hw_5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e573717c",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "98f469be",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bbacb4c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [\n",
    "    ['ozon.ru/product/293783766', 0.8, 2776, 4.8, 68],\n",
    "    ['ozon.ru/product/151517600', 0.65, 6394, 4.7, 80],\n",
    "    ['ozon.ru/product/8751202', 2.4, 4468, 4.9, 84],\n",
    "    ['ozon.ru/product/147927590', 1.5, 8090, 4.85, 76],\n",
    "    ['ozon.ru/product/278676526', 1.2, 2490, 4.8, 75],\n",
    "    ['ozon.ru/product/185555856', 0.6, 12143, 4.8, 69],\n",
    "    ['ozon.ru/product/146189876', 1.3, 5257, 4.75, 83],\n",
    "    ['ozon.ru/product/143167442', 2, 7590, 4.9, 79],\n",
    "    ['ozon.ru/product/141378152', 2.5, 5690, 4.9, 83],\n",
    "    ['ozon.ru/product/29636513', 4, 12790, 4.9, 78]\n",
    "]\n",
    "\n",
    "df = pd.DataFrame(data, columns=['link', 'volume', 'price', 'rating', 'noise'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "482f9b16",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.scatter(df, x='volume_rank', y='price_rank')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "108cdff1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.sort_values(['volume_rank', 'price_rank'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26b237dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edecfd55",
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import reduce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5990f10e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mult(el_prev, el):\n",
    "    return el_prev*el"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ac8387a",
   "metadata": {},
   "outputs": [],
   "source": [
    "is_dominated({'volume_rank': 5,\n",
    "              'price_rank': 5}, df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6381599f",
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {x[0]: {'volume_rank': x[6],\n",
    "                 'price_rank': x[7]} for x in df.to_records()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e664b037",
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2dfc8807",
   "metadata": {},
   "outputs": [],
   "source": [
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9286bbc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "pareto_1 = [[key, is_dominated(params[key], df)] for key in params.keys()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "685b6f2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "pareto_1_df = df.join(pd.DataFrame(pareto_1, index=[x[0] for x in pareto_1],\n",
    "                                   columns=['index', 'is_dominated'])).drop(columns=['index'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be2f8531",
   "metadata": {},
   "outputs": [],
   "source": [
    "pareto_1_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b11ece3",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.scatter(pareto_1_df, x='volume_rank', y='price_rank', color = 'is_dominated')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5af5159",
   "metadata": {},
   "outputs": [],
   "source": [
    "params_1 = {x[0]: {'volume_rank': x[4],\n",
    "                   'price_rank': x[5]} for x in pareto_1_df[pareto_1_df['is_dominated']].to_records()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f263e62d",
   "metadata": {},
   "outputs": [],
   "source": [
    "pareto_2 = [[key, is_dominated(params[key], pareto_1_df[pareto_1_df['is_dominated']])] for key in params.keys()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74afcdd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "pareto_2_df = pareto_1_df.join(pd.DataFrame(pareto_2, index=[x[0] for x in pareto_2],\n",
    "                                   columns=['index', 'is_dominated_2'])).drop(columns=['index'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1edc9716",
   "metadata": {},
   "outputs": [],
   "source": [
    "pareto_2_df['pareto_set'] = pareto_2_df['is_dominated'].astype(int) + pareto_2_df['is_dominated_2'].astype(int)\n",
    "pareto_2_df['pareto_set'] = pareto_2_df['pareto_set'].astype(str)\n",
    "pareto_2_df['size'] = 1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2583e8f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.scatter(pareto_2_df, x='volume_rank', y='price_rank', color = 'pareto_set', size='size')\n",
    "fig.update_layout(\n",
    "    xaxis = dict(\n",
    "        tickmode = 'linear',\n",
    "        tick0 = 0,\n",
    "        dtick = 1\n",
    "    )\n",
    ")\n",
    "fig.update_layout(\n",
    "    yaxis = dict(\n",
    "        tickmode = 'linear',\n",
    "        tick0 = 0,\n",
    "        dtick = 1\n",
    "    )\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76fb7edf",
   "metadata": {},
   "outputs": [],
   "source": [
    "pareto_2_df['ip'] = (10 - pareto_2_df['volume_rank'])**2 + (10 - pareto_2_df['price_rank'])**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "160e777d",
   "metadata": {},
   "outputs": [],
   "source": [
    "pareto_2_df[pareto_2_df['pareto_set']=='0'].sort_values('ip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcad4955",
   "metadata": {},
   "outputs": [],
   "source": [
    "pareto_2_df[pareto_2_df['pareto_set']=='1'].sort_values('ip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f70686ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "98f5dfe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "79083cd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_dominated(list1, list2):\n",
    "    if len(list1) == len(list2):\n",
    "        compared = []\n",
    "        for i in range(len(list1)):\n",
    "            compared.append(list1[i]>list2[i])\n",
    "        return np.prod(compared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9396811a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dominates(series):\n",
    "    bool_list = []\n",
    "    for record in series:\n",
    "        dominators = []\n",
    "        for pos_d in series:\n",
    "            if np.prod(np.array(pos_d)>np.array(record)) == 1:\n",
    "                dominators.append(pos_d)\n",
    "        if len(dominators) > 0:\n",
    "            bool_list.append(True)\n",
    "        else:\n",
    "            bool_list.append(False)\n",
    "    return bool_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3cf2dbe2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pareto_set(df_raw, param_dict):\n",
    "    df_proc = df_raw.copy()[list(param_dict.keys())]\n",
    "    for param in param_dict.keys():\n",
    "        df_proc[param+'_rank'] = df_proc[param].rank(ascending=param_dict[param], method='dense')\n",
    "    ranks = [param+'_rank' for param in param_dict.keys()]\n",
    "    df_proc['is_dominated'] = dominates(df_proc[ranks].values.tolist())\n",
    "    return df_proc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "26402ece",
   "metadata": {},
   "outputs": [],
   "source": [
    "pareto_1 = pareto_set(df, {'volume': True,\n",
    "                           'price': False})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e0ee7919",
   "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>volume</th>\n",
       "      <th>price</th>\n",
       "      <th>volume_rank</th>\n",
       "      <th>price_rank</th>\n",
       "      <th>is_dominated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.80</td>\n",
       "      <td>2776</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.65</td>\n",
       "      <td>6394</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.40</td>\n",
       "      <td>4468</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.50</td>\n",
       "      <td>8090</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.20</td>\n",
       "      <td>2490</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.60</td>\n",
       "      <td>12143</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.30</td>\n",
       "      <td>5257</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.00</td>\n",
       "      <td>7590</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.50</td>\n",
       "      <td>5690</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.00</td>\n",
       "      <td>12790</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   volume  price  volume_rank  price_rank  is_dominated\n",
       "0    0.80   2776          3.0         9.0          True\n",
       "1    0.65   6394          2.0         5.0          True\n",
       "2    2.40   4468          8.0         8.0         False\n",
       "3    1.50   8090          6.0         3.0          True\n",
       "4    1.20   2490          4.0        10.0         False\n",
       "5    0.60  12143          1.0         2.0          True\n",
       "6    1.30   5257          5.0         7.0          True\n",
       "7    2.00   7590          7.0         4.0          True\n",
       "8    2.50   5690          9.0         6.0         False\n",
       "9    4.00  12790         10.0         1.0         False"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pareto_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3338d93",
   "metadata": {},
   "outputs": [],
   "source": [
    "pareto_set(df, {'volume': True,\n",
    "                'price': False})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "593a11ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "pareto_2 = pareto_set(pareto_1[pareto_1['is_dominated']], {'volume': True,\n",
    "                                                           'price': False})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "09492510",
   "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>volume</th>\n",
       "      <th>price</th>\n",
       "      <th>volume_rank</th>\n",
       "      <th>price_rank</th>\n",
       "      <th>is_dominated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.80</td>\n",
       "      <td>2776</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.65</td>\n",
       "      <td>6394</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.50</td>\n",
       "      <td>8090</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.60</td>\n",
       "      <td>12143</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.30</td>\n",
       "      <td>5257</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.00</td>\n",
       "      <td>7590</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   volume  price  volume_rank  price_rank  is_dominated\n",
       "0    0.80   2776          3.0         6.0         False\n",
       "1    0.65   6394          2.0         4.0          True\n",
       "3    1.50   8090          5.0         2.0          True\n",
       "5    0.60  12143          1.0         1.0          True\n",
       "6    1.30   5257          4.0         5.0         False\n",
       "7    2.00   7590          6.0         3.0         False"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pareto_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "47ec8afc",
   "metadata": {},
   "outputs": [],
   "source": [
    "pareto_12 = pareto_1.join(pareto_2['is_dominated'], how='left', lsuffix='2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1f6e0a8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pareto_12['group'] = pareto_12['is_dominated'].fillna(False).astype(int)+pareto_12['is_dominated2'].fillna(False).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1520fad1",
   "metadata": {},
   "outputs": [],
   "source": [
    "pareto_12['group'] = pareto_12['group'].astype(str)\n",
    "pareto_12['size'] = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "933ca628",
   "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>volume</th>\n",
       "      <th>price</th>\n",
       "      <th>volume_rank</th>\n",
       "      <th>price_rank</th>\n",
       "      <th>is_dominated2</th>\n",
       "      <th>is_dominated</th>\n",
       "      <th>group</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.80</td>\n",
       "      <td>2776</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.65</td>\n",
       "      <td>6394</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.40</td>\n",
       "      <td>4468</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.50</td>\n",
       "      <td>8090</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.20</td>\n",
       "      <td>2490</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.60</td>\n",
       "      <td>12143</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.30</td>\n",
       "      <td>5257</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.00</td>\n",
       "      <td>7590</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.50</td>\n",
       "      <td>5690</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.00</td>\n",
       "      <td>12790</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   volume  price  volume_rank  price_rank  is_dominated2 is_dominated group  \\\n",
       "0    0.80   2776          3.0         9.0           True        False     1   \n",
       "1    0.65   6394          2.0         5.0           True         True     2   \n",
       "2    2.40   4468          8.0         8.0          False          NaN     0   \n",
       "3    1.50   8090          6.0         3.0           True         True     2   \n",
       "4    1.20   2490          4.0        10.0          False          NaN     0   \n",
       "5    0.60  12143          1.0         2.0           True         True     2   \n",
       "6    1.30   5257          5.0         7.0           True        False     1   \n",
       "7    2.00   7590          7.0         4.0           True        False     1   \n",
       "8    2.50   5690          9.0         6.0          False          NaN     0   \n",
       "9    4.00  12790         10.0         1.0          False          NaN     0   \n",
       "\n",
       "   size  \n",
       "0     2  \n",
       "1     2  \n",
       "2     2  \n",
       "3     2  \n",
       "4     2  \n",
       "5     2  \n",
       "6     2  \n",
       "7     2  \n",
       "8     2  \n",
       "9     2  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pareto_12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f7668a09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "group=1<br>volume_rank=%{x}<br>price_rank=%{y}<br>size=%{marker.size}<extra></extra>",
         "legendgroup": "1",
         "marker": {
          "color": "#636efa",
          "size": [
           2,
           2,
           2
          ],
          "sizemode": "area",
          "sizeref": 0.005,
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "1",
         "orientation": "v",
         "showlegend": true,
         "type": "scatter",
         "x": [
          3,
          5,
          7
         ],
         "xaxis": "x",
         "y": [
          9,
          7,
          4
         ],
         "yaxis": "y"
        },
        {
         "hovertemplate": "group=2<br>volume_rank=%{x}<br>price_rank=%{y}<br>size=%{marker.size}<extra></extra>",
         "legendgroup": "2",
         "marker": {
          "color": "#EF553B",
          "size": [
           2,
           2,
           2
          ],
          "sizemode": "area",
          "sizeref": 0.005,
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "2",
         "orientation": "v",
         "showlegend": true,
         "type": "scatter",
         "x": [
          2,
          6,
          1
         ],
         "xaxis": "x",
         "y": [
          5,
          3,
          2
         ],
         "yaxis": "y"
        },
        {
         "hovertemplate": "group=0<br>volume_rank=%{x}<br>price_rank=%{y}<br>size=%{marker.size}<extra></extra>",
         "legendgroup": "0",
         "marker": {
          "color": "#00cc96",
          "size": [
           2,
           2,
           2,
           2
          ],
          "sizemode": "area",
          "sizeref": 0.005,
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "0",
         "orientation": "v",
         "showlegend": true,
         "type": "scatter",
         "x": [
          8,
          4,
          9,
          10
         ],
         "xaxis": "x",
         "y": [
          8,
          10,
          6,
          1
         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "legend": {
         "itemsizing": "constant",
         "title": {
          "text": "group"
         },
         "tracegroupgap": 0
        },
        "margin": {
         "t": 60
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "volume_rank"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "price_rank"
         }
        }
       }
      },
      "text/html": [
       "<div>                            <div id=\"e9d392c2-dbd2-4d6b-b763-c8cff325d929\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"e9d392c2-dbd2-4d6b-b763-c8cff325d929\")) {                    Plotly.newPlot(                        \"e9d392c2-dbd2-4d6b-b763-c8cff325d929\",                        [{\"hovertemplate\":\"group=1<br>volume_rank=%{x}<br>price_rank=%{y}<br>size=%{marker.size}<extra></extra>\",\"legendgroup\":\"1\",\"marker\":{\"color\":\"#636efa\",\"size\":[2,2,2],\"sizemode\":\"area\",\"sizeref\":0.005,\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"1\",\"orientation\":\"v\",\"showlegend\":true,\"type\":\"scatter\",\"x\":[3.0,5.0,7.0],\"xaxis\":\"x\",\"y\":[9.0,7.0,4.0],\"yaxis\":\"y\"},{\"hovertemplate\":\"group=2<br>volume_rank=%{x}<br>price_rank=%{y}<br>size=%{marker.size}<extra></extra>\",\"legendgroup\":\"2\",\"marker\":{\"color\":\"#EF553B\",\"size\":[2,2,2],\"sizemode\":\"area\",\"sizeref\":0.005,\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"2\",\"orientation\":\"v\",\"showlegend\":true,\"type\":\"scatter\",\"x\":[2.0,6.0,1.0],\"xaxis\":\"x\",\"y\":[5.0,3.0,2.0],\"yaxis\":\"y\"},{\"hovertemplate\":\"group=0<br>volume_rank=%{x}<br>price_rank=%{y}<br>size=%{marker.size}<extra></extra>\",\"legendgroup\":\"0\",\"marker\":{\"color\":\"#00cc96\",\"size\":[2,2,2,2],\"sizemode\":\"area\",\"sizeref\":0.005,\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"0\",\"orientation\":\"v\",\"showlegend\":true,\"type\":\"scatter\",\"x\":[8.0,4.0,9.0,10.0],\"xaxis\":\"x\",\"y\":[8.0,10.0,6.0,1.0],\"yaxis\":\"y\"}],                        {\"legend\":{\"itemsizing\":\"constant\",\"title\":{\"text\":\"group\"},\"tracegroupgap\":0},\"margin\":{\"t\":60},\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"volume_rank\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"price_rank\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('e9d392c2-dbd2-4d6b-b763-c8cff325d929');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.scatter(pareto_12, x='volume_rank', y='price_rank', color = 'group', size = 'size')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7aed99ce",
   "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>link</th>\n",
       "      <th>volume</th>\n",
       "      <th>price</th>\n",
       "      <th>rating</th>\n",
       "      <th>noise</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ozon.ru/product/293783766</td>\n",
       "      <td>0.80</td>\n",
       "      <td>2776</td>\n",
       "      <td>4.80</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ozon.ru/product/151517600</td>\n",
       "      <td>0.65</td>\n",
       "      <td>6394</td>\n",
       "      <td>4.70</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ozon.ru/product/8751202</td>\n",
       "      <td>2.40</td>\n",
       "      <td>4468</td>\n",
       "      <td>4.90</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ozon.ru/product/147927590</td>\n",
       "      <td>1.50</td>\n",
       "      <td>8090</td>\n",
       "      <td>4.85</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ozon.ru/product/278676526</td>\n",
       "      <td>1.20</td>\n",
       "      <td>2490</td>\n",
       "      <td>4.80</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        link  volume  price  rating  noise\n",
       "0  ozon.ru/product/293783766    0.80   2776    4.80     68\n",
       "1  ozon.ru/product/151517600    0.65   6394    4.70     80\n",
       "2    ozon.ru/product/8751202    2.40   4468    4.90     84\n",
       "3  ozon.ru/product/147927590    1.50   8090    4.85     76\n",
       "4  ozon.ru/product/278676526    1.20   2490    4.80     75"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bbdbc44e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pareto_1_2 = pareto_set(df, {'volume': True,\n",
    "                             'price': False,\n",
    "                             'rating': True,\n",
    "                             'noise': False})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f4f6706d",
   "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>volume</th>\n",
       "      <th>price</th>\n",
       "      <th>rating</th>\n",
       "      <th>noise</th>\n",
       "      <th>volume_rank</th>\n",
       "      <th>price_rank</th>\n",
       "      <th>rating_rank</th>\n",
       "      <th>noise_rank</th>\n",
       "      <th>is_dominated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.80</td>\n",
       "      <td>2776</td>\n",
       "      <td>4.80</td>\n",
       "      <td>68</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.65</td>\n",
       "      <td>6394</td>\n",
       "      <td>4.70</td>\n",
       "      <td>80</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.40</td>\n",
       "      <td>4468</td>\n",
       "      <td>4.90</td>\n",
       "      <td>84</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.50</td>\n",
       "      <td>8090</td>\n",
       "      <td>4.85</td>\n",
       "      <td>76</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.20</td>\n",
       "      <td>2490</td>\n",
       "      <td>4.80</td>\n",
       "      <td>75</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.60</td>\n",
       "      <td>12143</td>\n",
       "      <td>4.80</td>\n",
       "      <td>69</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.30</td>\n",
       "      <td>5257</td>\n",
       "      <td>4.75</td>\n",
       "      <td>83</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.00</td>\n",
       "      <td>7590</td>\n",
       "      <td>4.90</td>\n",
       "      <td>79</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.50</td>\n",
       "      <td>5690</td>\n",
       "      <td>4.90</td>\n",
       "      <td>83</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.00</td>\n",
       "      <td>12790</td>\n",
       "      <td>4.90</td>\n",
       "      <td>78</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   volume  price  rating  noise  volume_rank  price_rank  rating_rank  \\\n",
       "0    0.80   2776    4.80     68          3.0         9.0          3.0   \n",
       "1    0.65   6394    4.70     80          2.0         5.0          1.0   \n",
       "2    2.40   4468    4.90     84          8.0         8.0          5.0   \n",
       "3    1.50   8090    4.85     76          6.0         3.0          4.0   \n",
       "4    1.20   2490    4.80     75          4.0        10.0          3.0   \n",
       "5    0.60  12143    4.80     69          1.0         2.0          3.0   \n",
       "6    1.30   5257    4.75     83          5.0         7.0          2.0   \n",
       "7    2.00   7590    4.90     79          7.0         4.0          5.0   \n",
       "8    2.50   5690    4.90     83          9.0         6.0          5.0   \n",
       "9    4.00  12790    4.90     78         10.0         1.0          5.0   \n",
       "\n",
       "   noise_rank  is_dominated  \n",
       "0         9.0         False  \n",
       "1         3.0          True  \n",
       "2         1.0         False  \n",
       "3         6.0         False  \n",
       "4         7.0         False  \n",
       "5         8.0         False  \n",
       "6         2.0         False  \n",
       "7         4.0         False  \n",
       "8         2.0         False  \n",
       "9         5.0         False  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pareto_1_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "41a66a1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ideal(df_raw, param_dict):\n",
    "    df_proc = df_raw.copy()[list(param_dict.keys())]\n",
    "    value_cols = []\n",
    "    for param in param_dict.keys():\n",
    "        df_proc[param+'_rank'] = df_proc[param].rank(ascending=param_dict[param], method='dense')\n",
    "        df_proc[param+'_value']= (df_proc[param+'_rank'].max()-df_proc[param+'_rank'])**2\n",
    "        value_cols.append(param+'_value')\n",
    "    df_proc['ideal'] = 0\n",
    "    for col in value_cols:\n",
    "        df_proc['ideal'] += df_proc[col]\n",
    "    return df_proc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ca6050e2",
   "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>volume</th>\n",
       "      <th>price</th>\n",
       "      <th>volume_rank</th>\n",
       "      <th>volume_value</th>\n",
       "      <th>price_rank</th>\n",
       "      <th>price_value</th>\n",
       "      <th>ideal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.80</td>\n",
       "      <td>2776</td>\n",
       "      <td>3.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.65</td>\n",
       "      <td>6394</td>\n",
       "      <td>2.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.40</td>\n",
       "      <td>4468</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.50</td>\n",
       "      <td>8090</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.20</td>\n",
       "      <td>2490</td>\n",
       "      <td>4.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.60</td>\n",
       "      <td>12143</td>\n",
       "      <td>1.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.30</td>\n",
       "      <td>5257</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.00</td>\n",
       "      <td>7590</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.50</td>\n",
       "      <td>5690</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.00</td>\n",
       "      <td>12790</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   volume  price  volume_rank  volume_value  price_rank  price_value  ideal\n",
       "0    0.80   2776          3.0          49.0         9.0          1.0   50.0\n",
       "1    0.65   6394          2.0          64.0         5.0         25.0   89.0\n",
       "2    2.40   4468          8.0           4.0         8.0          4.0    8.0\n",
       "3    1.50   8090          6.0          16.0         3.0         49.0   65.0\n",
       "4    1.20   2490          4.0          36.0        10.0          0.0   36.0\n",
       "5    0.60  12143          1.0          81.0         2.0         64.0  145.0\n",
       "6    1.30   5257          5.0          25.0         7.0          9.0   34.0\n",
       "7    2.00   7590          7.0           9.0         4.0         36.0   45.0\n",
       "8    2.50   5690          9.0           1.0         6.0         16.0   17.0\n",
       "9    4.00  12790         10.0           0.0         1.0         81.0   81.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ideal(df, {'volume': True,\n",
    "           'price': False})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8cd16ec9",
   "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>link</th>\n",
       "      <th>volume</th>\n",
       "      <th>price</th>\n",
       "      <th>rating</th>\n",
       "      <th>noise</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ozon.ru/product/293783766</td>\n",
       "      <td>0.80</td>\n",
       "      <td>2776</td>\n",
       "      <td>4.80</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ozon.ru/product/151517600</td>\n",
       "      <td>0.65</td>\n",
       "      <td>6394</td>\n",
       "      <td>4.70</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ozon.ru/product/8751202</td>\n",
       "      <td>2.40</td>\n",
       "      <td>4468</td>\n",
       "      <td>4.90</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ozon.ru/product/147927590</td>\n",
       "      <td>1.50</td>\n",
       "      <td>8090</td>\n",
       "      <td>4.85</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ozon.ru/product/278676526</td>\n",
       "      <td>1.20</td>\n",
       "      <td>2490</td>\n",
       "      <td>4.80</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        link  volume  price  rating  noise\n",
       "0  ozon.ru/product/293783766    0.80   2776    4.80     68\n",
       "1  ozon.ru/product/151517600    0.65   6394    4.70     80\n",
       "2    ozon.ru/product/8751202    2.40   4468    4.90     84\n",
       "3  ozon.ru/product/147927590    1.50   8090    4.85     76\n",
       "4  ozon.ru/product/278676526    1.20   2490    4.80     75"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a7068724",
   "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>volume</th>\n",
       "      <th>price</th>\n",
       "      <th>rating</th>\n",
       "      <th>noise</th>\n",
       "      <th>volume_rank</th>\n",
       "      <th>volume_value</th>\n",
       "      <th>price_rank</th>\n",
       "      <th>price_value</th>\n",
       "      <th>rating_rank</th>\n",
       "      <th>rating_value</th>\n",
       "      <th>noise_rank</th>\n",
       "      <th>noise_value</th>\n",
       "      <th>ideal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.20</td>\n",
       "      <td>2490</td>\n",
       "      <td>4.80</td>\n",
       "      <td>75</td>\n",
       "      <td>4.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.80</td>\n",
       "      <td>2776</td>\n",
       "      <td>4.80</td>\n",
       "      <td>68</td>\n",
       "      <td>3.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.50</td>\n",
       "      <td>5690</td>\n",
       "      <td>4.90</td>\n",
       "      <td>83</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.00</td>\n",
       "      <td>7590</td>\n",
       "      <td>4.90</td>\n",
       "      <td>79</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.40</td>\n",
       "      <td>4468</td>\n",
       "      <td>4.90</td>\n",
       "      <td>84</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.50</td>\n",
       "      <td>8090</td>\n",
       "      <td>4.85</td>\n",
       "      <td>76</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.30</td>\n",
       "      <td>5257</td>\n",
       "      <td>4.75</td>\n",
       "      <td>83</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.00</td>\n",
       "      <td>12790</td>\n",
       "      <td>4.90</td>\n",
       "      <td>78</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.65</td>\n",
       "      <td>6394</td>\n",
       "      <td>4.70</td>\n",
       "      <td>80</td>\n",
       "      <td>2.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.60</td>\n",
       "      <td>12143</td>\n",
       "      <td>4.80</td>\n",
       "      <td>69</td>\n",
       "      <td>1.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   volume  price  rating  noise  volume_rank  volume_value  price_rank  \\\n",
       "4    1.20   2490    4.80     75          4.0          36.0        10.0   \n",
       "0    0.80   2776    4.80     68          3.0          49.0         9.0   \n",
       "8    2.50   5690    4.90     83          9.0           1.0         6.0   \n",
       "7    2.00   7590    4.90     79          7.0           9.0         4.0   \n",
       "2    2.40   4468    4.90     84          8.0           4.0         8.0   \n",
       "3    1.50   8090    4.85     76          6.0          16.0         3.0   \n",
       "6    1.30   5257    4.75     83          5.0          25.0         7.0   \n",
       "9    4.00  12790    4.90     78         10.0           0.0         1.0   \n",
       "1    0.65   6394    4.70     80          2.0          64.0         5.0   \n",
       "5    0.60  12143    4.80     69          1.0          81.0         2.0   \n",
       "\n",
       "   price_value  rating_rank  rating_value  noise_rank  noise_value  ideal  \n",
       "4          0.0          3.0           4.0         7.0          4.0   44.0  \n",
       "0          1.0          3.0           4.0         9.0          0.0   54.0  \n",
       "8         16.0          5.0           0.0         2.0         49.0   66.0  \n",
       "7         36.0          5.0           0.0         4.0         25.0   70.0  \n",
       "2          4.0          5.0           0.0         1.0         64.0   72.0  \n",
       "3         49.0          4.0           1.0         6.0          9.0   75.0  \n",
       "6          9.0          2.0           9.0         2.0         49.0   92.0  \n",
       "9         81.0          5.0           0.0         5.0         16.0   97.0  \n",
       "1         25.0          1.0          16.0         3.0         36.0  141.0  \n",
       "5         64.0          3.0           4.0         8.0          1.0  150.0  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ideal(df, {'volume': True,\n",
    "           'price': False,\n",
    "           'rating': True,\n",
    "           'noise': False}).sort_values('ideal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "759d69fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def linear_convolution(df_raw, param_dict, weights):\n",
    "    df_proc = df_raw.copy()[list(param_dict.keys())]\n",
    "    value_cols = []\n",
    "    for param in param_dict.keys():\n",
    "        df_proc[param+'_rank'] = df_proc[param].rank(ascending=param_dict[param], method='dense')\n",
    "        df_proc[param+'_value']= df_proc[param+'_rank']*weights[param]\n",
    "        value_cols.append(param+'_value')\n",
    "    df_proc['convolution'] = 0\n",
    "    for col in value_cols:\n",
    "        df_proc['convolution'] += df_proc[col]\n",
    "    return df_proc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "43968143",
   "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>volume</th>\n",
       "      <th>price</th>\n",
       "      <th>rating</th>\n",
       "      <th>noise</th>\n",
       "      <th>volume_rank</th>\n",
       "      <th>volume_value</th>\n",
       "      <th>price_rank</th>\n",
       "      <th>price_value</th>\n",
       "      <th>rating_rank</th>\n",
       "      <th>rating_value</th>\n",
       "      <th>noise_rank</th>\n",
       "      <th>noise_value</th>\n",
       "      <th>convolution</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.65</td>\n",
       "      <td>6394</td>\n",
       "      <td>4.70</td>\n",
       "      <td>80</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.60</td>\n",
       "      <td>12143</td>\n",
       "      <td>4.80</td>\n",
       "      <td>69</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.00</td>\n",
       "      <td>12790</td>\n",
       "      <td>4.90</td>\n",
       "      <td>78</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.50</td>\n",
       "      <td>8090</td>\n",
       "      <td>4.85</td>\n",
       "      <td>76</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.30</td>\n",
       "      <td>5257</td>\n",
       "      <td>4.75</td>\n",
       "      <td>83</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.00</td>\n",
       "      <td>7590</td>\n",
       "      <td>4.90</td>\n",
       "      <td>79</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.50</td>\n",
       "      <td>5690</td>\n",
       "      <td>4.90</td>\n",
       "      <td>83</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.40</td>\n",
       "      <td>4468</td>\n",
       "      <td>4.90</td>\n",
       "      <td>84</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.80</td>\n",
       "      <td>2776</td>\n",
       "      <td>4.80</td>\n",
       "      <td>68</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.20</td>\n",
       "      <td>2490</td>\n",
       "      <td>4.80</td>\n",
       "      <td>75</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   volume  price  rating  noise  volume_rank  volume_value  price_rank  \\\n",
       "1    0.65   6394    4.70     80          2.0           0.4         5.0   \n",
       "5    0.60  12143    4.80     69          1.0           0.2         2.0   \n",
       "9    4.00  12790    4.90     78         10.0           2.0         1.0   \n",
       "3    1.50   8090    4.85     76          6.0           1.2         3.0   \n",
       "6    1.30   5257    4.75     83          5.0           1.0         7.0   \n",
       "7    2.00   7590    4.90     79          7.0           1.4         4.0   \n",
       "8    2.50   5690    4.90     83          9.0           1.8         6.0   \n",
       "2    2.40   4468    4.90     84          8.0           1.6         8.0   \n",
       "0    0.80   2776    4.80     68          3.0           0.6         9.0   \n",
       "4    1.20   2490    4.80     75          4.0           0.8        10.0   \n",
       "\n",
       "   price_value  rating_rank  rating_value  noise_rank  noise_value  \\\n",
       "1          3.0          1.0           0.4         3.0          0.9   \n",
       "5          1.2          3.0           1.2         8.0          2.4   \n",
       "9          0.6          5.0           2.0         5.0          1.5   \n",
       "3          1.8          4.0           1.6         6.0          1.8   \n",
       "6          4.2          2.0           0.8         2.0          0.6   \n",
       "7          2.4          5.0           2.0         4.0          1.2   \n",
       "8          3.6          5.0           2.0         2.0          0.6   \n",
       "2          4.8          5.0           2.0         1.0          0.3   \n",
       "0          5.4          3.0           1.2         9.0          2.7   \n",
       "4          6.0          3.0           1.2         7.0          2.1   \n",
       "\n",
       "   convolution  \n",
       "1          4.7  \n",
       "5          5.0  \n",
       "9          6.1  \n",
       "3          6.4  \n",
       "6          6.6  \n",
       "7          7.0  \n",
       "8          8.0  \n",
       "2          8.7  \n",
       "0          9.9  \n",
       "4         10.1  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_convolution(df,\n",
    "                   {'volume': True,\n",
    "                    'price': False,\n",
    "                    'rating': True,\n",
    "                    'noise': False},\n",
    "                   {'volume': 0.2,\n",
    "                    'price': 0.6,\n",
    "                    'rating': 0.4,\n",
    "                    'noise': 0.3}).sort_values('convolution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59a5034d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "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": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}