JZXYcs3ltYVjvBOc+ZAlaOFnsx6yYa+f3mFvTA+Z3ZbDW1fV8NfXOnGPxudJPNQ1jNs3FlOhfzLRlOallPQMe2MWFp/M1evqONw9zOsnzT1opZR8+r49/PnVxGU/4iWVxsecojyqivNSOtJyd0s/e9sGuXFjvTbae34Z+9oHEwrRxUvrDI9avnzNPHItwvSo4od2t5GXa+Hej5yHRcDtD78Wl/HfM+TlP/5ygHPqK7hhw8Koy9ZXFpJvtcR1TqPlhTZUFeH1B2K+vO1pGcDrD8SV0A8E891i5ZEpg0wxLTQti+3pOd47woO72njfuYt433kLEYK46ice7HSyvCZ8YjyMJ0m/mmDYMl7Ji1DWL9LyrqKFLQ92OqmvLIyYjwSa/MWFepgwnlGbTtMhS225wUnlk7YfdZBrEWzQhW4NCmy5nGsyry0cqX6bN0tduZ1Op8dU8rrRcS8Oo0E2mWvW1THs9fPkgfhCf/Eq9E9mRW0JR7qH8fqnGn79Lh++MWnKswvwjtW12HIspgcm7DjWx4O729nyQrOp9RIh1Rp2q+aVsK89dR6s32xrpigvl2vOmg9oL4XDXj/HprEKwgmHi3yrJe4RtclSUWijaVkVj+xpj9vQHPUH+POrHVxyRg2nzy3ms5cu49lDPTwah3bf1/+8H/foGN+85syYL8c5FqHLwsR+qYim3RYUS+6JbthtP6bpNZ67OE6DrCC+AuOGwaZGWSpSSkNVbE/Pj54+jC3Xwv9raqC6OJ+zF5bz99ejG2ThSiZNZtW8UnIsIuGwpaHSb4bTqosoyc+Nmth/oMMZNVxp0LSsimGvP+YgAQgJWcZrkEV4U9t+zMHaBWVhjcV4vZ3RCCfEOJPUldmDo9/ipbl3YlHxaJy3pJJ5pfk8FGcppb2tA5QVWE2H4JbXluAPSI52T5Ui6XIaorDmZCHKCmy8aXk1j+49acpg/c22ZkCrUmGmdFgipPr6WbOgjMPdwwx7k9fd6hny8pd9HVx39nyK9Pq0xkvh7mlM7E+m0HqiXHPWfLqc3mBB7Vj8441uBlw+rjmrDoAPNNazdkEZX3tsf1Tj5JmDXTy29yS3XLR0gtZkNFbUlnCgwxnT+9bS56IqgnZbvNUrth91sGpeadyDqYLlk2IYZI5hwyCLb7vpRhlkWYIQgs3LInt6jnQP8fCe9v/P3nmHt1Vef/z7yvLe247tONN2pu0kQAaQwXACAZIwCg17lVla2lLaX1u6oC1QyipQdgqBQCEhzGySkB1nO85yhp043nvbst7fH0fXlm2NK1nWlZLzeR49jvarm6urc8/5nu/BHVOGdImQc8YkIL+k3qbGoHtkkvWALNDPB+nxoU6JatsMnShvaFPtQaag0wlMTI1ErpUgqqnNgMLqZlUddVOHR0OvE6qyUvUtHfDT6/r4sVkjIrCvhqyhtQN5xXVW9RBqsp32KKpuQnSwX9cPlrvpsr5wQEdmz4PMHJ1O4Lps9aOU9p6uRWZyhMM/pqNtaGW6TWEdz5jMn5CEysY2/FCg7oe2uLYFq/JLceHQKBglsEnl85ylsKoZEUG+qrWS9shMiYDspz2Owsc7itDRKXH7lNSu24bFBCM0QO/wGLe84jrc/f5ONKkIFMnywr0SgFkZcQgN0KvOpi7dfQYxIf64ZEQMAMpk/f36cahv6cBfv8m3+JzGNgN+tywPI+NC8OCM4arXNioxDDXNHV3jw6xRWNVsdfZnRJAfYkL8bAZkrR2d2FNUq1o/BnSXIO15kdU0tyM80Bf6flq7uAvvWCUDgDIr1jI9L645hiBfH/xkevcXLsfUQWMrS6b8EPUePdSbzJQI7Dtd67AVQUktZRkczZABwKQhUSgob7R4FnSkjEYm2QokFUIDfDFpSKQq3ZYjY5OAbnuMOrMM2c5T1eg0Sqt6CDXZTnsUuahDzlm6zGEdyPIVVjUhJsRfdRC5IJtGKdkrxzS1GXC0rEG1/5g5Q6JphJKl0kx5vfqxSb2ZmR6HiCBf1Y0Ji7cVAgCevyETYQF6bDjaP42hPSz5RvWHLFOpuL/WFB2dRizeXohL02IxzEx0rtORjszRk8JPc09j3eFyfGdHuqFYN7hbAhDg64O54xOxIq/UbtBY29yOdYfLcW3moB4BRkZCGB6YPhxLdxfjh2N9T/KeX3kEJfWt+Pv141R3INPrmk5W7JQt7Xm3DYsNsTm9YldhDdo71evHACCiq5nKToasiQaLewsckHkR00bEWMz0HC6tx9f7S3DntCE9xIuDo4MwKjHMpo7scEkDfHQCI+Ntp7GzUyJQ32rASQdd5rtMYR3MkAHk2A9Y9iNTG0gqTE+Lw+HShq4ylDXqWx0LyPz1pkG3ZmdqWwqq4KfXYUJqpMXn2Mt2qoF8f7QLyAaZArIzDmXImjE0Rv2aR8aHYmxSGJbtsV22zCuug1ECWXYGiltCGaFkSbzclSFzsGQJkIP93PGJWHmwFA2tts/iWzs6sWTnaVw+Kh6Do4NwychYbDhaMaBdmq4yQFWIDPZDanSQwxms3qw8WIqy+jbcYZYdU8hMCcfhkgbVljFSSqw3ZaGX2il9Vza2o7m9UxMbmfnZyWjp6LQrL/l6fwk6OmVXudKcR2aNwLCYYPzfsrwejTB7imqwaOsp3DY5FRNNuly1ZCTat4VRvNtsnRwq0yus7c9bj5Nf4wVD1a9PGTBuV0PW5D2DxQGNAjIhRIQQ4jMhxGEhxCEhxBQhRJQQYrUQ4pjpr+Vfs/OYEH+9xUzPi6uPIdRfj/suGdbnObPHJGBXUY3VDkllZJK/3naJTsk+OHrA7TKFVelB1uM9kyPg6yMsli0PlzRYHZlkCbVlwrqWDtUu/Qq93fq3nqjChMERNsueM2xkO+3R0WnE2dpWzQT9AB0QY0L8HSpZFlbZ9yDrzfzsZOQV1+NomXVRsJKVcVTQr6B0Wvb+wahoaEOov97puYYLJiSjzWC0m535at9ZVDe1486pQwBQJrysvs3unE1nMXQacaamxeX7T3/tcQDS0Q2OCuqaamFOZnIEDEapaoIHQCcAyjSLrSeqUFJnfV/tGrSugdHypNRIJEcG2u22XLanGCPjQjBmUN+qQICvD55ZMA5F1c14cc1RAHSc+M3SA4gPDcCvctIdXld4II1QsmWcrHi32To5HBEbgrqWDlQ2Wg6etp6owvjkcIflF5FBfnY1ZNVeNDYJ0C5D9hKAFVLKDACZAA4BeBLAWinlSABrTdeZXsxI75npySuuw4qDpbjnkqGIsLDj5YyNh5TA6nzL3WokjLdf9hsRF4JgPx+HD7hnalsgBJAQ7niWIdDPB2MGhWNXYd9Oy0Ml1kcmWSIjIRSDwgOwysp2UHC0ZAlQ+lwRYdc2tyO/pB5ThsXYfM7UETHw9VGna+vN2doWdBqlpiVLgLKeakuWze0GlNW32fUg6821mfZHKe07U4uUqEBEO9kdl5EQhqqmdlQ09tTKlDe0OqUfU8hOicDQmGCbZUspJRZtPYW0+JAuDc105eTBhYPozSmpa4XBKF0ekGUmR/RrxNHBs3XYeaoGt09JtTgOLCtFKYuqC8iUE9e/LRgHKYEv9lgvfRdVU+Zfi++UTicwPzsJmwsqrWbwC6uasKuwBgsmJFs95k0eFo2bL0jB25tOIq+4Dm9uPIHDpQ34y7yxPcypHcGWLQygrtvblrC/qc2AfRb8GtUQFeyHahW2F1yytIEQIhzApQDeAQApZbuUshbAdQAWmR62CMA8d6/NG+htdPrimqMID/TF3RcPtfj49PhQDIkOwkoLzuF1zR04W9eqKiDz0QmMSw53KkMWHxrgkHbBnEmpkdh3pq6HLYGUEodLbXeG9kYIgSvHJGDjsQqbWo36FoNTAZmSIdt+shpSwq5ANcRfj0mpUU7pyLrO5jUOyJIj1Adkip+coxmI2FB/XDoyBsv3Wh+ltO90HbJSnE+oj+oqzfTMBJTXtzlVrlQQQmBeVhK2nazCWSvbaXdRLfKK63H7lCFdP7TxYQHISAjtt1edNQbKb6sri+6ko/6iLacQ6OuDGyemWLw/LiwAg8IDVJ8UbjhagWExwbhkZCwmpkZi2R7rZr1FVXTiqDbj7mrmZyfBKGF1lNKyPcUQApiXPcjm6/xmzihEBvnhZ5/sxUtrj+GqcQm4YnS80+salRiGE5XWRyipsU8Z3mV90Tcg23mq2qJfoxoig20PGJdSoqaZS5b2GAqgAsB7Qog9Qoi3hRDBAOKllCWmx5QCcH4vOofJSAhFfJg/NhytwN7TtVhzqBz3XzrMareUEAI5YxKwpaCyj1eWItZUq8PKTIlAfkm9Rc8maxTXNjulH1OYNCQK7QYj8oq7z4rP1LSgsc3gUEAGUJNDu8FoM/PgXIbMD7Wmbbv1eBUCfHVdZ/O2mJ4eq0rX1hsluNHK8kJByZCp8VBSPMiGOFESmj8hGSV1rV2z7swpb2hFcW0LMpMd148pjLIy+qe8wXFT2N7Mz06i7IyVH9pFW04hNECP+dk9dUEz0uOQe6rGJTYSvSkcIA+7MYPCoHfSHqemqR3L957FvOwkm9YHSnORPVo7OrH1eFVXtnF+dhKOllk36y2qbkZCWIDq7mpXMyw2BFkpERYzwVJKLNtTjCnDopEYbvtYGh7kiz9dOwYF5Y3w1+vwx2vG9GtdGQm2Ryh1ebfZaHwZFB6AID8fHLfwGltPVMHXR3T5TjpCVJCvzZJlY5sBHZ2SM2R20AOYAOB1KWU2gCb0Kk9KOo2xeJQXQtwvhMgVQuRWVAxMSt+TEUJgelosNh6rwPMrjyAyyBd3mLQn1sgZmwCDUWLd4Z5ZssN2Rib1JjslAh2dEvkOOJA7YwprjiLsNx81lO+goF/hgiGRiAr2s9rkYDRK1Ld2qPYgU4g0DRgHKCC7YEiUqoygs/YXp6ub4afXIb4f2RtXMDYpHO0GI/YU2dfBnVIyZA6I+hWuHB2PEH89llrQ2CgzVtUEwNaICPJDYnhAj9KMlJJKlk50WJozODoIk1IjsXR3cZ/sTHl9K749UIIbJ6YguJd+ZnpaLAxGic0DYH9RVN0MXx9h98fdUQJ8fTAqMcwpYf8nuafRZjDijql9xfzmZKVEoKi62a6Ye/vJarQZjF0VBcWs15pOq6i6SXMJwIIJSThc2tDn+Lq7qAaFVc19gnZrXDUuAb+8Mg2v3JKNuLD+HSOUkxVrY7HUeLcJITA8NgTHLWTIth2vQnZKpFM6zQg7GjJlH+EMmW3OADgjpdxuuv4ZKEArE0IkAoDpr8V8vZTyTSnlJCnlpNjYWLcs2NOYkR6HhlYDNhVU4oHpw+2KIbOSIxAf5o+VeT0DMmVkktrhyY4K+zuNEiW1rf3KkMWG+mNIdBB2mgVkh1WMTLKE3keHK0bF4/vD5RazfA1tBkip3qVfISLQD7XN7ahoaMORsgZMVqmHSI8PRUJYgMNaoaLqZqREOjaKaiCYmR4LPx+dqmkQpyrJN80Z36sAXx9cNS4B3x0o6TNKae/pGvjoBMYmOZ8hAxQTzO6SZUObAa0dxn6VLBUWTEhGQXkj8op7/qh9tKMIBmNPvy2FiamRCPHXD4iOjITuQRZ1Wv0lMyUc+8/UOWSP02mU+GBrISYPi7LrK6j2GLT+SDn89bqu72JEkB9mZsRi+V7LZr1aWF70Zu54ZZRSz47QpbuLEeCrw5xxiapeRwiBR2aNtNgY4SipphFK1oT9art1lU5Lc+pbO3CguA6TnShXAqQha2gzWO1U7x6b5B2msIAGAZmUshTAaSGE0vZxGYB8AF8CuMN02x0Alrt7bd7CtBEx8NEJxIT44TYLB/Pe6HQCV45OwPqj5T1+0A6XktO9WmF8QlgA4kL9sU+l+WN5A4mH+5MhA4CJqVHYXdQ9aPxQif2RSdbIGRuPhjYDthzvW/6qd9ClXyEiyBdGCawxjflRq4dQsp0/HKtwcASR9j8eAPm7TRsRjZX59ofYn6pq6pdNx/zsZDS1d2JVfs/gb9/pOmQkhPa71JSREIrjFd0jlMrrnTeF7c3V40yjlMx+aNsNRizeXoQZ6bEYYmGUlJ9eh6nDo7HhiOvtL4qqBs7DLislEo1tBovZEGusO1yO4toW3DFliN3HjksKh07Y9zvbcKQCk4dF99gv5mcnWzTrbe3oRFl9m+aaTBqlFIfle892yQDaDJ34en8JcsYkaGIC7aMTSE8Isyjs7/Zusy9DGB4bjJK61h4l+B0nqmGUcErQD3RnvmpbLGfJugMy94zCcgVadVk+CmCxEGI/gCwAzwD4O4ArhBDHAFxuus5YIDzQF49fkYZn5o9THZTMHpuA1g4jNpqMA7tGJqlwulcQQjjU2q5YIvQnQwZQqbG6qR0nKkmHpASSzjB1eAxC/PVYaSGr4+hgcQWlrfrbAyUI8ddjvAPZmunpsahvNajeplJKl3tI9YfZYxNwurrFaklDobCq2Sn9mMJFQ6OQFBHYQ2NjNErsO1Pbr3KlwijTCCXlLF4xhVWbPbZFeJAvLhsVh6/2nUWHKfD+Lq8EFQ1tNuUGM9LjUFzb4lBwowb6ER0Y8briBeeIjuzLfWcRFeynSnwe7K9Hmp2pIUVVzThR2dQlCVCYmRGL8MC+Zr3uHipuiwUTklDe0NZVqv7+cAXqWjpUlysHgtGJNNOy94mBI95tSqel+SzSrSfIrzF7sHPf36iu8UmWhf1dAdm5YnshhMgw/Z1g6eLsm0op95rKjuOllPOklDVSyiop5WVSypFSysullNanSjN4eOYIXGly4lfDhUOjEB7o2xWInKykkUkZDgrjM1MicLKyCbV22o2BblPY5H5myCYNUXRk1V0jkxwJJM0J8PXBjPRYrM4v6yNG78qQOVhWU1yjST8W6dCYDiXbuV6ljqy2uQMNbQYM1sAvyRKXj4qHTsBiF69Ca0cnSupaLWaC1KLTCVyXNQg/HKvo8tQ7UdmEhlaDUw79vekW9lNppj+msJaYn52Eysb2Lif1/24txJDoIEwfaV12oQjS1e4baqhr7kBdSwdSB2hE0LCYEIT661V3WrYZOvH94XJcMSpe9fcmM5mE/dYyh8qUA0U/puCv98E1mYlYlV/aI1OjdC1rrSEDukcpKVq3ZXtoVNLFI2zb6AwkGQmWRyg5Mg/VkvXF1uNVmJQa6XR2W5lPaU1PqHS+R4WcIwEZgMdNf/9p4fL8AK6LcTG+PjpcPioeaw6VoaPT2DUqxtFMU7cXkP0D7hkXZciGxYQgIsgXuadqcLiURiY5GkiaM3tsAqqa2pF7qmfM72yGTPF/c6Z9OzzQFxMGR2C9ylE5A9Uh5yzRIf64YEiUxYyjQrflRf/WvGACWQN8uZf8pBQdkSsyZEOig+Gv13WVZpSgzxUlS4CyXZFBvli6uxh5xXXYVViD26YMsakDTIoIxMgydOY8AAAgAElEQVS4EJfqyAY6+NDpBManhKvOkG0pqEJjmwGzx6o/ucwaHIGa5o6uz9KbDUcrMDgqCEMtnADMz05Ga4cR3x0o6brNU2xkAGWU0iCsyCvFmZpmrDtcjuuyBmk6i3GUFcd+xbtNTckyNToYep3oCshqmtpxqLTe6XIl0F2ytDY+qaqpHX4+OgQ7aeysBTb/l6WU95v+zrRwmeWeJTKuImdMPOpbDdh2ogqHSuqh14muMxe1ZKVEwE+vU/UjUVzbgqhgP6e0XubodAKTUiOxq7DG6UDSnBnpcfDT6/pkdepNI25std1bIsLs8fYMYa2tJ6+4XtUQbTVGjO4mZ0wCjpQ14GSl5bFap/pheWHOiLhQjE8O78oe7DtTixB/PYbHOrYPW0Lvo0N6QmiXFUx5fRsCfHUIdZFux0+vwzWZg7A6vwyvritAkJ8PbpyUbPd509Nisf1ENZrbXWN/4Y79JzM5QvWIoxV5pQjx12PqCPU/zJk25ma2GTqx5XgVpqfFWtTGThgcgSHRQT26LQurmhHs59Nj7JyWLJiQhJaOTvz8k71WRyW5k3QrMy2LqkwVEBUn3L4+OgyODuoKyLafrFLl12iLKDsBGY1N8lWtkfYEVIfdQoixQoibhBC3K5eBXBjjei5Ni0Wgrw9WHizF4ZIGDI8NsTsyqTfB/npcOjIGqw6W2RUbF9f0z/LCnImpUThR2YTNBZUIDdD363VD/PW4ZEQMVh7sKUav6ypZOj7CQ3neaAtjTeyhlFYsDQbujRojRneTM9b2EPv+eJD1Zn52Eg6erceR0gbsPV2LcUnhLusWzEigmZZkeUGmsK48mM/PTkKbwYgVB0uxYEKSqtL4jPQ4tHcaLXqwOYMjZSZnyUxRRhzZ1hV2GiVWHyrDzIw4h45DafEhCPS1PDUk91QNmts7++jHFIQQmJedhK0nus16T1dTk4On/HBPSo1ESlQgdp6qQXp8qGpbooFCGaHU2zjZUe+2EbEhXeawW49XIdDXB+OdHHcGmA0Yt1KyrG5q9ypBP6AyIBNCPAXgFdNlJoBnAVw7gOtiBgBFP7XyYBnyS5wXxueMSUBxbUufNv7enKlpdllApujIVh0sw6iEsH4fPHPG0mcw/9Goa+mAj0443M2klDgvGhbtVHAwOjEMMSH+qrRChVVNiA31d3q+4kCQFBGI8cnhVu0vTlU1IzLI1+HMoyWuMY1SWrKzCIdK6l2iH1MYlRiG6iayLylvaEW8i8qVClmmUUoAVHUUAsAFQyMR6OvjMh1ZUTXZjwxkx162SlnDzlPVqG5qx2wHtLAAZTPHJVmeGrL+SDn8fHQ2My+9zXqLqpv7XU53JUIIzM+irNj8CUkeESiOSgzrY5xcVN3kUGA/Ii4ERVXN6Og0YuuJKlwwVJ1fozX89T4I9vNBtQ1RvzdZXgDqM2Q3gOwpSqWUd4HmT/bP+IfRhNljE1DR0IaSulandViXj4qHj05gxcESq4+RUpIprItGkYxLCoefjw4Go0RGP8qVCooY3TyIqG8xICxA7/AB0Ecn8MD04bh7muXxVfbQ6QQuTYvBD8cq7Lree4JfkiVyxiRg7+lai3MMT1U6PlTcGjEh/pieFosPtxWio1O6RD+moHhgHSpt6MqQuRIhBJ7IScejs0ZgZLy6fdhf70P2Fy7SkRVVD5zlhUJcWAASwwPseoWtPFgKP73OajbLFpkp4cg7W9/Vtaqw4WgFLhwaZVMmkRodTKOUdtM4Lk/8Ti2cnIprMwfhpkmWx0i5m1GJoX1GKDm63UbEhcBglNhVWIOjZY390o8p0Pgka6L+jnMzQwagRUppBGAQQoSBTFs9Y09hHGJmRhx8fSjgcHT0kEJksB8uGhpls7OuuqkdrR1Gl2XIAnx9MM40HsfZdZsTFeyHi4ZG9yizOTM2SeHJORn90kNMT4tFTXMH9tvpTjtd3eIR4uPe5JiyHL19wgDF8sJ1a56fnYSOTgpcXRmQjTYTL1fUt7nE8qI3c8Yl4hdXptt/oBnT02NRWNVsVaPnCO7KBmUmR9jstJRSYtXBMlw6MqbPlAJVr58SgXaDsYdh6dnaFhwta+zTXWmJBROScKy8Ed8fKUebwehxAVl8WABeviXbY3RtoxJphNKxMio5Kt5tjmw3Rev54bZCAP3TjylE2QjIqhrbEOWCrLw7URuQ5QohIgC8BWAXgN0Atg7YqpgBIyzAF1OHk/C8P8L4nDEJKChvtDrjTLG8cFWGDCBtBeCagAygJodjZp+hrsXxsUmu4tKRsRACNjMh7QYjzta1eER7fm9GxIVgeGxwHx1Za0cnzta1uCxDBgBXjI5HqL8eCWEBSAh3XRYrPMgXg8IDsLuwBg1tBpd1WPYXJcDYYGfY+Ke5p/G7Lw50Naf0pqPTiLO1rW4JPrIGR6CwyvqIo7ziehTXtjhk3dPj9ZWyqFnQp3x31GTc5o4bBD8fHV5aewyAZ1heeDIZvYT9ipbVkeBeGTKuNHKMdUJv25tIK+OTOjqNqG81eNXYJEBlQCalfEhKWSulfAPAFQDuMJUuGS/kgenDcfuUVMSGOP+Dc+UYMnG0JuTuMoV1UYYMAG6YmIwF2UkuE7kqPwbKZ+hPhqy/RAb7ITM5wqZW6ExNM6T0LEG/ObPHJmDbieoeB0hlzZYsCJwlwNcHT8xOxwPTh7nsNRUyEsO6pji4umTpLKnRwRgaE4z1VoL11o5O/Pqz/Xjis/34cFsRrnt1M46U9h11c9Y0CN4dwYfSCWktS7biYAl8dAKXj7JvBmuJpIhAxIT4YW9R9+uvP1KOQeEBqjrHw4N8MSsjDvtNU0dcecJwLpIaHYxAX58u6wtn7FNC/PVIDA+AwShx4dAol1h5RAb5otpChkyZLexNg8UB+8awlsxgowDo+2MMy2jLlOHR+PN1Y/slFk0MD0RmSoT1gKxWfUu0WkbGh+KFH2X1SwhqzqCIQGQmh2OV6TM4M1jclcxIj8W+M7VWu4aKnDgrdSc5YxLQaZRYe7g7k3OycmDWfNuUIbjTSc2eLUYlhnaZhvZ3sLgrmZ4Wi20nqvpYSZypacZN/9mKT3JP45GZI/DxfZPR0GrAvH9vxlf7zvZ4rDv9tsYn04gjazqylQfLcNHQKKdLckKIHmXRjk4jNhdUYXp6nOrj2nyTnYQQrj1xPBehEUqhXSViZ+1TlGB5qgvKlYBJQ2ZB1O+Ng8UB+xkyS4awbAzLAABmj0nA/jN1XcGXOWdqWhDs56NZxkktOWMTsO9MHc7WtqC+pcOp4deuYnpaLKRE13ir3nii5YU545LCMSg8oEejhCstL9yB+XBrTylZAqQja+0wYvvJbjPjTccqcc0rm3Cyoglv3jYRv8xJx5Th0fjmpxdj9KAwPPrxHvzl6/wu4bti0OuOEUHB/nqMjAu12GmpSB1ynCxXKmSlROB4RSPqWzuwq7AGjW0GVfoxhZnpcYgI8sWg8ECXneSdy4xKJJ8+KWWXd5ujGShFRzbZBYJ+gMYiNVoYMO6NY5MA+8awlgxh2RiWAUAaLABdGSZzlA5LT2jZtkWOWdlSy5IlAIxPjkBkkK9VHVlhVTMCfHUDIjZ3BUII5IxNwA/HKtBkyjKdqmpCWIC+h3muJ2OuT/SUkiUATB4aTYbMpmHjr60vwO3vbkdsqD+WPzKthxYrPiwAH983GXdMScU7m07i1re3o6KhDaerm+Gn1yHeTZ8rMyXc4ogjJauuyB6cf/0ISAkcOFOHDUcroNcJTHPAYNZPr8OvctJxx9TUfq3jfGFUYhhqmztQWt/qtHfbNZmDcOPEZJfpgLsGjPcqW3YFZF40NgkAbLa3CCFmSSnXCSEWWLpfSrl0YJbFeAPDYkOQFh+ClQdLcVev8pErTWEHkuGxIRgZF4Iv9p5FR6fUNCDz0QlcMjIWG49WwGiUfcbqKG3mnhzk5oxJwHubT2HD0QpcNS4RhVXNGBoT7NFrNmdoDI1QMkrZNSvPEwj088HkYdFYe7gMxbXNWHmwDHPHJ+If14+32KXop9fhT9eNRdbgCPxm6QHMfeUHRAf7IyUy0Oa4JleSlRKJT3PPmDo7uzOkKw+WIjMlAonh/Ts+mDv2rz9SgYmpkQh1MMO98CIOxtSiZI8PlzSgqLrZKV3oxNRITDQ1Z7kCxZS7urkdcWHdJxqKruycypABmG76e43pMtd0Uf7NnOfkjEnAjpPVqGrsOfbHlR5kA83ssQldWhetS6wz0mNR2diO/JK+prue6JfUmwuGRCE62K+rbHnShR5k7kDRysSG+HtcEDk9jewv1hwqx++uHoVXbsm2axkxPzsZSx+cBn+9D/JL6t26/2SmkE2NedmyuLYF+8/UOWwGa4nwIF8MiwnGqvwyHCqpx4z0uH6/JmMdxf8xv6TeY8x0I4MtDxivORc1ZFLKp0z/fBDAGgDHARQCOGW6MOc5OWMSYJTAmkPdnmSNbQbUtXQgKUL7L6wazLUsYYED52CuhktGkgZmfS+LAymlW0w9+4vSOff94XI0thlwtrbFpR5k7uC2yalYONnzMifXZCbiytHxWHzvRbj3kmGqA8bRg8Lw1SMX45YLU3CjG41G0+NDEeCrw77TdV23KfKGnH6WKxUyUyK6TqYc0Y8xjhMW4IvkyEBsOFrhMd5tXfMsewn7q5vaERqgh6+GQ9mdQe1qvwBlxToANJpdmPOcMYPCkBQR2MMktsvywksyZMpnALTPkMWG+mNcUngfHVlVUzua2zs90hS2N7PHJqChzYBPdp6GUXqfpcCNk1Lw8MwRWi+jD3GhAXjz9klOCaLDg3zxtwXjcdW4xAFYmWWUEUd7T9d03bYirxRp8SEY5oKB8ACQaTKLjgv175evIqOOjIQw5J6ixhJPODlUSpK9zWFpbJJ3ZccA9QFZspTyZinls1LKf5ouLwzoyhivQAiB2WMTsOlYJRpMhpTFtdTN5Q0aMsAkRjdlybQOyAA6099dVNs17Bxwz1BoVzF1RDRC/PV4d9NJAMAQF3qQMd5FZnJE14ijqsY27DxV3e/uSnOyBpMeaXparMeVmM9FRieGQpnu5gknWhFKQNa7ZNl8bgdkW4QQ4wZ0JYzXkjMmAe2dxi5TUyVDluIlGTIAWDh5MC7LiFNlKjnQTE+PRadRYnNBZddtRVWebXlhjr/eBzMz4rrsULytZMm4DmXE0ZHSBqw5VAajhEsDstGJYZgzNsEjS8znIsr8Y52HeLf56XUI8df3MYetamz3OkE/oD4guxjALiHEESHEfiHEASHE/oFcGOM9TEyNREyIH1aY9CFnalvg56NDTD8mAbib4bEheOfOC2wOJXYX2SkRCA3Q99CRKRmy5EjvCG4UjVCov94rz1QZ16CMONpzuhYrD5YhOTIQY1wwMkfBT6/D67dOdOlMU8Y6il1Fogd5t0UG+54zGTK1vz5zBnQVjFfjoxO4YnQ8vtx7Fq0dnSiuacGgiAC3tdefa+h9dLhkZAw2HCXPKSEEiqqbkRAWgABfH62Xp4oZ6XHw0+uQGuPZNh3MwJIcGYjoYD9sPlaJTccqcduUVN4fvJjUqCAE+vp4VKY+KsgPNc3d8g4pJaq8VEOmKiCTUhYO9EIY7yZnTAI+3nEamwsqvcrywlOZkRaHbw+U4nBpA0YlhqGoyvMtL8wJ8dfjwenDPcrLi3E/QggasZZfCimp4YPxXnQ6gbumDfGoyRsRQX49RP3N7Z1oNxi9zvICUF+yZBibTB0eg1B/PVYeLMUZLzGF9WQuNbXwK92WRdXNXiHoN+fnV6QNyLxJxrvIMjnqx4T4YcJg15mCMtrwxOwM3HSB++xT7BEV7NfDh6zLpZ8DMuZ8xU+vw6xRcViVX4aKhjav8SDzVBLCA5CREIr1R8rR2tGJ0vpWr8qQMYxCpknfdcXoBPiwjIFxMZFBfj00ZN46xxLggIxxITljElBrquVzybL/TE+PRe6pGhwubQDgHR2WDNObSamRuGRkDG6dPFjrpTDnIFHBvmhq70SboROA2dgkL5tjCXBAxriQ6Wmx8Dd13nDJsv/MSIuDwSixZEcRAO/wIGOY3gT76/HBPRdhzKBwrZfCnIN0DxinZEANZ8gYhg68yuifZM6Q9ZuJqZEI9vPBF3uLAXCGjGEYpjddA8ZNgVi1l86xBDggY1zM3dOGYGZ6LBLDA7Reitfjp9dh2ogYtHYYEeTng2gvPMAwDMMMJJG93Pqrm9qh1wmEBWjvKeko3rdixqOZOiIGU0fEaL2Mc4bp6bFYlV+GwVHs58UwDNMbpZtS0Y5VN7UjMtjPK4+XnCFjGA9musn+gsuVDMMwfYkMJq9DxRy2uqnda6sJnCFjGA8mOTIIC7KTcPFIzjoyDMP0JiKwZ8myprm9q4zpbXBAxjAezgs/ytJ6CQzDMB6Jn16HUH99l5i/qqkdoxJcNy/VnXDJkmEYhmEYryUyuHt8Uo2XzrEEOCBjGIZhGMaLoYCsA51GidqWDq+0vAA4IGMYhmEYxouJDPJFTVM7apvbISW8VtTPARnDMAzDMF5LVBANGFfKlpwhYxiGYRiGcTOKhqyq0XvHJgEckDEMwzAM48VEBfuhub0TpfWtXde9EQ7IGIZhGIbxWiKCyBz2eEUTAA7IGIZhGIZh3I5Sojxe3gig273f2+CAjGEYhmEYr0UR8R+vaESIvx7+eh+NV+QcHJAxDMMwDOO1KCXKE5VNXpsdAzggYxiGYRjGi1E0ZO0GI6KC/TVejfNwQMYwDMMwjNdiPkw8KogzZAzDMAzDMG7H10eH0AA9AO81hQU4IGMYhmEYxstRdGTeOjYJ4ICMYRiGYRgvJ8JUtuQMGcMwDMMwjEYo2jHOkDEMwzAMw2iEkhmL9NI5lgAHZAzDMAzDeDmKW7+3jk0COCBjGIZhGMbLUTJkHJAxDMMwDMNoRFZKBEbEhSAxPFDrpTiNXusFMAzDMAzD9IdpI2Kw5vHpWi+jX2iWIRNC+Agh9gghvjZdHyqE2C6EKBBCfCKE8N68I8MwDMMwjANoWbJ8DMAhs+v/APAvKeUIADUA7tFkVQzDMAzDMG5Gk4BMCJEM4GoAb5uuCwCzAHxmesgiAPO0WBvDMAzDMIy70SpD9iKAJwAYTdejAdRKKQ2m62cAJGmxMIZhGIZhGHfj9oBMCDEXQLmUcpeTz79fCJErhMitqKhw8eoYhmEYhmHcjxYZsmkArhVCnAKwBFSqfAlAhBBC6fpMBlBs6clSyjellJOklJNiY2PdsV6GYRiGYZgBxe0BmZTyN1LKZCnlEAA3A1gnpVwI4HsAN5gedgeA5e5eG8MwDMMwjBZ4kjHsrwE8LoQoAGnK3tF4PQzDMAzDMG5BU2NYKeV6AOtN/z4B4EIt18MwDMMwDKMFnpQhYxiGYRiGOS/hgIxhGIZhGEZjOCBjGIZhGIbRGA7IGIZhGIZhNIYDMoZhGIZhzm8qjgKGdk2XwAEZwzAMwzDnL+1NwL8vBH74p6bL4ICMYRiGYZjzl/JDACSQMFbTZXBAxjAMwzDM+UvpAfobzwEZwzAMwzCMNpQdBPxCgYhUTZfBARnDMAzDMOcvZXlA/BhAp21IxAEZwzAMwzDnJ1JShkxj/RjAARnDMAzDMOcrtYVAWz1lyDSGAzKGYRiGYc5PSvPob/w4bdcBDsgYhmEYhjlfKcsDIID40VqvhAMyhmEYhmHOU8rygKhhgF+w1ivhgIxhGIZhmPOU0jyPEPQDHJAxDMMwDHM+0tYA1Jz0CP0YwAEZwzAMwzDnI2X59JczZAzDMAzDMBpRpoxM0t7yAuCAjGEYhmGY85HSPCAgHAhP0XolADggYxiGYRjmfKTsIA0UF0LrlQDggIxhGIZhmPMNo7E7IPMQOCBjGIZhGOb8ouYk0NHkMYJ+gAMyxhLGTjp7YBiGYZhzkTJlZBIHZIwns+ga4OvHtF4FwzAMwwwMpXmA0AFxo7ReSRcckDE9qS8BCjcDecsAQ7vWq2EYhmEY11OWB0SPAHwDtV5JFxyQMT05vo7+tjcAhZu0XQvDMAzDDARleR5VrgQ4IGN6U7AGCI4F9IHAke+0Xg3DMAzDuJbWOqC2yKME/QAHZIw5nQbKkI28Ehg+kwIyKbVeFcMwngAfC5hzhbKD9NdDZlgqcEDGdHN2N9BaC4y4HEifA9Sd7t5xGYY5P5ES+PYJ4JUJQHuz1qthmP5Tauqw5AwZ47EUrKGuk2EzgLTZAASXLb2doyuB5Q+TlQnDOIqUwOo/ADv+A1SfAA5/rfWKGKb/lB0AAqOA0EStV9IDDsiYbgrWAEmTgKAoICQOSJoIHPlW61UxzmI0Aqt+B+z5ENi3ROvVMN7ID88DW14GLrgXiEilfYlhvJ3SPBoo7iEjkxQ4IGOIpiqgeDeVKxXS51AZs75Eu3U5QkcL0Nao9So8h+NrgcqjgH84sO4vQHuT1itivInt/wHW/RUYfzMw5zkgayFwcgNQU6j1yhjGeYydQPkhIMGz9GMAB2Ta0lqv9Qq6Ob4OgARGmgdkV9Hfoys0WZLDfHo78NYsoKNV65V4Blv/TSn5mz8EGkroOsOoYe/HwHdPABlzgev+Deh0QNYtAASw72OtV8cwzlN9AjC0eJzlBcABmXZsex34xxDg+Pdar4QoWAMERQOJ2d23xY2iMoU36MiqjgPHVgGVR4CNz2m9Gu0pywdOfA9ceB8w9FJg1DXApheBhlKtV8Z4Ooe+ApY/BAydDlz/DuCjp9sjBgPDpgN7FvNoNcZ7KT1Afz1M0A9wQKYNB78AVvwGkJ3Axue1Xg0dXI+vBYbPojNhBSEoS3ZiveeXu3a9D+j0wMgcYPOL3B267TXykpt4F12//E9AZzvw/dPqX6OmEPjkVgp2mfOD4+uAz+4mLenNHwG+AT3vz7oVqCsCTv2gzfoYpr+U5dFvRWyG1ivpAwdk7qZwC7D0fiDlQuCyP5Ab/umd2q6pdD/QVNFTP6aQPgfobKOgzFMxtAF7F9Na570OBIQDX/70/O0sbKwA9n9KJaagKLotejhly/Z8qC5YbakBFt9I2ZIdbw3sehnPoGgbsGQhEJMGLPwU8A/p+5hRc0mTuHex+9fHMK6gNI/2cb2/1ivpAwdk7qTiCPDxLZT6v2UJcOFPgIAIyuhoScFq+jv8sr73pU6lA7And1se+gporqJsUHA0kPM3oDgX2Pm21ivThtx3KYie/FDP2y/9FeAfCqz6ve3nG9qAJbcCNSeBuNFA/nIuUZ3r1JcAi28izeFty4DASMuP8w0Exl1P+0RrnXvXyDCuwANHJilwQOYu6kuAD68HfPyAWz+jzIV/CHDh/eTtU3FEu7UVrAUSs4CQ2L73+fiS0P/ICs/NOO16H4gcAgybSdfH30TB5do/A3VntFyZ++loBXa+RdMWYkb2vC8oCrj0CSpPF6yx/HwpybescBNw3WvAJb8AGs4CZ3YM/NoZ7dj3MdBWRyeKIXG2H5t9K2BoBfKWumdtDOMqmquB+mKyvPBAOCBzB631wEc30s6w8FMKHhQu+glpfTa/rM3aWmqB0zsslysV0q8CmiuB4l3uW5daKo+RnmXCHd36NyGAuS8A0gh884vza+RL3mdUfu6dHVO48D7a/1b93nKAve4vwIH/UTl9/I1AWg7g40+6R+bcREryqRs8BYhNs//4QROA2FFctmS8D0Wu4YGCfoADsoGns4PsGMrygZv+CwzK7nl/cAww4XZg/ydAXbH713dyAzUX2ArIRlxGIkhPLFsqYv7sW3veHjkEmPlbsuw4uKz/71NT6JkBqTlSAltfA+LG0LQFS+j9SeBfng/s+aDnfbveB374JwW3Fz9Ot/mHAiOvAPK/4LLlucrZPdSdPP5H6h4vBJC9EDizU9vMPsM4SplpZJKHzbBU4IBsIJGSxOUnvgeufbmnx5c5Ux6mbM6219y7PoBKV/7hQPIF1h8TGElaMlfYX3zzS+Crx/r/OgCV5/YuJq8kS2WWix6kUux3vyaRurMYjcDHNwPvzvHsjsOTG4Dyg8DkB207UI++Dki5CFj3NNDWQLcdWwN8/TgF5le/0PP5Y+aTjxmXLc9N9n9CWdAx89Q/Z/yP6ESInfsZb6I0DwiOBULjtV6JRTggG0i+fwbY9xEw47d9MzjmRKYC426gDEVzdf/ft+YU8J/p1nVCClLSD/HwGd1eQ9ZIvwqoOEymes7SWEGC813vu6YEduhLCrQm3WX5fh89BcLNVfaF7LY48i1llIwdwNc/99wS6NbX6GAz7kbbjxMCuPJpoKmcSuUl+4H/3UG6ihvf77svpOUA+gDXZBoZz6Kzg0rU6XOsC/ktERJHFjP7ltBrMIw3UHbAYwX9AAdkA0fxLmDjszRuZPoT9h8/7TGgvRHY+U7/33vzS0DJXuDTO6lUao3yQyTYtlWuVEibTX+P9MO1P+8zKo9GDgG+/WX/g8/c94CoYcCQS60/JjGTMpB7PgBOOuGdJCUZzUYNA2b/nbJQ+z9xfs0DReUx4NhKmjnY2zvKEikXAGMWAFteIXuLgAjgx59SibI3/qG0j3C35cBSfZLkDe4UyxesoROWzFscf272Qgrq7Z34MYwrkJIyXM42l3UagPLDHqsfAzggGxikBL57krIVs/+mboBp/Bg649z+BtDe7Px7N1YAez+ijJZfMPDRj4DGcsuPVQ6kluwuehM1lIS8/dGR7fuYAqQffUiZrRVPOv9a5YeAoi3AxDt7mtlaYsZvKAj86jGad+kIBWspuL34ceCC+4DkC4GVv6XZn57Ettep7DTpHvXPufwpCpA7moGF/wPCEq0/Vilbnt7e/7UyPTEayaLl9WkU9K78rfvGf+37GAiKIZ2oo4y8ko5xXLZ0PfVnqUt893+pKuGpWXl30NlBvopvXAy8Mc35pEXVMbID8lD9GMAB2cBw4H+kt7nsKTIpVcvFP6Nuxv50L+14k3ykrvgzcMvH1HG35MeWA8ZvvYcAAB+FSURBVJGCNSQAD09S99rpc8jY1hk9Vlk+ULKPzsQTxpGdwv5PgKMrHX8tgMqePn6UgbSHXxAw919A9XFg/d/Vv4eUlOUMTzFpZnTANS+R/9LqfpRAXU1zNf2wjr/RsnWJNSKHUCB213dA/Gjbj02bTWXLfO62dCm1p4EP5lE38OCLyNi4oQTY64Ygp6WGdKHjbiR7G0fx8aXvxdEVdCI4UDRXu0bK4S3UnALenU0NNl8+CrycDbwwGvj8PjruVRacHwFaexOw7Q3g5QnA0vsAowEIHUQ2Uc6gdFh6qOUFwAGZ62lrBFb/gcTkaoIFcwZPIbH1lpcpveoo7U3kQZV+FXlQJU0AFrxJ3VDLH+75JW5rBIq2OnZmnH4VZVSOOVGi2L8EED7A2Bvo+iW/JNPRr37muMFkRwsFIKOuoS5VNQyfRTq+LS8DZ1R2S57aRBmhaY8Bej+6LX40MPWnFDSf3OjYugeKXe9Tlsua1YUths1Ql8L3D6Gy5UEv7rZsrqb/+/3/o+HZznzHXIWUlP14bQpwJpdOGG5dSicsyRfS3NGB1mYdXEbjtDJvdv41sm+lH8oDn7puXeY0VwP/uRT494XdMwjPZSqOUvNQax1w7zrgoe3A1f8EBk+maSlfPQa8OhH4Zwaw8v/OzcCsqZIajv41BljxayA8GbjlE+DBreQxWbiZrKQcpfQAoPMll34PhQMyV7PpX3SGO+dZ+6W03ggBTPsZUFvknIB6j6kUOM2si3H0tZSpy/u8Z3bo1A90MFajH1NImkglCkfLlsZOSjmPvKI7g6P3A657FWgspQDWEQ4uowPWRCtifmvkPENO5MsfUlcS2vgcEBIPZN/W8/bpTwCRQymYdFdpyRqdHTTaaNiMgT/zGzOf/r9ObxvY93EFR1dSU83n9wJvzgT+ngo8OxR4exaw9F7giweALx7Uxuy4/izw0U2U/RiUBTy0BZh0N33/haCJCnWnB16ruG8JyRASM51/jbhRdFzYs9j1wYHRCCx7AGgso47O968GihwomXd2UNDyTDLw1mXAit/SCUVDqWvX6SpK9gPvzaEA965vgeSJQFwG6UJvfA/45VHgkVxg7otA8iRg66t0gnkuYOykgHP5IxSIbXwWGDwVuGc1cPd3QPps+j1Ny6Htc3yd4+9xdjfNr1ROrj0QDshsYTQ6JmKvPkki6XE3UfnBGdJm006z+SXHDnCdBvqCplzU970v/jkNBd7wd8oOAFSu9A2mMy+16HS0voI1gKFd/fNObqAgtfeZeNJEYMojlOFxZFZm7ntA9EhgyMXqnwNQ+fial6hbdIOd0uXpHbTuqT/tK5L3Dewugf6g8XD47f+hxowpjw78eyllS083id34PAU8G5+jDGdAODD2euosvfljyjrM+h1ldb581LGMX0crlZJObXZubQc+A16bTA0mc54Fbv+yp1E0QCcuCePpfQYqYKw6Ttsm80fqNK62yFpIditn97hmbQpbXqZGlSufph/moBgq76r5MW4oBRZdS8fEYdOpvLrzbeom/mc68OI4CtZ3vEUeg1pzeiewaC59v+76zvLJlRBU+Zh0F+lwR88D1vzJ+X1xIKg+CfxrHPBODq3t2Jpua53eGI0kgfnml/R/8t/rqKFl3I3AwzuBWz6imc/mJF9IDUiOSl2aqmg7jbzCuc/lJux4HZzn7PuYsilTf0qaLHsHrtW/B3Q+wOV/dP49dTrKcH3xIAnKrXmX9Sb/C8qszbYQaAhBAUTNKfo8EYOBY6vpQOXogNX0q0wdixvU79z7lpDXWdqcvvfN/C1w+Bvya3toKzUi2KLsIOnzrnzauR+SkVdQmWXzS0DGNXQWaomNzwOBUdYtNYbPJP3MphepDBuX4fha+ktNIfD90xQoOSPKdhT/EJNJ7HLazxzNAKvF0Ebl4PqzVMJz5Ix2yys0bWDcTZSBtbZ/x2VQsLP+b5R9mfui/c9TW0RdkGf3ABCUKb30CfuWMQDJCb57grLYyRcC89+gge+WULJkn95G2eBxN9h/fYB+dGoLSapgj/2f0GcYd5O617bF2OupEWHnO+reWw2FW0nUPnoeTZcQArh7BfDBfGpUuuFdkixYfO4W4H93UiCw4C0qcwF0Elm6n4aon95O+9iB/1EQNOv35N+n83HN+h3hxAaacRwaD9y+nI7P9hACuPYVKsN9djfwwA/2R165gzVPkQ46NJ4C6k0vAEJHWdjUaXQJiibLoryldDKpD6DM15gF1CjiF2T99X30dAw6tooCOrXHoMNfkdxmzHzXfM4BgjNktsi8mdLFW14mDZYtzcmJDTTk+pLH1YvkrTH2BiAsmdK2as6QpaQ1Ro+0HPQA9KP2ow+oHr/4RjpwO/MjPnwmrW3Nn9RpcNoaaLuMnW/ZjsE3kH44awuBtX+x/3q571E3YdaPHV+7glK6/OJByyXHkn10Zj7lYdsBYs4zFKR89Zj7dVVSkhAcArjq+f5nOdQyet7AlC3bm4D8Lylr8dwIYPENwFc/Bd6/igIzNWz/D7Dqd7TGea/bP9mY/mtqLtm9iIIlWxnp4+vI26/qOHD9O7T/bfgHsOga+/NSy/KpbLpnMb3fXd9ZD8YUMuZSpnzj8+r2rbYGyrC8fRltR1soo5KGTe//sQoAAiPoOLn3QzrR6y9NlRRkRAwmH0Fl3w6JA+78mvS5n95O3eTmSAlseRV4fy7gFwLcu7Y7GAPoGJg8CZj6CB0Lf3EEeHQ36UtX/R+VRN1t/Hx0JR2PIwbTfqEmGFMICKPpL621wOf3uC6b2lwNFO92/HlF2+hkbdpjwL1rgF8XArd9QXph3yDKRi65BXj3Smo+G5QFLHgb+FUBfY4x82wHYwppsynoO+vAGg8uA6KGU0OZB8MBmS10PvRjN+M3JOL+ZKFlS4pOA1k4RAx2TelI70eZo9PbgW8et1+6PLmBgoipj9o+YwiKIq8p5bdbjd1Fb3wDgZynyWAvV0X78aGvSHBuy+codSoNWd/+Bn2prdHeRGf2o6+jz+IsAeF0oK88Yrl0ufF5yuhdeJ/t1wmOoUzd6W30o+4MzdXU6Wbrc1vi4FKgYDVw2e+BiBTn3tsZusqWLjCJba0jbeGShcCzwykjVLCWdI8//pQCn/JDJOo+tcn2a+W+R0FVxlzg+rfVZa2EoMzI1EepGWblb/t+14xG2h8+WACEJgD3r6eM1bzXgPlvUsbljYuBwxZ0lVLSut6aSdrO25bRjFA1a9Pp6Ies4hBw5BvbjzUagaU/oTFGMWn042zLc69oG50AOeM9Zo1Zv6cmnS8e6l/HpdEILL2fvNFuWtS3Sz0wkrbj0EvphGrbG3R7WwNlxVb9H3WD3/+9/c5hISgwvvkjCuDL8un/cvub7jnBOvAZdcDHjybNWGiC46+RMJZE/yc3Ura3v7TUAu9dRYG9I3o9o5G+P6GJ9H0C6GR1+Exg1v/R53uyiILOG94DfnmMXADG32jZ+9AWw2dRg9hRlXKixgraPmPmu+/E1VmklG69AEgB8D2AfAAHATxmuj0KwGoAx0x/I+291sSJE6Xb2PG2lE+FS/n2lVI2V/e8b/ubUj4VJuXB5a59z9V/pNdd94ztx/13npTPjpCyvUXd657JlXLr686vy2iUctF1Uj6TImVDme3Hvj9XyhfH03Ns0dog5QtjpXx5opSHv5Nyx1tSrn5Kys/ulfLdOVL+a5yUf4qm7XFqs/NrN+eLh6X8Y4SUp3O7byvLp/dY+1d1r2E0Svne1bQtTudKWV8iZUeb9cdWn5Jy7xIpv3xMylcvpPdSLnuXqHvP5mopnx0u5X+mS9lpUPccV7JkoZTPpTn/3p0GKTe/IuVfE+hzP58u5de/kPL4eikNHT0fW36Y9ok/RtJzLO1Huz+k1/nwBik7Wh1fj9Eo5bdP0Gus+n33ezTXSPnRzXT7Z/dI2dbY97mVBVK+cQk95ptfdX8HW2ql/PQOun3Rdfa/J5YwdEj5Uha9vq3vz9q/0vtsfU3Kpirar55JlvLsPsuPX/6olH9NpO+cKynNk/LPsVIuvsn+990aG56jz7LjbduP62iV8uMf02O/e1LKVybRd3nTi86/d+0ZKf87n17z/bn0XR0IKgu696t3cqRsqev/ay57iF7v6GrnX6OjlY5lf4qi7/eL49XvI/s+pfffs9j593eEd2ZL+fo0dY/d8TatrTRvYNekEgC50kpMI6Sb22aFEIkAEqWUu4UQoQB2AZgH4E4A1VLKvwshnjQFZL+29VqTJk2Subm5A77mLg5+QX4oUcOB25YCYYMow/FyNpA4ngS6rozApaSuk70f0nzBCyyYfpYeoLO6y/5A5RB3UXmMWvbH3QjMf93yY2pPk3h2xpN0scfx70m0q6DTk+9MeBKVWsOSqORgTTviKK119Bn8QoCfbKSS6uf3Urbj53nqs3CVBWRYaDArf/qH0/ODouni40vao3rTAHn/MBKsKlYnG58l0emPPgAyrrb9fl8+SuWv+9fTfudu8j6nktKd3wJDpjn23LJ84MtHaJJF2mzaZ5Mm2c7sttZTNuTw13SWe+2rdPYNUJPK0vuoy/SWJeqmFFhCKQHnvkO6sDHzgE9uJd1YzjOUwbX23Ta0AWv+SLNoE8ZRZmvNU7T/z/oddU47q7fb/QFtr4WfWdZsHlxGmaGsW6n0LwRQVwy8cyV1Ud+zikydFTpagOfTgYyrSMfmara9QVYFVz1vP8Pcm1ObqAQ8Zj5lR+0dSzsNtG32fUzd3ze8Bwy9xPm1AyY7kkXUnQlQNSDDxvFG76c+w9NaR40m296gcvoljwOTH3Z+nzWnvRl45woq7/9ko+NZc6MRWHY/aerm/4eqPe9dBUy4naoJtuhoAV69gLKX928YOG2pOZtepO/Yz/Ptl93fn0udug/v8IgMmRBil5RyksX73B2Q9VmAEMsBvGq6zJBSlpiCtvVSynRbz3V7QAaQVmzJQtJN3LaMdCu57wAPbLafIneGTgOVSo+tAm5cROUccz6/j0Txjx90bBadK1jzR7L5uHuV5a7SH/5Jwtyf7u35o2CLM7sASAq+QuIGXmRbsAb48HrqRM2+DXh1EnV+XqlCz2ZO1XEqXzVXmYwsq3pe2puoc27wFCB1CpV3zD9bWwPw33n0Ggv/RwGGJU5tIq3LtMeo0UQL2hqB54bTwfqq59Q9x9AG/PAC7RMBYdRhOPZ69QdIKYHNL9L+FJNGXWZlBykwTJ1KJU41+hNbGI3A14+RP5jOlwLpmxap70Q+soICx5ZqMhO+/h3nu60VDO3AKxOoFHTPqp7bq2Q/8G4Ozea78+uemrmKI3RfQAQ9TxF85y0FPruLxOPDZvRvbZaQkvR/pzbRj7PaZpfGcuCNSyjQvn+9+iDHaKTyfeo025MmHKWmkHTDp1SMW0sYT/ZBIy6nk6zeJrvGTtqn1v2VjgVZC0lq4EyJ0hZVx0nnGJtOpUFHmmGUY7n5if3qP1Dz0y2fkO2ENZTj/B1fUSnZHZQfBl67iJpxrDVeAUBDGfBCBjXJzPyte9ZmB48NyIQQQwBsBDAWQJGUMsJ0uwBQo1y3hiYBGQCc3UsHHaOBznom3QNcPYD2B+3N1BJcso8yc4rdQ20R8FIWcNEDwOxnBu79ra6ric6MgqKA+9b31MZISWaOQdHUHeXJLH+ENIIpF1EW67H91CXkbpqrKdiqKQTu+JKygeZ0tFI2tLMdeGhb/wOQ/vDJbaRxfPyQ/aD59E7KZFQcps7UnL8BwdHOve+J9RSEGdoBQwtl1279vDtj1l+MRtKi1RZSJs7R/aD+LGUZJtzuuhOkHW/R7FfzH7ymSmoUMBoogLG0zjO5lHGKHgHc+Q0Fwotvoqz6z/MG7mSnoQx4fSoFkfettd9cYewEPlxA2rZ713rOrEGjkbKytnzLWmtpnyzaRl18/mHULKEEaNUngRW/Ic3t4Ck0Sm9Q9sCt+eAXZO1x0YPAHJVTSZT9a+Jd1I2vBP2GNuCtWRQsP7TVsgl3Yzk56Q+9lGwq3IWUwEuZdGL74yXWH6d8toe2kWeeB2ArIHO7hky5AAgBlSsXmK7X9rq/xsrz7geQCyB38ODBrirrOk5lAdXY/z6EdBsDTVOVlK9cQFolpRb+3ZOkrakpGvj3t0beMqrPb/tPz9vP5NLtue9psiyHaKmV8p+junVAWlJfIuWLmVL+bXBfzcO6p2mNx9ZoszZzDnxGazm5yfpjWhtoH30qXMp/jpbyyErXvHdNkZRvX+E6/Y2n094i5XMjSdckJWkU350j5V/ipDyzy/Zzj64iTdD7c2m7/TFSylV/GPg1H/6O9o8Vv7X9uKIdpFt6KkzK3PcHfl0DRUstaYiXP9p9LFEuL4yV8sDnzmvbHEXRQ67/h5SNlbYfe+gb0t4tvqmvflNKky4whvR6ltb/5WO0f1Ucc83aHeGbX0r5l3gp25utP+bdOVK+epH71qQCeJKGDACEEL4AvgawUkr5gum2I/CGkqU5bQ2UJXJ16tkadWeAt68ApBFY+CmN2Bg1l8YjaYWUpPsq3gM8uqvbif/bXwG7FpG7dKDNRKdncHIj2W7c+L5rrAD6gzLLThq7LRLKD1N2bMx84Pq3tF0fYCpbjiBPt5yngaoCyoBVHKHOyIojZJxrNJAlwmVPUYbGlUjpEZoQt7DlVeogvHsVjSHLfZcsA8bfaP+5+z4hfVBoIhk0uytb8M0vyIz1tmXUGWdOaR6V8I5+R/qv6b+m/eRc+P+Ukvb/gtVU+p54B3WnuwtDO+kfj62k98+4mjK2w2b21HedySV9VdwoKnlbs/jZ/BKVL+e93tNuqCyftLMX3g/M+cfAfiZLKHKTH/8PSLuy7/31JcALo8glYYZNObpb8aiSpakcuQgk4P+Z2e3PAaiS3aL+KCnlE7ZeS/OATAvKD5E2pKOFSlcPbNLeW6XiKJUoxt9EdgCGdnJeHjadAhzGccoP0xgV/xAKyj67h2w6Ht7p2ADxgeST22iMlpRUrgHIBDJyKB3kY9PJFy/lAm3XeS7Q3gT8ayxZjjScdVxDqAR0iVnATzYM3DrNaW8G3pxBso4Ht1CZurIAWP8Madn8w4BpPyXJhatKzkw3Zflk4r1vSbeuMftW0rB1tlMTgH8oTUGwZSpr7KTArfQAjflSvNI+vJ7mJP90b/9siJyloxV4dhj5hc59oe/9SoPJwzuBWM+ZX+lpAdnFAH4AcACAYvbyWwDbAXwKYDCAQgA3SSmrbb3WeRmQAaRX+O91pCW79XOtV0OsfopE1/esBpoqyF/nx5+SAzPjHMW7afyLzoe0Kte9BmQ7OLB+ICneTaNpooaRiWlsBumVXNE1xvRl4/M0hWDkldRR6qgGbN8n9MM0kBqm3pTsJx3S8JlUSdizmDRlkx8kvyp3NyKdjxjaqPFrzwfUyQ5Qtlr40PE6ZoT916gpBF6fRo77d3xFRsmLr6cO5CkPD+z6bbFkIWmrf3agb3b1nRygvRF40INGS8HDAjJXct4GZACVtQKjXF8Gcpa2RpOIPwoIH0zjjR4/1LfjiHGMU5voTDTlQtfbqjDeRXszNZ+Mv6mvYaons+UVmqDg40dD1C/5hWeM+TkfqS2ioPj4Wgqmes+KtMWeD6nz9PI/UdbN0EpWEloO6979X7IBenBLz/mfdcXAv0aT7cylv9JufRawFZDxLEtvpfdAYq3xDyEt0f/upNT25Ic4GHMFQy4GHsmlblUOxs5v/IIc9/byBCY/TPq1lIvcO1WC6UvEYGDmb+jiKFkLyaNxzVN0/aYPtA3GAMoWA+Tabx6Q5S+nv6M9e3Zlb3h0EuM6Rs/r9jbKvFnLlZxbRKRoa3HBMP1Bp6NxUxyMeTdCANe8BATHAUMucZ1Bd38ITSBd5NGVPW8/uJS01WrKsR4EZ8gY1yEEMO8N8uVJ0MBBnmEYhhk4QmKBR3ZQc4mnZOzTZgMb/gE0VVHjSG0RNRtc9pTWK3MYzpAxriUsEci6xXO+rAzDMIzrCIx0r42HPdJyAEiywQC6y5Vj5ll9iqfCARnDMAzDMN5JYhaVUY+aJsIcXEa3RQ3Tdl1OwAEZwzAMwzDeiU5HxrAFa2meZ/EuMtD2QjggYxiGYRjGe0mbDbTVAd+ZHPk5IGMYhmEYhnEzw2aQz13BaiBpIhCZqvWKnIIDMoZhGIZhvBf/UCB1Gv3bS7NjAAdkDMMwDMN4O6OvA3z8yQ/TS2EfMoZhGIZhvJsJdwDpVwGh8VqvxGk4Q8YwDMMwjHej03l1MAZwQMYwDMMwDKM5HJAxDMMwDMNoDAdkDMMwDMMwGsMBGcMwDMMwjMZwQMYwDMMwDKMxHJAxDMMwDMNoDAdkDMMwDMMwGsMBGcMwDMMwjMZwQMYwDMMwDKMxHJAxDMMwDMNojJBSar0GpxFCVAAoHOC3iQFQOcDvca7B28xxeJs5Dm8zx+Ft5ji8zRyHt5l1UqWUsZbu8OqAzB0IIXKllJO0Xoc3wdvMcXibOQ5vM8fhbeY4vM0ch7eZc3DJkmEYhmEYRmM4IGMYhmEYhtEYDsjs86bWC/BCeJs5Dm8zx+Ft5ji8zRyHt5nj8DZzAtaQMQzDMAzDaAxnyBiGYRiGYTSGAzIbCCFmCyGOCCEKhBBPar0eT0QI8a4QolwIkWd2W5QQYrUQ4pjpb6SWa/Q0hBApQojvhRD5QoiDQojHTLfzdrOCECJACLFDCLHPtM3+ZLp9qBBiu+k7+okQwk/rtXoSQggfIcQeIcTXpuu8vWwghDglhDgghNgrhMg13cbfSxsIISKEEJ8JIQ4LIQ4JIabwNnMODsisIITwAfBvAHMAjAZwixBitLar8kjeBzC7121PAlgrpRwJYK3pOtONAcAvpJSjAUwG8LBp3+LtZp02ALOklJkAsgDMFkJMBvAPAP+SUo4AUAPgHg3X6Ik8BuCQ2XXeXvaZKaXMMrNt4O+lbV4CsEJKmQEgE7S/8TZzAg7IrHMhgAIp5QkpZTuAJQCu03hNHoeUciOA6l43XwdgkenfiwDMc+uiPBwpZYmUcrfp3w2gA1gSeLtZRRKNpqu+posEMAvAZ6bbeZuZIYRIBnA1gLdN1wV4ezkDfy+tIIQIB3ApgHcAQErZLqWsBW8zp+CAzDpJAE6bXT9juo2xT7yUssT071IA8VouxpMRQgwBkA1gO3i72cRUftsLoBzAagDHAdRKKQ2mh/B3tCcvAngCgNF0PRq8vewhAawSQuwSQtxvuo2/l9YZCqACwHum0vjbQohg8DZzCg7ImAFFUhsvt/JaQAgRAuBzAD+TUtab38fbrS9Syk4pZRaAZFAGO0PjJXksQoi5AMqllLu0XouXcbGUcgJIqvKwEOJS8zv5e9kHPYAJAF6XUmYDaEKv8iRvM/VwQGadYgApZteTTbcx9ikTQiQCgOlvucbr8TiEEL6gYGyxlHKp6WbebiowlUS+BzAFQIQQQm+6i7+j3UwDcK0Q4hRIbjELpPXh7WUDKWWx6W85gGWgwJ+/l9Y5A+CMlHK76fpnoACNt5kTcEBmnZ0ARpq6kvwA3AzgS43X5C18CeAO07/vALBcw7V4HCYtzzsADkkpXzC7i7ebFYQQsUKICNO/AwFcAdLefQ/gBtPDeJuZkFL+RkqZLKUcAjp2rZNSLgRvL6sIIYKFEKHKvwFcCSAP/L20ipSyFMBpIUS66abLAOSDt5lTsDGsDYQQV4F0GD4A3pVSPq3xkjwOIcTHAGYAiAFQBuApAF8A+BTAYACFAG6SUvYW/p+3CCEuBvADgAPo1vf8FqQj4+1mASHEeJA42Ad0IvmplPLPQohhoAxQFIA9AG6VUrZpt1LPQwgxA8AvpZRzeXtZx7Rtlpmu6gF8JKV8WggRDf5e/n97dxBiVRXHcfz7y6bCCnPR0tCFoTRpQiC1cOsiKswkESxrJZSCELhoEyRCmxYVLds00jKFFomLVhMhSM7IGG6aRdAsihAkUGjm3+IcYQxnckS7+Px+Vu++d3nn3AsPfvzveee/pCTP0f448hDwC/A2/TeK92xFDGSSJEkD85GlJEnSwAxkkiRJAzOQSZIkDcxAJkmSNDADmSRJ0sAMZJJ0G5KsT7Jv6HlIGg0GMklaob7b/XrAQCbpjnAfMkkjozdr/w44R2vhMgO8CWwGPgEeA/4ADlTVXJLDwEHgb+BiVe3tu7R/BowDY8CHVXUqyQHgtf4dq4CH+/fO0jat/Qb4Cni0T+e9qvrhLl+ypBFhIJM0Mnogm6U1iZ5M8iWtxdIu4NWq+j3JG8DOqnonyW/Ahqq6luSJqrqc5DgtnE30dk1ngW3AHuAYsKWq/ly8A34fezWwUFVXk2wEvq6q5//XGyDpnvXgf58iSfeUX6tqsr+eoLWlGgfOtDairALm+ufTwIkkJ2ktv6D1MHwlyfv9+BFaCxiAM8u0gBkDPu+tZOaBp+/Q9Ui6DxjIJI2af5f9rwAzVfXCTc59CdgBvAx8kORZIMDuqrq0+MQk24G/lhn3CK2f61ba+tyrtzd9SfcjF/VLGjVPJbkevvYBPwJPXn8vyViSZ5I8AKyrqu+Bo8Aa2vqw08Ch9HJakm1LjHMFeHzR8RpgrqoWgP20Spwk3RIDmaRRcwl4N8nPwFraAv3XgY+TTAHngRdpgWkiyQXgJ+DTqroMfER7/DidZKYf38w0MJ9kKskR4AvgrT7GJpavpknSDVzUL2lk9EX931bV+MBTkaQVsUImSZI0MCtkkiRJA7NCJkmSNDADmSRJ0sAMZJIkSQMzkEmSJA3MQCZJkjQwA5kkSdLA/gHnj4kbcre8hAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l5YpGzJ4Laub",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "outputId": "2b980f98-b55c-4fa2-a350-a280478b5d91"
      },
      "source": [
        "#ScatterPlot\n",
        "df6[[' \"Weight (Sep)\"', ' \"Weight (Apr)\"']].plot(kind='scatter', x=' \"Weight (Sep)\"', y=' \"Weight (Apr)\"', \n",
        "                                                 figsize=(10, 6), color='darkblue')\n",
        "\n",
        "plt.title('Hubungan berat badan bulan september dan april', fontsize = 20)\n",
        "plt.ylabel('April', fontsize = 10)\n",
        "plt.xlabel('September', fontsize = 10)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "py8d7sL__JVC"
      },
      "source": [
        "Scatter plotnya seperti membentuk garis lurus miring kanan, artinya hubungan berat badan bulan april dan september erat dan positif (selengkapnya akan dipelajari di materi eksplorasi data)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6OW8qF9l-3GO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "ec9d2d01-6c1d-4f78-9cff-204f6ec4bd02"
      },
      "source": [
        "#Histogram\n",
        "df6[' \"Weight (Apr)\"'].plot(kind='hist', figsize=(7, 5))\n",
        "\n",
        "plt.title('Histogram dari berat badan bulan april')\n",
        "plt.ylabel('frequensi')\n",
        "plt.xlabel('berat')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OFdFu9AsJ4ei",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "outputId": "ecc3a620-f885-4513-f37e-652e9ac013ea"
      },
      "source": [
        "rata2berat_lk = df6[df6['Sex']=='M'][' \"Weight (Sep)\"'].mean()\n",
        "rata2berat_pr = df6[df6['Sex']=='F'][' \"Weight (Sep)\"'].mean()\n",
        "\n",
        "fig = plt.figure(figsize =(7, 5))\n",
        "plt.title('Bar Chart dari rata-rata berat badan Laki-laki dan Perempuan bulan September')\n",
        "berat = [rata2berat_lk, rata2berat_pr]\n",
        "bars = ('laki-laki', 'perempuan')\n",
        "kelompok = np.arange(len(bars))\n",
        "\n",
        "plt.bar(kelompok, berat)\n",
        "plt.xticks(kelompok, bars)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ov4Tv--0NV7v",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "69954d83-0077-4ba5-ca3f-f32cd8771b18"
      },
      "source": [
        "# Pie chart\n",
        "labels = 'Laki-laki', 'Perempuan'\n",
        "jlh_anggota = np.array([len(df6[df6['Sex'] == 'M']), len(df6[df6['Sex'] == 'F'])])\n",
        "colors = ['blue', 'pink']\n",
        "fig = plt.figure(figsize =(7, 5))\n",
        "plt.pie(jlh_anggota, labels = labels, colors=colors)\n",
        "plt.show()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "605babKcSl0c"
      },
      "source": [
        "### Dengan Seaborn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7L2EA6EXO-DS"
      },
      "source": [
        "import seaborn as sns"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "rtfhQSerVEXB",
        "outputId": "3d24204c-0673-4402-f071-1f27a84c0743"
      },
      "source": [
        "#LineChart\n",
        "sns.lineplot(data=df6[[' \"Weight (Sep)\"', ' \"BMI (Sep)\"']])"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9373dbfa10>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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nuPvuu6HX62E0GvH0008DAH7xi1/gq1/9KpYvX45gMIgNGzbgscceAyBIAl944YUYGBjAv//7v6O8XFCbff/993HllUlyF2kIhsMghEDJjXt6hjx+WCMeu1JBUGTQRGLu44TCFP1OwXMf9vhnfSkkpYAnEIJJm/uPQeuAGyoFQZXVkPNzc6YftbW1GBsbk7xv48aNkiWL0fXkzEjHs379evzrv/4rRkZGYLFY8MYbb0g+zm6344UXXpC8b/ny5fjDH/6QcPtf//pXvPbaa5LHpCMYolArSELIJ5PMmLDMiCcAi2FcE1lKgmDQ5UOYAiVmLawGDUY8foRnoXiY2x8a/3+e4u7f+fN+fO25vXk5N2d28eCDD6KjoyOjz9nf34/vfOc7MeWaEyEQym4DEzCTPHf3uOcOADaJLlVWBllSoEMgRBGmgMMbgMWgwWwi2qA7vUGUFuR+DaeHxjDg8uHMyBgqLNmZIcnhAMA555wz6WOTdZsWFxdPSSc+GKbQZNm4zxjPfdgj6MowbKZEz511p7KEKjA7JQg8vnHPPR8VM4FQWNT+eetgT87PP1uZDlPXOALBEBWr+uQwmfdu5hh3tx9FcWGZ+Jg789xLC7RiCGc2lkO6fEFxrFc+Kmb6nILkMgC8dYgb91yg0+kwODjIDfw0IEwpguEw1DI9d0opBgcHJcs0UzEjwjLBUBgObxBFUWEZq1GLkchADpaR7nV4QQhgN2nRb4zI/s5Gz90fRGmBFqNjgbyIh/WMCjuoxspC7GobwoDLB7spfbchZ/JUVlais7MT/f39+V7KrCcUpugd9cJvUGNIZjGDTqdDZWXlhM4zI4z7SGToRnRYxm7SRDTL/aLh6IvqTmWPnY36Mi5fCIusBhzvdeVFX4Ypc96+thbf+fPHePdwL/5pTXXO1zGbUKvVqKury/cyOAAOdI7gzv/diidua0bzotKsnWdGhGVE6YEYz134/2DURKY+hw8lZl3MY0dmoXH3+IMoKxBeh3zE3LsjnvuFC0pQZdXz0AxnVsHEC+N1rzLNzDDuTHogLuYOIGZoR6/Ti9IC4QU1apTQKBUYcs+umHs4TOHxh1DKjHuePHeNSgGLQY3LF5dha8sgnHyeK2eWwCbBlXDjnp6hKNEwBgvFRFfDMOkBQNCHsBjUs85zZ92phXo1dGpFXjz3nlEvygp0IITgiqVl8IfCeP8YjwVzZgfMc7eZsluCPSOM+/igjsSwDDPuwVAYAy5fzNUy1azVmYon0sBk0Cph0qrhzIdxd3jFsNDK6iIUm7W8JJIza+h3eWExqKFVydPHmSwzyrhHe+5FBg0IAQYiMfcBlx+UCg1MDMFzn13hAOapm7QqmHWqvIVlygqF90GhILh0cSneP9YHbyCU5kgO5+xHyP1lvzpsZhh3tx86tQJ6zfiVUKkgsOjVGIrE3KMbmBhWo2bWVcuwBiaDRgWTVpXzsAylFN2j48YdAK5YUgaPP4QtJwZyuhYOJx/0u3xZT6YCM8W4ewIxXjvDZtKKYZfoBiaGxTD7ZH+ZMTdqlYJxz7HnPuIJwB8Mx1xkz51rg1mn4lUznFlBdNVeNpmScSeEfJsQcogQcpAQ8jwhREcIqSOE7CCEtBBCXiCEZF24RehOTTyN0KXKjLvguUe/qFaDBiNjgVklHsamMBk1Kph0qpzH3Hsi70NZlHHXqBS4ZFEp3jnSKznUnMOZKVBKp7/nTgipAPANAM2U0qUAlAD+CcBPATxMKa0HMAzgy5lYaCqGPbGiYYxo8bA+sTs1Ki5v1CAUprNqUPS4566CWavKeYeqaNwLYz/cly8pw4gngN3twzldD4eTSxzeIPzB8FkRc1cB0BNCVAAMALoBXATgpcj9zwCYvHSaTIbj5H4Z0dUwfU6hxT1aZrNI1JeZPaEZVi1j1CphykNCtXeUGfdYJcjllYUABJ13Dmem0h/J/U1rz51SegbAAwA6IBj1UQB7AIxQSpnF6ARQMdVFpiNe7pdhM2kx7PELWg4Ob0y8HRjvUp1NSVV3lOfOEqq5FJPqHhV2UPGeC6v5HXD6pA6bNrT0ObG7bSjfy+CcpfQ5c9OdCkwtLFME4BoAdQDKARgBXDGB4+8ihOwmhOyeipiRIBomrcluMwr6MiMev9DAFJfEYHH62ZRUFcMykZh7IEThC+Yuzt3r8MJm1CYo4mlVShToVBhwTW/j/uDbx/GdP3+c72VwzlL6nbnpTgWmFpa5BEArpbSfUhoA8AqAdQAskTANAFQCOCN1MKX0t5TSZkppc3Fx8aQXMToWAKWANUlYBhBmqfY5vSiJ89ytzLjPolp3jz8EnVoBpYLAHFGky2U5ZI/DmxBvZxSbtWJfwnRl0OVH18gYQrMoCc/JHP2i5z69q2U6AJxLCDEQYRDgxQAOA3gfwI2Rx9wOYHJDBmUi6sokSagCQunRoNufUH5kMUZi7rPMc2czU026iHHPYdydSQ9IYTdpxQ//dGXY40cwTMW+CQ5nIvQ5fdCoFCjQZV+Qdyox9x0QEqd7AXwSea7fAvgegO8QQloA2AA8lYF1JkWqO5Vhi+jLHO1xgNLYBiYAMGtVUCnI7Eqo+oIwaCLGXRsZ2JFDz13IfSQx7mbttA/LMGeia0R6mDOHk4p+p9Cdms3B2IwpXT4opT8E8MO4m08BWDOV550IzOuWSqiy2450OwEgIaEqiIdpZpVxd/lCMDLPPfIzV6Wg3kAIw54A5hRKG/dikxabp7Fxp5SKQnOdw2NYVZPnBXHOOvqc3pwkU4EZ0KHKDLNUKSQrdTzc7QAAya4wq1GN4Vkk++vxB2GMyDSYdbmNubNGsmSee7FZC6c3OG01Zly+IIKRWPsZ7rlzJgHz3HPBWW/cmR67lOeuUipQZFCjpU/acweEcM5sK4WM99xz1cjUI9a4J4u5R8ohp6n3Hi0yN53CMsNuP0ZnUVHA2UyfMzfdqcAMMO4jHj80KgX0amn5TKtRg0CIQkHGY/DRFM0yfRm3PwSjVnitcp1QlZIeiIZp8E/Xiplo435meHoY91CY4qbHt+H/eXF/vpfCSYMvGMKIJ5ATXRlgBhj3IbcfVoMmaYLCZhQMRrFZKw7KjqbIqJlVpZBuXxBGTVzMPddhmWQx94hHM10rZqLnBnSNTI9qmTcP9qClz4WjPc58L4WTBqZzxT13mSSTHmCwcE2yq2VRZBpTLrs080l0WEarUkCtJLnz3Ed9MGqUYn19POOe+/Q27kvKC3BmZCzvnxlKKX71fgsAIUzkz2EzGmfi9OWwgQmYEcZdWnqAwdrapeLtgGD8g2Gal4lEuYZSGhOWIYTkVNO9xzGG0kJd8l3WNJcgYGGZJeWFcPmCcIzl9zOz6Vg/Dnc7cO5cK8KUJ3mnO/05lB4AZohxl2pgYrBGppIkcV7LLJIg8AXDCIWp6LkDgsZM7jz35A1MwLgEQf809dyZcV9cXgAgv8aUUopH329BhUWPb1zUAABoH+Sia/lib8cw+hypQ3V9ORQNA2aCcXf7xZJHKZhXH68rM34/U4ac+XF3d5SuDMOkzZ2me6/Dl9K4A0yCYHoa92GPH2atCtVWA4D8GvedrUPY0z6MuzbMxbwSEwCgY8iTt/XMZoKhMD735A785oOTKR/HPHe7RGFHNsh+D2wWCYUpRscCokaMFNbICxmvK8OYTeJhbh+T+x1/23M1RzXMlDmTJFMZdpMWA87p+V6MePywGNWosAhyxfksh/zVppOwmzS4ZXUVtCoFdGoF2genj3GnlCI+JUEIctKZmWtODbjh8YfS2pA+pw9WoyZBNC9bnNXG3TEWQJhCUhGSURwx7slqq5lxH5oNxl2cwjReNmrSqnJSejjoFjRZ0nnudrMWh7scWV/PZGDjHO0mDTQqRd489wOdI9h8vB/fu2IhdJES4GqrYVoZ9889tQNbWwZjbtOrlXjjm+tRZzfmaVXZ4VDXKID0nd79Tp9oj3LBWW3chzzJpQcYa+qseOCmRqyvt0vez+L1s0GCIFrLnWHSqdGWA6OQroGJUWzSTuOEqh+WSNlthUWfN+P+6/dPwqxT4XPnVou3VVuN6BiaHjF3Sin2tA9jdW0Rzq8XFF89/iAe33wKO04NzjzjfkZwRtIZ99Gx1JV9measNu4jKaQHGEoFwY2rKpPeX6BTQTlLxMPcUVOYGCatKifaMukamBjFZi2cPkGCQJekMS1fDHsCqI0YpgqLPi+NTC19Trx5qAdfv6geZt34577GZsCWln5QSvMe+nB4g/AGwrhscRnu3DAXgBCWe3ZHBw5N013ZVGB/U7rclWMsgMoiQy6WBOAsT6imkh6QCyEERQb17EqoxsfccyA/MD47NV3MffpKEIx4/LDoBYNabtHlxXNnoY5b11TH3F5jM8AbCIu11PmEVY1E57kUCoLFcwrEEMZMgVIaFZZJ/T1yeoM5kfplnNXGPZXc70SwzBIJgmTVMt5AGIFQdhtgeke9UCpI2koBdv9061IVJn4FxfxOhcWAfqcPvmBuRc56HV6oFCRhB8QqeKZD3J1dYOIF4haXF+BIt3NGDTrpHB6DwxuETq1IuwN2egMo0OcuLHN2G/eIQU5V5y4Hq0EzOxKqUjH3yP/dWS6H7HF4UWySloCIhtUATzd9mdGxyFAYw7jnDgDdOZYh6HUIwlOKuNexxiaEi6ZDrXsy9c8l5QUYC4Rm1BB0FpJprrGmnEccDlO4fEFRiTUXnNXG/fIlZXj0n1fEVH9MhiKjOkYUaqbCYu6G6GoZXW403Xsd3rQhGWD6ShDET/yqKMpPOaQwLjLxdayw6KEg06PWvdch3Wa/pLwQAGZUaOZw1ygUBGiuLUIoTOHxS+/k3P4gwhTcuMul1m7EVcvLp5xAmg6yv3/a2YEnPzyV1XO4fUGoFARa1fjbnqs5qt1pulMZTIJguoVlxpP3LCwjGPfOXBt3hw+lEh2OGpUC5Rb9tAjL9Dq8MGlVMTtEAGgoNUGjVEzbUlcA+OP2drywq0P24w91OTCv2CTuOJM5Sez2Ah0Py+SUIqMm7+Jhz+86jfv/7yg6h7P35XT7gjBolDEXQ1OOBnb0jsrz3LUqJQr16unruUfCMnMK9SAk99K/vc7kYwprbIZp4bn3O32STYNqpQLzy0zTumLmj9vb8b/b22U//lCXA0vKC8TKpWTFCcy4m7lxzy1FBjUCIZoRAzfs9uPYJORXB5w+BMMUT2yemPd+oteJQZmG0O0PiTF2hjiwI4thGbcvCKcvmNQoxWM3aWQb92AojN1tQ1NZnixEz10veO4alQIlZm1OwzLeANMDl05KC7Xu+TfuvQ5vUrmPJXMKcbjbkXdFzWT0OryyL9iDLh96HF4sKS8Ud8COJN8jR6SShodlcgyrtslE3P2Rf5zAjY99hPAEKgIopRhw+aAgwJ92nZ5QSOJzT+3Af71xRNZjo+V+GezDlk19mfEySHndeXaTVvZr8M7hXtz42Dbs6xie9PrkwD4bFuO451We40am/iRVKIwamwFDbn/akrxs0+v0JpX7WFJRgCG3X/xMTCd8QWHG77AnAI8//feB7UAEzz117srJjXt+yKQEwcl+F5zeILpG5X/pXb4gfMEwblldjUAojKe2tMo6zukNoNfhw952eYbN7Q/BkOC5R7aTWfTce0dTz06Nx27Wyq6WYZ7qB8f7J7c4mQx7/FApSIwWfYVFn1PPnakKJjOcNdOgHJJSil6HL+l7vSSiqMm6OqcT0Q6FnPeVzWZeHBWWSXZhFWPuvBQyt7AKiEwkVdkXq6XPJfsY9qFaU1eETy+bgz9ub5c1E5Odq23QI6tO3+0LwqSNrSwaj7lnx9vrGfWKMcw5hXpZx0xEgoB5gB+eGJjcAmXChsJE5ysE4+6d0C5tKrAqlGSGs9omGPd8hmYcY0H4g+GkoaOFZQUgBNMy7s5eX0CoX0/HoS4HKix6WAyatJ67Q4y5c889p7AO15EpGvdAKCxu00/2y6/lZV6q3aTFVy+sh8sXxDPb2tIeF/0l/rhzJO3jhYRq7IfLoFaCkMx77kNuP/7r74dxwc/fx7tHenH3hrmotclrvY6WIEgHq6nef3pEjGtmA6YrE01FkR7+UBgD7twkf9nfmjzmnn/PvdeZepdm1KpQZ8/IzjUAACAASURBVDdOy3LIaD12OWMUD3WNijuRceMu/Rl0RPokclktc1Zry2QKVgHB5AwmS9fImNh9NxHPnSUP7SYtFs0pwMULS/C7ra348vl1CTHyaNiXmBDg49Oj2LigJOV53P5gQkJVoSAwaTKr6f67La148O1jGAuEcN2KSnzrkgZUWeVratijyiHTHdc96oU5okm/7eQgLl9SJuscp4c8eH5nB+Kd7muayrFoTkHC44c9iXMDyiM7kTPDYzkZetzr8EGtJEk7ss06NaxGTV4FxNJdgACh3l1uKDGX9EYZ9zMjqS+Qbl8QrQNuXNNYAUDo+k7lJDm9QWiUipgy5GzDPXcIV1OdWoHTU9zOMmOrUSlwchJhGdbAc8+F9RjxBPD8ztT1th1DbtiMGtQXm7D/dPovi8cXimlgYpgyqOk+5g/hR68fxpKKQrz1rQ148ObGCRl2ILpLNb1H3DvqxYULS2DUKPHhCflx92c+asOvN53E77a2iv8e++CkOJM0nhFPAIX6RM8dyN3Qjj6nFyVmXUJ3ajT5lv5NFzoCxmfQTnWnnGn6nD6oFEQMt6XiaI8DlI7nEJiTlKxaxukNwKxT5VTUjRt3CG/MsopCWaGNVLRHLg5r59nQ0j8xz11BxsNDq2qKcN5cG5748FRK7ZL2QQ+qbQY0VVnwcedo2vIyly/RcweQdI5qIBSesA4I835ubq5CQ6l5Qscy5OrLhMMUfU4fKov0OG+ebUJx9xN9LiwpL8Dx+z4l/lvfYE8arx7xBBI891x3qfY5pOvHo6mx5de4p0v6AsDiyM5oujUz9Tp8KDFrUVGUXvGT5QzYyEVACM2kirnnMt4OcOMu0lRlwaEux5QmyHcMuqFRKbBunh1Dbr/s6psBlw9WY6zuyj0XzkOvw4c3D/YkPa590IMaqwGNVRYMuf0pk0DBUBi+YDgh5g5EPHcJ4/7ZJ3bgP147KOtvYIyLRk1+KMG4BEHq12/ALfQGlBXqsL6hGO2DHtnaKi19LtRHxtMxUhlGqVm9BTo1zFpVzhqZeh3elOEOQKiY6R4dm9LneCr0OXww61SSnzOGWDEzzYw7k3aolFHieuiMA0UGNeZENeaZdeoU1TKBnDYwAdy4izRWWeAPhnG0Z/IfuPZBD6qtBtSXCkbjpEzvvd/pF+PMjPPm2qBRKZJ+AfzBMLpHx1BtM6KpygIA2Hc6+c5DSsudIeW5ewMh7OkYnnAVynjMdfIxaJtM2d/eUeH+sgId1jcIw1jkrNfjD+LMyBjmFcca92qrAaNjgYRKpTF/CL5gWHJuQEWRHmdyJB7W60jencqothkRpshqp3Mq5FyAbCYtygp00y6pKry+WpRb9OhxeBFMoZR6qHsUS8oLY8IsqTx3pzeIAj333PMCM5AfpzCQ6egYEjzp+ojRkJtU7Xf5Eiaiq5QK1BebcDRJt2vnsAdhKnhqC8rM0KoUKdcupQjJkJqjerRHkGbtGPLI7oAFohUBJ++5y5Ug6I70EpQV6lBnN6LCopcVdz8VqWSK99yrrRFlxbiEZCpp6Vw1MnkDITi86bt8ayIVSe15KoeUcwECBO89l567NxDCj/52GHtTNLv1OX0oMetQUaRHKEzRmyQsGAiFcbzHJe5AGOYkO2Ag4rlrueeeFyosethNmpTebyooFQxhtc2ACoseOrVCtnEfSDJbcWGZGceTGHf25a22GaBWKrC0ohD7U6ydddxJGXcpzz3aqzrQKd/D6nf6oFEpUDjFZg27SZM25t4bNd2JEIL1DXZ81DKY0uMCxndUUmEZILGUcFiUHpDw3C16nMmBl9yXRGkxHtbI1JGnuHufM3kDUzRLygtwst+FsSQqipkkEArja8/txe+2tuIve89IPoZJOzDPHUieSznR64I/FI6JtwOpwzKOMR5zzxuEEDRWWibtufe7fPD4Q6ixGqBQEMy1m2QZdyY9YJf40s4vM6PH4ZVsaGJfXvZlbqqy4OCZ0aRDN1w+4UsU38Qk3KZO8NwPdTlg1CihIEh50YiHbW2nWhVgN2nTeu49DmEAiC1yYVzfUAynL5g2Md7S54KCjBtzBqsTj0+qstdfahB7uUUPhzeY9Zb/XjFRmX5MoV6tnFRSlVI6pbAkpVRI+qa5AAHA4vJChCmmdD45hMIU335hP9490geDRpl0R8MciZICnaj4mSyXwhwfJmHMMKUMy+R2UAfAjXsMTVUWnOx3T6oZRjS2kaEJ9SXyjLszIj0QH3MHgAWRapNjvYnee/ugB3q1UgznNFZZ4AuGk4qWeSKeedKEqj8Y02l5qMuBZZWFmF9qnqBx92Wk5rtYhgRBz6hgSFgiel29DYQAm4+njru39LlQYzNCq4q90Bm1KthN2oSk7LiWu3TMHZDX9DIV5Ia7CCGothomVev+6v4zuOJ/PsRxic+bHEY8AfhD4bQXICA3SdVwmOL7Lx/A6we68a+fWogLF5SgI0nCPXrAiGjck3juh7oc0KuVCYO+k8Xcg6Ew3P4Q99zzSWMk7n7g9MQTPcxTYi3g9SUmnBkZS7vtZG328TF3AFhQFjHuEt5Nx5CQvGUeclOlsPZkhpiFXaRKIc1aFSgFPJGO0GAojKPdDiwpLxR2M50jslX8BEnaycfbGXLEw+IHgFgMGiyvtKSNu5/sdyUkUxlSFTOpYu4VabbwmUKsH5dx4ayeZDnk3w90AwCOdE/O4I53p6Z//yuL9CjUq7Nm3CmluPdvh/Dink588+IG3H3BPFTbDOgcHpMM27EqrxKzFnqNElajJqlxP9zlwKI55oSpYgU6NfyhcEJnNfvu8WqZPNIYMZDJtvX/9ffD+PUm6SaX9iEPCBE+tABE45GuYiZaeiCeOYU6mHUqSc+9Y8gtXkgAoMqqh9WoSRpW8khMYWKI+jIRr+PUgBu+YBiL5xSgscqCEU9Atl5JfwY9d1caCYLu0bGEASAbGuzYf3pEHIsXTzAURuuAOyHezqixJmqijw/qkI65A8DpLMfd+5xeaJQKyTXEw/6GicjqunxBbI5UGk2kuzpmjTIamBiECAOzX9nbiXX3vyf+W/+z99KKwN3710NJv4eMJz48hT9sa8ed6+vwrUsaAAivSzBM0T2auMuKHw0o5FISjXs4THE44vjEk0xfxpkHXRmAG/cYCg1qzLUbJb3fXocXT21pxR+3SQv5nx7yoLxQL271mfFIb9xju1OjIYRgQak5IdTCkrc1UZ2fQs4geSNWKs9d1HSPiIeJMcWKArGKSE5oZqK67amwy5jIJKU+uL6hGGEKbDs5KHlMx5AHgRDFvGKj5P3VNgN6HN6Yi8qwJwCDRpkQxgEEL9Vu0mJ3W3bb6fsis1Pl5DJqbAb4gmHRG5XDB8f64Q+GoVKQSRt3OdID0Xz9onp8prEc582zif9GPQG8tKcz6TFuXxDP7mjH/7x7QmyYisflC+JX75/ERQtL8INPLxJfs+okCXNh7UzaYXxGrtRurGPIA5cvmFApA4wb7/jiBBbmzaWuDMCNewKNVRbsP50Yhnhl7xmEKdA16pWsIW4fdIsJOQCotRugIOm9oP4UYRlASKoe63HGrKfP6YM3EE5ICDZWWXCizyWZ3GPVMvGSv0DiHNVDZxzQqBSYV2zC/FITdGqFLOMevbWdKuz16E+SVHX5gnD5ggnTnVZUW1JKEbD3I6nnbjOAxtWJD3v8kpUyAMQqna0tAynVIfe0D6Glb3KxbGA8US2HanFYtvzdxJuHemAzarC+wS67PyOe8fdf3sV9bb0dD9zUGPPvksWl2HKiP+lruaN1EIEQhT+YXBr72e3tGB0L4JsXN8RcDMUh4hL5iD6HIO3AHl9hMeDMyFiCHWAyv5Keu1Za9tcxxkbscc89rzRVWdDv9MVs3SileHH3aTEEsEti8k/HkCfG2GpVStTYjLI8dwWRjucCQjmkwxuMGW4wHt+P9T6bqiygFPjkTGLOgFXLGNSJ3mf8HNVDXQ4sLDNDrVRApVQI0gwyjHuyqfeTQexSTeJ99oyOl0FGo1YqcN48OzYnM+6R92NeEuMu1rpHGcZRT0CyUoaxvsGOQbdf/OLHEwyFccczu3HvXw8nfY50yK0fByCqb7YNyEuq+oIhvH+0D5cuLsX8MjNaB9xpy0mTrbFAp4J+CgPrNzQUY9gTSBqL33x8ADq1ApcvKcUft7Un6NN4AyE88WEr1jfYxRwao6xAB41SIVkm2hc3GrDcooPHH0oI7x3qGoVKQTC/LPHzk2zY/PigDu6555VGiTDEnvZhnBpw49uXNsCsU2Fna+wW3OULYsDlj4mBA8C8YmNaz11KeiCa+axiJio0w6o5auIEucScgURCmM1PlRKdio65U0pjpEyBSJmlDGmGTEgPMNJJEKS6kKxvsOP00JikFMHJPjdKC7RJt8hSte6C9EDyL+b59am7Y3e2DWHYE8DHp0cmrf0ut34cACqLDNCoFLL1jT5qGYTLF8TlS8tQX2xCIEQnpQnfl2JIh1zWRV7LZBfnD0/045w6G751yXy4/SE881FsmPTF3acx4PLhno31CccqFQSVVukh4vGjAVnuLF7S41CXA/UlJskQXTLZ3/FBHdxzzyuL5pihUcZ2e764uxMGjRJXLS9Hc01Rguc+XnMe60nPKzGl9YL6nYndqdEskDDuHUMeKBVELMNjFBk1qLEZJBUiPf7EEXsMFnN3+oLoHB6DwxvE4qhtJ5NmSDcblulhyymFS4ctTcyd7azmSAzdZlIEmyWMbUuKShkAsBk1MGqUMcZtJI3nXlKgw8Iyc9JQ0FsRfSCnL4hTAxMPeXj8QTi9wZSfk2iUCoK59vSOBePNgz0waVVYO88m7miSHTvi8WPVj9/Bu4d7E+5LNV5PLsVmLRbPKZB8Lc+MjOFkvxvrG+xYNKcAlywqwe8/ahW7rwOhMB774BRWVltw7lyr5PPXWA2Ste7xYa8Ki0E8ZzSHuhwJzUsM5jDEK0Nyz32aoFUpsai8QPTc3b4gXj/QhauWz4FRq8LqOita+lwxLfmsprg6zpOW4wX1uxJ1ZaIpMmpQYtbGVMy0D3pQbtFBrUx8+5qqLJKeu8sXgjHJdtkcNWovei4kg+0I9qdpDup1eKFVKTISW0wnQSB2p0oYd1GKIK7qglKKkxKCYdEQQlBtM8Z4/VJa7vFsmF+M3W3DCbM3w2GKtw71iufcP4ky24lUoTDmlZhkxc5DYYp3jvTiooUl0KqUUYUA0iGdvR3DGHT78fqBLsl1yinVTMf6Bjv2tA+LRpuxJWLwN8wvBjAujf3cDkEa+6/7u3BmZAxfu6g+aeK5xmZEx6A7JpbOpB2inZJyi/D/6KRqn9OLfqdPMt4OJK+WyccUJmCKxp0QYiGEvEQIOUoIOUIIOY8QYiWEvEMIORH5WZSpxeaKpspCfHJmFKEwxRufdMPtD+Hm5ioAwJpawSPYFVUdEV/jzqhP4wUByaUHollQFlsx0z7kSdglMBorLehxeMWYNMMjMRybwcTEXL4gDnc7oCDAorJx415ZpIfNqMH+jnTGXdiWZ0qz2m7SJDXuPaNeFOrV0EnkEAgh2DDfjm0nY6UIeh0+uHzBlMYdiC2HDIcpRscCSXMijPUNdvhDYexojd3VHTgzih6HF3dvmAuTVjWpDujJ6PXUF5twesiTdprVrrYhDLn94pCTAp0aJWZt0s8suzhtiUsgU0pFVcWpsr6hGIEQxc6413LziQGUFmjREHn/VlYXYe08QRp7zB/Crze1YGGZGRemGFpTbTXA7Q9hMEqxVUrawWrUQKdWxJRDSjk+0YhVZxKeu06tkHTGsslUz/YIgDcppQsBNAI4AuD7AP5BKW0A8I/I72cVTdUWePwhnOhz4sXdnZhrN2JVjXCNWlZZCI1KEROaaR/ywGJQJ+ipzEvjBaWSHohmQakZJ/pcorZ6x6A74ULCWFEteNl72hPzAsmMu0qpgE6tEIx71yjmFptikmKEkIhmfGrD1JehBiZGKgmCHoc3IZkajZQUgVgpkyIsAwhx99PDYwiHKZzeIMIUabVyVtdaoVEpsCUuFPTmwR6oFASXLi7F8srU+j/JGM9lTMxzD1OgNU1S9a1DPdCoFNi4oFi8rb7ElDRezy5OAy4/jkQ11w17AgiEaEbe/+baImhVipi4eyhMsbVlAOsbimOch69eWI8+pw/3PLsHJ/vd+OqFyb12QDqnIjUakBCCcos+ZtD9YQkN92hUSgX0aqVkzD3XZZDAFIw7IaQQwAYATwEApdRPKR0BcA2AZyIPewbAtVNdZK5hYYhX93VhZ9sQbmquEj8wWpUSK6osMca9Y9CTkNwE0ntBTHpAjufuD4bRNihIIwx7ApLnA4ClFYXQq5UJeQGPP3lYBhD0ZZyRsIyUZ9JYZcHJfldKaQZhmETmxs0Vm5N3qfY6vCiVCMkw1s6zQREnRXAyTaUMo9pmgD8YRo/Dm7I7NRqdWolz6qwxsWJKKd461IPz5tlgMWjQWGXBkW6HrNmw0Uy0fhyALGVSSinePtSLDQ32mAv/vGITTvW5EsoAKaX4uHMEFy0UPOPoBHImpJ4ZOrUS58yNHb5y8MwoRjwBMZ/CWDvPhsYqC94/1o86uxGfXjYn5XMz4x49dS1Zcj6+kelQ1yiqrYaUhlpKgsARmcKUa6biudcB6Afwe0LIPkLIk4QQI4BSSml35DE9AEqnushcU2szokCnwlNbTkGpILhhZUXM/WvqrDjU5RBjgu1D7oSyREYqL4iV+dnNqQ0HkyE43uOM0rCRNu5qpQIrqi0JW1p3Cs8dED6Up4c86B71JjXulAKfpFCIlKPlPRFKC3RJdbW7R72Yk+JCIiVF0NLnglmrkqGsOF4OKRr3FNUyjPUNdhzvdYkhsRN9LrQOuHFZJOTRWGlBMNLhOBH6JqG0ObfYCEJSN9EdPOPAmZGxhLmz9SUmOH3BhCao9kEPRjwBXLq4NCGBnAmp52g2NNjR0ucSY97sXKyahkEIwdcvFCpjvrJxXtKqM0ZlkQGExHnuDukqr8qiWDnnZI5PNGadCk5fouee62QqMDXjrgKwEsBvKKUrALgRF4KhwqVfsvaLEHIXIWQ3IWR3f7/82Ze5QKEgaKyyIBCiuGB+cYI3urrWilCYYm/HMAKhMLpGvEk96foSE05KeEFAaumBaBpKzCBEEBBjH8pUc0lX11pxpMcR42W7/UEYU0zHMWlVYihHKmHUWCnclkq7xu0PZaTGnbG8shDeQBhHumOrdAKhMAZcvpSeO5AoRdDS58K8ElPanAC7cHYMuTGSQhEynvUNQmiDGaI3D/aAEODyxYJ/w0Jm6XIX8UxGaVOnVqKqyJDSc3/zUDeUCoJLFsX6X8lyRey9b6qyYH2DHbtah0XtpMkkfVPBXksW5tp8YgBLygskvyuXLC7Fm99aj5tWVaZ9Xp1aibICXUwjU5/DK3nxLC/UY8DljyRcA2gf9Mgw7mrJhOrZ5rl3AuiklO6I/P4SBGPfSwiZAwCRn31SB1NKf0spbaaUNhcXF0s9JK+wtvubmxM/MCtriqAgwK7WIZwZHkMoTJPGwOtLTHD5gqJ3EE267lSGXqNEjdWAYz1O8UNZk2SnAAg7C0pj4+5uXyil527SqjAWCRewGZfRWAwa1NmNSROCfRn23ADh7wCEOvFo+p0+UJrYwBTP+vlMiiCimdKfulKGMadQB5WCxHruMoz7wjIz7CatGE5461APVlRZROegtECHsgLdhGf1TrYKJV2fxVuHenFOnTVhfGAq465XK9FQYsL6huJIAlmQeWBSAHLLNdMxv9SEErMWm0/0w+ULYm/7sGjwpVhYViD74ldtNcQ0MglDOhIvntEzco90Je9MjcasSxySnQ+5X2AKxp1S2gPgNCFkQeSmiwEcBvBXALdHbrsdwGtTWmGeuHFVJb58fh0uWpgYVTJpVVhSXogdrUNizWxSzz2FgFgqXZl4FpSZcaxXCMvYjBpJjRjGimoLVAqCXZHQDKVU8NwltNzFvyniWZQX6hK+7IzGSEJQahcyEdVCucwp1KOySC/+HYwesQwy9evWVGWBSavC5hMDGB0LoN/pS1njzlApFago0qN9yCPK/SaTH4iGEIINDXZsaRlAx6AHh7ocuGJpbMhDKFWdoOc+yfrx+hITTg24JYec9zm8aOlzSVaWlJi1MGtVCZ/ZjztHsKyyECqlAmvqhAQyu5D1OnxJq5cmgyDrUIwtLQPY2jKAYJhiQ1y8fbLU2GJr3ZN1/44P7fCmrZRhFOjUcEnID+RaegCYerXM1wE8Swg5AKAJwE8A3A/gUkLICQCXRH4/66ixGfHvVy2GRiX9Eq2utWL/6RHRu0nmSadqCkknPRDNglIz2gbcON7rTLpLYBg0KiytKBTj7mOBECiVnsLEYBIEi1N4Jk1VFvQ5fTFSCAw5U+8nw5paK3a1DcVcUHpH5ckcCFIENmw5MZB0+lIymHc36vGDEMj2vNbPF4aj/8+7xwEgIZ7dWGVB26AHwzKHpwORRPUkLpr1JSb4g2FJdUO2G1pdl9jsQwjB3Lh5BP5gGIe6HOKOVqdWYk3teAJ5Ito3ctkw344RTwCPf3ASOrUCq2ozU1VdYzOi3+kTexKSrX1c192Dw90O2E3atAUDJm1iQjUfw7GBKRp3Sun+SGhlOaX0WkrpMKV0kFJ6MaW0gVJ6CaU0UYhlBrCmrgi+YBh/P9AFrUqRNElXYtaiUK8Wy6iiSSc9EM2CsgKEqbA1TrZLiF2fFQc6R+ENhETNmJTVMhHPIpVnIkozSMSMezPYnRrNmjorBt1+nIoq6etOoisjxYYGOzqGPHjviBAdlGvcBV13N4Y9ARTq1bLeI2A84ffKvjNYWGZOuOg3VgkXT7mhGXdEIG0ysWwxvNKf2Fm8q3UIerUy6ftdXxxr3I/2CPITrJIMGE8g9zq86J2APIJc2Gu5t2ME5861Sbb8T4b4iVvJLp5lhTooiDCRKVVnajTx1TK+oDBc/Wz03GctqyPNTHs7RlAdGa0nBSEEq2qKsKs98RqXTnogmgURoaIwTRQMS7Y+fyiMj0+PwBMRDUsXcwdSG/dFcwqgVhLJTtU+hw96tVLcAWQK5llGV//0OgRtc2uS8FE0LE773M4OaJQKVMVJNiSjxmqEwxtE26Bb1s6KUWIWpAgAJIRkAGB5pQVkAqMLp6LXMy9FOeTOtmGsrLEkbaypLzGhz+kTk/IslNRUHW3cWQJ5AP0RVcVMYjdpxfzP+fWZCckAsbXuHr8gUy2141QrFSgt0KF10IMTvc60IRlASKiOBULiuMtxLfezzHOfzdhMWlETPFlZImNNnRWn+t0JDTnppAeiqbUZoYl8EeV47s2RpqtdbUOi5y41Yo/BKgWWVCQPy+jUSiyeUyAZMxY8t6nPTo1nrt0Iu0kTE3fvcXhRWihf27yySI8htx+1dgNUMrsEWejr49MjsgZkRMPa4+NDMoBwEW0oMcmOu0+lftxi0MBu0iQY99GxAI72OLCm1pb0WFGGIHLsvtMjsJu0KI+qUGIJ5A+O9yeoKmaK9fMFo54qmTpRqqOGiPelyRVVWPT48EQ/gmEq07jHdqnma1AHwI37lGDVHNVJpAAYzMuPTwzKkR5gqJQKMX6f7mICCJo080tN2Nk2LE5hSpWEvWFVJX556woxzpiMxioLPukcTUjS9WbBcwOEnU9zjTWmYqZnNHV3avzxzDDIDckA46+xwxuckOcOAHeun4uHbm4UPfh4hNGFo7ImJU21fnxesSmhQ3pP+xAoBVbXJY9hx1fMfHx6BE1VhTEXVIVC0LJ/93AvgmGK0gz2ODDuWj8XD97UiPml8t+7dFgMGhToVGgfcqeVqS636MVy2HSVMkCivky+RMMAbtynBDPa6YztsopC6NSKGANFKUW/S35YBgAWRD7g6RKq0evb2z4MR6TO25CiWsZu0uIzjeVpn7Ox0gK3P5TgDfY5pq4ImIzVdVZ0Do+hO9IKPhFtcwBilUU62YFookXg5FTKRFNs1uL6lZVJdxZN1RYMuf04PRSb6NzTPoQ/7zodO5iF6Z5MMp7NBrVHP+fO1mGolQQrqpIb96oiPTRKQTZ4dCyAk/1uMZkazfoGu1hCm+mYOyDskG9Ylfy1nCw1NiPaBz3oZQNGknx2WTmkSauStWNmRpw1Molyv9xzP7vYML8YS8oLcN685NtbANCoFGiKkyxw+oLwB8OyyiAZVywtw/oGu2xvf02dFS5fELsj9e6pPHe5sJhrdFhBEI3KfEKNwcTadrYKVTPpdGXiOb/BjsbKQlywQP7W3qBRiRdeOQ1ME0FKZXP7qUH88xM78N2XD+CX743PB+1zeqFTT15ps77EhNGxQIwu/q62IUGmIkWCXaVUoNZuwMk+t9iVHD/8AoiNhWc6mZ5Nqm2COJzYn5EiLAMIUuDJ8mrRxHvuzLHinvtZht2kxd+/sV4cqJGKNbVWHO5yiNs0udID0VyxdA7+98vnyPZiWNho0zGhUiRVQlUudTYjzDoV9kUZd5cvCI8/lPFSOMaiOWYYNYJezuhYAN5AWFLqNxlmnRqvfe18rKqR1vhOBvPU0sn9TpQFZWZoVeMzA/Z1DOPLT+9CldWAa5rK8dA7x/Hkh6cACPXj0ePfJkp8UtUbCOFA54h4wUxFfUQ2mFX2LK9MNO5Myx7IzHjFXFFjNeDM8Bi6RiIy1UkGaTDjLickA0iFZXjMfcazus6KMBWqawD50gNTgTUBHY3IBacqhZSLQkESGnF6HRObnTlRVEoFVtYUYVfrcFQDU/a9RBb+ssioypkI6sjowv2nR3CoaxS3/24n7GYtnr3jHDx4UyOuXDYH9/39CP64vX3K9ePxg9r3nx5BIETFkGLKY4tNaB90Y2frEOYWG5Nq22xcUAKNSpG1sFw2qLEZEAxT7Ds9nFKmutYu5NNYCWs6xLBMxIkTh2OfTR2qnImxsroIyqiuzQVmVgAAIABJREFUUbnSA1Ml2kNLVS0zERorLTjW6xzXFclSA1M059RZcazXKeraTyQsM1mYgFimPXcgkpg+M4rbntoJk1aFZ+84B6UFOqiUCjx8SxMuXliCf3v1ID7uHJlSuGNOoQ5GjVL03Nnnr1lGQxCTDd7SMoAmCa+d8Y2L6/HqPesyVoeeC1gRxMEzoyl3HHV2I/5yz1pc3ViR9DHRJIRlIj8zERKdKNy45wijVoWl5QViUnUi0gNTgdWJa5SKpN22E6WxyoJQmOJglxCLzbRolBTM03z9QHfWz8VgifKJVsvIgY0uJITg2TvPRWXReLJOo1LgV59diXX1NngD4SmFOwghMVOZdrYNYUGpWVYegYV0QmEqGW9nGDQqWQ0+0wn23goa9Kk/Sysijpkc4ueoOr0BmLQq2cdnEm7ccwiTLPAFQxOSHpjqOQGk1JWZKGKXZSQ0k66cLDPntECjVOCDY/1ZPxdjfYMd16+owPJKeVvyiXBBQzGuWj4Hz95xDursiaW0OrUST9zWjH9aXZVWozwd8yLdpsFQGHvbh1OWQMYfx6IVUpUyZzNlBTrR2cnkjlOrUkKjUsDpG4+55yPeDnDjnlNW11nhD4ZxoHMU/U4fbCZ50gNTYV6xETajJmMhGUCIrVdY9GKXZa/DB4NGmdWtp06txPLKQvhDYdiMmoztQlJhM2nx0C1NWal0KDSo8eg/rxS1+qUwaFS4/4blsuLjqagvMaF71ItdbcNw+0NYU5e6uouh1yhRYRFKIhfOSV80cDahUBCxWznTjoI5Sl/G6Q3kZQoTwI17TlkdVdI34PJlPSQDCNvyjQtKUGWV13Yvl6Yqi2jchfF62fekWYgpF8nUmQQLr/xplzBIWk6lDGNFdRHW1FnPqni6XJjuT6arvKL1ZRxj+fPc83PWWYrVqEF9iQm72oYw7AnIlh6YKj+5filkNENOiMaqQvz9k24MuHwR4aXsX6jW1FrxG5zMSTJ1JsEqZv7vYA+qrPoJXRx/fuPybC0r77BGtUxXeQkDOyIxd19Adl9KpuGee45ZXWvFnrZh9Dm8Wa+UYWhVyozpbDNYI86BzhH05shzX1lTBEKQdgITJ5YamwEqBYE/GJ5wiEenzvxnZ7pQG0mqZtNzd3qDeSmDBLjnnnPW1BXh+Z0dcPqCebuiZ4JllYVQEEH+N9OzU5NRqFfjwZsasSyFuBknEbVSgRqbASf73RMKycx0rmmqQIhC1gCXiWDWqTA4IMgJO8byMxwb4MY950Qns3IRc88WBo0K80vN+LBlAN5AOCeeOwBcvzL9nExOIkK3qVtyOMdspciowZfPr8v485q0QliGUpq34dgAD8vknAqLXmxpnoj0wHSkqcqCfZGO27OpO3E2snFBCVbVFGGuRNklJ7OwsIw3EEYwTHkp5GxidaQ7sNh0dseOo2ufc+W5cybHrWuq8fJX1mZcXZGTSIFOBZc/iNGIaBgvhZxFnDtXCM2c7SV90V2LZ5NoFIeTTcw6NSiFKFHNY+6ziBtWVaKsUDeh4RHTkfmlZhg0Snj8obNK7pXDySbMmHeNCJ3b3HOfRaiVCmxcUJLvZUwZpYJgaUUhTFpVXoSROJzpCEugdo0InnsyOeFsw7+RnCnxuXNrcCgiIMbhcABTxHM/M8LCMrzOnXMWcnVjOa6WMZ6Pw5ktjIdl8htz52EZDofDySBsJGLXaH49d27cORwOJ4MwY35meAwKkpkJaJOBG3cOh8PJICwMM+wJwKxT5623gBt3DofDySB6tVKc05CveDvAjTuHw+FkFEKIWBqcrxp3gBt3DofDyTjMY+eeO4fD4cwgWFI1X5UyADfuHA6Hk3GYx17APXcOh8OZOTCjnq8pTAA37hwOh5NxxsMy3HPncDicGQOrluHGncPhcGYQ4zF3HpbhcDicGQOvluFwOJwZCK9z53A4nBkIN+4cDoczAzm/3o7Pn1uDxeUFeVvDlI07IURJCNlHCHk98nsdIWQHIaSFEPICIUQz9WVyOBzO2YPNpMWPr10KrSo/cr9AZjz3bwI4EvX7TwE8TCmtBzAM4MsZOAeHw+FwJsCUjDshpBLAlQCejPxOAFwE4KXIQ54BcO1UzsHhcDiciTNVz/1/AHwXQDjyuw3ACKU0GPm9E0DFFM/B4XA4nAkyaeNOCLkKQB+ldM8kj7+LELKbELK7v79/ssvgcDgcjgRT8dzXAbiaENIG4E8QwjGPALAQQlj9TyWAM1IHU0p/SyltppQ2FxcXT2EZHA6Hw4ln0sadUvqvlNJKSmktgH8C8B6l9LMA3gdwY+RhtwN4bcqr5HA4HM6EyEad+/cAfIcQ0gIhBv9UFs7B4XA4nBRkpH2KUroJwKbI/08BWJOJ5+VwOBzO5OAdqhwOhzMD4cadw+FwZiDcuHM4HE4+oFT4lyW4cedwOJx80H8M+Ek5cOzNrDw9N+4cDoeTD4ZbgYAHMNqz8vTcuHM4HE4+GGoVfhbVZeXpuXHncDicfFCyCFh9B2CwZuXp8zcmhMPhcGYz8y4U/mUJ7rlzOBxOPmjfBjh7svb03LhzOBxOrgmHgGc+A2z/TdZOwY07h8Ph5JrRTiAcAKzZSaYC3LhzOBxO7hnObqUMwI07h8Ph5B5WBsk9dw6Hw5lBDLcCCjVQkL0ppNy4czgcTq4xlQILPgUolFk7Ba9z53A4nFxz3leFf1mEe+4cDoeTSygF3INZVYQEuHHPPCOngVOb8r0KDoczXXEPAD+fC+x6Mqun4cY90zx1GfCHa4CAN98r4XA40xFWBmmpzuppuHHPNM4u4Wf3/vyug8PhTE+yrAbJ4MY9k3hHx/+v1ORvHRwOZ/oy3AqAAEU1WT0NN+6ZxNEN2BcAn38VqFiZ79VwOJzpyFCrUN+u0mb1NNy4Z5KShcDXdgKVq4HWzVnPhnM4GG4Dfn8lMHgy3yvhyCUcBIoXZP003LhnEr9b+PnJi4Li29Cp/K5nuhEO53sFM4tQEHjlbqDnAKBU53s1HLnc+BTwuZezfhpu3DMFpcAvVgL/932g+lzhttM7sn/egROAZyj755kK7/0X8KtzgVfuzPdKZhZbHwZObwcu+SHwzn8AB7NvMDgZgpCsn2J2Gvfuj4Gjf8/sczq6AFePIARkXwBoC7Nv3EMB4NFm4DfrsnueqTBwAtj8M2CwBTj8qpCX4EydM3uATfcDS28Amr8MdGwHDr+W71Vx0tF7CHhosRC2zTKzz7iPjQDP3QK8+EXA68jc857ZI/ysWAUoFEDVaqAjy8adhX2cXUDXvuyea7IcflX4edtrQqxx7zPSjwuHgIOvAIGx3K3tbCUwBrx8J2AqA658UPAC6y8GTm4SQjWc6cvgScBxBtAWZP1Us8+4v3cf4OwGQr7Meu9n9ggqb2XLhN+rzgX6jwgXk2xRvAD4biugNgIfPpi980yFQ68Jr0XtOqD+EmD374UdRzw7HgNe+iLw8Z9yv8azDZUOWPt14LrHAH2RcFv9pYBvFOjcmd+1cVIznH2pX8bsM+4b/gW4/kmhO+yTFzP3vGf2CIadlTfVbQAWXwP4Mrg7iIZSIYFrsALn3QMc+RvQdzQ755osgyeB3k+E1wEAVt8phK6Ovh77uKFTwD9+LPy/7cPcrvFshBCg+YtA3frx2+ZuBIgSOPFOvlbFkcNQK6C3ArrCrJ9q9qhCeh0AKGAuA5bfJHjvrl7BSE41ucEMbWXz+G3V5wj/ssXAceCx84Gb/wCc8xXA58zJB2ZCHPqL8JMZ94ZLhQugeyD2cX1HBA/U1gy0bcnMezJTefvfAGcvcMMTsbfrLUDVORFdox/mY2UcOQy35sRrB2aTcX/rB0DrB8A9OwCNAVj3jcw9NyHAXe8nlvr5XMDQSWBOY+bOxTj2BhDyC8bSaAM+9dOJHe8dFeJ+2TSizV8CihcChZGBBAolcPeHiedceKUQsuk9KKyLG3dpKBWSpqVLpe+/9leCTjhn+jLcBlQ0p31YJpgdYZmWd4F9/wssvVEw7AzPUGYqDMIh4aci7uX8x4+A331KXpJr3x+BHb+Vf85jbwJly4HCyvHb9v4B+ODn6Y919QEPLgK2/Ur++SaDwQosuir2NkKEXUbbFmFI8Lv3An6PEM6qWAXMuyjxdeQIDJ0CRjqE10gK61xAY8ztmjgT4yvbJu6ITZKZ/y3yOYG/fkPwIDd+P/a+Pb8H/nwbMNwu77n++g1g+28Sb3/jX4Dfbky8vWoNEHALHmkqKBUSvW/9QJAMTod7QEicLfhU7O2du4HNPwecPamP/+QlYV1b/yd71Sm7ngLe+H/HL3zRvPX/Ac/eDLz6FWDH44C7b/y+Ay9Kv8Yc4OR7ws9kxh0Q+iz+9q3crIczcTQGwGjPyalmvnHf84xQevSZXyRqOSy9Qfgpp/nD1S+U8QV9ifed2SMd766KxNxPp6lg6D8q5ADCAcHgpuPE2wANA/OviL193TeF50jnkR/4E2CwAe5+YP+z6c83GfY8DZzZKz1GbNXtwsWldTNwyb1AUe34fSfeAj58aOZJN4TDQs3/VDj5HmCpETz0ZPidwKFXZlZJZNCf7xWkxucCtj4i9M+komO7UIY93JaTZc1s4x4OATsfB6rXSic3i2qByjXyjDur1264VKgp97mE3wNjQmNCxarEYworAXO50EWYipZ/CD8v/qFQ4pYO7yhQvAiY0xR7u22ecMHa/bvkXavhMND0WeDTDwBX/1IIVWWaoVNCS/ySa6Xvr1gF1KwDatcLFTTR1J4vePJTNYTThZ5PhDDYP/4TeGy9sJOcDOGwsDOrvzh1PqL+UuHz0blrcueZbmz6KfBfZcBHv8z3SqQ5+nfgV+cIHcKbH0j92J5PgONvAsrsCoYxZrZxJwrgml8L3mEylt0khE36jqR+roOvCAZVWwA8eQnw/k+E27sPCM05UsadEOGiks5zn3+5YGzXf0e44KTzWs/9CnDPNunY9PnfAfwuYPuvpY9VKIBz7gaWXg+svE2ossi0l8zyGKxKRorbXgM+/5fEv6E2Ut6Xy5JISrPjHZ76QMi5/O1bQggtOCaUrE4GhQL4/9s78/AoqmyB/45sooBsiiBhU9RBZFFEQQXcHUTkKfpU9OE2jo44LsM44hvHBUFBR8ANBVncBllERVAUEHFkT2QJiMi+QwgkbAlrzvvjVL/uJN2d7qRJd2fu7/vqq6pbt26fqr516txzz731xDK44u/h8zXpZCGRq4sREnk4pziSHT9WfgM/9LcWsK8TMn0CjO5ifTbxZubL8Omd1mpPudheqOGepd3roHxli9grBcq4cheLBQ4XknheN3sJpE8InWfPFtg4x6zi6immFOcPNXdM4MjUYJx1DdRrHf7LTLWbQlvPgt0wB4a2t3C3YBzItIcwlPVWpxk06wYb5hauaHl59lLKXO1PSx0Fo2+I7aRey7+AeheE/9JMuQrBJ7uq2QSq1j3+D2/2RpjaxxTFgIbWwgPr5PVNAFcSlk2ET7pb663zq/bw12hUskFa5StZZFQ4fCGR0ca7Hzti/Ubf97PrLxiu6mNzKgw8E4ZfBYvHRPcb0ZC5GiY+aK3TuydCw3b+Y7vXwbie+b+fECsO54RvXe3PMAsczEC65kX44yy4th/cOCS8ct+12upAKUWCJb9y358RvNNu21KbmbGoqVCrnAbXvlTYfx2IL167+c22vvp5Czmb9JiFOlarH/pt3LoH/PdHUOHE4Me3pNnoUl9FrVLHfPBz3gief9YAGHReeJ9q1zfhnsmFK9GGn+z8wK9EVTgJNsw2X3csyN5oLaHzi+nuETHXjC/e/XhwJNc6dBeOgKMH7aV9egvIzTLXyTd/C36eKkx7Dj57ILxsC0fAhPvshX/fNxYKKgItbrd+hj1bopf5g64WfRUJTa+2OpSbFXn5qaMgc6UZIh/eBGPvKtya2bYEPr7Z6ozmwd7Nlp65ymSLVevn0H4Y2wNOKO89O5X9x87vDrd/Ajm7LHgg1nz0XzCgsc0VBf7nbOdvMOlRGNQcvuxlaaeeY/1c5SrYdCNnXxc60ivvmLln65dOGCQku3Lfv9MexhkvFD42/13YnGbheEXR7hH7c0LRugfc9pH5tMGaYZ1ftdGX1RtAryL8m0cPmSUdjGUTbQKoE7whB7XOhPNvM7/5/p3586paCGSDS6BcmCEKJ3rx69uW5ve9LxkLFavCOZ39ac1vsWuIVSdm9Qbw58XWuikuFz9sQ+ujlefQflgw3HygozqHtsC+fcamhrjjX/DAdOgyCJp0tIFUbe61sNmCLTlVi/KZPdg6o0NZX+t/gilP2oN+9+f+6QEAWtwGaOiR0QuGw+cPF04/sMteCpH6atvcD0+tzf/b4cjNMvdH447mPrr4Idg41+6Tjx2/wIfdzC157xQb13F5bzu2bYkZKP+6rfh9CoGUqwAN2kH3kcFbf/Va2TM5793YzmO/Oc0U8HndoFo9SxtxNQy9DN6+CJaOs9/tPjL4+Ys+Ce0B2J5uBlyjy4MfPw4UW7mLSIqIzBSRX0RkuYg85qXXFJFpIrLKW0dYw4pBlVNtAMzsIfmbu/sz7AFqdUfkFXzNTHszB1MolWtAs6750353I5zbxSpYsIiQQL7vCx92De5qWTPTlHVgfHKH3mZdzn0rf94dy2HPxvCtDB/Zm2BYR3/kzOEc84U365o/1r9ceWj/Zwut3DA7fJlFuW5ys6x5Xz0FKlUtWsZQ1L/QOg4jjXfP2gBTn7HZ9r7ubXP87Frjt74K0vBS6NTHBk4VpFMf62T/6nH/ty4BZvaDeW9D2z/C9a9Y2uIxhSefa3gpdBlsxkCgxQn24r7w3uCfV8tcZbJXqlL42LofAA0fAhlI5ep2/w/uiUzZznrV5kC6rp+9tM7vbh37C4fb+AuwCI9KVayvxKdwfS+487tb39a6H83NtT8j6M9ExME95n66cTCceUXofFf+w/LNGhhd+aHcTQALhpnx02WQ7ecds3te8WTo+DQ8sdyO+Yy8giz62IzKYNQ5D+6fbgEZpYWqFmsB6gIXeNtVgd+AZsBA4Gkv/WlgQFFlXXjhhVpsjh5WHXWD6ounqm5aaGkzX1F9rprqzt8iL2feu3ZO2gf50+cPU538pOqxo4XP2btddV9G0WVnrlZ97hTV6S/mT9+z1X7z34MKnzP+XtUXaqnm7Lb9j7ur9jvD8u/dFtk1jf0fO+fALtWl4+3cNT8Uznc4R3XgmaojO9t+Xp7qof22nb3J7s3oLlZeOCY+pPpW2+D3KlqWjled+05keZeMVX2hpur4+1Q3LvDkP2DHjhxSPXLQtg/nRFbe7vWqL6eoDrvC6tePr9m9+7KX6rFjlmfnKvvN4Vep5marznlLdfWM6K7RR16e3d/+Kar7dpjM2Zv8xz//k+rLDaK7rzm7VQc0LlznCpK52urZl73ypx89ojr6RrvuTamW5ruPoVg5VbVvHdXBLVV3rYlcVlW7tukvWn3dMDeyc9b8oJqTVXS+vDzV/Zmqc4eq9qtn97gg+zJUX6ytOqV3dHIH8u3frYyi7lMMAVI1hF4ttuWuqttU9Wdvex+wAjgDuAnwzev6ARAiHi5GlKsAt35gPu9P7zRra+H71pFZu2nk5Vz0B2syTe2Tf1BT6kizmINZ51XrWOuhKGqdaS2M1BH5O+vWzrR1MIusw1/N8vNNDVr7bGuJ+K41Ejr+zSJn5r5tTcpqZwRvFlaobE3sytVt/+Ae6F8PXk4x//43T1mro05zczF99yxsKuCKyt4I6eOgyRVFt2Qi4bci4t2PHfHP6tmsGzy+zL5wk3KRWZQVT7KWxti7zP995KC5FaY+E7y8QGo0tH6LbUss4mXZRIuq6jLY35qofRbcOhq2LoZ32psLY2mEE9FtT4dfv/bvp08wq/eqZ60PaGwP+ORWu9eqFt/epFN097VyDTtn3jvhB7VVrgFtHywchVOuvF3fKQ1gpXefi/rm59nXWV/PwT3+fqpfp8DsN2yZ/17wGO/9GfBRN/j3a+YSiXS6jiYdrc7m7A7e7+YjfQK80dpcLUdyrV4VyjPepvMoGJobDSltrYyC8e7HjsKnPWykfGkSSutHswCNgI1ANSA7IF0C90MtJbLcfez4RbV/fdVv+pgVump69GXsXm+Ww6gbzELbvtwsl/nDSi7f+jmFy5pwv+rAs/zWYEH2ZYQ+Finjeto1bV2sunZW+LwHdtk6d4/qj/9UnfJXa1UEtoBys1Vfb27W2cF9/vQpvc0CDLQ4S0LqaLtfGb8WPpabrfrBTXZ865Lw5cwdavkGt7T1knGRy7B7va1zssySDcavX1urccIDofMUZMydqq82tfy52VYH3uvot8xXTjVZv/uHatZGs4ZTR0cut4/M1da6mPxk9Of6OLAr8paijz1bzVpWVf20h11L4DLqBnteVVXX/aT66tl2jT9/HL18u9ZaK2vhiODH9+1QfaWhtbCOHVX94k9mXResp8eORt5iCMXe7XZ9s9/Mn745zdKXji9Z+UEgjOUeC8VeBUgDbvb2swsczwpx3oNAKpDaoEGD2Fzp/p22Ppzrr1zR4lMq895TndFX9fnqwZtx0ZKXZ838wS39D/HWJaorJpe87HBsX24uoRl9Y1fm+tlWpq8pvy9Dte9p9uDEiszV9j8sGJ4/PWuj6tuXmNKKVBn43CqfPxw7+QI5uDe6+rb8S5Nn1TRTTsOvUt3yc/48XzxidW/DPKvPPjdTtHz1hN2rzNX503etUX3/2uhedsXhcI4ZAQf3qe5epzproOqbF1mdyc02V9SQ1qrblxWv/Lw81ZG/NxfU7nWFj33aw16+PiMha4PnhnrUny/Sl3IkDGquOvbu/Gk/Dfbcqdtj9zsex025AxWAb4EnA9JWAnXV75dfWVQ5MbHcY0Venj1Yq6arDmml+kHX2JW9dpbqiiklt8ajZclY/4svVkx73irsL19526dE18dRFHl5qq+da30NqmZBDr/aHtT+KaprZkZX3uY082UnAkcOmg99wgO2H+zFkLvH30Ly9X8Uh73bVF863fpwfL+VOkr1pbpm8a6ZWfyyY8H62XatJWHrErP8X6hlrZQ9Wy09/TOroz/+M3/+Kb1Vn6/hf+F92K1kvvZA0icU7tf66BbVN9vEpvwChFPuJYmWEWAEsEJVA51Yk4Ce3nZPILk+7CgCN71lc3hkrffPPxMLGneAczub3zZ9Avw0qHTmUGlxW+wnK+rUx3yjkx41H3WH3tH1cRSFbwDa2h9sv3INC0G9+EH4g+eDjoYzLoDyFWMnX0koX8lG76aPsxDZYGGVJ1aDbu/Y/N8LRxT/t6qebqOfL37IfNtjboevHrN464fnRH8fY03D9natJaFuC3g0DVrfZXMafdLd+ium9rG4/fYFpve+vDe072V1audK69OoWrdkMvhofov1Bfg4dtTCSksxBPL/CaX1i1qAywAFlgKLvaUzUAuYAawCpgM1iyoroSz3QPbtyO9XjgXZm83n+tq5qkMvjW3ZpU3GStVx91gkwvFg9/ri+WGTgU2pZlUuHhM+34a5/r6QkpCXp/puB2v5zH2n9FuPpcWutebKUrV+pmB9NoFM/ov54GPVsj2cY/5/X4TRpoX2P6d/FpvyC0AYy120NCzHImjTpo2mpqbGW4zS4dA+eP08+97lpY/Z8GXHfya52f4IpdJg0wKLfz/td6X3m4nM7CE24VfLO2zQXCw4ehheSbGBZNf3t6iurYss2u04/NcikqaqQYe9JvcI1WSkUlVoc49tRzooxVE2KU3FDhaq5xS7H9+gwrYlCH8sSPmK5gryfai8XAW776X9X+OUe3y4vLfFTDfqEG9JHI7/XK7ta599DDXpX3Gpf5HFuh/aB2PutFHoccAp93hwYjWbw8R9Ts7hiB8nlLPO2FhT/yIbzJQ22gaAHdpb5CnHA6ddHA6HI5aktLW1byRsw8viIoZT7g6HwxFLqp4OVz5rUyLUaV70HPzHCafcHQ6HI9a0f9RcM43iY7WDU+4Oh8MRe7Yuss8q1g/znYjjjFPuDofDEWtqnQXtetlssHEizOd8HA6Hw1EsTq5tHz+JI85ydzgcjjKIU+4Oh8NRBnHK3eFwOMogTrk7HA5HGcQpd4fD4SiDOOXucDgcZRCn3B0Oh6MM4pS7w+FwlEES4ktMIrIT2FDM02sDmTEUp7RIVrkheWV3cpcuTu7jT0NVPTXYgYRQ7iVBRFJDfWYqkUlWuSF5ZXdyly5O7vji3DIOh8NRBnHK3eFwOMogZUG5D4u3AMUkWeWG5JXdyV26OLnjSNL73B0Oh8NRmLJguTscDoejAEmt3EXkehFZKSKrReTpeMsTChEZKSIZIrIsIK2miEwTkVXeukY8ZQyGiKSIyEwR+UVElovIY156QssuIieKyAIRWeLJ/YKX3lhE5nv1ZayIVIy3rMEQkXIiskhEJnv7CS+3iKwXkXQRWSwiqV5aQtcTABGpLiITRORXEVkhIu2SQe5ISFrlLiLlgLeB3wPNgDtEpFl8pQrJaOD6AmlPAzNUtSkww9tPNI4Cf1HVZsAlwCPePU502Q8BV6pqS6AVcL2IXAIMAAap6llAFnB/HGUMx2PAioD9ZJH7ClVtFRBGmOj1BGAIMFVVzwVaYvc9GeQuGlVNygVoB3wbsN8H6BNvucLI2whYFrC/EqjrbdcFVsZbxgiu4UvgmmSSHTgJ+Bm4GBuYUj5Y/UmUBaiPKZQrgcmAJInc64HaBdISup4ApwDr8Poek0XuSJektdyBM4BNAfubvbRkoY6qbvO2twN14ilMUYhII6A1MJ8kkN1zbSwGMoBpwBogW1WPelkStb4MBp4C8rz9WiSH3Ap8JyJpIvKgl5bo9aQxsBMY5bnB3heRk0l8uSMimZV7mUHNREjYsCURqQJ8BjyuqnsDjyWq7Kp6TFVbYZZwW+DcOItUJCLSBchQ1bR4y1IMLlPVCzA36SN/8+SlAAABpElEQVQi0iHwYILWk/LABcBQVW0NHKCACyZB5Y6IZFbuW4CUgP36XlqysENE6gJ464w4yxMUEamAKfZPVHWil5wUsgOoajYwE3NnVBcR30fhE7G+XAp0FZH1wKeYa2YIiS83qrrFW2cAn2Mv1ESvJ5uBzao639ufgCn7RJc7IpJZuS8EmnqRBBWB24FJcZYpGiYBPb3tnpg/O6EQEQFGACtU9fWAQwktu4icKiLVve3KWD/BCkzJd/eyJZzcqtpHVeuraiOsPn+vqj1IcLlF5GQRqerbBq4FlpHg9URVtwObROQcL+kq4BcSXO6IibfTv4QdIp2B3zB/6v/GW54wco4BtgFHMGvhfsyXOgNYBUwHasZbziByX4Y1SZcCi72lc6LLDrQAFnlyLwP+4aU3ARYAq4HxQKV4yxrmGjoBk5NBbk++Jd6y3PcsJno98WRsBaR6deULoEYyyB3J4kaoOhwORxkkmd0yDofD4QiBU+4Oh8NRBnHK3eFwOMogTrk7HA5HGcQpd4fD4SiDOOXucDgcZRCn3B0Oh6MM4pS7w+FwlEH+D9H92fXp9H8RAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "bnXKw0X3VblN",
        "outputId": "fc5293c4-c680-4480-e6f4-b34c69970d1c"
      },
      "source": [
        "#ScatterPlot\n",
        "sns.relplot(x=' \"Weight (Sep)\"', y=' \"Weight (Apr)\"', data = df6)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f9373cf9190>"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8E_3ApHG-qIC"
      },
      "source": [
        "Scatter plotnya seperti membentuk garis lurus miring kanan, artinya hubungan berat badan bulan april dan september erat dan positif (selengkapnya akan dipelajari di materi eksplorasi data)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "ZXq1VYeVWl3b",
        "outputId": "fc4f9693-4b4e-49ca-f17e-9b2ee4b5e75f"
      },
      "source": [
        "#Histogram\n",
        "sns.displot(data=df6, x=' \"Weight (Apr)\"',hue=\"Sex\", kde=True)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f9373c7f650>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 404.625x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xR0rzImH-Zso"
      },
      "source": [
        "Terlihat data histogram per kategori laki-laki dan perempuan"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "ceZrdlroX9iG",
        "outputId": "9ab0a849-bb8a-4dcf-b0b4-780e95fc6f19"
      },
      "source": [
        "#boxplot\n",
        "sns.boxplot(data=df6, x=' \"Weight (Apr)\"')"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9371bd6050>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pyP8BOQ6WKl1/nvIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ovdYy-C97ch"
      },
      "source": [
        "Terlihat ada outlier di bagian atas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "deZlR5nPZndA",
        "outputId": "54526463-2107-4262-c6b5-84ae7d42b993"
      },
      "source": [
        "#Catplot\n",
        "sns.catplot(data=df6, x='Sex', y=' \"Weight (Apr)\"')"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f9371b2f710>"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DG0yiNxx-O9V"
      },
      "source": [
        "Sebaran data berat badan bulan april per kategori laki-laki dan perempuan"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 396
        },
        "id": "QZdj2rxDbpze",
        "outputId": "956aaa5f-3e84-4bbb-f279-3e87d01336c5"
      },
      "source": [
        "#HeatmapKorelasi\n",
        "corrmat = df6.corr()\n",
        "f, ax = plt.subplots(figsize=(9, 6))\n",
        "sns.heatmap(corrmat, vmax=1)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9371b144d0>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1PbfvIND9tlJ"
      },
      "source": [
        "Warna merepresentasikan keeratan hubungan, bisa diliat di nilai hubungan/korelasinya bisa dilihat di penggaris sebelahnya"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DHsEXxrbcYxH"
      },
      "source": [
        "### Latihan-6 (Visualisasi data)\n",
        "\n",
        "Gunakan data dari: https://people.sc.fsu.edu/~jburkardt/data/csv/cities.csv\n",
        "\n",
        "Cari:\n",
        "*   Deskriptif data\n",
        "*   beberapa visualisasi datanya\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vxXMS8aOec-f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "f8170d46-92b7-48b3-bf42-3b2fd6983a5b"
      },
      "source": [
        "# Contoh mengimport dari web\n",
        "url = 'https://people.sc.fsu.edu/~jburkardt/data/csv/cities.csv'\n",
        "df = pd.read_csv(url)\n",
        "\n",
        "df"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>LatD</th>\n",
              "      <th>\"LatM\"</th>\n",
              "      <th>\"LatS\"</th>\n",
              "      <th>\"NS\"</th>\n",
              "      <th>\"LonD\"</th>\n",
              "      <th>\"LonM\"</th>\n",
              "      <th>\"LonS\"</th>\n",
              "      <th>\"EW\"</th>\n",
              "      <th>\"City\"</th>\n",
              "      <th>\"State\"</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>41</td>\n",
              "      <td>5</td>\n",
              "      <td>59</td>\n",
              "      <td>\"N\"</td>\n",
              "      <td>80</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "      <td>\"W\"</td>\n",
              "      <td>\"Youngstown\"</td>\n",
              "      <td>OH</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>42</td>\n",
              "      <td>52</td>\n",
              "      <td>48</td>\n",
              "      <td>\"N\"</td>\n",
              "      <td>97</td>\n",
              "      <td>23</td>\n",
              "      <td>23</td>\n",
              "      <td>\"W\"</td>\n",
              "      <td>\"Yankton\"</td>\n",
              "      <td>SD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>46</td>\n",
              "      <td>35</td>\n",
              "      <td>59</td>\n",
              "      <td>\"N\"</td>\n",
              "      <td>120</td>\n",
              "      <td>30</td>\n",
              "      <td>36</td>\n",
              "      <td>\"W\"</td>\n",
              "      <td>\"Yakima\"</td>\n",
              "      <td>WA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>42</td>\n",
              "      <td>16</td>\n",
              "      <td>12</td>\n",
              "      <td>\"N\"</td>\n",
              "      <td>71</td>\n",
              "      <td>48</td>\n",
              "      <td>0</td>\n",
              "      <td>\"W\"</td>\n",
              "      <td>\"Worcester\"</td>\n",
              "      <td>MA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>43</td>\n",
              "      <td>37</td>\n",
              "      <td>48</td>\n",
              "      <td>\"N\"</td>\n",
              "      <td>89</td>\n",
              "      <td>46</td>\n",
              "      <td>11</td>\n",
              "      <td>\"W\"</td>\n",
              "      <td>\"Wisconsin Dells\"</td>\n",
              "      <td>WI</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>39</td>\n",
              "      <td>31</td>\n",
              "      <td>12</td>\n",
              "      <td>\"N\"</td>\n",
              "      <td>119</td>\n",
              "      <td>48</td>\n",
              "      <td>35</td>\n",
              "      <td>\"W\"</td>\n",
              "      <td>\"Reno\"</td>\n",
              "      <td>NV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>50</td>\n",
              "      <td>25</td>\n",
              "      <td>11</td>\n",
              "      <td>\"N\"</td>\n",
              "      <td>104</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "      <td>\"W\"</td>\n",
              "      <td>\"Regina\"</td>\n",
              "      <td>SA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>40</td>\n",
              "      <td>10</td>\n",
              "      <td>48</td>\n",
              "      <td>\"N\"</td>\n",
              "      <td>122</td>\n",
              "      <td>14</td>\n",
              "      <td>23</td>\n",
              "      <td>\"W\"</td>\n",
              "      <td>\"Red Bluff\"</td>\n",
              "      <td>CA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>40</td>\n",
              "      <td>19</td>\n",
              "      <td>48</td>\n",
              "      <td>\"N\"</td>\n",
              "      <td>75</td>\n",
              "      <td>55</td>\n",
              "      <td>48</td>\n",
              "      <td>\"W\"</td>\n",
              "      <td>\"Reading\"</td>\n",
              "      <td>PA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>41</td>\n",
              "      <td>9</td>\n",
              "      <td>35</td>\n",
              "      <td>\"N\"</td>\n",
              "      <td>81</td>\n",
              "      <td>14</td>\n",
              "      <td>23</td>\n",
              "      <td>\"W\"</td>\n",
              "      <td>\"Ravenna\"</td>\n",
              "      <td>OH</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>128 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     LatD   \"LatM\"   \"LatS\"  \"NS\"  ...   \"LonS\"   \"EW\"              \"City\"  \"State\"\n",
              "0      41        5       59   \"N\"  ...        0    \"W\"        \"Youngstown\"       OH\n",
              "1      42       52       48   \"N\"  ...       23    \"W\"           \"Yankton\"       SD\n",
              "2      46       35       59   \"N\"  ...       36    \"W\"            \"Yakima\"       WA\n",
              "3      42       16       12   \"N\"  ...        0    \"W\"         \"Worcester\"       MA\n",
              "4      43       37       48   \"N\"  ...       11    \"W\"   \"Wisconsin Dells\"       WI\n",
              "..    ...      ...      ...   ...  ...      ...    ...                 ...      ...\n",
              "123    39       31       12   \"N\"  ...       35    \"W\"              \"Reno\"       NV\n",
              "124    50       25       11   \"N\"  ...        0    \"W\"            \"Regina\"       SA\n",
              "125    40       10       48   \"N\"  ...       23    \"W\"         \"Red Bluff\"       CA\n",
              "126    40       19       48   \"N\"  ...       48    \"W\"           \"Reading\"       PA\n",
              "127    41        9       35   \"N\"  ...       23    \"W\"           \"Ravenna\"      OH \n",
              "\n",
              "[128 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "UlUSr-yde0xO",
        "outputId": "96ecde74-0ce9-464e-882f-e0aa1c03d6df"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>LatD</th>\n",
              "      <th>\"LatM\"</th>\n",
              "      <th>\"LatS\"</th>\n",
              "      <th>\"NS\"</th>\n",
              "      <th>\"LonD\"</th>\n",
              "      <th>\"LonM\"</th>\n",
              "      <th>\"LonS\"</th>\n",
              "      <th>\"EW\"</th>\n",
              "      <th>\"City\"</th>\n",
              "      <th>\"State\"</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>41</td>\n",
              "      <td>5</td>\n",
              "      <td>59</td>\n",
              "      <td>\"N\"</td>\n",
              "      <td>80</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "      <td>\"W\"</td>\n",
              "      <td>\"Youngstown\"</td>\n",
              "      <td>OH</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>42</td>\n",
              "      <td>52</td>\n",
              "      <td>48</td>\n",
              "      <td>\"N\"</td>\n",
              "      <td>97</td>\n",
              "      <td>23</td>\n",
              "      <td>23</td>\n",
              "      <td>\"W\"</td>\n",
              "      <td>\"Yankton\"</td>\n",
              "      <td>SD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>46</td>\n",
              "      <td>35</td>\n",
              "      <td>59</td>\n",
              "      <td>\"N\"</td>\n",
              "      <td>120</td>\n",
              "      <td>30</td>\n",
              "      <td>36</td>\n",
              "      <td>\"W\"</td>\n",
              "      <td>\"Yakima\"</td>\n",
              "      <td>WA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>42</td>\n",
              "      <td>16</td>\n",
              "      <td>12</td>\n",
              "      <td>\"N\"</td>\n",
              "      <td>71</td>\n",
              "      <td>48</td>\n",
              "      <td>0</td>\n",
              "      <td>\"W\"</td>\n",
              "      <td>\"Worcester\"</td>\n",
              "      <td>MA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>43</td>\n",
              "      <td>37</td>\n",
              "      <td>48</td>\n",
              "      <td>\"N\"</td>\n",
              "      <td>89</td>\n",
              "      <td>46</td>\n",
              "      <td>11</td>\n",
              "      <td>\"W\"</td>\n",
              "      <td>\"Wisconsin Dells\"</td>\n",
              "      <td>WI</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   LatD   \"LatM\"   \"LatS\"  \"NS\"  ...   \"LonS\"   \"EW\"              \"City\"  \"State\"\n",
              "0    41        5       59   \"N\"  ...        0    \"W\"        \"Youngstown\"       OH\n",
              "1    42       52       48   \"N\"  ...       23    \"W\"           \"Yankton\"       SD\n",
              "2    46       35       59   \"N\"  ...       36    \"W\"            \"Yakima\"       WA\n",
              "3    42       16       12   \"N\"  ...        0    \"W\"         \"Worcester\"       MA\n",
              "4    43       37       48   \"N\"  ...       11    \"W\"   \"Wisconsin Dells\"       WI\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "BqD8NR9We2zX",
        "outputId": "56ed72bb-7659-4bc4-e8d7-0a7d48eec33e"
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>LatD</th>\n",
              "      <th>\"LatM\"</th>\n",
              "      <th>\"LatS\"</th>\n",
              "      <th>\"NS\"</th>\n",
              "      <th>\"LonD\"</th>\n",
              "      <th>\"LonM\"</th>\n",
              "      <th>\"LonS\"</th>\n",
              "      <th>\"EW\"</th>\n",
              "      <th>\"City\"</th>\n",
              "      <th>\"State\"</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>39</td>\n",
              "      <td>31</td>\n",
              "      <td>12</td>\n",
              "      <td>\"N\"</td>\n",
              "      <td>119</td>\n",
              "      <td>48</td>\n",
              "      <td>35</td>\n",
              "      <td>\"W\"</td>\n",
              "      <td>\"Reno\"</td>\n",
              "      <td>NV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>50</td>\n",
              "      <td>25</td>\n",
              "      <td>11</td>\n",
              "      <td>\"N\"</td>\n",
              "      <td>104</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "      <td>\"W\"</td>\n",
              "      <td>\"Regina\"</td>\n",
              "      <td>SA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>40</td>\n",
              "      <td>10</td>\n",
              "      <td>48</td>\n",
              "      <td>\"N\"</td>\n",
              "      <td>122</td>\n",
              "      <td>14</td>\n",
              "      <td>23</td>\n",
              "      <td>\"W\"</td>\n",
              "      <td>\"Red Bluff\"</td>\n",
              "      <td>CA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>40</td>\n",
              "      <td>19</td>\n",
              "      <td>48</td>\n",
              "      <td>\"N\"</td>\n",
              "      <td>75</td>\n",
              "      <td>55</td>\n",
              "      <td>48</td>\n",
              "      <td>\"W\"</td>\n",
              "      <td>\"Reading\"</td>\n",
              "      <td>PA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>41</td>\n",
              "      <td>9</td>\n",
              "      <td>35</td>\n",
              "      <td>\"N\"</td>\n",
              "      <td>81</td>\n",
              "      <td>14</td>\n",
              "      <td>23</td>\n",
              "      <td>\"W\"</td>\n",
              "      <td>\"Ravenna\"</td>\n",
              "      <td>OH</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     LatD   \"LatM\"   \"LatS\"  \"NS\"  ...   \"LonS\"   \"EW\"        \"City\"  \"State\"\n",
              "123    39       31       12   \"N\"  ...       35    \"W\"        \"Reno\"       NV\n",
              "124    50       25       11   \"N\"  ...        0    \"W\"      \"Regina\"       SA\n",
              "125    40       10       48   \"N\"  ...       23    \"W\"   \"Red Bluff\"       CA\n",
              "126    40       19       48   \"N\"  ...       48    \"W\"     \"Reading\"       PA\n",
              "127    41        9       35   \"N\"  ...       23    \"W\"     \"Ravenna\"      OH \n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "spszci2ne6z8"
      },
      "source": [
        "### Membuat Deskriptif"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "YrV-IL7ie-Zr",
        "outputId": "9e01fce0-944d-4d0d-9222-0b838d8ec219"
      },
      "source": [
        "# DataNumerik\n",
        "df.describe()"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>LatD</th>\n",
              "      <th>\"LatM\"</th>\n",
              "      <th>\"LatS\"</th>\n",
              "      <th>\"LonD\"</th>\n",
              "      <th>\"LonM\"</th>\n",
              "      <th>\"LonS\"</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>128.000000</td>\n",
              "      <td>128.000000</td>\n",
              "      <td>128.000000</td>\n",
              "      <td>128.000000</td>\n",
              "      <td>128.000000</td>\n",
              "      <td>128.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>38.820312</td>\n",
              "      <td>30.765625</td>\n",
              "      <td>27.492188</td>\n",
              "      <td>93.250000</td>\n",
              "      <td>27.742188</td>\n",
              "      <td>26.960938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.200596</td>\n",
              "      <td>16.426158</td>\n",
              "      <td>18.977814</td>\n",
              "      <td>15.466499</td>\n",
              "      <td>16.927937</td>\n",
              "      <td>18.727807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>26.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>35.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>11.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>39.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>89.500000</td>\n",
              "      <td>26.500000</td>\n",
              "      <td>23.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>42.250000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>103.250000</td>\n",
              "      <td>40.250000</td>\n",
              "      <td>47.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>50.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>123.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>59.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             LatD      \"LatM\"      \"LatS\"      \"LonD\"      \"LonM\"      \"LonS\"\n",
              "count  128.000000  128.000000  128.000000  128.000000  128.000000  128.000000\n",
              "mean    38.820312   30.765625   27.492188   93.250000   27.742188   26.960938\n",
              "std      5.200596   16.426158   18.977814   15.466499   16.927937   18.727807\n",
              "min     26.000000    1.000000    0.000000   71.000000    0.000000    0.000000\n",
              "25%     35.000000   16.000000   11.000000   80.000000   14.000000   11.000000\n",
              "50%     39.000000   31.000000   24.000000   89.500000   26.500000   23.500000\n",
              "75%     42.250000   45.000000   47.000000  103.250000   40.250000   47.000000\n",
              "max     50.000000   59.000000   59.000000  123.000000   58.000000   59.000000"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "id": "oQYis3hzfC__",
        "outputId": "f8b984ff-53d1-413d-da03-f8c824016412"
      },
      "source": [
        "df.hist()"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f9371a5a9d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9371a15450>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f93719c8a90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9371989150>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f937193e7d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f93718f4e50>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "Tx8zI-DxfI-9",
        "outputId": "25fecacf-f0c0-4025-e559-df16c639a648"
      },
      "source": [
        "#data non-numerik\n",
        "df.describe(include=[\"object\", \"bool\"]) "
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>\"NS\"</th>\n",
              "      <th>\"EW\"</th>\n",
              "      <th>\"City\"</th>\n",
              "      <th>\"State\"</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>128</td>\n",
              "      <td>128</td>\n",
              "      <td>128</td>\n",
              "      <td>128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>\"N\"</td>\n",
              "      <td>\"W\"</td>\n",
              "      <td>\"Springfield\"</td>\n",
              "      <td>CA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>128</td>\n",
              "      <td>128</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        \"NS\"  \"EW\"          \"City\"  \"State\"\n",
              "count    128   128             128      128\n",
              "unique     1     1             120       47\n",
              "top      \"N\"   \"W\"   \"Springfield\"       CA\n",
              "freq     128   128               4       12"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4NFE6GyKfNso",
        "outputId": "5bbd59d5-231d-4b28-af5f-306c9afdd035"
      },
      "source": [
        "#List nama kolom\n",
        "df.columns"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['LatD', ' \"LatM\"', ' \"LatS\"', ' \"NS\"', ' \"LonD\"', ' \"LonM\"', ' \"LonS\"',\n",
              "       ' \"EW\"', ' \"City\"', ' \"State\"'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OIiQpd-nfRYY"
      },
      "source": [
        "### Visualisasi Data dengan Matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "p1Gr7O2yfTWV",
        "outputId": "cb103619-6dc1-40cc-c5ce-b9b77f96114d"
      },
      "source": [
        "#Line Chart\n",
        "df[[' \"LonD\"', ' \"LonM\"']].plot(kind='line', \n",
        "             figsize=(10, 5))\n",
        "\n",
        "plt.title('Perbandingan LonD dan LonM ', fontsize = 20)\n",
        "plt.ylabel('banyak', fontsize = 10)\n",
        "plt.xlabel('data', fontsize = 10)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "id": "NTGQFF7rfY0Z",
        "outputId": "5a2c61b2-f98b-4483-cefe-8916402d293f"
      },
      "source": [
        "\n",
        "#ScatterPlot\n",
        "df[[' \"LonD\"', ' \"LonM\"']].plot(kind='scatter', x=' \"LonD\"', y=' \"LonM\"', \n",
        "                                                 figsize=(10, 6), color='darkblue')\n",
        "\n",
        "plt.title('Hubungan LonD dan LonM', fontsize = 20)\n",
        "plt.ylabel('LonD', fontsize = 10)\n",
        "plt.xlabel('LonM', fontsize = 10)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "QH-OhgEMfbK0",
        "outputId": "8a8e9632-8cf7-41c1-93b2-bf6c5a3b955c"
      },
      "source": [
        "#Histogram\n",
        "df['LatD'].plot(kind='hist', figsize=(7, 5))\n",
        "\n",
        "plt.title('Histogram dari Latitude D')\n",
        "plt.ylabel('frequensi')\n",
        "plt.xlabel('banyak')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "rVa02wTufes-",
        "outputId": "3e6148cc-2a89-44cb-f1d1-b705ea030839"
      },
      "source": [
        "rata2_n = df[df[' \"NS\"']=='N']['LatD'].mean()\n",
        "rata2_s = df[df[' \"NS\"']=='S']['LatD'].mean()\n",
        "\n",
        "fig = plt.figure(figsize =(7, 5))\n",
        "plt.title('Bar Chart dari rata-rata Latitude D bagian Utara dan Selatan')\n",
        "data = [rata2_n, rata2_s]\n",
        "bars = ('Utara', 'Selatan')\n",
        "kelompok = np.arange(len(bars))\n",
        "\n",
        "plt.bar(kelompok, data)\n",
        "plt.xticks(kelompok, bars)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "5drLKN8SfiQs",
        "outputId": "3b88e28f-703d-466e-c9a2-927c47fa589b"
      },
      "source": [
        "# Pie chart\n",
        "labels = 'Springfield', 'Yokohama'\n",
        "jumlah_anggota = np.array([len(df[df[' \"City\"'] == 'Springfield']), len(df[df[' \"City\"'] == 'Yokohama'])])\n",
        "colors = ['purple', 'orange']\n",
        "fig = plt.figure(figsize =(7, 5))\n",
        "plt.pie(jumlah_anggota, labels = labels, colors=colors)\n",
        "plt.show()"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FvaSNPi1flNR"
      },
      "source": [
        "### Menggunakan Seaborn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rYub1Uq3fnDh"
      },
      "source": [
        "import seaborn as sns"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "B1eUNz2gfq3z",
        "outputId": "2de274c9-5637-4bbf-9e19-6801a095f9f5"
      },
      "source": [
        "#LineChart\n",
        "sns.lineplot(data=df[[' \"LonD\"', ' \"LonM\"']])"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f936fddbd50>"
            ]
          },
          "metadata": {},
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "duGqiTQQftJB",
        "outputId": "8cb37701-6a2f-469e-bd40-a7b6766e5fd5"
      },
      "source": [
        "#ScatterPlot\n",
        "sns.relplot(x=' \"LonD\"', y=' \"LonM\"', data = df)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f936fcf50d0>"
            ]
          },
          "metadata": {},
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "q_ByiFYNfwp5",
        "outputId": "1e364a7d-e90f-44e2-a852-3b825ec658df"
      },
      "source": [
        "#Histogram\n",
        "sns.displot(data=df, x=' \"LonD\"',hue=' \"City\"', kde=True)"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f936fc7c9d0>"
            ]
          },
          "metadata": {},
          "execution_count": 78
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 499.875x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "7dW06dKKfz9r",
        "outputId": "2303e59b-52d4-4010-f2e5-80401e5a9ea7"
      },
      "source": [
        "#boxplot\n",
        "sns.boxplot(data=df, x=' \"LonD\"')"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f936f4972d0>"
            ]
          },
          "metadata": {},
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "zbEoE_gQf4Il",
        "outputId": "e068b9b2-98c0-4106-edb1-fbc06a9fa1fb"
      },
      "source": [
        "#Catplot\n",
        "sns.catplot(data=df, x=' \"City\"', y=' \"LonD\"')"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f936f404a10>"
            ]
          },
          "metadata": {},
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 396
        },
        "id": "cJM6ep_8f6Yi",
        "outputId": "c8f3c69f-611c-4812-b85c-6e52425a9a8b"
      },
      "source": [
        "#HeatmapKorelasi\n",
        "corrmat = df.corr()\n",
        "f, ax = plt.subplots(figsize=(9, 6))\n",
        "sns.heatmap(corrmat, vmax=1)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f936ed98d10>"
            ]
          },
          "metadata": {},
          "execution_count": 81
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}