Matplotlib_На_одной_координатной_сетке_Практика_hist.ipynb

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    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.4"
    },
    "colab": {
      "name": "Matplotlib На одной координатной сетке. Практика hist.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/oObelix/DataScience/blob/master/Matplotlib_%D0%9D%D0%B0_%D0%BE%D0%B4%D0%BD%D0%BE%D0%B9_%D0%BA%D0%BE%D0%BE%D1%80%D0%B4%D0%B8%D0%BD%D0%B0%D1%82%D0%BD%D0%BE%D0%B9_%D1%81%D0%B5%D1%82%D0%BA%D0%B5_%D0%9F%D1%80%D0%B0%D0%BA%D1%82%D0%B8%D0%BA%D0%B0_hist.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3-ZakYfzEAED"
      },
      "source": [
        "# Продвинутая визуализация с matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vKubjZDEEAEE"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fqj1loxYEAEE"
      },
      "source": [
        "## Урок 2. Визуализация в цикле, несколько графиков на одной координатной сетке \n",
        "\n",
        "### Практика\n",
        "\n",
        "#### Задание 1\n",
        "\n",
        "Сравните распределения минимальных зарплат в 1980, 1990 и 2000 годах, построив 3 графика на одной координатной сетке. При постройке графиков на одной координатной сетке убедитесь, что столбцы всех трех графиков имеют одинаковую ширину столбцов, и все 3 графика одновременно видно."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a1kFxYYVEAEF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e8385b9d-93df-4c40-d015-b68da42f9e22"
      },
      "source": [
        "!ls sample_data"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "anscombe.json\t\t      mnist_test.csv\t     wage-data-coast.csv\n",
            "california_housing_test.csv   mnist_train_small.csv\n",
            "california_housing_train.csv  README.md\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "e7ZOWyVBd-ur",
        "outputId": "8c547296-edfc-448f-b4e2-389d7bb7a2cf"
      },
      "source": [
        "df = pd.read_csv('sample_data/wage-data-coast.csv')\n",
        "df"
      ],
      "execution_count": 3,
      "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>Year</th>\n",
              "      <th>State</th>\n",
              "      <th>Salary</th>\n",
              "      <th>IsCoastal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1968</td>\n",
              "      <td>Alabama</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1968</td>\n",
              "      <td>Alaska</td>\n",
              "      <td>2.10000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1968</td>\n",
              "      <td>Arizona</td>\n",
              "      <td>0.66000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1968</td>\n",
              "      <td>Arkansas</td>\n",
              "      <td>0.15625</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1968</td>\n",
              "      <td>California</td>\n",
              "      <td>1.65000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2745</th>\n",
              "      <td>2017</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>7.25000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2746</th>\n",
              "      <td>2017</td>\n",
              "      <td>Washington</td>\n",
              "      <td>11.00000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2747</th>\n",
              "      <td>2017</td>\n",
              "      <td>West Virginia</td>\n",
              "      <td>8.75000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2748</th>\n",
              "      <td>2017</td>\n",
              "      <td>Wisconsin</td>\n",
              "      <td>7.25000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2749</th>\n",
              "      <td>2017</td>\n",
              "      <td>Wyoming</td>\n",
              "      <td>5.15000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2750 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Year          State    Salary  IsCoastal\n",
              "0     1968        Alabama   0.00000          0\n",
              "1     1968         Alaska   2.10000          1\n",
              "2     1968        Arizona   0.66000          0\n",
              "3     1968       Arkansas   0.15625          0\n",
              "4     1968     California   1.65000          1\n",
              "...    ...            ...       ...        ...\n",
              "2745  2017       Virginia   7.25000          1\n",
              "2746  2017     Washington  11.00000          1\n",
              "2747  2017  West Virginia   8.75000          0\n",
              "2748  2017      Wisconsin   7.25000          0\n",
              "2749  2017        Wyoming   5.15000          0\n",
              "\n",
              "[2750 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "DnOMw06veFM-",
        "outputId": "5a4295db-cba6-4577-c1f5-ece9f6ec0cca"
      },
      "source": [
        "max_salary = df[df['Year'] <= 2000]['Salary'].max()\n",
        "step = 0.25\n",
        "bins = [i * step for i in range(int(max_salary/step) + 1)]\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(15,6))\n",
        "\n",
        "for year in range(1980, 2000 + 1, 10):\n",
        "  ax.hist(df[df['Year'] == year]['Salary'], label=year, alpha=0.4, bins=bins)\n",
        "\n",
        "ax.set_title('Распределения минимальных зарплат в 1980, 1990 и 2000 годах')\n",
        "ax.set_xlabel('минимальная зарплата $/час')\n",
        "ax.set_ylabel('колличество записей')\n",
        "ax.set_xticks(bins)\n",
        "ax.legend()\n",
        "plt.show()"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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MqweV/ndKJ1H0fF0Xxb39Ftbsr6A453cSxWt5MrB/FueoUubwNYrDF+8v8/tilXmryszfUxwufjFFj+V65XKJiM9Q/Gj09q7cKX782j4iPtPoc4Ki6L80M39T9i4eAfwoigu9SGqhyMx25yBpKRfFjYOPLL9M9GW+w4DxmXlCXfsYikv5H9akFKVKIuIEilsv9HROnyqK4rzIiZnZ355nNVn5/r6vPIy2tv1tFLdwOKENaUmqoK9XeJOkgfQ0xb3C6i2iuL+SpA6VmT9pdw56jScp9rv1nqf7fbGk1wmLOkmvW5k5vYf2ByiueidJapLM/EYP7dfxysVxJL0OefilJEmSJHUwL5QiSZIkSR3Mok6SJEmSOlhHnFO3xhpr5Pjx49udhiRJkiS1xaxZsx7OzG7vOdqyoq68Yej/UNykcjBwUWZ+MSLWBS6guIfLLODgzHyht2WNHz+emTNfc99hSZIkSVomRMQ/ehrXysMvnwd2zcwtgC2BPSJiO4oblZ6WmetT3LjyiBbmIEmSJElLtZYVdVlYWD4dUj4S2BW4qGw/B3h3q3KQJEmSpKVdSy+UEhGDIuJGYAFwJXA38HhmLionmQuM7mHeoyJiZkTMfOihh1qZpiRJkiR1rJZeKCUzXwK2jIhhwC+Ajfsw7+nA6QCTJk3yZnqSJEnSMujFF19k7ty5PPfcc+1OZUAMHTqUMWPGMGTIkMrzDMjVLzPz8Yj4E7A9MCwiBpe9dWOAeQORgyRJkqTOM3fuXFZZZRXGjx9PRLQ7nZbKTB555BHmzp3LuuuuW3m+lh1+GRFrlj10RMQKwGTgduBPwP7lZIcCl7YqB0mSJEmd7bnnnmP11Vdf6gs6gIhg9dVX73OvZCt76kYB50TEIIri8cLM/FVE3AZcEBEnATcAZ7YwB0mSJEkdblko6Lr0Z11befXLv2fmmzNz88zcNDP/s2y/JzPfkpnrZ+YBmfl8q3KQJEmSpCV1+OGHM3LkSDbddNPFbTfddBPbb789m222GXvvvTdPPvkkUJwDeOihh7LZZpuxySab8NWvfnXxPJdffjkbbbQR66+/PieffHLT8huQc+okSZIkqRnOm3F/U5f3/m3HNZzmsMMO45hjjuGQQw5Z3HbkkUfy9a9/nZ122olp06Zxyimn8KUvfYnp06fz/PPPc/PNN/PMM88wYcIEDjroIMaOHcvRRx/NlVdeyZgxY9hmm23YZ599mDBhwhKvQ0tvaSBJkiRJnW7HHXdkxIgRr2q744472HHHHQGYPHkyF198MVAcPvn000+zaNEinn32WZZffnlWXXVVrr32WtZff33e9KY3sfzyy/O+972PSy9tzuVFLOokSZIkqY8mTpy4uCibPn06c+bMAWD//fdnpZVWYtSoUYwbN45PfvKTjBgxgnnz5jF27NjF848ZM4Z585pzIwCLOkmSJEnqo2nTpvH973+frbfemqeeeorll18egGuvvZZBgwbxz3/+k3vvvZdTTz2Ve+65p6W5eE6dJEmSJPXRxhtvzBVXXAEUh2L++te/BuC8885jjz32YMiQIYwcOZK3vvWtzJw5k7Fjxy7uzYPi/nujR49uSi4WdZIkSWqq6XdMb0vcAzY8oC1xtWxasGABI0eO5OWXX+akk05i6tSpAIwbN44//vGPHHzwwTz99NNcc801HHfccUyYMIE777yTe++9l9GjR3PBBRdw3nnnNSUXD7+UJEmSpF4cdNBBbL/99syePZsxY8Zw5plncv7557Phhhuy8cYbs/baazNlyhQAjj76aBYuXMjEiRPZZpttmDJlCptvvjmDBw/mu9/9LrvvvjubbLIJBx54IBMnTmxKfpGZTVlQK02aNClnzpzZ7jQkSZJUgT11aqbbb7+dTTbZpN1pDKju1jkiZmXmpO6mt6dOkiRJkjqYRZ0kSZIkdTCLOkmSJEnqYBZ1kiRJktTBLOokSZIkqYNZ1EmSJElSB7OokyRJkqReHH744YwcOZJNN910cdtNN93E9ttvz2abbcbee+/Nk08+CcALL7zAlClT2Gyzzdhiiy246qqrFs8za9YsNttsM9Zff32OPfZYmnV7ucFNWYokSZIkDYSZZzV3eZOmNJzksMMO45hjjuGQQw5Z3HbkkUfy9a9/nZ122olp06Zxyimn8KUvfYkzzjgDgJtvvpkFCxaw5557ct1117HccsvxkY98hDPOOINtt92Wvfbai8svv5w999xziVfBnjpJkiRJ6sWOO+7IiBEjXtV2xx13sOOOOwIwefJkLr74YgBuu+02dt11VwBGjhzJsGHDmDlzJvPnz+fJJ59ku+22IyI45JBDuOSSS5qSn0WdJEmSJPXRxIkTufTSSwGYPn06c+bMAWCLLbbgsssuY9GiRdx7773MmjWLOXPmMG/ePMaMGbN4/jFjxjBv3rym5GJRJ0mSJEl9NG3aNL7//e+z9dZb89RTT7H88ssDxfl3Y8aMYdKkSRx33HHssMMODBo0qKW5eE6dJEmSJPXRxhtvzBVXXAEUh2L++te/BmDw4MGcdtppi6fbYYcd2HDDDRk+fDhz585d3D537lxGjx7dlFzsqZMkSZKkPlqwYAEAL7/8MieddBJTp04F4JlnnuHpp58G4Morr2Tw4MFMmDCBUaNGseqqq3LNNdeQmZx77rnsu+++TcnFnjpJkiRJ6sVBBx3EVVddxcMPP8yYMWM48cQTWbhwId/73vcA2G+//ZgypbiK5oIFC9h9991ZbrnlGD16ND/+8Y8XL+f73/8+hx12GM8++yx77rlnU658CRZ1kiRJkjpJhVsQNNv555/fbftHP/rR17SNHz+e2bNndzv9pEmTuOWWW5qaG3j4pSRJkiR1NIs6SZIkSepgFnWSJEmS1MEs6iRJkiSpg1nUSZIkSVIHs6iTJEmSpA5mUSdJkiRJPZgzZw677LILEyZMYOLEiXzrW98C4NFHH2Xy5MlssMEGTJ48mcceewyAzOTYY49l/fXXZ/PNN+f6669fvKxzzjmHDTbYgA022IBzzjmnaTl6nzpJkiRJHWP6HdOburwDNjyg1/GDBw/m1FNPZauttuKpp55i6623ZvLkyZx99tnstttuHH/88Zx88smcfPLJ/Nd//Re//e1vufPOO7nzzjuZMWMGH/nIR5gxYwaPPvooJ554IjNnziQi2Hrrrdlnn30YPnz4Eq+DPXWSJEmS1INRo0ax1VZbAbDKKquwySabMG/ePC699FIOPfRQAA499FAuueQSAC699FIOOeQQIoLtttuOxx9/nPnz5/O73/2OyZMnM2LECIYPH87kyZO5/PLLm5KjRZ0kSZIkVXDfffdxww03sO222/Lggw8yatQoAN74xjfy4IMPAjBv3jzGjh27eJ4xY8Ywb968HtubwaJOkiRJkhpYuHAh733ve/nmN7/Jqquu+qpxEUFEtCkzizpJkiRJ6tWLL77Ie9/7Xj7wgQ+w3377AbDWWmsxf/58AObPn8/IkSMBGD16NHPmzFk879y5cxk9enSP7c1gUSdJkiRJPchMjjjiCDbZZBM+/vGPL27fZ599Fl/B8pxzzmHfffdd3H7uueeSmVxzzTWsttpqjBo1it13350rrriCxx57jMcee4wrrriC3XffvSk5evVLSZIkSerBX//6V3784x+z2WabseWWWwLwla98heOPP54DDzyQM888k3XWWYcLL7wQgL322ovf/OY3rL/++qy44oqcddZZAIwYMYLPf/7zbLPNNgB84QtfYMSIEU3JMTKzKQtqpUmTJuXMmTPbnYYkSZIqaPYl56tqdGl6dabbb7+dTTbZpN1pDKju1jkiZmXmpO6m9/BLSZIkSepgFnWSJEmS1MEs6iRJkiSpg1nUSZIkSXpd64TrgDRLf9bVok6SJEnS69bQoUN55JFHlonCLjN55JFHGDp0aJ/m85YGkiRJkl63xowZw9y5c3nooYfancqAGDp0KGPGjOnTPBZ1kiRJkl63hgwZwrrrrtvuNF7XWnb4ZUSMjYg/RcRtEXFrRHy0bD8hIuZFxI3lY69W5SBJkiRJS7tW9tQtAj6RmddHxCrArIi4shx3WmZ+vYWxJUmSJGmZ0LKiLjPnA/PL4aci4nZgdKviSZIkSdKyaECufhkR44E3AzPKpmMi4u8RMS0ihvcwz1ERMTMiZi4rJ0VKkiRJUl+1vKiLiJWBi4HjMvNJ4L+B9YAtKXryTu1uvsw8PTMnZeakNddcs9VpSpIkSVJHamlRFxFDKAq6n2bmzwEy88HMfCkzXwbOAN7SyhwkSZIkaWnWyqtfBnAmcHtmfqOmfVTNZO8BbmlVDpIkSZK0tGvl1S/fChwM3BwRN5ZtnwEOiogtgQTuAz7cwhwkSZIkaanWyqtf/gWIbkb9plUxJUmSJGlZMyBXv5QkSZIktYZFnSRJkiR1MIs6SZIkSepgFnWSJEmS1MEs6iRJkiSpg7XylgaSJC0dZp7VnriTprQnriSpo9hTJ0mSJEkdzKJOkiRJkjqYRZ0kSZIkdTCLOkmSJEnqYBZ1kiRJktTBLOokSZIkqYNZ1EmSJElSB7OokyRJkqQOZlEnSZIkSR3Mok6SJEmSOphFnSRJkiR1MIs6SZIkSepgFnWSJEmS1MEs6iRJkiSpg1nUSZIkSVIHs6iTJEmSpA5mUSdJkiRJHcyiTpIkSZI6mEWdJEmSJHUwizpJkiRJ6mAWdZIkSZLUwSzqJEmSJKmDWdRJkiRJUgezqJMkSZKkDmZRJ0mSJEkdzKJOkiRJkjqYRZ0kSZIkdTCLOkmSJEnqYBZ1kiRJktTBLOokSZIkqYNZ1EmSJElSB7OokyRJkqQOZlEnSZIkSR3Mok6SJEmSOphFnSRJkiR1MIs6SZIkSepgFnWSJEmS1MEs6iRJkiSpgw1uNEFEfLy79sz8RoP5xgLnAmsBCZyemd+KiBHAz4DxwH3AgZn5WN/SliRJkiRBLz11EfEv5eDngUOAVeoejSwCPpGZE4DtgKMjYgJwPPCHzNwA+EP5XJIkSZLUD70dfvmx8u96wJXAbsBfM/PEzDyx0YIzc35mXl8OPwXcDowG9gXOKSc7B3h3P3OXJEmSpGVeb0XdCwCZ+Whmfgp4H3BARFweEdv0JUhEjAfeDMwA1srM+eWoBygOz5QkSZIk9UNv59RNBYiIX1KcEwcQwDjgGmBQlQARsTJwMXBcZj4ZEYvHZWZGRPYw31HAUQDjxo2rEkqSJEmSljk9FnWZeVs5+PX+LjwihlAUdD/NzJ+XzQ9GxKjMnB8Ro4AFPcQ/HTgdYNKkSd0WfpIkSZK0rGt4S4PMvJriKpVDyuHrgOsbzRdFl9yZwO11V8q8DDi0HD4UuLSPOUuSJEmSSg2Luoj4EHAR8MOyaTRwSYVlvxU4GNg1Im4sH3sBJwOTI+JO4B3lc0mSJElSPzS8Tx1wNPAWiouckJl3RsTIRjNl5l8ozsHrzm6VM5QkSZIk9ahhTx3wfGa+0PUkIgbzyoVTJEmSJEltVKWouzoiPgOsEBGTgenAL1ubliRJkiSpiipF3fHAQ8DNwIeB3wCfa2VSkiRJkqRqqpxTtwIwLTPPAIiIQWXbM61MTJIkSZLUWJWeuj9QFHFdVgB+35p0JEmSJEl9UaWoG5qZC7uelMMrti4lSZIkSVJVVYq6pyNiq64nEbE18GzrUpIkSZIkVVXlnLrjgOkR8U+K+869EfiXlmYlSZIkSaqkYVGXmddFxMbARmXT7Mx8sbVpSZIkSZKqaFjURcR+dU0bRgSZ+fMW5SRJkiRJqqjK4Zc/A24DZlEcfgmQgEWdJEmSJLVZlaJuU+BLwMrA5zNzdmtTkiRJkiRVVeWcutnAgeVVL79RXjDlhMyc1/LsJEmSJEm9qnJO3XcoDrcEuAfYCbgT71UnSZIkSW1X5fDLmQ2eS5IkSZLapMrhl+cMRCKSJEmSpL6rcvjlPfVNQGbmm1qTkiRJkiSpqiqHXy4Edml1IpIkSZKkvqtS1C3KzEdanokkSZIkqc+qFHWDImI4r9x4HIDMfLQ1KUmSJEmSqqpS1K0GzOLVRV0CnlMnSZIkSW1W5eqX4wcgD0mSJElSP1TpqSMiNgUmAEO72jLz3FYlJUmSJEmqpsotDb4I7DV2kc4AABxHSURBVExR1P0G2BP4C2BRJ0mSJElttlyFafYHdgMeyMwpwBYU59lJkiRJktqsSlH3bGa+DCyKiFWBBcDY1qYlSZIkSaqiyjl1MyNiGHAGxVUwFwL/29KsJEmSJEmVVLn65b+Wgz+IiMuBVTPz761NS5IkSZJURcPDLyNizYj4UESsCUwC3lMOS5IkSZLarMo5dRcCOwFXArsAw4GftDIpSZIkSVI1Vc6pG5aZu0TE3Mw8GiAibmlxXpIkSZKkCqr01L1U/v1iN22SJEmSpDaqUtR9BiAzzwSIiNWA01uZlCRJkiSpmipXv7yi7vkTwPdalpEkSZIkqbIqPXWSJEmSpNcpizpJkiRJ6mCVi7qIWDkiVm5lMpIkSZKkvqly8/HNIuIG4FbgtoiYFRGbtj41SZIkSVIjVXrqfgh8PDPXycxxwCfw6peSJEmS9LpQpahbKTP/1PUkM68CVmpZRpIkSZKkyhre0gC4JyI+D/y4fP5B4J7WpSRJkiRJqqpKT93hwJrAz8vHmmWbJEmSJKnNqtx8/DHg2AHIRZIkSZLUR1WufvkvEXFRROwWEf8XEQsi4oMDkZwkSZIkqXdVDr/8EnABcDHwLmBz4D9amZQkSZIkqZoqRd3TmXkR8I/MvCszHwCebzRTREwre/VuqWk7ISLmRcSN5WOvJchdkiRJkpZ5Va5+OToivg2MKv8GMLrCfGcD3wXOrWs/LTO/3qcsJUmSJEndqlLUfar8O6umbWajmTLzfyJifD9ykiRJkiRVVKWoe0Nmnt7EmMdExCEUheEnyqtrvkZEHAUcBTBu3LgmhpckdbLzZtw/4DHfP2jAQ0qSVFmVc+qmNjHefwPrAVsC84FTe5owM0/PzEmZOWnNNddsYgqSJEmStPSo0lM3LCL2q2/MzJ/3NVhmPtg1HBFnAL/q6zIkSZIkSa+oUtStRnErg6hpS6DPRV1EjMrM+eXT9wC39Da9JEmSJKl3VYq6+zPz8L4uOCLOB3YG1oiIucAXgZ0jYkuKovA+4MN9Xa4kSZIk6RVVirpb+7PgzDyom+Yz+7MsSZIkSVL3qlwo5fMRMbTrSUSs4K0KJEmSJOn1oUpRNx14ueb5S2WbJEmSJKnNqhR1gzPzha4n5fDyrUtJkiRJklRVlaLuoYjYp+tJROwLPNy6lCRJkiRJVVW5UMpU4KcR8T2Kq1bOBQ5paVaSJEmSpEoaFnWZeTewXUSsXD5f2PKsJEmSJEmVNDz8MiLWiogzgemZuTAiJkTEEQOQmyRJkiSpgSrn1J0N/A5Yu3x+B3BcqxKSJEmSJFVXpahbIzMvpLytQWYuoritgSRJkiSpzaoUdU9HxOoUF0khIrYDnmhpVpIkSZKkSqpc/fLjwGXAehHxV2BNYP+WZiVJkiRJqqTK1S+vj4idgI2AAGZn5ostz0ySJEmS1FDDoi4i9qtr2jAiyMyftygnSZIkSVJFVQ6/3Ltu+JcU59dZ1EmSJElSm1U5/HJK13BE3FD7XJIkSZLUXlWufglARKxPtZ49SZIkSdIAqXJO3VMUh1s+Cnys5RlJkiRJkiqrcvjlKgORiCRJkiSp7/pz9UsAr34pSZIkSa8DVc6R+xlwGzCL4j514NUvJUmSJOl1oUpRtynwJWBl4POZObu1KUmSJEmSqqpyTt1s4MCI2Br4RkT8EzghM+e1PDtJkiRJUq+qnFP3HYrDLQHuAXYC7gRWbGFekiRJkqQKqhx+ObPBc0mSJElSm1Q5/PKcgUhEkiRJktR3fbn5+OImIDNz1ZZlJUmSJEmqpMrhl3dl5ptbnokkSZIkqc+qFHVDI2IL4HlgfmY+0eKcJEmSJEkVVSnqHgC+A6wAjIqIx4ApmekFUyRJkiSpzapcKGWX2ucR8TbgB8CkViUlSZIkSapmub7OkJl/Aaa2IBdJkiRJUh9Vufrlt3sY5eGXkiRJktRmVc6p2xf4QqsTkSRJkiT1XZWi7hFvQC5JkiRJr09VirqNI+JG4Dngn8Bfge9l5nMtzUySJEmS1FCVom4TYBDFLQ3WBg4AfgR8sIV5SZIkSZIqqHJLg3/UPL0VuDIi/qt1KUmSJEmSqqrSU0dEbAG8vXz658z899alJEmSJEmqquF96iLio8BPgZHl4ycR8W+tTkySJEmS1FiVnrojgG0z82mA8tDL/wW+08rEJEmSJEmNNeypAwJ4qeb5S2WbJEmSJKnNqvTUnQXMiIhflM/fDUxrXUqSJEmSpKqqXP3yGxFxFfC2smlKZt7Q0qwkSZIkSZU0LOoiYnRmXg9cX9M2NTN/0NLMJEmSJEkNVTmn7tcRsTFARGwUEVcDWzaaKSKmRcSCiLilpm1ERFwZEXeWf4f3P3VJkiRJUpWi7iDg/Ig4DfgZ8PnMnFphvrOBPerajgf+kJkbAH8on0uSJEmS+qlhUZeZtwPvBHYFvpqZ/1NlweV0j9Y17wucUw6fQ3HRFUmSJElSP1W5+fjNwOXAqhQ3Hv97RPy9n/HWysz55fADwFr9XI4kSZIkiWq3NHhXKwJnZkZE9jQ+Io4CjgIYN25cK1KQJEmSpI5X5ZYG/2hivAcjYlRmzo+IUcCCXuKeDpwOMGnSpB6LP0mSJElallW5UEozXQYcWg4fClw6wPElSZIkaanSsqIuIs4H/hfYKCLmRsQRwMnA5Ii4E3hH+VySJEmS1E9Vzqnrl8w8qIdRu7UqpiRJkiQtawb68EtJkiRJUhNZ1EmSJElSB7OokyRJkqQOZlEnSZIkSR3Mok6SJEmSOljLrn65LJgx/dQBj7ntAZ8Y8JiSJEmSXr/sqZMkSZKkDmZRJ0mSJEkdzKJOkiRJkjqYRZ0kSZIkdTCLOkmSJEnqYBZ1kiRJktTBLOokSZIkqYNZ1EmSJElSB7OokyRJkqQOZlEnSZIkSR3Mok6SJEmSOphFnSRJkiR1MIs6SZIkSepgFnWSJEmS1MEs6iRJkiSpg1nUSZIkSVIHs6iTJEmSpA5mUSdJkiRJHcyiTpIkSZI6mEWdJEmSJHUwizpJkiRJ6mCD251AJ7t60ewBj7ntgEeUJEmS9HpmT50kSZIkdTCLOkmSJEnqYBZ1kiRJktTBLOokSZIkqYNZ1EmSJElSB7OokyRJkqQOZlEnSZIkSR3Mok6SJEmSOphFnSRJkiR1MIs6SZIkSepgg9udgCS10nkz7m93CgPm/duOG/CYy9Lr2xYzzxr4mJOmDHxMSdISsadOkiRJkjqYRZ0kSZIkdTCLOkmSJEnqYBZ1kiRJktTBLOokSZIkqYNZ1EmSJElSB2vLLQ0i4j7gKeAlYFFmTmpHHpIkSZLU6dp5n7pdMvPhNsaXJEmSpI7n4ZeSJEmS1MHa1VOXwBURkcAPM/P0+gki4ijgKIBx48YNcHqS1HnOm3F/u1OQJElt0K6eurdl5lbAnsDREbFj/QSZeXpmTsrMSWuuuebAZyhJkiRJHaAtRV1mziv/LgB+AbylHXlIkiRJUqcb8KIuIlaKiFW6hoH/B9wy0HlIkiRJ0tKgHefUrQX8IiK64p+XmZe3IQ9JkiRJ6ngDXtRl5j3AFgMdV5IkSZKWRt7SQJIkSZI6mEWdJEmSJHUwizpJkiRJ6mAWdZIkSZLUwSzqJEmSJKmDteOWBpK0VFvv/ultiXv3uAPaEleSJLWXPXWSJEmS1MEs6iRJkiSpg1nUSZIkSVIHs6iTJEmSpA5mUSdJkiRJHcyiTpIkSZI6mEWdJEmSJHUwizpJkiRJ6mAWdZIkSZLUwSzqJEmSJKmDDW53ApLa47wZ9w94zPdvO27AY0rqo5lnDXzMSVMGPiYw/Y7pAx7zgA0PGPCYaq1l5X3UjvUE/2eqsqdOkiRJkjqYRZ0kSZIkdTCLOkmSJEnqYBZ1kiRJktTBLOokSZIkqYNZ1EmSJElSB7OokyRJkqQOZlEnSZIkSR3Mok6SJEmSOtjgdicgLevOm3F/W+Kud//0gQ+67ScGPqZaqi3vozaYvtrctsQd98SYAY+57bojBjympL6Zfseyse+F9qzrARseMOAxl5Q9dZIkSZLUwSzqJEmSJKmDWdRJkiRJUgezqJMkSZKkDmZRJ0mSJEkdzKJOkiRJkjqYRZ0kSZIkdTCLOkmSJEnqYBZ1kiRJktTBLOokSZIkqYMNbncCktrj6kWzBzzm3TPuH/CY690/fcBjtsvce04a8JjrDd5owGNKnWz6HQO/TzrgyYUDHpNVVx74mLTn9ZVeD+ypkyRJkqQOZlEnSZIkSR3Mok6SJEmSOphFnSRJkiR1MIs6SZIkSepgFnWSJEmS1MHaUtRFxB4RMTsi7oqI49uRgyRJkiQtDQa8qIuIQcD3gD2BCcBBETFhoPOQJEmSpKVBO3rq3gLclZn3ZOYLwAXAvm3IQ5IkSZI6XjuKutHAnJrnc8s2SZIkSVIfDW53Aj2JiKOAo8qnCyNidjvz6cEawMMDGfDf33/GgMcstSOuMZe6uG15/y4rMdsV15hLX9w2xDzc19eYnRzXmEtf3HatayPr9DSiHUXdPGBszfMxZdurZObpwOkDlVR/RMTMzJy0tMdsV1xjLn1xjbn0xTXm0hd3WYnZrrjGXPriGnPpi9uudV0S7Tj88jpgg4hYNyKWB94HXNaGPCRJkiSp4w14T11mLoqIY4DfAYOAaZl560DnIUmSJElLg7acU5eZvwF+047YTdaOw0PbdUjqsrKuy0rMdsU15tIX15hLX9xlJWa74hpz6YtrzKUv7uv6FLDuRGa2OwdJkiRJUj+145w6SZIkSVKTWNT1ICL2iIjZEXFXRBzfzfg3RMTPyvEzImJ8zbj/KNtnR8TuTYz58Yi4LSL+HhF/iIh1asa9FBE3lo/KF56pEPOwiHioZtlH1ow7NCLuLB+HNjHmaTXx7oiIx5uwntMiYkFE3NLD+IiIb5c5/T0itmrCejaK+YEy1s0R8beI2KJm3H1l+40RMbNqzIpxd46IJ2pexy/UjOt12yxBzE/VxLul3I4jlmRdI2JsRPyp/J+4NSI+2s00Td2uFWM2dbtWjNnUbVoxZiu26dCIuDYibirjntjNNE3d91aM2dR9b8WYrdj3Vonb9P1vOe+giLghIn7Vzbimf55WiNn0z9MKMZu+TSvEbNX27PV/PArN/kxtFLNVn6mN4rbiM7VRzFbsf4dFxEUR8X8RcXtEbF83vhXbtFHMVm3TRnGbvk0HRGb6qHtQXMDlbuBNwPLATcCEumn+FfhBOfw+4Gfl8IRy+jcA65bLGdSkmLsAK5bDH+mKWT5f2KL1PAz4bjfzjgDuKf8OL4eHNyNm3fT/RnExnX6vZznfjsBWwC09jN8L+C0QwHbAjCVZz4oxd+haFrBnV8zy+X3AGi1a152BXy3ptulLzLpp9wb+uKTrCowCtiqHVwHu6Ob929TtWjFmU7drxZhN3aZVYrZomwawcjk8BJgBbFc3TbP3vVViNnvfWyXmYTRx31s1bt30Tdn/lvN+HDivh/dpU7dpxZhN3aYVYzZ9mzaK2cLt2ev/OK35TG0Us1WfqY3i7tzD9l6Sz9TK+dK8/e85wJHl8PLAsAHYpo1itmqbNorb9G06EA976rr3FuCuzLwnM18ALgD2rZtmX4o3BcBFwG4REWX7BZn5fGbeC9xVLm+JY2bmnzLzmfLpNRT3+FsSVdazJ7sDV2bmo5n5GHAlsEcLYh4EnF8xpx5l5v8Aj/Yyyb7AuVm4BhgWEaPo/3o2jJmZfyuXCc3ZnpXi9qLf74c+xmzWNp2fmdeXw08BtwOj6yZr6natErPZ27XievakX9u0HzGbtU0zMxeWT4eUj/oTv5u6760Ss9n73orr2ZMl2Sf1NW5TtmtEjAHeCfyoh0ma/XnaMGYLPk+rrGdP+r1N+xizKduzoqZ/pjbSqs/UJbAk37H6Yom3a0SsRvHj7JkAmflCZj5eN1lTt2mVmK3YphXXtScDtU37xaKue6OBOTXP5/LaLzSLp8nMRcATwOoV5+1vzFpHUPxi0mVoRMyMiGsi4t0V4vUl5nvL7u+LIqLrxvEtX88oDodZF/hjTXN/1rOKnvLq73r2Vf32TOCKiJgVEUe1IN72URyC9duImFi2tXxdI2JFip39xTXNS7yuURyu9WaKnodaLduuvcSs1dTt2iBmS7Zpo/Vs9jaN4lCyG4EFFF8UetymTdr3VolZqxn73qoxm7nv7UvcZu9/vwl8Gni5h/FN36YVYtZqyjatGLPZ27TSerbg87TR/3gr9r192a80c99bZd5m738r5dvE/e+6wEPAWVEcyvujiFipbppmb9MqMWs1a5tWjduW70lLoi23NNCSiYgPApOAnWqa18nMeRHxJuCPEXFzZt7dhHC/BM7PzOcj4sMUv6bu2oTlVvE+4KLMfKmmrVXr2TYRsQvFzuptNc1vK9dzJHBlRPxf2RvWDNdTvI4LI2Iv4BJggyYtu5G9gb9mZm2v3hKta0SsTPGBdlxmPtnkfPsds9nbtUHMlmzTiq9tU7dp+f++ZUQMA34REZtmZrfnajZL1ZjN3PdWiNmSfW8fXt+m7H8j4l3AgsycFRE7L2n+VfQlZrO2acWYTd2mfXxtm/152srPqCWK2YLP1EbztmL/WzXfZu1/B1OcQvFvmTkjIr4FHA98fgnXoykxm7xNq8Rt5/ekfrOnrnvzgLE1z8eUbd1OExGDgdWARyrO29+YRMQ7gM8C+2Tm813tmTmv/HsPcBXFL+tLHDMzH6mJ8yNg677k25+YNd5H3SEF/VzPKnrKq7/rWUlEbE7xuu6bmY90tdes5wLgF1Q85KiKzHwyy0Owsrhn5JCIWIMWr2upt23a53WNiCEURcdPM/Pn3UzS9O1aIWbTt2ujmK3YplXWs9TUbVqzjMeBP/Haw3iave+tErPZ+96GMVuw760Ut0az9r9vBfaJiPsoDlXaNSJ+UjdNs7dplZjN3qYNY7Zgm1Zaz1JTP08r/I83fd9bZb/Sis/URvO2Yv/bh3ybtf+dC8zNV3ruL6IofGo1e5tWidmKbdowbpu/J/Vfvg5O7Hu9PSiq+Hsoumi7ToScWDfN0bz6xO4Ly+GJvPrE7nuodrJ+lZhvpjhBc4O69uHAG8rhNYA7qXYxhCoxR9UMvwe4phweAdxbxh5eDo9oRsxyuo0pToKNJV3PmvnH0/PFQ97Jq08AvnZJ1rNizHEU54jsUNe+ErBKzfDfgD36+B7uLe4bu15Xip3g/eV6V9o2/YlZjl+N4ry7lZqxrmXO5wLf7GWapm7XijGbul0rxmzqNq0Ss0XbdE3KE9aBFYA/A++qm6bZ+94qMZu9760Ss6n73qpxy3FN3/+W8+1M9xcfaOo2rRizqdu0Ysymb9NGMVuxPanwP07z971VYjb9M7Vi3GbvfyvlS/P3v38GNiqHTwBOaeU2rRizJd+TKsRtyfekVj/ansDr9UFxlZ87KHb6ny3b/pPiFz2AocD08s12LfCmmnk/W843G9iziTF/DzwI3Fg+LivbdwBuLt9cNwNHNDHmV4Fby2X/Cdi4Zt7Dy/W/C5jSrJjl8xOAk+vmW5L1PB+YD7xI8SvNEcBUYGo5PoDvlTndDExqwno2ivkj4LGa7TmzbH9TuY43la/9Z/v43m0U95iabXoNNTvL7rZNM2KW0xxGcdGD2vn6va4Uh2Ek8Pea13CvVm7XijGbul0rxmzqNq0Ss0XbdHPghjLuLcAX6vcPNHnfWzFmU/e9FWO2Yt/bMG75/ASauP+tWcbOlIVHK7dpxZhN/zytELPp27RRzFZsT3r4H6e1+94qMZv+mVoxbrP3vw1jls8Po7n73y2BmRT7h0soCrRWf09qFLNV35MaxW3696SBeHRVoZIkSZKkDuQ5dZIkSZLUwSzqJEmSJKmDWdRJkiRJUgezqJMkSZKkDmZRJ0mSJEkdzKJOkqRlTESc0EP7DyLirQOcjiRpCVnUSZK0jIiItSPit8DUiLgpIj5WN8l2FPdlkiR1EIs6SVrGRMT4iMiImFo+HxQR8yLi7PL52RGxfzl8ZDntGuV8t9QsZ/+K8/QWa++ImBERN0TE7yNirZrln1BOe2NELIyISfU51Ey7c0T8qub5J7t6oyLiQxFxXVnEXBwRK5bt60XEteXy7+3KqW65h0XErIi4OSIurZn3krL91og4qmb6hRFxWtn+h4hYs2y/KiJml7FujIiX6nJ/omx/ICI+WbZ/ocz7log4PQpvL6e7LSKe7VpeT9N3s/mPoyjafgBMAi6vyWMT4I7MfCki7ouINWq2Q1dOPb2Wa0XEL8r2myJih25iS5JaxKJOkpZNdwHvLof3AObUTxARQ4GpwIKqC+1hnt5i/QXYLjPfDFwAfLpm3CDg1MzcEphZNYdu/Dwzt8nMLYDbgSPK9n8FLiyX/6nuZszMszNza2BLYATw9nLU4WX7JODYiFi9bF8JmJmZE4GrgS/WLO4DmbllGe/ZuvW8umz/QU37d8u8NwVWAN6VmX8up9sLuLtmed1O380qvQAMB4Zk5ouZeXvNuD15pch7GeiuKOzptfx2uQ5bAFsBt3YzrySpRSzqJGnZ9DxwV0RMBA4GftzNNEcD5/DqAmS9mt6hUyrO01usMcDvIuJmisJqYs24FYDnuomxOIeI+GxN+9trcqs9rHDTiPhzGeMDNTFeAlbpZvmvEhFfAB4AFgF/LpuPjYibKHq9xgIblO0vAz8rh38CvK3R8ul5PXcpezFvBnbl1a9Nd6pMf0oZ78NlT+LONeN255Wibi7w5m7m7+m13BX4b4DMfCkzn2iQqySpiSzqJGnZdRZFz9hg4MG6casC7wN+WNde2ztU37vV0zy9xfoORQ/TZsCHgaE149YG/tnNsu4u4+8AHBoRG5Xtf67J7bSa6c8GjiljnFgT45vA5Ii4n+4LVAAy8z+BUcDjwJvLQugdwPZlz9QNdXm/avaellvjNetZ9nh+H9i/zPuMXmJUnj4zn8jMDwPfBT4HXBgRQ8vDKIdlZlcenwG+VhbIU2sWcTbdv5aSpDayqJOkZVRmzgJGUhRc9T4GfCczX+jDInucp5dYqwHzyuFDuxrL87neDszoJd6zwDPAkAZ5rQLMj4ghFL1LXR4BXgTeSQ+HX0bEsHLw5fLvhmXOj2XmMxGxMcXFRbosB+xfDr+f4vDSHkXEIGA/4K91o7qKpYcjYuWaZfak0vQRsUlEdH3230yxXkOAXYA/dU2XmX+pKZBrDwnt6bX8A/CRrnWKiNUa5CtJaiKLOklahmXmnpn5625GBcXhg33R6zw9xDoBmB4Rs4CHa9r/ApyQmfO7WdS6EfEX/n87d4wSQRBEAfQ3bOhJDA00MPMmnsDQSFgEwcwrCGaCBzATQRPRREHBSwhqZFAGs8Gu4q6KOAy8l3Uz0xSdFV1VXZ/deVV9GpzywU665PAiycPU/kGSw6q6nfPv9qTM8j5d+eVxuhLFUWvtPsl+ZqdFviZZnQxz2UiyuyC2oySPSU6mN6vqKd1r212S0yRX8w75wffrSS6TbKa7k72qes5sP908X93lVrryz9sk10mWv3EWAH+kVX2nMgQAWKS19lJVS33HsUhrbVxV46n1TZK1qnrrLyoAfmvUdwAAwL87m15U1UpPcQDwB7zUAQAADJieOgAAgAGT1AEAAAyYpA4AAGDAJHUAAAADJqkDAAAYMEkdAADAgL0D513I4J1XrUYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rn0ZFb7jgm6l"
      },
      "source": [
        ""
      ],
      "execution_count": 60,
      "outputs": []
    }
  ]
}