VizPack.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/invest41/Exploring_Libraries/blob/main/VizPack.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Note: you may need to restart the kernel to use updated packages.\n"
          ]
        }
      ],
      "source": [
        "!pip install VizPack -q"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "#Access Data Analysis Libraries\n",
        "\n",
        "# NumPy \n",
        "from VizPack.vizpack import np\n",
        "\n",
        "# Pandas\n",
        "from VizPack.vizpack import pd\n",
        "\n",
        "# Seaborn\n",
        "from VizPack.vizpack import sns\n",
        "\n",
        "# Matplotlib.pyplot\n",
        "from VizPack.vizpack import plt\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "from VizPack.vizpack import Viz_Func"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0V3ncA-52En2",
        "outputId": "a9d9d104-9a51-425e-f5d9-8186f8a5b6d1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            " ========== Welcome to the Visualization Tool kit ========== \n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "viz = Viz_Func()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VzQjPH1675kr",
        "outputId": "917a4fb7-caf3-4655-a59d-4ebd98d15093"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Help on Viz_Func in module VizPack.vizpack object:\n",
            "\n",
            "class Viz_Func(builtins.object)\n",
            " |  Methods defined here:\n",
            " |  \n",
            " |  __init__(self)\n",
            " |      Contains Methods for plotting visualizations from Pandas DataFrame\n",
            " |      - label_plot : Labelling a plot\n",
            " |      - plot_bar : Plotting a Bar Chart\n",
            " |      - plot_donut : Making a Donut Plot\n",
            " |      - annot_bar : Annotating a Barplot\n",
            " |  \n",
            " |  annot_bar(self, plots, horizontal=False)\n",
            " |      Parameters:\n",
            " |        plots : Barplot plots\n",
            " |                i.e. plots = sns.barplot(x, y)\n",
            " |  \n",
            " |  label_plot(self, title='Distribution Plot', x='X-axis', y='Y-axis')\n",
            " |      Parameters:\n",
            " |        title : str\n",
            " |                Default - 'Distribution Plot'\n",
            " |        \n",
            " |        x : X-axis label (str)\n",
            " |                Default - 'X-axis'\n",
            " |                \n",
            " |        y : Y-axis label (str)\n",
            " |                Default - 'Y-axis'\n",
            " |  \n",
            " |  plot_bar(self, dataset, limit=None, column=None, horizontal=False, figsize=(20, 10), color='darkcyan')\n",
            " |      Parameters:\n",
            " |        dataset : Pandas DataFrame, Pandas Series\n",
            " |      \n",
            " |        limit : Limit of Variables to Plot\n",
            " |                Useful in large datasets (None, int)\n",
            " |                Default - None\n",
            " |        \n",
            " |        column  : Dataset Column (None, str)\n",
            " |                  Default - None\n",
            " |        \n",
            " |        horizontal  : Should it be an Horizontal Plot (boolean)\n",
            " |                  Default - False\n",
            " |        \n",
            " |        figsize  : Figure Dimension (Tuple)\n",
            " |                  Default - (20,10)\n",
            " |        \n",
            " |        color  : Color of the bar (str)\n",
            " |                  Default - 'Darkcyan'\n",
            " |  \n",
            " |  plot_donut(self, dataset, limit=None, column=None, title='Donut Plot', figsize=(20, 10), shadow=False, colors=False, explode=False, background='white')\n",
            " |      Parameters:\n",
            " |        dataset : Pandas DataFrame, Pandas Series\n",
            " |        \n",
            " |        limit : Limit of Variables to Plot\n",
            " |                Useful in large datasets (None, int)\n",
            " |                Default - None\n",
            " |      \n",
            " |        column  : Dataset Column (None, str)\n",
            " |                  Default - None\n",
            " |        \n",
            " |        title : str\n",
            " |                Default - 'Donut Plot'\n",
            " |        \n",
            " |        figsize  : Figure Dimension (Tuple)\n",
            " |                  Default - (20,10)\n",
            " |        \n",
            " |        shadow  : Presence of Shadow (Boolean)\n",
            " |                  Default - False\n",
            " |        \n",
            " |        colors  : Colors of the bar (boolean, list)\n",
            " |                  Default - False\n",
            " |        \n",
            " |        explode  : Presence of Explosion (Boolean)\n",
            " |                  Default - False\n",
            " |        \n",
            " |        background  : Color of the background (str)\n",
            " |                  Default - 'white'\n",
            " |  \n",
            " |  ----------------------------------------------------------------------\n",
            " |  Data descriptors defined here:\n",
            " |  \n",
            " |  __dict__\n",
            " |      dictionary for instance variables (if defined)\n",
            " |  \n",
            " |  __weakref__\n",
            " |      list of weak references to the object (if defined)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "help(viz)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_A8qHxMK77tx",
        "outputId": "7a5ef6da-8573-48a9-b9fd-6454482a1a9f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "=========== Custom Methods ===========\n",
            "-------------- annot_bar --------------\n",
            "-------------- label_plot --------------\n",
            "-------------- plot_bar --------------\n",
            "-------------- plot_donut --------------\n"
          ]
        }
      ],
      "source": [
        "# Printing out the custom methods\n",
        "\n",
        "print('=========== Custom Methods ===========')\n",
        "spacer = '--------------'\n",
        "\n",
        "for i in dir(viz):\n",
        "   if not i.startswith('_'): print(spacer, i, spacer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [],
      "source": [
        "AlBet = ['Alpha', 'Beta', 'Gamma', 'Delta', 'Epsilon', 'Zeta', 'Eta', 'Theta', 'Iota', 'Kappa', 'Lamda', 'Mu', 'Nu', 'Xi', 'Omicron', 'Pi', 'Rho', 'Sigma', 'Tau', 'Upsilon', 'Phi', 'Chi', 'Psi', 'Omega']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [],
      "source": [
        "#Generating random data points\n",
        "import random\n",
        "random.seed(42)\n",
        "np.random.seed(42)\n",
        "\n",
        "\n",
        "A = np.random.randint(20, 500, 50_000)\n",
        "B = np.random.randint(2, 50, 50_000)\n",
        "C = random.sample( AlBet * 50_000, 50_000)\n",
        "\n",
        "indices = [f'numeral_{i}' for i in range(50_000)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Omicron</th>\n",
              "      <th>Gamma</th>\n",
              "      <th>Theta</th>\n",
              "      <th>Iota</th>\n",
              "      <th>Lamda</th>\n",
              "      <th>Sigma</th>\n",
              "      <th>Alpha</th>\n",
              "      <th>Mu</th>\n",
              "      <th>Rho</th>\n",
              "      <th>Zeta</th>\n",
              "      <th>...</th>\n",
              "      <th>Delta</th>\n",
              "      <th>Xi</th>\n",
              "      <th>Phi</th>\n",
              "      <th>Psi</th>\n",
              "      <th>Epsilon</th>\n",
              "      <th>Omega</th>\n",
              "      <th>Nu</th>\n",
              "      <th>Tau</th>\n",
              "      <th>Eta</th>\n",
              "      <th>Beta</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Count</th>\n",
              "      <td>2207</td>\n",
              "      <td>2192</td>\n",
              "      <td>2148</td>\n",
              "      <td>2145</td>\n",
              "      <td>2127</td>\n",
              "      <td>2119</td>\n",
              "      <td>2112</td>\n",
              "      <td>2106</td>\n",
              "      <td>2101</td>\n",
              "      <td>2093</td>\n",
              "      <td>...</td>\n",
              "      <td>2074</td>\n",
              "      <td>2065</td>\n",
              "      <td>2062</td>\n",
              "      <td>2056</td>\n",
              "      <td>2035</td>\n",
              "      <td>2020</td>\n",
              "      <td>2019</td>\n",
              "      <td>2006</td>\n",
              "      <td>1995</td>\n",
              "      <td>1986</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 24 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Omicron  Gamma  Theta  Iota  Lamda  Sigma  Alpha    Mu   Rho  Zeta  \\\n",
              "Count     2207   2192   2148  2145   2127   2119   2112  2106  2101  2093   \n",
              "\n",
              "       ...  Delta    Xi   Phi   Psi  Epsilon  Omega    Nu   Tau   Eta  Beta  \n",
              "Count  ...   2074  2065  2062  2056     2035   2020  2019  2006  1995  1986  \n",
              "\n",
              "[1 rows x 24 columns]"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Alphabet Count\n",
        "pd.DataFrame(pd.Series(C).value_counts(), columns = ['Count']).T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "pX3SmKJg2GQS",
        "outputId": "32a7cbc1-36f9-4557-a2a3-5fd024fd3d05"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>numeral_0</th>\n",
              "      <td>122</td>\n",
              "      <td>4</td>\n",
              "      <td>Eta</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>numeral_1</th>\n",
              "      <td>455</td>\n",
              "      <td>39</td>\n",
              "      <td>Mu</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>numeral_2</th>\n",
              "      <td>368</td>\n",
              "      <td>36</td>\n",
              "      <td>Lamda</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>numeral_3</th>\n",
              "      <td>290</td>\n",
              "      <td>10</td>\n",
              "      <td>Omega</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>numeral_4</th>\n",
              "      <td>126</td>\n",
              "      <td>44</td>\n",
              "      <td>Lamda</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             A   B      C\n",
              "numeral_0  122   4    Eta\n",
              "numeral_1  455  39     Mu\n",
              "numeral_2  368  36  Lamda\n",
              "numeral_3  290  10  Omega\n",
              "numeral_4  126  44  Lamda"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Creating a dataframe\n",
        "\n",
        "df = pd.DataFrame({\n",
        "    'A': A,\n",
        "    'B': B,\n",
        "    'C': C\n",
        "}, index = indices)\n",
        "\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Help on class Viz_Func in module VizPack.vizpack:\n",
            "\n",
            "class Viz_Func(builtins.object)\n",
            " |  Methods defined here:\n",
            " |  \n",
            " |  __init__(self)\n",
            " |      Contains Methods for plotting visualizations from Pandas DataFrame\n",
            " |      - label_plot : Labelling a plot\n",
            " |      - plot_bar : Plotting a Bar Chart\n",
            " |      - plot_donut : Making a Donut Plot\n",
            " |      - annot_bar : Annotating a Barplot\n",
            " |  \n",
            " |  annot_bar(self, plots, horizontal=False)\n",
            " |      Parameters:\n",
            " |        plots : Barplot plots\n",
            " |                i.e. plots = sns.barplot(x, y)\n",
            " |  \n",
            " |  label_plot(self, title='Distribution Plot', x='X-axis', y='Y-axis')\n",
            " |      Parameters:\n",
            " |        title : str\n",
            " |                Default - 'Distribution Plot'\n",
            " |        \n",
            " |        x : X-axis label (str)\n",
            " |                Default - 'X-axis'\n",
            " |                \n",
            " |        y : Y-axis label (str)\n",
            " |                Default - 'Y-axis'\n",
            " |  \n",
            " |  plot_bar(self, dataset, limit=None, column=None, horizontal=False, figsize=(20, 10), color='darkcyan')\n",
            " |      Parameters:\n",
            " |        dataset : Pandas DataFrame, Pandas Series\n",
            " |      \n",
            " |        limit : Limit of Variables to Plot\n",
            " |                Useful in large datasets (None, int)\n",
            " |                Default - None\n",
            " |        \n",
            " |        column  : Dataset Column (None, str)\n",
            " |                  Default - None\n",
            " |        \n",
            " |        horizontal  : Should it be an Horizontal Plot (boolean)\n",
            " |                  Default - False\n",
            " |        \n",
            " |        figsize  : Figure Dimension (Tuple)\n",
            " |                  Default - (20,10)\n",
            " |        \n",
            " |        color  : Color of the bar (str)\n",
            " |                  Default - 'Darkcyan'\n",
            " |  \n",
            " |  plot_donut(self, dataset, limit=None, column=None, title='Donut Plot', figsize=(20, 10), shadow=False, colors=False, explode=False, background='white')\n",
            " |      Parameters:\n",
            " |        dataset : Pandas DataFrame, Pandas Series\n",
            " |        \n",
            " |        limit : Limit of Variables to Plot\n",
            " |                Useful in large datasets (None, int)\n",
            " |                Default - None\n",
            " |      \n",
            " |        column  : Dataset Column (None, str)\n",
            " |                  Default - None\n",
            " |        \n",
            " |        title : str\n",
            " |                Default - 'Donut Plot'\n",
            " |        \n",
            " |        figsize  : Figure Dimension (Tuple)\n",
            " |                  Default - (20,10)\n",
            " |        \n",
            " |        shadow  : Presence of Shadow (Boolean)\n",
            " |                  Default - False\n",
            " |        \n",
            " |        colors  : Colors of the bar (boolean, list)\n",
            " |                  Default - False\n",
            " |        \n",
            " |        explode  : Presence of Explosion (Boolean)\n",
            " |                  Default - False\n",
            " |        \n",
            " |        background  : Color of the background (str)\n",
            " |                  Default - 'white'\n",
            " |  \n",
            " |  ----------------------------------------------------------------------\n",
            " |  Data descriptors defined here:\n",
            " |  \n",
            " |  __dict__\n",
            " |      dictionary for instance variables (if defined)\n",
            " |  \n",
            " |  __weakref__\n",
            " |      list of weak references to the object (if defined)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "help(Viz_Func)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### **Creating a Barplot using VizFunc methods**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "id": "VuDeaMqx8aB5",
        "outputId": "5b5354f1-8fb0-43a1-d961-f0ff2b441e31"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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vvPGGGjdurHfeeUd37txxZpkAAAAAABfltJC7b98+dezY8ZF7+P3xxx/66quvkm375JNP1KlTJ4WFhals2bKaNWuWs8oEAAAAALgwp4XcJUuWaMyYMfLx8bFti4uL0+jRo9W/f3/btvv372vnzp22m84HBQUpLCzMWWUCAAAAAFyYu7NeaMKECY9smz59utq0aaOCBQvatl2/fl1Zs2aVu/uD0ry9vVN1/8O/un79jhITXefuSHnyZNXVq9FGl2Fq9Njx6LFz0GfHo8eOR4+dgz47Hj12PHrseK7WYzc3i3LlevL9z50Wcv9q69atunjxooYNG6bt27fbtj/utr0WiyXN50/pTadXefJkNboE06PHjkePnYM+Ox49djx67Bz02fHosePRY8czU48NC7lr1qzR8ePH1bJlS8XExOjKlSv64IMPNHXqVEVHRyshIUEZMmRQVFRUsinOqXX1arRLjeR6e2dTVNRto8swNXrsePTYOeiz49Fjx6PHzkGfHY8eOx49djxX67GbmyXFUG5YyJ04caLt6+3bt2vmzJn67LPPJElVqlRRaGioAgMDFRISIn9/f4OqBAAAAAC4knR5n9wxY8ZoyZIlatq0qXbt2qUPPvjA6JIAAAAAAC7A6SO5GzdufGRbtWrVVK1aNdvjAgUKaP78+c4sy+GmTv1UCQkJGjp0lKQH1x4vWPCDVq1arqtXr6pEiRfVtetbqlmz1mOfP336ZG3btkXBwaudWTYAAAAAuJR0OZJrJlarVd9+O1srVy5Ptn3RooVauPB7vf/+AM2fv1gNGzbUsGEf6ejRI4+cY/v2bVqxYqmzSgYAAAAAl0XIdaDz58/p/ff7KCQkWL6+eZPti429p759P9Brr9VWgQIF1adPH2XO7KW9eyOSHXfr1k1NnDhWFSpUcmbpAAAAAOCSCLkOdPDgfvn4+OqHHxYpX778yfZ169ZLzZu3kiTFxsZq4cKFio29p4oVKyc7burUiapVK0BVqrzirLJd1tSpn2rSpHG2x1arVfPnf6927Vqobt1XFRQUpPDwLcmes2XLJnXv3kl1676qdu1aauHCHx57Gyv8T0p9LleunHr06EyfnxGfZQAAgKdHyHWgRo2aatSoscqT54UnHrNp00bVr19LY8eOVdeuPVWyZCnbvnXrQnXs2BG9915/Z5Trsp52SvihQwc1YsRgNWjQWPPnL1bfvv31/fffKTh4sRFvI91LTZ/Xrl2r2rXr0uenxGcZAADg2RFyDVa6dFnNmbNQw4YN09y539h+uY2MvKTPP5+u4cM/VubMmQ2uMv16linhUVGRatv2dXXq1EUFChRUQEBdVanyiiIidhjxVtK11Pa5UKFC6tKlB31+CnyWAQAA7IOQazBvbx+VKFFS3bp1U2BgK/300wJZrVZ9+uknatashcqXr2B0ienas0wJr127nvr1+1CSlJiYqIiIndq7d7eqVq3u1PfgCtLS5+XLl9Lnp8BnGQAAwD6cfgshPBAevkWFCxdRwYKFbNuKFXtRYWFrFRl5SRERO3Xw4H6FhARLkuLj4xUfH68GDV7TtGn/UvnyFY0qPV1p1KipGjVqmuIxmzZt1MiRQ2S1WtWrV59kU8Il6fr162rVqrESEhJUrVpNtWwZ5MiSXRJ9djx6DAAAYB+M5Bpk1qzPtWTJj8m2HT58SEWLFtMLL3hr0aIV+uGHRZo790fNnfujgoLa6YUXvDV37o8qVeolg6p2TU+aEp4kU6ZM+vrrHzR+/GT98ccxTZjwsTGFurjSpcsqJCRE/fp9SJ8dhM8yAADA3yPkGqRDhze0atUKhYau1rlzZ/Xtt99q/fqf1bNnb7m7u6tgwULJ/mXLll0ZMmRQwYKFlDFjJqPLdymPmxL+sMyZM8vPr5Rq166n998foP/8J0xRUZcNqtZ1eXv7qFSpUurQ4Q367CB8lu3naVawTnLr1k21bNlY+/btdVK1rokeOwd9BoBHEXINEhjYSh98MFDz589Vly4dtGrVKk2YMEU1atQyujTTCA/fonPnzibbVqzYi7py5cEv/UeO/K79+/c+sl+SrlyJckqNZkCfHY8e28/TrmCd5OrVKxowoJ+uXr3izLJdCj12DvoMAE9GyHWSmTO/1tCho5Jta9WqrX76abk2bgzXqlWrVKtWwBOf361bLwUHr3Z0maaS0pRwSVqzZpWmTZuY7F6ihw8f+v8j6YWdWqsro8+OR4/t41lWsJakDRvWqVu3TkpMTHR26S6DHjsHfQaAlBFyYVopTQmXpKCgtjp37qxmzpyhs2fP6L///UWzZn2uDh3eULZs2Qyu3nU83OfTp0/rxx/n0Wc747NsH8+ygrUk/frrf9WtW0+NGzfJeUW7GHrsHPQZAFLG6sowrcDAVkpIiNf8+XMVGXlJ//jHP5JNCS9W7EVNn/6FZs+eqZCQZcqZM5c6dOiszp27Gly5a3m4z9OmTVShQkXos53xWbaPZ13B+pNPJkqSLl684NA6XRk9dg76DAApI+TCNGbO/PqRba1atVWrVm0lSd7e2RQVdTvZ/ooVK+urr+Y6pT6zSKnPj+uxRJ/Tis+ycZJWsD527ICmTJmiXLlycysmO6PHzkGfATzPCLlplCVHJnl5ejjk3N7e9p9WGBN3X3du3rP7eQHAjLy9feTt7aOaNSvr8OFj+umnBQQDO6PHzkGfATzPCLlp5OXpIcu0aUaXkWrWgQN1R4RcAEhJePgWFS5cRAULFrJtK1bsRYWFrTWwKnOhx85BnwGAhacAAPjbFazx7Oixc9BnAGAkF+kQU8Idz9V6LLlenx3ZY4nPsr116PCGpk+fpFKlSqtcuQpauTJc69f/rEmTphtdmmnQY+egz/Y3deqnSkhISHYryGXLFmvZsiW6fDlSBQoUUNu2HRUY2Mq2//jxo/rXv/6pI0cOK1u2bKpfv6F69XpHnp6eBryD9I8ew94IuUh3mBLueK7WY8n1+kyPXcvfrWCNZ0ePnYM+24/VatV3332llSuXq3nzlrbtK1YEa/bsmRo4cJjKli2n48cP6uOPP5aHh4caN26mW7du6cMP+6p27boaMmSkLlw4r/Hjx8hqld57r7+B7yj9ocdwFEIuAOC58zQrWCfJly+/tmzZ5dD6zIAeOwd9dozz589p0qRxOnnyhHx98ybbFxKyTK1bt7PdxqlChZe0bdsOhYauVuPGzXTgwD7duHFd7777vry8sqhgwUJq3Liptm/fRgB7CD2GI3FNLgAAAPCQgwf3y8fHVz/8sEj58uVPtu+DDwaqVas2yba5ubnp9u1bkqScOXNJkpYvX6r4+HhdunRJ4eFb5ef3knOKdxH0GI7ESC4AAADwkEaNmtpGEf+qYsXKyR5fuHBBGzasU5s2HSRJZcqUVZcuPfTtt7P1zTf/VkJCgsqXr6iPPhri8LpdCT12rrRe9zxhwsf6+ec1jz3XiBEfq0mT5s4o+6kxkgsAAAA8hevXr+vtt99W7tx51LlzN0lSbOw9nT9/Vo0aNdVXX83VhAlTdfHiBU2dOtHYYl0UPX42VqtV3347WytXLk+2Pem6565de+qHHxape/fumj59ku12Y/37D9TKlWHJ/tWsWUtFihRVQEAdI95KmjCSCwBwWa62UrgrrmDtaj2WXK/PrMbums6fP6eBA9/X/ftx+vzz2cqaNask6aefFujEiROaN2+R3NzcVKpUaWXNmlX9+7+jjh3fVPHiLxpcueugx8/mWa57zpo1q63fkvTLL+u1ffs2zZmzUF5eWZz6Pp4GIRcA4LJcbRVrV1zB2tV6LLlen+mx6zl69IgGDnxf2bNn16JFi+Tu/r8wcOjQAZUs6Sc3t/9NmCxduqwk6dy5swSwVKLHzy7puuePP56gMWOGJ9v3wQcDHwm+D1/3/LDY2Hv68svP1aFDJ5fpLdOVAQAAgFQ6ffqUPvzwPeXNm0+zZn2rfPnyJdvv7e2jEyf+SLbtzz9PSJIKFSrktDpdGT22j0aNmmrUqLHKk+eFR/ZVrFhZ+fMXsD1Ouu65WrWajxy7cuUK3bkTrS5dejq0Xnsi5AIAAACpNH78aHl6emrUqLGKj49XVFSUrl69ohs3bkiSWrduq9OnT+qf/5yss2fPaPfuXZo48RPVrPmaihVzjVEwo9Fj53rcdc9JEhMTFRy8SK1bt0s2fTm9Y7oyAAAAkApnzpzW4cO/S5I6dUp+i5sCBQpq8eIQlSjhp88//7e+/nqWevZ8U9myZdNrr9VW797vGlGyy6HHzvWk656THDiwXxcunLetuuwqCLkAAADAE8yc+bXt68KFi2jLll3J9nt7Z1NU1O1k28qXr6gvv/zGKfWZAT02RkrXPSfZsmWTXnqpjAoUKGhAhU+P6coAAAAA8Bz5u+uek+zbt0eVK1d1cnXPjpALAAAAAM+Rv7vuWXpwj90//jjuktc5M10ZAAAAAJ4TqbnuWZJu376luLhYZc+e3dklPjNCLgAAAEwtS45M8vL0cNj5vb2z2f2cMXH3deema92L2JF9psfP5mmue86ePccjx7kKQi4AAABMzcvTQ5Zp04wuI02sAwfqjlwrgLlan12xx0gdrskFAAAAAJgGI7kAAAAA4AJcbUq4ZMy0cEIuAAAAALgAV5sSLhkzLZzpygAAAAAA0yDkAgAAAABMg5ALAAAAADANQi4AAAAAwDQIuQAAAAAA0yDkAgAAAABMg5ALAAAAADANQi4AAAAAwDQIuQAAAAAA0yDkAgAAAABMg5ALAAAAADANQi4AAAAAwDQIuQAAAAAA0yDkAgAAAABMg5ALAAAAADANQi4AAAAAwDScHnKjo6PVvHlznTt3TpK0ePFiNW/eXIGBgRo2bJji4uIkSYcPH1abNm3UqFEjjRgxQvHx8c4uFQAAAADgYpwacvft26eOHTvq1KlTkqSTJ0/qu+++06JFi7Rq1SolJibqxx9/lCQNGjRIo0aN0rp162S1WrVkyRJnlgoAAAAAcEFODblLlizRmDFj5OPjI0ny9PTUxx9/rKxZs8pisahkyZK6cOGCzp8/r3v37qlChQqSpKCgIIWFhTmzVAAAAACAC3J35otNmDAh2eMCBQqoQIECkqRr165p4cKFmjhxoi5fvixvb2/bcd7e3oqMjHRmqQAAAAAAF+TUkPskkZGR6tWrl9q0aaNq1app9+7djxxjsVjSdM48ebLaqzyX5+2dzegSTI8eOwd9djx67Hj02Dnos+PRY8ejx45Hj53D2X02POSeOHFCb731ljp37qwePXpIknx9fXXlyhXbMVFRUbYpzql19Wq0EhOtdq1Vcs3/CFFRt40uIU3oseO5Yo8l1+ozPXYOV+wzPXYOV+ozPXY8euwcrthneuwc9u6zm5slxUFNQ28hFB0drZ49e6p///62gCs9mMacMWNGRURESJJCQkLk7+9vVJkAAAAAABdh6EhucHCwrly5ojlz5mjOnDmSpLp166p///6aNm2aRo4cqTt37qh06dLq0qWLkaUCAAAAAFyAISF348aNkqRu3bqpW7dujz2mVKlSCg4OdmJVAAAAAABXZ+h0ZQAAAAAA7ImQCwAAAAAwDUIuAAAAAMA0CLkAAAAAANMg5AIAAAAATIOQCwAAAAAwDUIuAAAAAMA0CLkAAAAAANMg5AIAAAAATIOQCwAAAAAwDUIuAAAAAMA0CLkAAAAAANMg5AIAAAAATIOQCwAAAAAwDUIuAAAAAMA0CLkAAAAAANMg5AIAAAAATIOQCwAAAAAwDUIuAAAAAMA0CLkAAAAAANMg5AIAAAAATIOQCwAAAAAwDUIuAAAAAMA0CLkAAAAAANMg5AIAAAAATIOQCwAAAAAwDUIuAAAAAMA0CLkAAAAAANMg5AIAAAAATIOQCwAAAAAwDUIuAAAAAMA0CLkAAAAAANMg5AIAAAAATIOQCwAAAAAwDUIuAAAAAMA0CLkAAAAAANMg5AIAAAAATIOQCwAAAAAwDUIuAAAAAMA0CLkAAAAAANMg5AIAAAAATIOQCwAAAAAwDUIuAAAAAMA0CLkAAAAAANMg5AIAAAAATIOQCwAAAAAwDUIuAAAAAMA0CLkAAAAAANMg5AIAAAAATIOQCwAAAAAwDUIuAAAAAMA0CLkAAAAAANMg5AIAAAAATMPpITc6OlrNmzfXuXPnJEnh4eEKDAxUw4YNNWPGDNtxhw8fVps2bdSoUSONGDFC8fHxzi4VAAAAAOBinBpy9+3bp44dO+rUqVOSpHv37mn48OGaNWuWQkNDdfDgQW3atEmSNGjQII0aNUrr1q2T1WrVkiVLnFkqAAAAAMAFOTXkLlmyRGPGjJGPj48kaf/+/SpSpIgKFSokd3d3BQYGKiwsTOfPn9e9e/dUoUIFSVJQUJDCwsKcWSoAAAAAwAW5O/PFJkyYkOzx5cuX5e3tbXvs4+OjyMjIR7Z7e3srMjLSaXUCAAAAAFyTU0PuX1mt1ke2WSyWJ25Pizx5sj51XWbj7Z3N6BJMjx47B312PHrsePTYOeiz49Fjx6PHjkePncPZfTY05Pr6+urKlSu2x5cvX5aPj88j26OiomxTnFPr6tVoJSY+GpaflSv+R4iKum10CWlCjx3PFXssuVaf6bFzuGKf6bFzuFKf6bHj0WPncMU+02PnsHef3dwsKQ5qGnoLofLly+vkyZM6ffq0EhIStGbNGvn7+6tAgQLKmDGjIiIiJEkhISHy9/c3slQAAAAAgAswdCQ3Y8aMmjRpkvr166fY2FgFBASocePGkqRp06Zp5MiRunPnjkqXLq0uXboYWSoAAAAAwAUYEnI3btxo+7pGjRpatWrVI8eUKlVKwcHBziwLAAAAAODiDJ2uDAAAAACAPRFyAQAAAACmQcgFAAAAAJgGIRcAAAAAYBqEXAAAAACAaRByAQAAAACmQcgFAAAAAJgGIRcAAAAAYBqEXAAAAACAaRByAQAAAACmQcgFAAAAAJgGIRcAAAAAYBqEXAAAAACAaRByAQAAAACmQcgFAAAAAJgGIRcAAAAAYBqEXAAAAACAaRByAQAAAACmQcgFAAAAAJgGIRcAAAAAYBqEXAAAAACAaRByAQAAAACmQcgFAAAAAJgGIRcAAAAAYBqEXAAAAACAaRByAQAAAACmQcgFAAAAAJgGIRcAAAAAYBqEXAAAAACAaRByAQAAAACmQcgFAAAAAJgGIRcAAAAAYBqEXAAAAACAaRByAQAAAACmQcgFAAAAAJgGIRcAAAAAYBqEXAAAAACAaRByAQAAAACmQcgFAAAAAJgGIRcAAAAAYBqEXAAAAACAaRByAQAAAACmQcgFAAAAAJgGIRcAAAAAYBqEXAAAAACAaRByAQAAAACmQcgFAAAAAJgGIRcAAAAAYBqEXAAAAACAaRByAQAAAACmQcgFAAAAAJgGIRcAAAAAYBqEXAAAAACAaaSLkLty5Uo1a9ZMzZo10+TJkyVJhw8fVps2bdSoUSONGDFC8fHxBlcJAAAAAEjvDA+5d+/e1YQJEzR//nytXLlSu3btUnh4uAYNGqRRo0Zp3bp1slqtWrJkidGlAgAAAADSOcNDbkJCghITE3X37l3Fx8crPj5e7u7uunfvnipUqCBJCgoKUlhYmLGFAgAAAADSPXejC8iaNav69++vJk2aKFOmTHrllVfk4eEhb29v2zHe3t6KjIxM03nz5Mlq71Jdlrd3NqNLMD167Bz02fHosePRY+egz45Hjx2PHjsePXYOZ/fZ8JB75MgRLVu2TP/3f/+nbNmyaeDAgdq6desjx1ksljSd9+rVaCUmWu1Vpo0r/keIirptdAlpQo8dzxV7LLlWn+mxc7hin+mxc7hSn+mx49Fj53DFPtNj57B3n93cLCkOaho+XXnLli2qUaOG8uTJI09PTwUFBWn79u26cuWK7ZioqCj5+PgYWCUAAAAAwBUYHnJLlSql8PBwxcTEyGq1auPGjXrllVeUMWNGRURESJJCQkLk7+9vcKUAAAAAgPTO8OnKtWrV0u+//66goCB5eHjo5ZdfVu/evdWgQQONHDlSd+7cUenSpdWlSxejSwUAAAAApHOGh1xJ6t27t3r37p1sW6lSpRQcHGxQRQAAAAAAV2T4dGUAAAAAAOyFkAsAAAAAMA1CLgAAAADANAi5AAAAAADTIOQCAAAAAEwj1SG3Xr16GjNmzCPbe/furaZNm9q1KAAAAAAAnkaKtxD65ZdfdPjwYUnS+fPntXPnTs2cOdO232q16tChQ4qOjnZslQAAAAAApEKKITdfvnzq16+frFarLBaLTp48qS+//DLZMVarVeXLl3dokQAAAAAApEaKIbd06dIaMmSIjh8/ruDgYOXPn181a9a07Xdzc1OuXLnUrl07hxcKAAAAAMDfSTHkSlLXrl0lSXnz5lWxYsW4/hYAAAAAkG79bchN0rdvX8XExOjQoUOKi4uT1WpNtr9SpUp2Lw4AAAAAgLRIdcj95ZdfNHTo0McuMmWxWPT777/btTAAAAAAANIq1SF3ypQpun37ttzd3ZUnTx5lyJDBkXUBAAAAAJBmqQ65Fy9eVL58+bRs2TLlzp3bkTUBAAAAAPBUUh1yy5cvr/j4eAIuAAAAACDdSnXI7d+/v959911NmTJFNWvWVObMmWWxWGz7WXgKAAAAAGC0VIfczp07y2KxaO7cuZo7d26yfSw8BQAAAABID1IdcvPnz+/IOgAAAAAAeGapDrkbN250ZB0AAAAAADyzVIfcyMjIFPf7+vo+czEAAAAAADyLVIfc2rVrP3Ef1+QCAAAAANKDVIdcq9X62O2ZM2dW3rx57VYQAAAAAABPK9Uh98iRI8kex8bG6sCBA3r//fc1aNAguxcGAAAAAEBauT3tEzNmzKgqVaooICBAn332mR1LAgAAAADg6Tz1wlOJiYm6ePGitm/frqtXr9q9MAAAAAAA0uqZF56yWq2qWLGiveoBAAAAAOCpPdPCU5kyZdLLL7+ssWPH2rUoAAAAAACexlMvPAUAAAAAQHqT6pCbZP/+/YqIiJDFYlGVKlVUtmxZR9QFAAAAAECapTrkJiQkaPDgwQoNDU22vWXLlpo4caIsFovdiwMAAAAAIC1SfQuhr776SmvXrpWXl5caNGigBg0ayMvLSytXrtTXX3/tyBoBAAAAAEiVVI/kLl++XDly5FBISIjy5csnSbpw4YJatWql4OBgvf322w4rEgAAAACA1Ej1SG5kZKRKly5tC7iSlD9/fpUpU0aXLl1ySHEAAAAAAKRFqkNuvnz59PvvvysyMtK27eLFizp06JDy58/vkOIAAAAAAEiLVE9XbtGihWbOnKnmzZurRo0akqRt27YpOjpaXbp0cViBAAAAAACkVqpDbp8+fXTs2DGtX79e69evt2339/dX7969HVIcAAAAAABpkeqQ6+7urn/961/av3+/du/erYSEBFWsWFGVKlVyZH0AAAAAAKTa316Tu2nTJjVs2FB//vmnJKlcuXLq1q2brl27phkzZujo0aMOLxIAAAAAgNRIMeTu3r1b7777rs6ePasdO3Yk27dhwwbt3LlTnTt31okTJxxaJAAAAAAAqZFiyJ09e7YSEhLUunVrNWzYMNm+WbNmqU6dOrp9+7ZmzZrl0CIBAAAAAEiNFEPunj17VLhwYU2YMEG5c+dOtq948eL617/+pXz58ikiIsKhRQIAAAAAkBophtzY2Fjlz59fFovlsfs9PDxUpEgRXbt2zSHFAQAAAACQFimG3EKFCunQoUNPDLHXrl3ToUOHlC9fPocUBwAAAABAWqQYcps0aaLbt2/rrbfe0rZt2xQdHS1Jun37trZu3aq33npL0dHRatKkiVOKBQAAAAAgJSneJ7dnz57auHGjDh06pB49ejyy32q1ys/PT71793ZYgQAAAAAApFaKI7mZM2fWggUL1LlzZ3l5eclqtdr+ZcyYUR06dNDChQvl5eXlrHoBAAAAAHiiFEdyJcnLy0sjR47U8OHDdfLkSd28eVNZsmRRsWLF5OHh4YwaAQAAAABIlb8NuUnc3NxUvHhxR9YCAAAAAMAzSXG6MgAAAAAAroSQCwAAAAAwDUIuAAAAAMA0CLkAAAAAANMg5AIAAAAATCNdhNyNGzcqKChIjRs31vjx4yVJ4eHhCgwMVMOGDTVjxgyDKwQAAAAAuALDQ+7Zs2c1ZswYzZo1S6tXr9bvv/+uTZs2afjw4Zo1a5ZCQ0N18OBBbdq0yehSAQAAAADpnOEh9z//+Y+aNm2qvHnzysPDQzNmzFDmzJlVpEgRFSpUSO7u7goMDFRYWJjRpQIAAAAA0jl3ows4ffq0PDw81LNnT0VFRalOnToqUaKEvL29bcf4+PgoMjIyTefNkyervUt1Wd7e2YwuwfTosXPQZ8ejx45Hj52DPjsePXY8eux49Ng5nN1nw0NuQkKCdu3apfnz58vLy0vvvvuuMmfO/MhxFoslTee9ejVaiYlWe5Vp44r/EaKibhtdQprQY8dzxR5LrtVneuwcrthneuwcrtRneux49Ng5XLHP9Ng57N1nNzdLioOahofcF154QTVq1FDu3LklSfXq1VNYWJgyZMhgO+by5cvy8fExqkQAAAAAgIsw/JrcOnXqaMuWLbp165YSEhL066+/qnHjxjp58qROnz6thIQErVmzRv7+/kaXCgAAAABI5wwfyS1fvrx69eqlTp066f79+3r11VfVsWNHFStWTP369VNsbKwCAgLUuHFjo0sFAAAAAKRzhodcSWrbtq3atm2bbFuNGjW0atUqgyoCAAAAALgiw6crAwAAAABgL4RcAAAAAIBpEHIBAAAAAKZByAUAAAAAmAYhFwAAAABgGoRcAAAAAIBpEHIBAAAAAKZByAUAAAAAmAYhFwAAAABgGoRcAAAAAIBpEHIBAAAAAKZByAUAAAAAmAYhFwAAAABgGoRcAAAAAIBpEHIBAAAAAKZByAUAAAAAmAYhFwAAAABgGoRcAAAAAIBpEHIBAAAAAKZByAUAAAAAmAYhFwAAAABgGoRcAAAAAIBpEHIBAAAAAKZByAUAAAAAmAYhFwAAAABgGoRcAAAAAIBpEHIBAAAAAKZByAUAAAAAmAYhFwAAAABgGoRcAAAAAIBpEHIBAAAAAKZByAUAAAAAmAYhFwAAAABgGoRcAAAAAIBpEHIBAAAAAKZByAUAAAAAmAYhFwAAAABgGoRcAAAAAIBpEHIBAAAAAKZByAUAAAAAmAYhFwAAAABgGoRcAAAAAIBpEHIBAAAAAKZByAUAAAAAmAYhFwAAAABgGoRcAAAAAIBpEHIBAAAAAKZByAUAAAAAmAYhFwAAAABgGoRcAAAAAIBpEHIBAAAAAKZByAUAAAAAmEa6CrmTJ0/W0KFDJUmHDx9WmzZt1KhRI40YMULx8fEGVwcAAAAASO/STcjdtm2bVqxYYXs8aNAgjRo1SuvWrZPVatWSJUsMrA4AAAAA4ArSRci9ceOGZsyYoT59+kiSzp8/r3v37qlChQqSpKCgIIWFhRlYIQAAAADAFaSLkDt69Gh9+OGHyp49uyTp8uXL8vb2tu339vZWZGSkUeUBAAAAAFyEu9EFLF26VPny5VONGjW0fPlySZLVan3kOIvFkqbz5smT1S71mYG3dzajSzA9euwc9Nnx6LHj0WPnoM+OR48djx47Hj12Dmf32fCQGxoaqqioKLVs2VI3b95UTEyMLBaLrly5YjsmKipKPj4+aTrv1avRSkx8NCw/K1f8jxAVddvoEtKEHjueK/ZYcq0+02PncMU+02PncKU+02PHo8fO4Yp9psfOYe8+u7lZUhzUNDzkzp071/b18uXLtWPHDk2cOFHNmzdXRESEKleurJCQEPn7+xtYJQAAAADAFRgecp9k2rRpGjlypO7cuaPSpUurS5cuRpcEAAAAAEjn0lXIDQoKUlBQkCSpVKlSCg4ONrgiAAAAAIArSRerKwMAAAAAYA+EXAAAAACAaRByAQAAAACmQcgFAAAAAJgGIRcAAAAAYBqEXAAAAACAaRByAQAAAACmQcgFAAAAAJgGIRcAAAAAYBqEXAAAAACAaRByAQAAAACmQcgFAAAAAJgGIRcAAAAAYBqEXAAAAACAaRByAQAAAACmQcgFAAAAAJgGIRcAAAAAYBqEXAAAAACAaRByAQAAAACmQcgFAAAAAJgGIRcAAAAAYBqEXAAAAACAaRByAQAAAACmQcgFAAAAAJgGIRcAAAAAYBqEXAAAAACAaRByAQAAAACmQcgFAAAAAJgGIRcAAAAAYBqEXAAAAACAaRByAQAAAACmQcgFAAAAAJgGIRcAAAAAYBqEXAAAAACAaRByAQAAAACmQcgFAAAAAJgGIRcAAAAAYBqEXAAAAACAaRByAQAAAACmQcgFAAAAAJgGIRcAAAAAYBqEXAAAAACAaRByAQAAAACmQcgFAAAAAJgGIRcAAAAAYBqEXAAAAACAaRByAQAAAACmQcgFAAAAAJgGIRcAAAAAYBqEXAAAAACAaRByAQAAAACmQcgFAAAAAJgGIRcAAAAAYBrpIuTOnDlTzZo1U7NmzTRlyhRJUnh4uAIDA9WwYUPNmDHD4AoBAAAAAK7A8JAbHh6uLVu2aMWKFQoJCdGhQ4e0Zs0aDR8+XLNmzVJoaKgOHjyoTZs2GV0qAAAAACCdMzzkent7a+jQofL09JSHh4eKFy+uU6dOqUiRIipUqJDc3d0VGBiosLAwo0sFAAAAAKRzhofcEiVKqEKFCpKkU6dOKTQ0VBaLRd7e3rZjfHx8FBkZaVCFAAAAAABX4W50AUmOHz+ut99+W0OGDJG7u7tOnjyZbL/FYknT+fLkyWrP8lyat3c2o0swPXrsHPTZ8eix49Fj56DPjkePHY8eOx49dg5n9zldhNyIiAi9//77Gj58uJo1a6YdO3boypUrtv2XL1+Wj49Pms559Wq0EhOt9i7VJf8jREXdNrqENKHHjueKPZZcq8/02Dlcsc/02Dlcqc/02PHosXO4Yp/psXPYu89ubpYUBzUNn6588eJFvffee5o2bZqaNWsmSSpfvrxOnjyp06dPKyEhQWvWrJG/v7/BlQIAAAAA0jvDR3K/++47xcbGatKkSbZtr7/+uiZNmqR+/fopNjZWAQEBaty4sYFVAgAAAABcgeEhd+TIkRo5cuRj961atcrJ1QAAAAAAXJnh05UBAAAAALAXQi4AAAAAwDQIuQAAAAAA0yDkAgAAAABMg5ALAAAAADANQi4AAAAAwDQIuQAAAAAA0yDkAgAAAABMg5ALAAAAADANQi4AAAAAwDQIuQAAAAAA0yDkAgAAAABMg5ALAAAAADANQi4AAAAAwDQIuQAAAAAA0yDkAgAAAABMg5ALAAAAADANQi4AAAAAwDQIuQAAAAAA0yDkAgAAAABMg5ALAAAAADANQi4AAAAAwDQIuQAAAAAA0yDkAgAAAABMg5ALAAAAADANQi4AAAAAwDQIuQAAAAAA0yDkAgAAAABMg5ALAAAAADANQi4AAAAAwDQIuQAAAAAA0yDkAgAAAABMg5ALAAAAADANQi4AAAAAwDQIuQAAAAAA0yDkAgAAAABMg5ALAAAAADANQi4AAAAAwDQIuQAAAAAA0yDkAgAAAABMg5ALAAAAADANQi4AAAAAwDQIuQAAAAAA0yDkAgAAAABMg5ALAAAAADANQi4AAAAAwDQIuQAAAAAA0yDkAgAAAABMg5ALAAAAADANQi4AAAAAwDQIuQAAAAAA0yDkAgAAAABMg5ALAAAAADCNdB1yV69eraZNm6pBgwZauHCh0eUAAAAAANI5d6MLeJLIyEjNmDFDy5cvl6enp15//XVVq1ZNL774otGlAQAAAADSqXQ7khseHq7q1asrZ86c8vLyUqNGjRQWFmZ0WQAAAACAdCzdjuRevnxZ3t7etsc+Pj7av39/qp/v5mZxRFmSpCLZszvs3I7gyF44Cj12PFfrseR6fabHzuFqfabHzuFqfabHjkePncPV+kyPncPeff6781msVqvVrq9oJ7Nnz9bdu3f14YcfSpKWLl2qAwcOaOzYsQZXBgAAAABIr9LtdGVfX19duXLF9vjy5cvy8fExsCIAAAAAQHqXbkNuzZo1tW3bNl27dk13797V+vXr5e/vb3RZAAAAAIB0LN1ek+vr66sPP/xQXbp00f3799W2bVuVK1fO6LIAAAAAAOlYur0mFwAAAACAtEq305UBAAAAAEgrQi4AAAAAwDQIuQAAAAAA0yDkAgAAAABMg5ALAAAAADANQi4AAAAAwDQIuQAAAADwnLh27ZrRJTgcIdfJ7t69q2nTpql+/fp6+eWXVb58eTVo0EDjxo3T7du3jS4PSLX4+Hj98MMPmjRpknbt2pVs3xdffGFQVeayYcMG29dLly5Vv3799OGHHyo0NNTAqoC0u337tiZMmKB+/fpp5cqVyfaNGjXKoKrMJT4+XkuXLtW6desUHR2tjz76SIGBgfr4448VHR1tdHmmcP78eQ0YMEBnzpzRxYsX9eabb6pixYrq3Lmzzpw5Y3R5psDn2Dn8/f319ddfy2q1Gl2KwxBynWzgwIHy8vLSggULtHfvXu3evVvz58+Xt7e3BgwYYHR5pvHjjz9KkuLi4vTZZ58pKChI7du319dff634+HiDqzOH0aNH6/Dhw/Lx8dHgwYM1e/Zs276NGzcaWJl5fPnll5Ie/NFgzZo1atmypZo2barly5drxowZBldnHvy8cLxhw4YpW7Zsat68uebNm5cs2B48eNDAysxj1KhR2rZtm3788Ue98cYbKliwoGbMmKE8efJozJgxRpdnCoMGDVK1atWUN29ejR07Vi1atNC2bdvUqVMnDRkyxOjyTIHPsXMULFhQJ06cUOvWrbVp0yajy3EMK5yqSZMmT9zXrFkzJ1Zibq1atbJarVbr2LFjrQMHDrT+/vvv1kOHDlmHDx9uHT58uMHVmUNgYKDt66tXr1oDAwOtc+fOtVqtVmvLli2NKcpkkj7HgYGB1nv37tm2x8XFWRs2bGhUWabDzwvHe/hnwt27d61vvvmmdeLEiY/sw9Nr3ry51Wq1Wu/cuWN95ZVXku2jx/aR9LPCarVaW7RokWxfUv/xbPgcO0fSZzk8PNzasWNHa5MmTaxffPGFNTw83Prnn38aXJ19uBsdsp83uXPn1s8//6xGjRrJze3BQLrValVoaKhy5cplcHXms3PnToWEhNh6PX78eDVp0sTgqszBarUqJiZGXl5eyp07t7755ht17NhRefLkkcViMbo8U4iJidGVK1eUP39+xcTEKGPGjJKke/fuyd2dH9/2xs8Lx4qKipK3t7cyZcqkL7/8Um+88YZmz57Nzws7sVgsunbtmnLnzq2pU6fatl+6dEmJiYkGVmYePj4+WrJkidq3b69q1app06ZNCggI0K+//qqcOXMaXZ4p8Dl2rho1aqhGjRr6448/tGHDBv3www86d+6c1qxZY3Rpz4zpyk42depUrVy5UlWrVlXt2rUVEBCgKlWqaPXq1Zo8ebLR5ZnGzZs3tW/fPhUuXFjnz5+3bb9w4YI8PDwMrMw8OnfurNatW2vbtm2SJF9fX3377bf65z//qRMnThhcnTlUqlRJ3bt3165duzR69GhJ0vr169WiRQt17tzZ4OrMg58Xjte3b18FBQXpl19+kSRly5ZN3377rdavX6+jR48aXJ059OvXT61bt1ZCQoL8/f0lSVu3blVQUJD69+9vcHXmMH78eP3888+qWbOmdu3apT59+qhq1aqaPHmyxo0bZ3R5psDn2DmS/pib5MUXX1SfPn00e/ZsUwRcSbJYrSa+4jgdi4+P1/Xr12W1WpU7d25GZezsyy+/1IEDB3Tw4EGVKVNGX331lZYtW6apU6dq3LhxatCggdElmsLJkyeVKVMm5cuXT0uXLtXRo0f18ssv6/r16+rWrZvR5ZlCUo/j4+P122+/ae3atXrllVf07rvvGl2aaTzp58W0adM0duxYfl7YSXR0tOLj45ONeCUmJmrjxo2qX7++cYWZyN27d5U5c2bb45s3b8pqtTLKaGfXr1/X2bNnFR8fL29vbxUqVMjokkzlr5/jW7duKTExkc8x0oSQ62SJiYmaN2+efvnlF0VFRcnDw0OFCxdWs2bN1LRpU6PLM6WkKbWXLl2Sp6encufObXRJpvD9999r/vz5SkxMVPXq1XXx4kU1aNBAGzduVMWKFdW3b1+jS3R5KfW4UqVKeu+994wu0XSSfl5cvHhRGTNm5OeFncTHxys4OFgNGjRQtmzZ9PXXX+vAgQMqW7asevfubZuKj6cXHx+vhQsX6uLFi6pXr56qVq1q2/fFF1+oX79+BlZnDomJiVqyZInCwsJ06dIlubm5ycfHRwEBAercuTMzP+wkPDxc2bJl00svvaQvvvhCR48eVZUqVdS9e3dlyJDB6PJMq3PnzlqwYIHRZdgNIdfJPv30U92/f1+1a9fWunXrVKpUKeXLl0/z5s1T9erV+aXVTviFyvECAwMVHBysK1euqHnz5vrtt9+UMWNGxcXFqW3btlq1apXRJbo8euwcD4eD+vXrq0qVKrZ9hAP7+OijjyRJI0eO1MyZMxUTE6PGjRvrv//9r27duqXp06cbXKHrGz58uBITE1WyZEktWLBA7du3V58+fSRJrVu31ooVKwyu0PWNGjVKiYmJat26tXx8fCRJly9f1sqVK223iMSzmTp1qnbv3q3o6Gj5+PgoT548atasmcLCwuTl5cUtx+ykXr16j2yLjIyUr6+vJNkuLXFlzJF1st9++832i+lrr72mN954Qz/99JMCAgLUokULQq6dJC3l36hRI02ePFkxMTHq1KmT/vvf/2r48OH8QmUHiYmJ8vT0VIECBdSjR49kfzhISEgwsDLzoMfOMXr0aFs4GDx4cLJwsHHjRkKuHRw7dkyrV6+W9GCBr5UrV8pisSggIIBZTHZy8OBB2+8XrVq1Urdu3ZQpUyZ169bN1PfCdKadO3cqLCws2bbChQurSpUqatasmUFVmcumTZu0evVq3bhxQw0aNNCOHTvk5uYmf39/tWrVyujyTGPUqFGaMmWK+vbtq/Lly8tqtertt9/W119/bXRpdsPCU06WkJCgq1evSnqw0uS9e/ckSffv3+e6XDs6duyYpk+frly5cmnnzp369NNPFRAQoDFjxujw4cNGl2cKDRs2VOfOnZWQkGALAUeOHFGnTp1YkdZO6LFzHDx4UJMmTVKPHj0UHBys0NBQff/995JEOLATLy8vHT9+XNKDUHDx4kVJD0YOPD09jSzNNJJWvJdkW/F+3rx5Wr16NStY20nWrFm1f//+R7bv2bNHXl5eBlRkTnFxccqVK5eGDBliWyDpzp073LfcjmrXrq358+dr1apVWrFihfLnz2/7o3qBAgWMLs8uSFVO1rNnT7Vp00YVKlTQvn379NFHH+n06dPq2rUrowV2lPQLVYkSJWy/UOXPn59fqOyof//+2rlzZ7LrYzw9PdWvXz8FBAQYWJl50GPn4HZYjjd06FB1795dlSpVUpYsWdS+fXuVL19ehw4d0ieffGJ0eaaQtOL9xx9/rBo1athWvO/Zs6ftj+t4NuPGjdPgwYMVGxsrb29vSdKVK1fk6emZ7HY3eHqdOnVSixYtFBoaqnbt2kmSdu/erYEDB9pm2MA+8uTJo9mzZ2v+/Pnq2rWr7t69a3RJdsU1uQb45ptvFBcXp2bNmqlo0aKKi4vTnTt3tH79enXo0MHo8kxhz5496tevnypVqqTMmTNr69atyX6hIiAASLJ48WLNmTPHFg4k6cSJE+rVq5euXr362JEbpN2RI0d05MgRRUVFKTY2Vt98843GjRunFi1aGF2aabDivXNcuHBBly9fltVq1dKlS/Xpp58aXZKpfP/998qQIYMCAgJUuHBhXbp0Sbdv39bu3bv5PdlBjh8/rmXLlmno0KFGl2I3hFwnmzZtmg4ePKjixYvr559/1pAhQ9SyZUtJLAxhb9HR0QoPD9fp06eVkJCgF154QbVq1VLevHmNLg1AOnPq1Cl5enoqf/78tm3R0dEKDg4mHNgBK4U7HiveO96wYcMe2bZx40bVrVtXkjRx4kRnl2Q6/J7sHM/DZ5npyk62adMmhYSEKEOGDHrzzTfVo0cPeXp6qkmTJlz7ZUcXLlyQJJUtW1Zly5a1bU9MTNSFCxeS/SIL4Pl24cIF22UMST87kjRs2NCIkkxn2bJlCg0NfWSl8Hbt2qlt27aEXDv4ux4Tcp9dzpw5FRISoj59+ih79uySHiwo+sorrxhcmXls2rRJK1askLu7O78nO9Dz8Fkm5DrZw/9BixYtqq+++krdu3dX7ty5ufbLjt5++22dOnVKPj4+tp5bLBZZrVZZLBZTLI0OwD4e9/MiCT8v7IOVwh2PHjvekCFD5O/vr88++0wDBgxQtWrV9MMPP6h169ZGl2YaSb+nSfye7EjPw2eZ6cpONnPmTIWHh2vo0KEqV66cJCkiIkJ9+/ZVXFycIiIiDK7QHKKjo9WpUyeNGTNGlStXNrocAOkYPy8c7/PPP9eOHTs0b94820JqR44c0ciRI1W7dm1GGe2AHjvPjRs3NGbMGOXPn19btmyx3R4Lz47fk53LzJ9lQq4Btm3bJh8fHxUvXty27eLFi5ozZ45GjBhhYGXmsn//fi1dulTjxo0zuhQA6Rw/Lxxv586dqlq1qu3xn3/+qbNnz7IQoB3RY+daunSpfv75Z82ZM8foUkyF35Odz4yfZUIuAAAAAMA03IwuAAAAAAAAeyHkAgAAAABMg5ALADC1unXrys/PTzNmzEi2ffny5fLz89PQoUMNquzvbd++XX5+fmrQoIHTztmkSRP5+fnp888/f2TfV199JT8/P7Vq1epvXyepv9xnGADgbIRcAMBzYc6cOTp9+rTRZaRJrly5VK9ePdWsWdNpr9msWTNJUlhY2CP71q1bJ0lq3ry50+oBACCtCLkAgOdCXFycy62eXLJkSc2aNUuffPKJ014zKcD++eefOnr0qG37+fPndejQIVksFlsQBgAgPSLkAgCeC25ubvr111/1n//857H7z507Jz8/P5UuXfqJ25Iet2zZUiEhIapbt64qVKig4cOH6+rVqxo4cKAqVKggf39/LVq0KNn5Fy5cqPr166ts2bJq2rSpQkJCbPuSphD37NlTAwYMUKVKlTRy5MjHTi2+ceOGhg8frurVq6t8+fJq1aqV1q9fb9t/8+ZNDR48WK+++qrKli2r1157TePGjVNcXFyq+lS0aFGVLVtWUvLR3KS+Va5cWfny5dO5c+f03nvvqVq1aipbtqzq1q2rL774Qk+6acPj3svjtp07d07vvPOOKlasqKpVq2rAgAGKiopKVe0AAEiEXADAc6Jdu3aSpIkTJ+ru3bvPdK6TJ09q7NixKliwoO7fv69ly5apcePG2rNnj4oVK6bIyEh98sknOnnypCRpxYoVGjt2rG7cuKFq1arp6tWrGjJkiNauXZvsvFu3btWOHTv00ksvqUKFCo+8bmJionr16qVly5YpY8aMqlixoo4fP67+/ftr27ZtkqRhw4Zp5cqVypYtm6pXr67o6GgtWLBAP/30U6rfX9Jo7sMhN2mqctIo7nvvvacNGzYoX758qlq1qi5fvqyZM2fql19+SX0j/yIuLk49e/bUxo0b9Y9//ENFihTR2rVr1aNHD92/f/+pzwsAeL4QcgEAz4Xu3burWLFiOn/+vGbPnv1M54qNjdWMGTM0b948tWnTRpKUOXNmrVmzRsuWLVOhQoWUmJhom+47c+ZMSdJPP/2k7777TkuWLJEkff3118nOa7VaNXfuXC1cuFBt27Z95HW3bt2qAwcOyNfXVz///LO+//57jR49WrVq1dKVK1ckSY0bN1afPn20atUqffvtt+rbt6+kB9OPU6tp06Zyc3OzTVmOiorS3r175e7ursaNGysuLk6dO3dW//79tWLFCs2dO1ft27dP8+v8VWhoqE6dOqVGjRpp+fLlCg4OVmBgoI4dO6ZNmzY99XkBAM8Xd6MLAADAGTw8PDR69Gh169ZNc+bMUY8ePf72OU+aeivJthiUj4+PJKlChQrKnDmzJMnX11dnz55VbGys7ty5o3Pnzkl6dMGmo0ePJhtVzpEjh0qUKPHE1zxy5IgkqUqVKvLy8pIkdejQQR06dLAd07hxY23YsEHjx4/X/v37bc9J7XTlpPqrVKmiHTt26Oeff5aPj48SExNVq1Yt5c6dW9KDVZjXrVun4cOHa+/evbZwm5bX+Wt/k/4osG7dOvn5+SXbt3fvXtWvXz/V5wYAPL8IuQCA50aNGjXUrFkzrV27Vt9///1jj0lMTLR9/aTAliFDBnl4eEh6cK2vJFvofHibJMXHx9u+rlev3iPnio2NtX2dNWvWVLyL5OdM+trd3V0JCQl68803tXfvXgUFBal37946c+aMZsyYkWJgf5zAwEDt2LFDYWFhypcvn6T/hfTo6Gi1bt1aFy5cUMeOHTVw4ECFh4drwYIFf/s6CQkJtq8ffu+SbFOS//GPf6hYsWLJ9iXVAADA32G6MgDguTJkyBBlyZJF9+7dS7Y9aRTWarXq0qVLkqSDBw8+8+vlyJHDNtrbq1cvzZo1S4MHD1a+fPlUr1495cyZ03ZshgwZUjxXqVKlJEkRERGKjo6WJC1evFgVKlTQxx9/rN9//1179+5Vzpw5NX78eDVt2tQ2ipxWDRs2lIeHh06ePKlt27YpU6ZMtpHULVu26MyZMypevLhGjhypunXr6tSpUymeL6m/169ft/X+0KFDyY4pWbKkJKlAgQL68ssvNWvWLFWuXFmlS5dW9erVn+p9AACeP4zkAgCeK76+vnr//fc1ceLEZNvz5MmjAgUK6Pz58+rRo4fKlCmjLVu2JBuVfVo9e/bUxIkT1b17d1WsWFGHDx/WjRs3UjVl+mGvvvqqypQpo0OHDqlJkyYqXry4du3apfj4eNWrV0958+aVh4eHbty4oVatWsnDw8MWJGNiYtL0Wjlz5lStWrX0f//3f7JarapTp46yZMkiSSpUqJCkB9OL27dvr9u3b9umKz/pdYoXLy4vLy/FxMSoc+fOKliwoMLDw5MdExgYqC+++EJbtmxRs2bN5OXlpQMHDihbtmy2hcMAAPg7jOQCAJ47nTt3to0aPmzatGkqWbKkzp49q5MnT2rmzJlyd3/2vwd369ZNQ4cOla+vr3bt2iUvLy+99957GjhwYJrO4+bmpu+++05BQUGKi4vTnj17VKJECc2YMUOvvfaavL29NWXKFBUpUkRnzpzR/fv3NWzYMFksFu3evTvZVOHUePga4oe/LlOmjEaMGKG8efPq2LFj8vLy0gcffCBJ2rlz52PPlSVLFk2dOlVFihTRsWPHdP36dX3++efJjsmcObPmzZunOnXq6NKlSzpx4oSqV6+uH374Qb6+vmmqHQDw/LJY03qRDgAAAAAA6RQjuQAAAAAA0yDkAgAAAABMg5ALAAAAADANQi4AAAAAwDQIuQAAAAAA0yDkAgAAAABMg5ALAAAAADANQi4AAAAAwDQIuQAAAAAA0/h/aOyQHuoTaFcAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Labelling the plot \n",
        "viz.label_plot(\n",
        "    title ='Data Distribution Plot - A', \n",
        "    x ='Numerical Value',\n",
        "    y = 'Count')\n",
        "\n",
        "# Plotting the Bars\n",
        "plots = viz.plot_bar(\n",
        "    dataset = df, \n",
        "    limit = 10, \n",
        "    column ='A', \n",
        "    horizontal = False, \n",
        "    figsize = (16,8), \n",
        "    color = 'darkcyan')\n",
        "\n",
        "#Anotating the Bars \n",
        "viz.annot_bar(plots, horizontal = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Labelling the plot \n",
        "viz.label_plot(\n",
        "    title ='Distribution plot of Greek Alphabet', \n",
        "    x = 'Count',\n",
        "    y ='Alphabet')\n",
        "\n",
        "\n",
        "# Plotting the Bars\n",
        "plots = viz.plot_bar(\n",
        "    dataset = df, \n",
        "    limit = 10, \n",
        "    column ='C', \n",
        "    horizontal = True, \n",
        "    figsize = (16,8), \n",
        "    color = 'maroon')\n",
        "\n",
        "\n",
        "#Anotating the Bars\n",
        "viz.annot_bar(plots, horizontal = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### **Creating a Donut plot using the plot_donut method**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Help on method plot_donut in module VizPack.vizpack:\n",
            "\n",
            "plot_donut(dataset, limit=None, column=None, title='Donut Plot', figsize=(20, 10), shadow=False, colors=False, explode=False, background='white') method of VizPack.vizpack.Viz_Func instance\n",
            "    Parameters:\n",
            "      dataset : Pandas DataFrame, Pandas Series\n",
            "      \n",
            "      limit : Limit of Variables to Plot\n",
            "              Useful in large datasets (None, int)\n",
            "              Default - None\n",
            "    \n",
            "      column  : Dataset Column (None, str)\n",
            "                Default - None\n",
            "      \n",
            "      title : str\n",
            "              Default - 'Donut Plot'\n",
            "      \n",
            "      figsize  : Figure Dimension (Tuple)\n",
            "                Default - (20,10)\n",
            "      \n",
            "      shadow  : Presence of Shadow (Boolean)\n",
            "                Default - False\n",
            "      \n",
            "      colors  : Colors of the bar (boolean, list)\n",
            "                Default - False\n",
            "      \n",
            "      explode  : Presence of Explosion (Boolean)\n",
            "                Default - False\n",
            "      \n",
            "      background  : Color of the background (str)\n",
            "                Default - 'white'\n",
            "\n"
          ]
        }
      ],
      "source": [
        "help(viz.plot_donut)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "id": "iefameJr9O7y",
        "outputId": "f335c326-a667-4fb4-da54-e062e868c10d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "viz.plot_donut(df,\n",
        "               limit = 7, \n",
        "               column = 'C', \n",
        "               title = 'Plot: Distribution of Greek Alphabets',\n",
        "               shadow = False,\n",
        "               explode = False, background = 'white'\n",
        "               )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### **Let's Connect**\n",
        "<p>\n",
        "  Author: Alao David I. <br/>\n",
        "  Date: 2nd of Jan. 2022\n",
        "  <!--ul>\n",
        "    <li>Author: Alao David I.</li>\n",
        "    <li>Date: 12th of Aug. 2021</li>\n",
        "  </ul-->\n",
        " \n",
        "  <a href=\"https://bit.ly/3kkaHhz\">\n",
        "    <img align=\"right\" height=100 width=100 src=\"https://cvs-disease.herokuapp.com/static/images/profile.259def32692b.jpeg\" />\n",
        "  </a>\n",
        "</p>\n",
        "\n",
        "<table>\n",
        "<tbody>\n",
        "<tr>\n",
        "<td><a href=\"https://invest41.github.io/AlaoDavid.github.io/\" target=\"_blank\">Project Portfolio</a></td>\n",
        "<td><a href=\"https://github.com/invest41\" target=\"_blank\">GitHub</a></td>\n",
        "<td><a href=\"https://mobile.twitter.com/Wilder_Maxim\" target=\"_blank\">Twitter</a></td>\n",
        "<td><a href=\"https://www.kaggle.com/welcomehere\" target=\"_blank\">Kaggle</a></td>\n",
        "<td><a href=\"https://www.linkedin.com/in/david-alao-72362113b/\" target=\"_blank\">Linkedin</a></td>\n",
        "<td><a href=\"https://public.tableau.com/app/profile/alao.david\" target=\"_blank\">Tableau</a></td>\n",
        "</tbody>\n",
        "</table>"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "authorship_tag": "ABX9TyPwAhNkPqlQZpNU5pTh4aKW",
      "collapsed_sections": [],
      "include_colab_link": true,
      "name": "Viz_Package.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.1"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}