Outlieres_AngelWithKarma.ipynb

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  "metadata": {
    "colab": {
      "name": "Outlieres_AngelWithKarma.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "gNTAE-5Qj3XY",
        "-ykeVR6qleq6",
        "CJYBdOYds1e3",
        "uPjf7Px4w1XK",
        "dOkVeVwVEuer",
        "yVOlTl7oE4aJ",
        "dh2tdmQNGrjv",
        "2mA2CY9oQpFp",
        "9ZNtzf6nOj_x"
      ],
      "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/Stuksus/Hackaton_SBER/blob/main/Outlieres_AngelWithKarma.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gNTAE-5Qj3XY"
      },
      "source": [
        "# Грузим данные"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FEuPDP2D-XNh"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.metrics import f1_score\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "pd.set_option('display.max_rows', 500)\n",
        "pd.set_option('display.max_columns', 500)\n",
        "import warnings\n",
        "from pandas.core.common import SettingWithCopyWarning\n",
        "\n",
        "warnings.simplefilter(action=\"ignore\", category=SettingWithCopyWarning)\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
        "warnings.simplefilter('ignore')"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d47ODAGk-jPt"
      },
      "source": [
        "train_df = pd.read_csv('https://raw.githubusercontent.com/BKHV/risk_models/master/data/PD-data-train.csv', sep=';')\n",
        "test_df = pd.read_csv('https://raw.githubusercontent.com/BKHV/risk_models/master/data/PD-data-test.csv', sep=';')\n",
        "desc_df = pd.read_csv('https://raw.githubusercontent.com/BKHV/risk_models/master/data/PD-data-desc.csv', sep=';')\n",
        "\n",
        "# train_df = train_df.drop(['record_id','ul_systematizing_flg'],axis = 1)\n",
        "train_df[\"ul_staff_range\"] = train_df[\"ul_staff_range\"].replace(['[1-100]','(100-500]','> 500'],[50,300,750])"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YZuM2Wd1kD06"
      },
      "source": [
        "# Наши размышления"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ykeVR6qleq6"
      },
      "source": [
        "##1. Дропать пропуски нельзя. \n",
        "Мы предполагаем что в train_df пропуски являются частью *предметной области*. Компании с пропусками просто не имеют/не предоставли своих бухгалтерских отчетов.\n",
        "\n",
        "**Детали**: пропуски в train_df составляют 46% выборки, а в test_df 59% выборки. Более того, в train_df 2% дефолтов произошло с компаниями без пропусков, а 4% дефолтов с компаниями с пропусками. Это слишком большая потеря данных\n",
        "\n",
        "**Мы предлогаем** сделать 2 отдельные модели для данных с пропусками и без, а потом уже их в конце объеденить их в 1 submit. Попробовать обучить первую модель вообще без данных бух отчетов\n",
        "\n",
        "Далее в коде подтверждение нашим словам"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MRcF5m6AYwAY",
        "outputId": "b9f9d98f-6c9d-46e4-ef0b-df3d0b67f488"
      },
      "source": [
        "target = train_df['default_12m']\n",
        "\n",
        "print(f'{(1-len(train_df.dropna()) / len(train_df))*100}% строк в train_df имеют пропуски')\n",
        "print(f'{(1-len(test_df.dropna()) / len(test_df))*100}% строк в test_df имеют пропуски')\n",
        "print()\n",
        "print(f'{(train_df.dropna()[\"default_12m\"].sum()/train_df[\"default_12m\"].count())*100}% строк c пропусками из train_df объявили дефолт')\n",
        "\n",
        "a = pd.concat([train_df, train_df.dropna()]).drop_duplicates(keep=False)['default_12m']\n",
        "print(f'{(a.sum()/train_df[\"default_12m\"].count())*100}% строк без пропусков из train_df объявили дефолт')\n",
        "print(f'{(target.sum()/train_df[\"default_12m\"].count())*100}% всего строк из train_df объявили дефолт')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "46.751041827442506% строк в train_df имеют пропуски\n",
            "59.00000000000001% строк в test_df имеют пропуски\n",
            "\n",
            "2.457169316252508% строк c пропусками из train_df объявили дефолт\n",
            "3.997530483099244% строк без пропусков из train_df объявили дефолт\n",
            "6.454699799351752% всего строк из train_df объявили дефолт\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CJYBdOYds1e3"
      },
      "source": [
        "##2. Стоит объеденить строки с идентичными столбцами бухгалтерского баланса \n",
        "Мы заметили, что существует не мало компаний прям с одинаковыми значениями в статьях баланса. Более того на частных примерах мы увидили, что компании с одинаковыми значениями в статьях баланса по остальным признакам имеют минимальные различия.\n",
        "\n",
        "**Детали**: Компании с одинаковым бухгалтерским балансом финансово *почти* на одно и то же\n",
        "\n",
        "**Мы предлогаем** Объеденить несколько строк с  идентичными столбцами бухгалтерского баланса  \n",
        "\n",
        "Далее в коде подтверждение нашим словам\n",
        "\n",
        "А также пример кода, который скидывает дубликаты (пример законментирован)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FP0uniEHlgKf",
        "outputId": "fd483547-60e0-47c9-caf9-634874a51b51"
      },
      "source": [
        "print(f'{train_df.duplicated().sum()/len(train_df)}% выборки train_df полные дубликаты')\n",
        "\n",
        "a = pd.concat([train_df, train_df.dropna()]).drop_duplicates(keep=False)\n",
        "print(f'{a.iloc[:,:24].duplicated().sum()/len(train_df)}% выборки train_df дубликаты по статьям расхода')\n",
        "print(f'{train_df.iloc[:,24:].duplicated().sum()/len(train_df)}% выборки train_df дубликаты по НЕ статьям расхода')\n",
        "\n",
        "# код удаляющий полные дубликаты\n",
        "# train_df = train_df.drop(['record_id','ul_systematizing_flg'],axis = 1)\n",
        "# train_df = train_df.drop_duplicates() "
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.0% выборки train_df полные дубликаты\n",
            "0.0% выборки train_df дубликаты по статьям расхода\n",
            "0.23762926377527396% выборки train_df дубликаты по НЕ статьям расхода\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "aoUbV6e_hOaS",
        "outputId": "b3a2fd98-4af2-4439-9259-c07d65c6c619"
      },
      "source": [
        "print('Вот например, 3 абсолютно одинаковые бухгалтерские статьи с минимальными отличиями в UL_,_AGE признаках')\n",
        "print()\n",
        "train_df.sort_values(by=['bus_age','ul_founders_cnt']).iloc[-6:-3,:]"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Вот например, 3 абсолютно одинаковые бухгалтерские статьи с минимальными отличиями в UL_,_AGE признаках\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>ab_inventory</th>\n",
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              "      <td>7470000.0</td>\n",
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            "text/plain": [
              "       record_id  ar_revenue  ar_total_expenses  ar_sale_cost  \\\n",
              "12174     102598   6508000.0          7470000.0     7470000.0   \n",
              "19056     161334   6508000.0          7470000.0     7470000.0   \n",
              "21846     185243   6508000.0          7470000.0     7470000.0   \n",
              "\n",
              "       ar_selling_expenses  ar_management_expenses  ar_sale_profit  \\\n",
              "12174                  0.0                     0.0       -962000.0   \n",
              "19056                  0.0                     0.0       -962000.0   \n",
              "21846                  0.0                     0.0       -962000.0   \n",
              "\n",
              "       ar_balance_of_rvns_and_expns  ar_profit_before_tax  ar_taxes  \\\n",
              "12174                      921000.0              -41000.0    7000.0   \n",
              "19056                      921000.0              -41000.0    7000.0   \n",
              "21846                      921000.0              -41000.0    7000.0   \n",
              "\n",
              "       ar_other_profit_and_losses  ar_net_profit  ab_immobilized_assets  \\\n",
              "12174                         0.0       -48000.0                    0.0   \n",
              "19056                         0.0       -48000.0                    0.0   \n",
              "21846                         0.0       -48000.0                    0.0   \n",
              "\n",
              "       ab_mobile_current_assets  ab_inventory  ab_accounts_receivable  \\\n",
              "12174                 5022000.0     5007000.0                 15000.0   \n",
              "19056                 5022000.0     5007000.0                 15000.0   \n",
              "21846                 5022000.0     5007000.0                 15000.0   \n",
              "\n",
              "       ab_other_current_assets  ab_cash_and_securities  ab_losses  \\\n",
              "12174                      0.0                 54000.0   -48000.0   \n",
              "19056                      0.0                 54000.0   -48000.0   \n",
              "21846                      0.0                 54000.0   -48000.0   \n",
              "\n",
              "       ab_own_capital  ab_borrowed_capital  ab_long_term_liabilities  \\\n",
              "12174       4339000.0            2541000.0                       0.0   \n",
              "19056       4339000.0            2541000.0                       0.0   \n",
              "21846       4339000.0            2541000.0                       0.0   \n",
              "\n",
              "       ab_short_term_borrowing  ab_accounts_payable  ab_other_borrowings  \\\n",
              "12174                 340000.0            2201000.0                  0.0   \n",
              "19056                 340000.0            2201000.0                  0.0   \n",
              "21846                 340000.0            2201000.0                  0.0   \n",
              "\n",
              "       bus_age  ogrn_age  adr_actual_age  head_actual_age  cap_actual_age  \\\n",
              "12174      532       137              25               25              25   \n",
              "19056      532       138              25               25              25   \n",
              "21846      534       140              28               28              28   \n",
              "\n",
              "       ul_staff_range  ul_capital_sum  ul_founders_cnt  ul_branch_cnt  \\\n",
              "12174              50       2003701.0              444              0   \n",
              "19056              50       2003701.0              444              0   \n",
              "21846              50       2003701.0              444              0   \n",
              "\n",
              "       ul_strategic_flg  ul_systematizing_flg  default_12m  \n",
              "12174                 0                     0            0  \n",
              "19056                 0                     0            0  \n",
              "21846                 0                     0            0  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uPjf7Px4w1XK"
      },
      "source": [
        "##3. Надо выбросить компании с отрицательными значениями статей баланса\n",
        "Мы заметили, что в некторых статьях есть отрицательные значения. По МСФО таких статей не должно существовать. Более того, таких компаний не много и они не объявляли дефолт. Однако если выбросить все отрицательные наблюдения, то мы теряем 40% выборки, так как столбцы с profit могут быть отрицательными. Поэтому тут функция по дефолту скидывает отрицательные значения в НЕ profit столбцах. \n",
        "\n",
        "\n",
        "Далее в коде подтверждение нашим словам\n",
        "\n",
        "А также функция, которая скидывает компании с отрицательными занчениями "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dOkVeVwVEuer"
      },
      "source": [
        "### 3.a Тута технический код на него не надо смотреть (надо запустить и скрыть)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W_bNEZvgd2CW"
      },
      "source": [
        "# Тута технический код на него не надо смотреть\n",
        "class Grapher:\n",
        "    def __init__(self, \n",
        "                 train, test=[], target_col = 'default_12m',\n",
        "                 figsize = (10,5), target_colors = {0:'gray',1:'red'}\n",
        "                 ):         \n",
        "\n",
        "        self.df_original = (train,test)\n",
        "        self.target = train[target_col]\n",
        "        self.df = train.drop(columns=[target_col])\n",
        "        self.dfs_by_target = [train[train[target_col]==target_value] for target_value in train[target_col].unique()]\n",
        "\n",
        "        self.test = test\n",
        "\n",
        "        self.plt_figsize = figsize\n",
        "        self.plt_colored_target = self.target.map(target_colors)\n",
        "        self.fplt_dims = {self.scatter:2,\n",
        "                          self.scatter3d:3,\n",
        "                          self.hist:1}\n",
        "\n",
        "    def show_cyclic(self,fplt):\n",
        "        '''\n",
        "        shows #[n-constant] fplt plots of df with cyclic change in the dimensions\n",
        "        Example:\n",
        "            g = Grapher( pd.DataFrame({a:[],b:[],c:[]}) )\n",
        "            g.show_cyclic(g.scatter) \n",
        "            -> \n",
        "            prints scatter plots with cyclic change in the dimensions:\n",
        "            g.scatter(a,b);g.scatter(b,c);g.scatter(c,a)\n",
        "        !: \n",
        "            it will show #[n-constant] graphics (it is almost always less than #qty of df columns)\n",
        "        '''\n",
        "\n",
        "        n=self.fplt_dims[fplt]\n",
        "        index_start = 0\n",
        "        for i in range(len(self.df.columns)-n):\n",
        "            fplt(*(self.df.columns[0+i:n+i]))\n",
        "\n",
        "    \n",
        "    def show_all(self,fplt):\n",
        "        '''\n",
        "        shows all possible fplt plots of df\n",
        "        Example:\n",
        "            g = Grapher( pd.DataFrame({a:[],b:[],c:[]}) )\n",
        "            g.show_all(g.scatter) \n",
        "            -> \n",
        "            prints all possible scatter plots:\n",
        "            g.scatter(a,b);g.scatter(a,c);g.scatter(b,c)\n",
        "        !: \n",
        "            if many df columns then there are too many graphs to show\n",
        "        '''\n",
        "\n",
        "        from itertools import combinations as combi\n",
        "        n=self.fplt_dims[fplt]\n",
        "\n",
        "        for dims in combi(self.df.columns,n):\n",
        "            fplt(*dims)\n",
        "\n",
        "    def show_separate(self, fplt):\n",
        "        '''\n",
        "        should show 3 fplt plots side by side: \n",
        "        {{default+alive,default,alive}}\n",
        "        In general should show n+1 fplt plots side by side:\n",
        "        {{everething, target_group_1, target_group_2, ... , target_group_n}}\n",
        "        '''\n",
        "        # all = self.df\n",
        "        # dead = all[self.target == 1]\n",
        "        # alive = all[all[self.target != 1]\n",
        "\n",
        "        # all = Grapher(train=all)\n",
        "        # dead = Grapher(train=dead)\n",
        "        # alive = Grapher(train=alive)\n",
        "\n",
        "        # all.scatter3d()\n",
        "        # dead.scatter3d()\n",
        "        # alive.scatter3d()\n",
        "        pass\n",
        "    \n",
        "    def scatter(self,\n",
        "                x_name = 'bus_age', y_name = 'ul_capital_sum',\n",
        "                alpha = 0.3,\n",
        "                xscale = 'linear',\n",
        "                yscale = 'linear'\n",
        "                ):\n",
        "        \n",
        "        fig, ax = plt.subplots(figsize=self.plt_figsize)\n",
        "\n",
        "        ax.scatter(\n",
        "                x = self.df[x_name], \n",
        "                y = self.df[y_name], \n",
        "                c = self.plt_colored_target,\n",
        "                alpha = alpha\n",
        "                )\n",
        "        ax.set_xscale(xscale)\n",
        "        ax.set_yscale(yscale)\n",
        "        plt.xlabel(x_name)\n",
        "        plt.ylabel(y_name)\n",
        "\n",
        "        plt.show()\n",
        "\n",
        "    def scatter3d(self,\n",
        "                  x_name='ul_capital_sum',\n",
        "                  y_name='ul_founders_cnt',\n",
        "                  z_name='bus_age',\n",
        "                  alpha = 0.3,\n",
        "                  xscale = 'linear',\n",
        "                  yscale = 'linear',\n",
        "                  zscale = 'linear'):\n",
        "        \n",
        "        fig = plt.figure(figsize=self.plt_figsize)\n",
        "        ax = fig.add_subplot(projection='3d')\n",
        "\n",
        "        ax.scatter(self.df[x_name], \n",
        "                   self.df[y_name], \n",
        "                   self.df[z_name], \n",
        "                   c = self.plt_colored_target,\n",
        "                   alpha = alpha)\n",
        "        \n",
        "        ax.set_xscale(xscale)\n",
        "        ax.set_yscale(yscale)\n",
        "        ax.set_zscale(zscale)\n",
        "        ax.set_xlabel(x_name)\n",
        "        ax.set_ylabel(y_name)\n",
        "        ax.set_zlabel(z_name)\n",
        "\n",
        "        plt.show()\n",
        "\n",
        "    def hist(self,\n",
        "             x_name='ar_revenue',\n",
        "             n_bins = 30,\n",
        "             alpha = 0.3,\n",
        "             xscale = 'linear',):\n",
        "        \n",
        "        fig, ax =  plt.subplots(figsize=self.plt_figsize)\n",
        "\n",
        "        \n",
        "        # for i,color in self.plt_colored_target.items():\n",
        "        dim_0 = self.df[self.target == 0][x_name]\n",
        "        dim_1 = self.df[self.target != 0][x_name]\n",
        "        ax.hist(dim_0, n_bins, stacked=True, density=False,color='Gray')\n",
        "        ax.hist(dim_1, n_bins, stacked=True, density=False,color='Red')\n",
        "\n",
        "\n",
        "        ax.set_xlabel(x_name)\n",
        "        # ax.set_title('Scores by group and gender')\n",
        "        # ax.legend()\n",
        "\n",
        "        plt.show()\n",
        "\n",
        "    def hist3d(self, n_bins = 10):\n",
        "        pass\n",
        "\n",
        "df_ab = train_df.iloc[:,:24]\n",
        "df_ab['default_12m'] = train_df['default_12m'] \n",
        "\n",
        "size = (6,3)\n",
        "g = Grapher(train = df_ab, figsize=size)\n",
        "gt = Grapher(train = df_ab[df_ab.default_12m == 0], figsize=size)\n",
        "gm = Grapher(train = df_ab[df_ab.default_12m != 0], figsize=size)"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yVOlTl7oE4aJ"
      },
      "source": [
        "### 3.b На графиках можно видеть что отрицательных данные есть и их вроде не много\n",
        "\n",
        "Каждая точка это одна компания. Серая точка НЕ объявила дефолт, крансная объявила дефолт."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "QyUnPC-FID-T",
        "outputId": "404a4426-1960-484b-bb41-756d0b6e00d1"
      },
      "source": [
        "g.show_cyclic(g.hist)"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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I3ZL+VNLXbW+XNHvP4mslvd/2Dknvk7Sux6rXqfoLPTvV3587u1HSybb3SvqYpK39tgnoB7c5BYDk6FEDQHIcTAQ62P5VvfQMkB9FxK+Poh5AYugDANJj6AMAkiOoASA5ghoAkiOoASA5ghoAkvs/j8XGHsw2CP8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+NtyWDFa5hHE38Aiw0XbV51v8DvBrwDP9POiMD3pJfyVp6wSvqnu1MTtJOhb4E+AS298fdnsGyfbTtpfSfKN+maSfHnabBknSzwOP2N7S72NPm7FuDpbttwy7DUPWepiJmNkkHUkT8n9g+4Zht+dwsf09SbfR3Jep+Qb8KcA5ks4GjgF+XNIXbf/ioR54xvfoo9UQFTHDlWG/Pwc8YPu3h92eQZM0IumFZfl5wBnA3w23VYNl+9dtz7e9iOb/8a39CHmoPOglvU3SLuB1wF9IumXYbeq3Miz02BAVDwB/bHvbcFs1WJKuA74KvEzSLkkXDbtNh8EpwHuAN0u6u7zOHnajBug44DZJ99J0Zjba7tvjhrNNvhkbEVG5qnv0ERGRoI+IqF6CPiKicgn6iIjKJegjIobsQAbqk/TJrievHpT0vZ775KmbiIjhkvRGYB9wre3W3wCW9EHgZNv/dart0qOPoZF0iaQf7VrfN9X2wyLpyjJU7pWS3ifpl0r5hZJ+YsDvfftUQ2xL+pakeYNsQwzeRAP1SXqppJslbZF0p6SXT7Dr+cB1vY4/44dAiOlD0pwDnBP4EuCLwON9eO8jypfHDmZf0fx2O9lAUquAF01wbhfSfCX/nw7mfSN6WAu8z/Y3Jf0s8CngzWOVkk4AFgO39jpQevTRmqQ/Lb2LbZJWlbJ9kn5L0j0030CeaL+fKxMp3FeuRR4t6UPAT9B8+/G2rm0/Xiab2CTpJaVsRNKfSNpcXqeU8tWSviDpK8AXJnnvCyX9WekZf1PS5aV8UZms5VqasF5QeuxbSzvfVbbbABwLbJH0rvKeH5F0HtAB/qBcK33eJO9/WWnzVklryw+VsZ76J9RMrvGgpDeU8udJWi/pAUk3AhMed5L3+nDXoH6XlLLnS/qL8plu7TqvK9RMYnKvpN/s8Tm/qeua8F2Sfqxtm+LgqBm87vXAl9SM4Plpmm8Ld1sJXN+qc2U7r7xavWh6tdCEz1bgxTRjhP+XKfY5hmYY5f9Q1q+lGXkR4FvAvK5tDfznsvwbwP8sy38InFqWF9KM9wKwGtgCPG+K978Q+HZp61i7O8AimqFgX1u2ewewEZgDvAT4R+C4Urev63irgY+U5duBTpvPrCx/oev8bgd+qyyfDfxVWf4wsK4sv4pmwpFJ32PsMwReA9wHPJ/mB9M2mqGM3wF8pmv7F5TPYjs/vEf3wh6f858Dp5TlY4Ejhv1vscZX+Te5tSz/OPDtHtvfBby+zbHTo48D8aHSc99EM2LmEuBpmhEVJ/My4CHbD5b1zwNvnGTbp4Cx8Uy20PzDB3gL8HulZ7OBZlS/Y0vdBttP9Gj3RtuPlu1uAE4t5f9ge1NZPhW4zs3QuP8X+BvgP/Y4bhunS/qapPtofu1+ZVfd2AiU3ef6RprLWdi+F7i35fucCtxo+1/dTNhxA/AGmvA/o/z28AbbjwGPAT8APifp7fzw0tlkn/NXgN8uv4W90Ad5iSzaczME9UOS3gnN5UVJJ43Vl+v1c2nGfOopQR+tSDqNJghe52Z6t7toeus/8IFdl5/Kv7l0VWh+gIzdQ/oRmp730vI63j+cfehfWxx3/KNlY+tt9j1oko6hua56nu2fAT5D85mNebL82X2ufVV+wL6aJvD/l6TLSlAvA64Hfh64uWw+4eds+wrgvTS/EX1lkpuCcQg08UB97wYuKp2rbTx75riVwPqu/y9TStBHWy8Avmv78fIfve38nduBRZJ+qqy/h6a3DPAvQJvrvX8JfHBsRdLSlu895gxJLyrX0c+l6aGOdyfwLjWzGo3Q9Ky/3uO4vdo/FurfKT3j81q09Q7gFwDUTLTxqhb7QNP+cyX9qKTnA28D7lTzVNDjtr8IXAm8urTlBbZvAn4VGOspTvg5S3qp7ftsf4JmJMkEfZ/ZPt/2cbaPdDNU8edsP2R7ue2TbJ9oe03X9qttX9r2+HnqJtq6GXifpAdowntTj+0BsP0DSb9Mc1PpCJqguLpUrwVulvRPtk+f4jAfAq5SM2TtETRh+L4DaPvXaS4vzQe+aHtUzXR83W6kuZl8D02P/9ds/3OP414DXC3pCZrfdJ51CcnNhBmfobkv8M80597L7wP/u3zOD9Bc1unJ9jckXcMPfzh91vZdks4CrpT0DPBvwPtpfjj9WfmNQzT3BWDyz/kSSafT3NPYBny5TZti+sgXpqJqki6kuZl58bDbEjEsuXQTEVG59Oijb8pz34vHFf932wOf2atcovjEuOKHbL9t0O9d3n+g5y7pa8DR44rfY/u+fhw/6pagj4ioXC7dRERULkEfEVG5BH1EROUS9BERlUvQR0RU7v8BG87TxhIZzIEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+eX+6Ged+CXygX/ceuglrbqE/gtu3qs4CqKqf9ut3A/42ySP6fe0L3J2uBN+Y5LXAR6vqcyO7W3yMjWz9E3EpewPvSnI/uv9Qdhu57bNV9cM+w9XAveiK8vyqWujXf4BuwqbtuRA4LckHRzI9DjgoyeIwxd50z9ORwDurf8OIqlpucv1Fnx643agjgQOy9T009uqP3i4E3pTkvcCZVbVlhfvVKqqqHyX5ZpJjq+qMdP9gB1XV5v41mO9VN23tS4FTx+3PoQ8BkORRdCXw0Ko6mO6trHZfYtOVTg7zDLoj2MOq6hC6yap2r25mskPpCvvVSV4xcp/FaWF/wfJzpr8KOK+qDgR+f5u8o1PLjtvPkqrqecDL6f7T2tiPI4buiP+Q/uPeVfWpZXZzI1t/z7Z9Pv93pZn6fT1k5PH3raobqhvDfi7dX0MXLjdkpNWX5HTgIuABSbYkOZHuZ//EJJuBq9j6ouGjgK8k+SrdQcvfjNu/Ra1FewPfr6qf9L/kD+nX345umlmAPwA+v9Sdq+rHwJYkT4Sb3pz4Tv1+r6+qn6ebjP1e/e33AH5SVe8BXk9X2juSeXGu4xMGbH8J3djg3foj/WOX2zjJfavqkqp6Bd00o/sDnwSe39+fJPdPcmfg08Bz+u+ZJHftd3MtcFi//FSG+THdvOBL+RQj7/iS5JCRrFdU1WvpplK1qKeoqo6vqn2qareq2q+q3lFV36yqo6rq4Ko6oKpe2W/7oaq6X1Xdv6qeWwPmq7eotegcYNck1wCvoZuHGrqjvsPTncj/GLoXurbnWXTDJ5fTjdf+BvBeYF2SK4BnA1/ut30Q8MX+hZa/Bl69A5lfB/xd/6LM2CPm6uZFPpnuyOdC4Joxd3l9/+LglXTfz2a6aTCvBi7t17+Nbjz4HLq5mOf77+mkfh9voCv2yxg++f95dMMbm7Z5kRXgRXTP5+X9kM7z+vUv6V+MvJxuCs8m3kJKq8NpTiWpcR5RS1LjPOtDK5bkLdzyHOR/rKp3TujxngO8eJvVF1bVC1Zp/y/jluPVZ1TV2Bd5pGlw6EOSGufQhyQ1zqKWpMZZ1JLUOItakhpnUUtS4/4f4UXwkObB5BoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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r3gX8YT9A/IFJXpvuiy2uTPKtJE97Yi2r1iXZiu4/69OTXAX8I7Bjv/g44KAkV9IN0HYX8MicO+oZ1Jqkt/e9iSXA+/pew5Z0jynvCXwbOGEt238B+GB/Lu8a4ITqBrTZIskvAwcC08CB/ZCnK+uxcb93pBsJ7jXAR9f0AUkOo/vV80VVtTfwsX7RmVX1wn7eDXSPCs9YTDfexKuBk5JsMbOgqu4ATgI+XlX7VNUlwHeAF1fVvnTjJX9gLT+zNgxPAu7vj4GZ13MAquo/q+r1/fFwfD/v/rXtzGFONUnvS3JkP/0MumEpfwGc1s/7InDmXBsm2QbYtqq+3c/6PN24LtANaHMA3bdt/BXdRZwAlwzs4qtV9Qvg+iE92FcAn5sJ+HpsWMrnJvkLYFtgK+AbA9t8ud/3zUluoxsgam12Ak7rvzhgM7qBmLQBq6oHktye5A1VdXr/hSZ7VdWKftS/e/tj6EPAycP2Z49aE5HkYLoQfEnfK72SbhCt2R7PYDMX0/Wmdwb+Fdibrvc8GNQPDUzncXzGKcB7qup5wFJWr312zcN+hn+g+4qz5wHvZO520HosyanAZcCzk9yZ5BjgTcAxSWYGYpq5aHgwcGOSm4CnAX85bP8GtSZlG+C+qvp5f7X7xf38JwEz54x/h+60wP9TVT8F7ktyYD/rLXSnSqAL5DcDN/e9knvpRlCbc19DnA+8beAc9Pb9/K2Bu5NsSvcPbtAbkjwpybPoRkW7cdbyn/Xbz9iGx8ZInuiX2WphVNUbq2rHqtq0qnaqqs9W1e1VdWhV7V1Ve1TVh/t1z6iq3arq16rqHTUwPv2aGNSalPOARUluoDtHvLyf/1/Afv2DAYfQ3Ue6Jm8F/jrJ1cA+M+v254FD17OGLqDvr8e++29kVXUecDYw3V/0Oa5f9GfA5cB3gR/O2uxHdMPqnks3vOV/z1r+NeDImYuJdEOynt5fBL1nXWuUHOZUWgdJTqH7NuwzFroWbTzsUUtS4+xRa8El+RTdXRyD/r6qPjfGz3ge8E+zZj9UVS8a12dIk2JQS1LjPPUhSY0zqCWpcQa1JDXOoJakxhnUktS4/wPhDWxxHEPgMAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Un_r7o8AEh9E",
        "outputId": "5bdf7656-88d5-43e8-9a40-1b131b04de7c"
      },
      "source": [
        "g.show_cyclic(g.scatter)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x108 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x108 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x108 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x108 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x108 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x108 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x108 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x108 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x108 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x108 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x108 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x108 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x108 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x108 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x108 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x108 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x108 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x108 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2eJjEvn10d3dTKpWYmJggEonYShMOh+3Qrra2NpLJ5B1mrNvtJpfLLWviOizF7XavOMfZiCe4EWV7EPh24IdF5N8CA8BzqvrJVY5ZrqDPty6z3w+IyKPANeBfqerN23cQkQ9ilrvb8HhEG1W4fBleeonEwgJzoRDtPT2oKuVymfHxcbLZLOVy2Vaezs5OO9N7YmICj8fDoUOHUFUmJiaYmpoiGAzi8XiIxWJMTExQKBQol8v2HxoMBpmbnKTN7WamWiXIa9478Xjw1sxPwA5GthTNyujOZDK2IqsqpVIJr9drf7XFY6jGfhLTZJ2ZmUFVaW1tpbW1ddtOP6wH0WgUwzAoFov2b5jNZgkGgw1NtDcyZjsvItcxVx79DuBfYK5os5qy1cP/BT6jqgUR+SnMUguPLfP5TwJPgpk8epefWR/nzsHXvgYdHYyHQvjTabIvvID7xAkC0SiJRIIrV67Yk7Eul4tsNktPTw/xeJxcLkelUqFYLDI5Ocn8/LztGJmbmyMSiRCLxRgfH7cvfitRdWJujn7DIB4KUcpkqNaURjMZ6Oggl8uRTqftu65lwk5MTOB2u5mbm+PGjRu88sorNDU1YRgG/f39BINBKpUK6XSa9vZ2ZmdnAXNcWY/i3bx5k8nJSTvHbmRkhIWFBfr6+raF13Aj8Hq99Pf3244qK1u+0SmJRsZsZzAL/Hwd0xv5qKreWP2otQv6qOrcos0/Zu3Cr5tDsQhnzsD+/ajLRSaVIhQOU5qcJDg3RzESYWpqClUlFovZ5qRlSpZKJXs+rFQq4Xa7bU+W1cukUin8fr9dLi+fz5NIJKhWq5TLZb7h8xFYWED8fhLpNMlcjoLbTSmZpOnSJSYmJuju7iYYDNpxhVbphng8TmtrKwCBQIBcLsfw8DAHDhzAMAz8fj/nz5+nVCohIni9Xh555BFWq1yWz+eZmppa4ijwer0kEgnbNN6thEIh3vCGN9gBBT6fr+GbSyM2xOOq2mim5ItAn4gcwlSy9wI/vHiH24qyvhPYHiFh2SxUq+B2I0CT202xWkV9PoxUirm5OVKpFG63m9nZWbLZLJVKxe41rEGzx+Nhfn7eNh0rlQotLS122FalUsHr9dp5a5aZ5nK5qDQ1oV4vgyMj+FXJ+Xxk3W4q+Txev59sNsvo6Kg9SZ5KpfB4PIRCIcLhMF6v1+5Z9+zZQyKR4OjRo4gIX/rSl/D7/bbTJJfL8fWvf53HH398ibm5GOtCu/0iE5FlI0J2GyJyV3VnGlG2ooj8PvBobfs54KOrzbWpallEfgb4G0zX/6dU9VUR+ShwRlW/APyciLwTKAPzwAdex/dYf5qawOWCUgk8HvYHg1xNJPC63Yyk04wMDdm9QjqdxuVy4fV6yefzlEolyuUyLpfLfhSLRfbv34/X67UVIpVK0d/fT7VaZXh4GBEhlUrZF7WqMpHJUGlpIa1KpVrFI0LA7aZYLOLxeBgbGyObzRIIBAgEAlSrVebn52lrayMUClGtVvF6vfaYTlWZnZ2lWq0uCYEKBALMz88zNTW14iT9So4CVV1RQR1eoxFl+xRwEfjnte0fAf4EePdqB6nqFzGraC1u+5VFr38Z+OUG5NgcPB74lm+B55+H9naiPh9HKxUGXS5Gy2Wq1SqqaocVWU4Ti3K5jNvtJhaL2RPfbW1teDweRkZGiMfjeL1ebt26RTabZWJiwh57Wa752dlZe8Uc69yBQADDMGyHijXNYClUsVikVCoxPz9PLBajWq0SDAbJ5/P4fD5CoRATEytXC1xcwu92mpqaCIVCpNNp2zGQy+Xw+Xw72rW/WTSibEdU9QcWbf+aiJxbb4E2i3g8zvT0NACdnZ3Lm0APPACBALz0EszO4tu/nzOZjFmm4Lbo9OXu+Pl8nunpaXw+H4VCgcHBQeLxOOVymWg0SmtrK4ODg5Rrymv1ZlbdEeuc1pivWCwuqe41NTVlV2/2eDy4XC7bE5rJZEilUrS0tNiOmtOnT2MYBu3t7faNwurxLCVbLTJFROjt7eXmzZvMz88D5jTDwYMHd7U3cr1oRNlyIvLtqvpVABF5M5Bb45htycWLF7ly5Yo99rh48SIPPfQQhw4dWrqjCPT3Q38/5XKZv/jsZ5mslberl3K5bPdKlsPE6/WSyWQYGBggn78z3NQaZ1n7Wu576zyGYeD1eikWi3ZZdFWlubkZr9dLqVRi7969PProo4yPj+P3++nv77cdJs3NzfT393Pt2jXbNKxWq9x3331rxvt5PB4OHz7MwYMHUdV1qe5lxR0uLCzgcrloaWnZ8blry9HIL/Uh4GkRseyFOPBj6y/SxhKPx7ly5QrRaNS+GxeLRV5++WU6OzuXTeWYnp7m7NmzDA8Pr4sMxWKx7pw2a7/F5p1lwi4u+JNOp+16kuVymdOnTzM3N0ehUMDn8zE2NrYk+fXkyZN0dnYyPj6OiLB///6G4i3XqydbXArBurFMTk7S09Ozqmd0J9LQPBtwUkSsMghLbvEi8mOqerflyDccq7Lw4ovF6mmmp6eXTJjfunWLr3zlK0xMTNiewu3CSpW14vE4DzzwAFevXiWXy+HxeOyLt1AocPz4cYLBIADt7e20t6+6nuWGk0ql7og7rFQqjI6O0tzcXFfPaXl1b6+vud1o2Aa4XckW8fPcfe3/DWfxWMgqB+fz+ZaMX8DsKZ599lkymcyS6I6dgLWIo+WhVFUGBwfp6upidHTUdv+D2bNYAdZbcaEmk0ncbveSz7ZKQORyuVWj/0ulEjdu3CAejwPmTbOnp2fbOmvumVJ2xWKRubk58vk8mUyGmZkZEomE7ZCIRqNEIhF7QvqVV14hkUiQz+frX3l0sSdvEx0GixXHeraWIrYK7VgeT8scPXLkCPPz84yPj1OpVPD5fHR3dxOLxRgYGOD8+fOkUilaW1s5deoU+/btA0zvoxW2tB5rHXg8nmVLIajqmgHSw8PDpFIpu/BSqVRiYGCAEydObMvM7vVUtm1768/n81y5coVyuWw7EGZmZuwAYYBMJsMzzzxDb2+vvSBiJpO5s0dbxTV+x36bpHCLZbTMLutCteqUeDweDMNgz549pFIpLl++TD6ft8viFYtFBgYGADh79qydkT47O8v/+/zneduhQ1TjcWaDQQpdXajHQ0tLCz09PXc1fovFYty6dYtSqWSnqmQyGYLBoG3uLocVbbPYi+zxeCgUCszNzbF//7Jram4p90TPNjExQbVatU2SZDJpV3GyLkQrsDgUCjE7O2t7+GyqVTMwuRE2UeEsTpw4YSeIZrNZisWiHbtppfFUq1WuX79OX1+frSjWTegf//EfaWpqsnuGSCJB7OxZbly+TOu+feypVKhMTJB57DHm5+fx+/13dWH7fD56e3sZHh4mn8/XHXdYqVSWfd/lcm3bFWLXU9m+to7nWlesxSnA/JMKhYKdn2TFK2azWTsjd1nT5jZFc5XLhFMpRJV0KERppQgKqyfcYKUzDINAIMD4+Dj79u1jfn7evhgNw6Cjo4Pe3l5cLpc9Br29R7I8nNYUAdUqbZcuUYpGmSmX6Whro2wYuCcn8QwP09Tby/T09F33ItFolAceeMCOnKnH0eH3+zEMY9mE3u0aNtZIIHIr8KuYCaSKmTj6USuQeDsmf6bTaZ577jkGBwdRVSKRiJ0CY81vLa6jD9hjuiWu+UrlNWUTIZxIcGRoCFelggBVEW4cOMDcaq7qDezlrPW/rRqLqVSKnp4eezIcYO/evfZFmc/naWlpWZIyAtihXVa7O5/Hk8+TrN2okjWzTcJhPGNj5Pv67DnAu3WuWPVG6sXlcnHgwAGGh4ftkLhCoUA0Gt221ZMb6dk+CzwPWFEk7wc+x2uLI24rKpUKzzzzDMlkkmg0SjweZ25ujunp6VUXpLAmnlFdGiVSrYJhEFhYILawwK2ODqqGgRoG7mqV1rk5UuEwxS0YmFu9lZWpHQ6HOXr0KJ2dnVQqFQYHB+0aJprL0SxCS3s715NJKpUKHo+HfD6Py+XigQce4MKFC2YyqttNJptlIZfD6/MxNDSE2+2mJxwmfOIE2WyWtra2LXO3t7W1EQgEbKdWV1fXpmSev14aUbZOVf31Rdu/ISI/tNZBddQg8QFPAw8Dc8APqepIA3Ity+joKIlEgubmZjsa3jCMJeFOdzg7rJ5ncXtN6VzlMl1jY1RdLgQIFotkQiEyXi+BUol0OIy3WFxd2WoKu95YF5fb7aZUKtl5ctaY9OjRo2TSaYrnzuG7cIGgYSCq+GIxJk+cIFet0tbWZtct8Xg8XLp0iVQ+TyEUojORwN3ZSS6fp5zNMjU8zMIjjxD1+Wwv5VbR1NS0Y6JNGlG2L4nIe4HP17Z/EDOaf0XqrEHyBBCvFRR6L+Z6Amsq8XIkk0kWFhbw+Xwkk0nbrV8qlez5Jisrelmv4nJt1SqIsHdqCjUMSh4P7mqVKhCLxym0tyOqlF2utd2xG9QDlEolgsEghUKBUCjEvn37lrjlRYRQMgnnz0NXl31TCc3M0DsyAu9855LznT59mocffphr167xtWoVpqYIjo/jB8ouF4P3309XS4td48ShPtZUNhFJYY7RBPgw8OnaWy4gDfzSKofXU4PkXZhjQYC/AD4mIqINzCJXq1UuXLjA4ODgkmDeSqWyxNFhGIbZCzSwplpt9RCaFxaY7ujAXVNIAYpeL5FUilQggEeE/Fom5OtQNpfLhd/vp1QqLRlHWuXe8vk8brebQCBAW1sbx44d49ChQ3eadlevQii0dNy4Zw+MjUEqBbdNHltKVHW7mT1xgnhvL0a5TDkQIFfz5jqK1hhrKpuq2v+CiLQAfdS/pG89NUjsfWr5bwmgFZhdvNNqNUjGx8cZGBigubnZNqmSySSGYbCwsGA7RKxaIZk6aj0u+exqFVmk+1XDQGo9ngKecplkOExlHXO63G43+/fvp1KpkMvl7CiLWCxmB+pa+W9WEPXtYWhLKJVWNmFXuPlYJmKpVAK/nwqvxWV2dXWtw7e8t2jEG/kvMUOyuoBzwCOYJRK+a2NEW8pqNUhGRkbsiAmLUChk108cHR0llUrR3t5OIBAgX6s8dYd3UPWO3kcBDINkOEwgmyUfCOCqVk3HSKHAQlsbeZ+P0lq9Wp29gLUGwP333097ezvBYJBMJkM8HufcuXPk83m7klahUCAYDLJv3761Ywj7+mBgAGKx175jMmlur+Aqj0ajvPGNb+Tll1+2rYJyuUxPT8+aVaAd7qSRMdvPA98CfENVv1NEjgG/ucYxa9YgWbTPLRFxA1FMR0ndLDfBaV0cDz74IG95y1vsVJQXX3yR1vZ22mdmmCoUyIhQBapeLypyp6nnciGFAuMtLRwZH8dQpezxINUq2aYm0oEAulqRzjqVzDAMurq6eNvb3mZPT1iEw2HC4TAtLS2cP3+e+fl5VJWmpiaOHz9en4Ogpwfuu880J91uc3zq98M73rGqeXv69Gn27dvHwMAA5XKZw4cP33XUyL1KI8qWV9V8rQaFT1WviMjRNY5ZswYJ8AXMVJ1/wHS6/F0j4zUwVwU9c+aMncUMZshPOBy287MMwyAajbJ//36zVztyhCMDA1REKBWLzGUyxEMhCsHgnYmhPh8lr5druRwhrxdvpULO5yNTqbx2oVoi13oYa+6rra2N48eP26FI5XKZiYkJpqeniUajdsjUsWPH6O7uXtVtHQqFeOSRR+wydU1NTXVX48Uw4LHH4P77YWoKgkHo7jaTY9egq6vLMRvXgUaU7ZaIxDCXjPpbEYkDq1bXqrMGySeBT4vIIGYNkvc2+iUOHDjA+Pg4ExMTdqVaj8fDww8/fMfFe+jQIQYHBym0tFB8+GFKk5MUCgX6+vp4w4MPcv7rX8ddKlHw+TCCQaouFxXD4NSpU3Zp8KmpKWKxGP39/XbW80pzTUNDQ/Yif2COxVpaWjhy5AgHDx5seE7I5XK9/ggJEejsNB8Om468ntQREXkLprn3rKo2vrLfXXLq1Ck9c+bMkjZrsYO5uTn8fj9dXV0rRn4nk0kuXrxoF1E9fPgwx44dw+12Mz09zY0b5j3EWlust7f3dV/g+Xyeq1ev2gV6rArEx44dc4rk7FJE5KyqnrqjfSflaVksp2zrSaFQsDOpToAAAATwSURBVBeCCIfDd536XyqViMfjdhXdlpaWdSkn4LA92VXKJiIzrGHCbjBt3DY1scNxvs/6clBV7wiU3ZHKttWIyJnl7lw7Fef7bA7bM2LTwWEX4iibg8Mm4Sjb6+PJrRZgnXG+zybgjNkcHDYJp2dzcNgkHGVzcNgkHGVrEBF5u4hcFZHB2nLHOxYR+ZSITIvIxa2W5W4RkW4R+XsRuSQir4rIz2+1TLfjjNkaoJZ5fo1FmefA+27LPN8x1NYxTwNPq+qJrZbnbhCRTszSHS+JSBg4C3z/dvpvnJ6tMezM81pMqJV5viNR1ecxg793PKo6oaov1V6nMFew3VaVWh1la4zlMs+31R/qACLSAzwIfHNrJVmKo2wOuwoRCQH/G/jwKovAbAmOsjVGPZnnDluEiHgwFe3PVPUvt1qe23GUrTHszHMR8WImun5hi2VyAMTM3v0kcFlVf3+r5VkOR9kaQFXLgJV5fhn4vKq+urVSvX5E5DOY5SiOisgtEXliq2W6C94M/AjwmIicqz2+Z6uFWozj+ndw2CScns3BYZNwlM3BYZNwlM3BYZNwlM3BYZNwlM3BoUYjgdkicqAW+PyyiFyox/PpKJuDw2s8Bby9zn0/gjn18yDmfOsn1jrAUbYdjIik197L3vcpEfnBjZRnp7NcYLaIHBGRZ0XkrIi8UFvjAsw1V6zKvVFgfK3zO5VCHRxW50ngQ6o6ICLfitmDPYa5puCXRORngSbqWO7a6dl2CCLyV7W766u1teqs9v9ca/uyiNxRGHSFc31XbazxSm2c4qu1/1Yt+fKCiPxere09InJRRM6LyPO1NpeI/K6IvFjb96dq7Z0i8nwteuOiiHzH+v8Sm0ctqPnbgD8XkXPAfweshRLeBzylql3A92CuV7G6PlmrdDqP7f0AWmrPAeAi5oKRCry/1v4rwMdWOf4pzFWC/JhpQv219qcxV5RtBa7yWlRRrPb8CrD/trYPAh+pvfYBZ4BDwC8C/67W7gLCW/27vY7fuQe4WHsdASZW2O9VoHvR9hDQvtq5nZ5t5/BzInIe+AZm5kEfUAU+V3v/fwLfXsd5jgLDqnqttv2nwKNAAsgDnxSRdwPZ2vtfA54SkZ/EVCCA7wZ+tHa3/yamovZhBmr/uIj8KvAGNZM4dyxqpugMi8h7wAx2FpGTtbdHqS0EKiL3Yd7EZlY7n6NsOwAReSvmmOBNqnoSeJnll1p+3YGuagZZn8Zc1/wdwLO19g9het66gbMi0oq5pPjPquoba49DqvolNR0Mj2KmHT0lIj/6euXZClYIzH4/8ETtRvcqr2Xm/yLwk7X2zwAf0FoXtxKOg2RnEAXiqpqtecMeqbUbmKbhZzEXmfxqHee6CvSISK+qDmJGyj9XG58EVfWLIvI1TLMIETmiqt8Evikij2Mq3d8APy0if6eqJRHpx1SwNuCWqv6P2jjwIUwzdUegqu9b4a07pgPUrG3y5kbO7yjbzuBZ4EMichlTWb5Ra88Ap0XkI8A08ENrnUjN1WN/HHPQ78Y0/f4IaAH+j4j4MXuuX6gd8rsi0ldr+zJwHriAObZ5qZZHNgN8P/BW4F+LSAmzkNCO6tk2GifFxsFhk3DGbA4Om4RjRu4yROTj3DmW+ANV/ZOtkMfhNRwz0sFhk3DMSAeHTcJRNgeHTcJRNgeHTcJRNgeHTeL/A3FSJjDqur3oAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 216x108 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x108 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x108 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 216x108 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dh2tdmQNGrjv"
      },
      "source": [
        "### 3.с Тута функция которая возвращает df с положительными"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AGR0NIxBJJdT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1c0d7815-80d6-4e4a-e94a-9b73bfbacb73"
      },
      "source": [
        "def remove_all_negs(df = train_df):\n",
        "    k = df.min()\n",
        "    return remove_cols_negs(df = train_df, cols=k[k<0].keys())\n",
        "\n",
        "def remove_cols_negs(df = train_df, cols=[1*'ar_total_expenses',\n",
        "                                              1*'ar_sale_cost',\n",
        "                                              1*'ar_selling_expenses',\n",
        "                                              1*'ar_management_expenses',\n",
        "                                              0*'ar_sale_profit',\n",
        "                                              0*'ar_balance_of_rvns_and_expns',\n",
        "                                              0*'ar_profit_before_tax',\n",
        "                                              1*'ar_taxes',\n",
        "                                              0*'ar_other_profit_and_losses',\n",
        "                                              0*'ar_net_profit',\n",
        "                                              1*'ab_accounts_receivable',\n",
        "                                              1*'ab_other_current_assets',\n",
        "                                              1*'ab_losses',\n",
        "                                              1*'ab_own_capital',\n",
        "                                              1*'ab_long_term_liabilities',\n",
        "                                              1*'ab_short_term_borrowing',\n",
        "                                              1*'ab_other_borrowings']):\n",
        "    for column_with_negs in cols:\n",
        "        if column_with_negs == '':\n",
        "            continue\n",
        "        df = df[(train_df[column_with_negs]>=0) | (train_df[column_with_negs].isna())]\n",
        "    return df\n"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Если убрать ar_total_expenses, теряется 6.621392190152804% выборки\n",
            "Если убрать все отрицательные значения, теряется 40.79950609661985% выборки\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y7eqQommO4mV",
        "outputId": "16d4c67a-d037-41e5-af91-ce21a8bb4aaa"
      },
      "source": [
        "print(f'Если убрать все кроме столбцов с profit, теряется {100-len(remove_cols_negs())/len(train_df)*100}% выборки')\n",
        "print(f'Если убрать все отрицательные значения, теряется {100-len(remove_all_negs())/len(train_df)*100}% выборки')"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Если убрать все кроме столбцов с profit, теряется 6.621392190152804% выборки\n",
            "Если убрать все отрицательные значения, теряется 40.79950609661985% выборки\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2mA2CY9oQpFp"
      },
      "source": [
        "## 4. Функции, которые разбивают на 2 выборки (с и без фин отчетности)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UCOcY6cOQ9NS",
        "outputId": "674b39aa-1892-4ee8-9712-c331a66e6162"
      },
      "source": [
        "def sep_df_byfin(df = train_df):\n",
        "    df_nofin = get_df_nofin(df = train_df)\n",
        "    df_fin   = get_df_fin(df = train_df)\n",
        "    return df_fin, df_nofin\n",
        "    \n",
        "def get_df_nofin(df = train_df):\n",
        "    return train_df.dropna()\n",
        "\n",
        "def get_df_fin(df = train_df):\n",
        "    return pd.concat([train_df, train_df.dropna()]).drop_duplicates(keep=False)['default_12m']\n",
        "\n",
        "print(f'{100*len(get_df_fin(df = train_df))/len(train_df)}% компаний имеют фин отчетность')\n",
        "print(f'{100*len(get_df_nofin(df = train_df))/len(train_df)}% компаний НЕ имеют фин отчетность')"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "46.751041827442506% компаний имеют фин отчетность\n",
            "53.248958172557494% компаний НЕ имеют фин отчетность\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ZNtzf6nOj_x"
      },
      "source": [
        "## 5. И да, тут очень странный чувак"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BxL9wg55OqPf"
      },
      "source": [
        "# train_df[train_df['record_id'] != 196113] #этот чувак очень странный"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}