D_IndividualItems.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3f2fac94",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/luisalvarez/opt/anaconda3/lib/python3.9/site-packages/outdated/utils.py:14: OutdatedPackageWarning: The package pingouin is out of date. Your version is 0.5.2, the latest is 0.5.3.\n",
      "Set the environment variable OUTDATED_IGNORE=1 to disable these warnings.\n",
      "  return warn(\n"
     ]
    }
   ],
   "source": [
    "import Useful_functions as uf\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8b46c5f1",
   "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>Unnamed: 0</th>\n",
       "      <th>Decoy</th>\n",
       "      <th>Item</th>\n",
       "      <th>Option_1</th>\n",
       "      <th>Option_2</th>\n",
       "      <th>Option_3</th>\n",
       "      <th>Choice</th>\n",
       "      <th>CatchTrials</th>\n",
       "      <th>Deliberation_RT</th>\n",
       "      <th>Choice_RT</th>\n",
       "      <th>Too_Slow</th>\n",
       "      <th>Survey_ID</th>\n",
       "      <th>CouplingCondition</th>\n",
       "      <th>PhantomCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>Kitchen Scale</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10000</td>\n",
       "      <td>620.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40781</td>\n",
       "      <td>High coupling</td>\n",
       "      <td>Highly_desirable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>Baby crib</td>\n",
       "      <td>C_A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10000</td>\n",
       "      <td>389.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40781</td>\n",
       "      <td>High coupling</td>\n",
       "      <td>Highly_desirable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>Baby high chair</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>S_B</td>\n",
       "      <td>B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10000</td>\n",
       "      <td>761.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40781</td>\n",
       "      <td>High coupling</td>\n",
       "      <td>Highly_desirable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>Leaf Blower</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A_B</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10000</td>\n",
       "      <td>308.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40781</td>\n",
       "      <td>High coupling</td>\n",
       "      <td>Highly_desirable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>P</td>\n",
       "      <td>Wifi extender</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>P_B</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10000</td>\n",
       "      <td>586.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40781</td>\n",
       "      <td>High coupling</td>\n",
       "      <td>Highly_desirable</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 Decoy             Item Option_1 Option_2 Option_3 Choice  \\\n",
       "0           0     F    Kitchen Scale        A        C        B      C   \n",
       "1           1     C        Baby crib      C_A        B        A      A   \n",
       "2           2     S  Baby high chair        A        B      S_B      B   \n",
       "3           3     A      Leaf Blower        A        B      A_B      A   \n",
       "4           4     P    Wifi extender        A        B      P_B      A   \n",
       "\n",
       "   CatchTrials  Deliberation_RT  Choice_RT  Too_Slow  Survey_ID  \\\n",
       "0          NaN            10000      620.0         0      40781   \n",
       "1          NaN            10000      389.0         0      40781   \n",
       "2          NaN            10000      761.0         0      40781   \n",
       "3          NaN            10000      308.0         0      40781   \n",
       "4          NaN            10000      586.0         0      40781   \n",
       "\n",
       "  CouplingCondition  PhantomCondition  \n",
       "0     High coupling  Highly_desirable  \n",
       "1     High coupling  Highly_desirable  \n",
       "2     High coupling  Highly_desirable  \n",
       "3     High coupling  Highly_desirable  \n",
       "4     High coupling  Highly_desirable  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_responses_filtered = pd.read_csv(\"all_responses_filtered.csv\")\n",
    "\n",
    "all_responses_filtered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0d9b5728",
   "metadata": {},
   "outputs": [],
   "source": [
    "decoy_S_df = all_responses_filtered[all_responses_filtered[\"Decoy\"] == \"S\"]\n",
    "decoy_C_df = all_responses_filtered[all_responses_filtered[\"Decoy\"] == \"C\"]\n",
    "decoy_A_df = all_responses_filtered[all_responses_filtered[\"Decoy\"] == \"A\"]\n",
    "decoy_P_df = all_responses_filtered[all_responses_filtered[\"Decoy\"] == \"P\"]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7c8ec5b",
   "metadata": {},
   "source": [
    "### Similarity Decoy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d782d11f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n"
     ]
    }
   ],
   "source": [
    "decoy_S_TargetA_df = decoy_S_df.loc[decoy_S_df['Option_1'].str.contains('S_A') | decoy_S_df['Option_2'].str.contains('S_A') | decoy_S_df['Option_3'].str.contains('S_A')]\n",
    "decoy_S_TargetB_df = decoy_S_df.loc[decoy_S_df['Option_1'].str.contains('S_B') | decoy_S_df['Option_2'].str.contains('S_B') | decoy_S_df['Option_3'].str.contains('S_B')]\n",
    "\n",
    "decoy_S_Items_TargetA_df = uf.itemFreq_Decoy(decoy_S_TargetA_df, \"Sim_Decoy\")\n",
    "decoy_S_Items_TargetB_df = uf.itemFreq_Decoy(decoy_S_TargetB_df, \"Sim_Decoy\")\n",
    "\n",
    "item_list = decoy_S_TargetA_df[\"Item\"].unique()\n",
    "item_list = np.sort(item_list)\n",
    "\n",
    "Sim_items_targets_A = np.array([])\n",
    "Sim_items_competitors_A = np.array([])\n",
    "Sim_items_decoys_A = np.array([])\n",
    "\n",
    "Sim_items_targets_B = np.array([])\n",
    "Sim_items_competitors_B = np.array([])\n",
    "Sim_items_decoys_B = np.array([])\n",
    "\n",
    "Sim_items_targets_A_sem = np.array([])\n",
    "Sim_items_competitors_A_sem = np.array([])\n",
    "Sim_items_decoys_A_sem = np.array([])\n",
    "\n",
    "Sim_items_targets_B_sem = np.array([])\n",
    "Sim_items_competitors_B_sem = np.array([])\n",
    "Sim_items_decoys_B_sem = np.array([])\n",
    "\n",
    "for item in item_list:\n",
    "    \n",
    "    current_item_A = decoy_S_Items_TargetA_df[decoy_S_Items_TargetA_df[\"Item\"] == item]\n",
    "    current_item_B = decoy_S_Items_TargetB_df[decoy_S_Items_TargetB_df[\"Item\"] == item]\n",
    "    \n",
    "    Sim_items_targets_A = np.append(Sim_items_targets_A, current_item_A[\"Target(%)\"])\n",
    "    Sim_items_competitors_A = np.append(Sim_items_competitors_A, current_item_A[\"Competitor(%)\"])\n",
    "    Sim_items_decoys_A = np.append(Sim_items_decoys_A, current_item_A[\"Sim_Decoy(%)\"])\n",
    "    \n",
    "    Sim_items_targets_B = np.append(Sim_items_targets_B, current_item_B[\"Target(%)\"])\n",
    "    Sim_items_competitors_B = np.append(Sim_items_competitors_B, current_item_B[\"Competitor(%)\"])\n",
    "    Sim_items_decoys_B = np.append(Sim_items_decoys_B, current_item_B[\"Sim_Decoy(%)\"])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bc6daa20",
   "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>Item</th>\n",
       "      <th>Target(num)</th>\n",
       "      <th>Competitor(num)</th>\n",
       "      <th>Sim_Decoy(num)</th>\n",
       "      <th>Target(%)</th>\n",
       "      <th>Competitor(%)</th>\n",
       "      <th>Sim_Decoy(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Air Fryer Oven</td>\n",
       "      <td>89</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>71.2</td>\n",
       "      <td>14.4</td>\n",
       "      <td>14.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air mattress</td>\n",
       "      <td>51</td>\n",
       "      <td>42</td>\n",
       "      <td>32</td>\n",
       "      <td>40.8</td>\n",
       "      <td>33.6</td>\n",
       "      <td>25.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Baby high chair</td>\n",
       "      <td>39</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>31.2</td>\n",
       "      <td>64.0</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Headlight bulbs</td>\n",
       "      <td>66</td>\n",
       "      <td>17</td>\n",
       "      <td>42</td>\n",
       "      <td>52.8</td>\n",
       "      <td>13.6</td>\n",
       "      <td>33.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Metal detector</td>\n",
       "      <td>33</td>\n",
       "      <td>78</td>\n",
       "      <td>14</td>\n",
       "      <td>26.4</td>\n",
       "      <td>62.4</td>\n",
       "      <td>11.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Night light</td>\n",
       "      <td>65</td>\n",
       "      <td>26</td>\n",
       "      <td>34</td>\n",
       "      <td>52.0</td>\n",
       "      <td>20.8</td>\n",
       "      <td>27.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Odor neutralizer</td>\n",
       "      <td>62</td>\n",
       "      <td>37</td>\n",
       "      <td>26</td>\n",
       "      <td>49.6</td>\n",
       "      <td>29.6</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Pressure washer</td>\n",
       "      <td>70</td>\n",
       "      <td>35</td>\n",
       "      <td>20</td>\n",
       "      <td>56.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Security camera</td>\n",
       "      <td>71</td>\n",
       "      <td>34</td>\n",
       "      <td>20</td>\n",
       "      <td>56.8</td>\n",
       "      <td>27.2</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Vacuum</td>\n",
       "      <td>64</td>\n",
       "      <td>30</td>\n",
       "      <td>31</td>\n",
       "      <td>51.2</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Item Target(num) Competitor(num) Sim_Decoy(num)  Target(%)  \\\n",
       "0    Air Fryer Oven          89              18             18       71.2   \n",
       "1      Air mattress          51              42             32       40.8   \n",
       "2   Baby high chair          39              80              6       31.2   \n",
       "3   Headlight bulbs          66              17             42       52.8   \n",
       "4    Metal detector          33              78             14       26.4   \n",
       "5       Night light          65              26             34       52.0   \n",
       "6  Odor neutralizer          62              37             26       49.6   \n",
       "7   Pressure washer          70              35             20       56.0   \n",
       "8   Security camera          71              34             20       56.8   \n",
       "9            Vacuum          64              30             31       51.2   \n",
       "\n",
       "   Competitor(%)  Sim_Decoy(%)  \n",
       "0           14.4          14.4  \n",
       "1           33.6          25.6  \n",
       "2           64.0           4.8  \n",
       "3           13.6          33.6  \n",
       "4           62.4          11.2  \n",
       "5           20.8          27.2  \n",
       "6           29.6          20.8  \n",
       "7           28.0          16.0  \n",
       "8           27.2          16.0  \n",
       "9           24.0          24.8  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoy_S_Items_TargetA_df.sort_values(by=['Item'], inplace=True, ignore_index=True) \n",
    "decoy_S_Items_TargetA_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ba86ab1c",
   "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>Item</th>\n",
       "      <th>Target(num)</th>\n",
       "      <th>Competitor(num)</th>\n",
       "      <th>Sim_Decoy(num)</th>\n",
       "      <th>Target(%)</th>\n",
       "      <th>Competitor(%)</th>\n",
       "      <th>Sim_Decoy(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Air Fryer Oven</td>\n",
       "      <td>52</td>\n",
       "      <td>48</td>\n",
       "      <td>25</td>\n",
       "      <td>41.6</td>\n",
       "      <td>38.4</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air mattress</td>\n",
       "      <td>60</td>\n",
       "      <td>22</td>\n",
       "      <td>43</td>\n",
       "      <td>48.0</td>\n",
       "      <td>17.6</td>\n",
       "      <td>34.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Baby high chair</td>\n",
       "      <td>38</td>\n",
       "      <td>14</td>\n",
       "      <td>73</td>\n",
       "      <td>30.4</td>\n",
       "      <td>11.2</td>\n",
       "      <td>58.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Headlight bulbs</td>\n",
       "      <td>69</td>\n",
       "      <td>20</td>\n",
       "      <td>36</td>\n",
       "      <td>55.2</td>\n",
       "      <td>16.0</td>\n",
       "      <td>28.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Metal detector</td>\n",
       "      <td>74</td>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "      <td>59.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>39.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Night light</td>\n",
       "      <td>63</td>\n",
       "      <td>43</td>\n",
       "      <td>19</td>\n",
       "      <td>50.4</td>\n",
       "      <td>34.4</td>\n",
       "      <td>15.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Odor neutralizer</td>\n",
       "      <td>79</td>\n",
       "      <td>19</td>\n",
       "      <td>27</td>\n",
       "      <td>63.2</td>\n",
       "      <td>15.2</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Pressure washer</td>\n",
       "      <td>71</td>\n",
       "      <td>13</td>\n",
       "      <td>41</td>\n",
       "      <td>56.8</td>\n",
       "      <td>10.4</td>\n",
       "      <td>32.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Security camera</td>\n",
       "      <td>76</td>\n",
       "      <td>21</td>\n",
       "      <td>28</td>\n",
       "      <td>60.8</td>\n",
       "      <td>16.8</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Vacuum</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>85</td>\n",
       "      <td>15.2</td>\n",
       "      <td>16.8</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Item Target(num) Competitor(num) Sim_Decoy(num)  Target(%)  \\\n",
       "0    Air Fryer Oven          52              48             25       41.6   \n",
       "1      Air mattress          60              22             43       48.0   \n",
       "2   Baby high chair          38              14             73       30.4   \n",
       "3   Headlight bulbs          69              20             36       55.2   \n",
       "4    Metal detector          74               2             49       59.2   \n",
       "5       Night light          63              43             19       50.4   \n",
       "6  Odor neutralizer          79              19             27       63.2   \n",
       "7   Pressure washer          71              13             41       56.8   \n",
       "8   Security camera          76              21             28       60.8   \n",
       "9            Vacuum          19              21             85       15.2   \n",
       "\n",
       "   Competitor(%)  Sim_Decoy(%)  \n",
       "0           38.4          20.0  \n",
       "1           17.6          34.4  \n",
       "2           11.2          58.4  \n",
       "3           16.0          28.8  \n",
       "4            1.6          39.2  \n",
       "5           34.4          15.2  \n",
       "6           15.2          21.6  \n",
       "7           10.4          32.8  \n",
       "8           16.8          22.4  \n",
       "9           16.8          68.0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoy_S_Items_TargetB_df.sort_values(by=['Item'], inplace=True, ignore_index=True) \n",
    "decoy_S_Items_TargetB_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0fab12fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1000 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 5, figsize=(20, 10))\n",
    "\n",
    "# Biased items: \"Baby high chair\", \"Metal detector\", \"Vacuum\"\n",
    "\n",
    "# Baby high chair would show a high similarity effect ... but the way we are computing the effect ignores the high attraction of the decoy item.\n",
    "\n",
    "counter = 0\n",
    "\n",
    "labels1 = [\"T_A\", \"C_A\", \"S_A\"]\n",
    "labels2 = [\"T_B\", \"C_B\", \"S_B\"]\n",
    "\n",
    "n = Sim_items_targets_A.shape[0]\n",
    "\n",
    "for x in range(2):\n",
    "    \n",
    "    for xi in range(5):\n",
    "        \n",
    "        if (counter == (n)):\n",
    "            counter = 0\n",
    "        \n",
    "        y1 = np.array([Sim_items_targets_A[counter], Sim_items_competitors_A[counter], Sim_items_decoys_A[counter]])\n",
    "        y2 = np.array([Sim_items_targets_B[counter], Sim_items_competitors_B[counter], Sim_items_decoys_B[counter]])\n",
    "        ax[x][xi].bar(labels1, y1, 0.5, capsize=8, label=\"Target A\")\n",
    "        ax[x][xi].bar(labels2, y2, 0.5, capsize=8, label=\"Target B\")\n",
    "        ax[x][xi].set_ylim(0, 105)\n",
    "        ax[x][xi].set_title(item_list[counter])\n",
    "        ax[x][xi].legend()\n",
    "        counter = counter + 1\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "94703853",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Air Fryer Oven' 'Air mattress' 'Baby high chair' 'Headlight bulbs'\n",
      " 'Metal detector' 'Night light' 'Odor neutralizer' 'Pressure washer'\n",
      " 'Security camera' 'Vacuum']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "51"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#decoy_S_Items_TargetA_df[decoy_S_Items_TargetA_df[\"Item\"] == \"Air purifier\"]\n",
    "\n",
    "#decoy_S_Items_TargetA_df.Item[\"Air purifier\"]\n",
    "\n",
    "item_list = decoy_S_TargetA_df[\"Item\"].unique()\n",
    "item_list = np.sort(item_list)\n",
    "print(item_list)\n",
    "\n",
    "decoy_S_Items_TargetA_df.loc[9, \"Target(num)\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "35be513d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/482233728.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_S_Items_Overall_df = decoy_S_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/482233728.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_S_Items_Overall_df = decoy_S_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/482233728.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_S_Items_Overall_df = decoy_S_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/482233728.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_S_Items_Overall_df = decoy_S_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/482233728.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_S_Items_Overall_df = decoy_S_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/482233728.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_S_Items_Overall_df = decoy_S_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/482233728.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_S_Items_Overall_df = decoy_S_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/482233728.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_S_Items_Overall_df = decoy_S_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/482233728.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_S_Items_Overall_df = decoy_S_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/482233728.py:23: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_S_Items_Overall_df = decoy_S_Items_Overall_df.append(current_item, ignore_index = True)\n"
     ]
    },
    {
     "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>Item</th>\n",
       "      <th>Similarity Effect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Air Fryer Oven</td>\n",
       "      <td>0.318841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air mattress</td>\n",
       "      <td>0.365714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Baby high chair</td>\n",
       "      <td>0.549708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Headlight bulbs</td>\n",
       "      <td>0.215116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Metal detector</td>\n",
       "      <td>0.427807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Night light</td>\n",
       "      <td>0.350254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Odor neutralizer</td>\n",
       "      <td>0.284264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Pressure washer</td>\n",
       "      <td>0.253968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Security camera</td>\n",
       "      <td>0.272277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Vacuum</td>\n",
       "      <td>0.380597</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Item  Similarity Effect\n",
       "0    Air Fryer Oven           0.318841\n",
       "1      Air mattress           0.365714\n",
       "2   Baby high chair           0.549708\n",
       "3   Headlight bulbs           0.215116\n",
       "4    Metal detector           0.427807\n",
       "5       Night light           0.350254\n",
       "6  Odor neutralizer           0.284264\n",
       "7   Pressure washer           0.253968\n",
       "8   Security camera           0.272277\n",
       "9            Vacuum           0.380597"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Overall similarity effect for individual items. \n",
    "\n",
    "#decoy_S_Items_TargetA_df\n",
    "#decoy_S_Items_TargetB_df\n",
    "\n",
    "#item_list\n",
    "decoy_S_Items_Overall_df =  pd.DataFrame(columns=['Item', 'Similarity Effect'])\n",
    "   \n",
    "for x in range(10):\n",
    "    \n",
    "    current_item_name = item_list[x]\n",
    "    \n",
    "    current_target_A = decoy_S_Items_TargetA_df.loc[x, \"Target(num)\"]\n",
    "    current_target_B = decoy_S_Items_TargetB_df.loc[x, \"Target(num)\"]\n",
    "    \n",
    "    current_competitor_A = decoy_S_Items_TargetA_df.loc[x, \"Competitor(num)\"]\n",
    "    current_competitor_B = decoy_S_Items_TargetB_df.loc[x, \"Competitor(num)\"]\n",
    "    \n",
    "    total_effect = (current_competitor_A+current_competitor_B)/(current_target_A+current_target_B+current_competitor_A+current_competitor_B)\n",
    "    \n",
    "    current_item = pd.DataFrame(data={'Item': current_item_name, 'Similarity Effect': total_effect}, index=range(1))\n",
    "    \n",
    "    decoy_S_Items_Overall_df = decoy_S_Items_Overall_df.append(current_item, ignore_index = True)\n",
    "    \n",
    "decoy_S_Items_Overall_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a59bbde",
   "metadata": {},
   "source": [
    "### Compromise Decoy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "87c7d2d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n"
     ]
    }
   ],
   "source": [
    "decoy_C_TargetA_df = decoy_C_df.loc[decoy_C_df['Option_1'].str.contains('C_A') | decoy_C_df['Option_2'].str.contains('C_A') | decoy_C_df['Option_3'].str.contains('C_A')]\n",
    "decoy_C_TargetB_df = decoy_C_df.loc[decoy_C_df['Option_1'].str.contains('C_B') | decoy_C_df['Option_2'].str.contains('C_B') | decoy_C_df['Option_3'].str.contains('C_B')]\n",
    "\n",
    "decoy_C_Items_TargetA_df = uf.itemFreq_Decoy(decoy_C_TargetA_df, \"Com_Decoy\")\n",
    "decoy_C_Items_TargetB_df = uf.itemFreq_Decoy(decoy_C_TargetB_df, \"Com_Decoy\")\n",
    "\n",
    "item_list = decoy_C_TargetA_df[\"Item\"].unique()\n",
    "item_list = np.sort(item_list)\n",
    "\n",
    "Com_items_targets_A = np.array([])\n",
    "Com_items_competitors_A = np.array([])\n",
    "Com_items_decoys_A = np.array([])\n",
    "\n",
    "Com_items_targets_B = np.array([])\n",
    "Com_items_competitors_B = np.array([])\n",
    "Com_items_decoys_B = np.array([])\n",
    "\n",
    "for item in item_list:\n",
    "    \n",
    "    current_item_A = decoy_C_Items_TargetA_df[decoy_C_Items_TargetA_df[\"Item\"] == item]\n",
    "    current_item_B = decoy_C_Items_TargetB_df[decoy_C_Items_TargetB_df[\"Item\"] == item]\n",
    "    \n",
    "    Com_items_targets_A = np.append(Com_items_targets_A, current_item_A[\"Target(%)\"])\n",
    "    Com_items_competitors_A = np.append(Com_items_competitors_A, current_item_A[\"Competitor(%)\"])\n",
    "    Com_items_decoys_A = np.append(Com_items_decoys_A, current_item_A[\"Com_Decoy(%)\"])\n",
    "    \n",
    "    Com_items_targets_B = np.append(Com_items_targets_B, current_item_B[\"Target(%)\"])\n",
    "    Com_items_competitors_B = np.append(Com_items_competitors_B, current_item_B[\"Competitor(%)\"])\n",
    "    Com_items_decoys_B = np.append(Com_items_decoys_B, current_item_B[\"Com_Decoy(%)\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7de363a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "decoy_C_Items_TargetA_df.sort_values(by=['Item'], inplace=True) \n",
    "#decoy_C_Items_TargetA_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f79b9894",
   "metadata": {},
   "outputs": [],
   "source": [
    "decoy_C_Items_TargetB_df.sort_values(by=['Item'], inplace=True) \n",
    "#decoy_C_Items_TargetB_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8102b6a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 5, figsize=(20, 10))\n",
    "\n",
    "# Biased items: \"Mobility scooter\", \"Smart lock\"\n",
    "\n",
    "counter = 0\n",
    "\n",
    "labels1 = [\"T_A\", \"C_A\", \"Co_A\"]\n",
    "labels2 = [\"T_B\", \"C_B\", \"Co_B\"]\n",
    "\n",
    "n = Com_items_targets_A.shape[0]\n",
    "\n",
    "for x in range(2):\n",
    "    \n",
    "    for xi in range(5):\n",
    "        \n",
    "        if (counter == (n)):\n",
    "            counter = 0\n",
    "        \n",
    "        y1 = np.array([Com_items_targets_A[counter], Com_items_competitors_A[counter], Com_items_decoys_A[counter]])\n",
    "        y2 = np.array([Com_items_targets_B[counter], Com_items_competitors_B[counter], Com_items_decoys_B[counter]])\n",
    "        ax[x][xi].bar(labels1, y1, 0.5, capsize=8, label=\"Target A\")\n",
    "        ax[x][xi].bar(labels2, y2, 0.5, capsize=8, label=\"Target B\")\n",
    "        ax[x][xi].set_ylim(0, 105)\n",
    "        ax[x][xi].set_title(item_list[counter])\n",
    "        ax[x][xi].legend()\n",
    "        counter = counter + 1\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7848f5d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/2043858046.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_C_Items_Overall_df = decoy_C_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/2043858046.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_C_Items_Overall_df = decoy_C_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/2043858046.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_C_Items_Overall_df = decoy_C_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/2043858046.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_C_Items_Overall_df = decoy_C_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/2043858046.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_C_Items_Overall_df = decoy_C_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/2043858046.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_C_Items_Overall_df = decoy_C_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/2043858046.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_C_Items_Overall_df = decoy_C_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/2043858046.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_C_Items_Overall_df = decoy_C_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/2043858046.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_C_Items_Overall_df = decoy_C_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/2043858046.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_C_Items_Overall_df = decoy_C_Items_Overall_df.append(current_item, ignore_index = True)\n"
     ]
    },
    {
     "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>Item</th>\n",
       "      <th>Compromise Effect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Baby crib</td>\n",
       "      <td>0.840659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Car vacuum</td>\n",
       "      <td>0.775330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Insect repellent</td>\n",
       "      <td>0.726496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lawn mower</td>\n",
       "      <td>0.604167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mobility scooter</td>\n",
       "      <td>0.697778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pesticide</td>\n",
       "      <td>0.767327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Puree machine</td>\n",
       "      <td>0.595652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Smart lock</td>\n",
       "      <td>0.794118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Water heater</td>\n",
       "      <td>0.504587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>White noise machine</td>\n",
       "      <td>0.789157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Item  Compromise Effect\n",
       "0            Baby crib           0.840659\n",
       "1           Car vacuum           0.775330\n",
       "2     Insect repellent           0.726496\n",
       "3           Lawn mower           0.604167\n",
       "4     Mobility scooter           0.697778\n",
       "5            Pesticide           0.767327\n",
       "6        Puree machine           0.595652\n",
       "7           Smart lock           0.794118\n",
       "8         Water heater           0.504587\n",
       "9  White noise machine           0.789157"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoy_C_Items_Overall_df =  pd.DataFrame(columns=['Item', 'Compromise Effect'])\n",
    "\n",
    "item_list = decoy_C_TargetA_df[\"Item\"].unique()\n",
    "item_list = np.sort(item_list)\n",
    "   \n",
    "for x in range(10):\n",
    "    \n",
    "    current_item_name = item_list[x]\n",
    "    \n",
    "    current_target_A = decoy_C_Items_TargetA_df.loc[x, \"Target(num)\"]\n",
    "    current_target_B = decoy_C_Items_TargetB_df.loc[x, \"Target(num)\"]\n",
    "    \n",
    "    current_competitor_A = decoy_C_Items_TargetA_df.loc[x, \"Competitor(num)\"]\n",
    "    current_competitor_B = decoy_C_Items_TargetB_df.loc[x, \"Competitor(num)\"]\n",
    "    \n",
    "    total_effect = (current_target_A+current_target_B)/(current_target_A+current_target_B+current_competitor_A+current_competitor_B)\n",
    "    \n",
    "    current_item = pd.DataFrame(data={'Item': current_item_name, 'Compromise Effect': total_effect}, index=range(1))\n",
    "    \n",
    "    decoy_C_Items_Overall_df = decoy_C_Items_Overall_df.append(current_item, ignore_index = True)\n",
    "    \n",
    "decoy_C_Items_Overall_df.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "813c2857",
   "metadata": {},
   "source": [
    "### Attraction Decoy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4318538b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:315: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_item_freq, ignore_index = True)\n"
     ]
    }
   ],
   "source": [
    "decoy_A_TargetA_df = decoy_A_df.loc[decoy_A_df['Option_1'].str.contains('A_A') | decoy_A_df['Option_2'].str.contains('A_A') | decoy_A_df['Option_3'].str.contains('A_A')]\n",
    "decoy_A_TargetB_df = decoy_A_df.loc[decoy_A_df['Option_1'].str.contains('A_B') | decoy_A_df['Option_2'].str.contains('A_B') | decoy_A_df['Option_3'].str.contains('A_B')]\n",
    "\n",
    "decoy_A_Items_TargetA_df = uf.itemFreq_Decoy(decoy_A_TargetA_df, \"Att_Decoy\")\n",
    "decoy_A_Items_TargetB_df = uf.itemFreq_Decoy(decoy_A_TargetB_df, \"Att_Decoy\")\n",
    "\n",
    "item_list = decoy_A_TargetA_df[\"Item\"].unique()\n",
    "item_list = np.sort(item_list)\n",
    "\n",
    "Att_items_targets_A = np.array([])\n",
    "Att_items_competitors_A = np.array([])\n",
    "Att_items_decoys_A = np.array([])\n",
    "\n",
    "Att_items_targets_B = np.array([])\n",
    "Att_items_competitors_B = np.array([])\n",
    "Att_items_decoys_B = np.array([])\n",
    "\n",
    "for item in item_list:\n",
    "    \n",
    "    current_item_A = decoy_A_Items_TargetA_df[decoy_A_Items_TargetA_df[\"Item\"] == item]\n",
    "    current_item_B = decoy_A_Items_TargetB_df[decoy_A_Items_TargetB_df[\"Item\"] == item]\n",
    "    \n",
    "    Att_items_targets_A = np.append(Att_items_targets_A, current_item_A[\"Target(%)\"])\n",
    "    Att_items_competitors_A = np.append(Att_items_competitors_A, current_item_A[\"Competitor(%)\"])\n",
    "    Att_items_decoys_A = np.append(Att_items_decoys_A, current_item_A[\"Att_Decoy(%)\"])\n",
    "    \n",
    "    Att_items_targets_B = np.append(Att_items_targets_B, current_item_B[\"Target(%)\"])\n",
    "    Att_items_competitors_B = np.append(Att_items_competitors_B, current_item_B[\"Competitor(%)\"])\n",
    "    Att_items_decoys_B = np.append(Att_items_decoys_B, current_item_B[\"Att_Decoy(%)\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c34ee94a",
   "metadata": {},
   "outputs": [],
   "source": [
    "decoy_A_Items_TargetA_df.sort_values(by=['Item'], inplace=True) \n",
    "#decoy_A_Items_TargetA_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f448214b",
   "metadata": {},
   "outputs": [],
   "source": [
    "decoy_A_Items_TargetB_df.sort_values(by=['Item'], inplace=True) \n",
    "#decoy_A_Items_TargetB_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7d5d43bd",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7nPUOPvjgyGQycffdd2fnvfTSS5HJZOLvf//7eve5dOnSGD16dHTr1i3q1q0bTZs2jX79+sWUKVOyyyxZsiSGDx8enTt3jjp16kTbtm3jjDPOKHOW4KpVq2Ls2LGx3XbbRUlJSbRo0SIGDx4cH3300QbHfu+990a9evXi5JNPjhUrVkRExPTp0+MHP/hBNGnSJOrWrRs777xz3HXXXTnrrb6M7mOPPRYnnnhiNG/ePOrVq5fzMwOoDjYmQzp16hQHHXRQPPjgg7HzzjtHaWlpdOvWLR588MGI+ObY2K1bt6hfv37svvvuMX369Jz1p0+fHkceeWR06tQpSktLo1OnTnHUUUfF+++/n7Pcuo6xw4cPj3PPPTciIjp37pxz+d4NeeONN6J///5Rv379aN68eZx55pnx9ddfZ59ffWmutZ3tl8lk8r481DPPPBPf+973orS0NNq2bRu/+tWvYuXKlXmtC1Ddre1S6plMJs4888y4/vrro1u3blFSUhK33HJLRES89957cfTRR0eLFi2ipKQkunXrFr/73e/y2tealyacNGlSZDKZuOOOO2LEiBHRpk2baNSoUey7777xzjvv5Kx3wQUXREREy5YtN3iMHzVqVPTs2TOaNGkSjRo1il122SVuuummSJIkZ5tvvPFGTJ48OZtN3/45fPDBB3HMMcfkjPPKK6+MVatWZZdZnUNjx46Niy++ODp37hwlJSVl/sAIUNM1bdo0DjnkkLj//vvj888/L/P822+/HVOnTo3BgwdHcXFxASoESKd+/frFO++8E7Nnz87OmzRpUuy2224xYMCAePHFF2PhwoU5z9WuXTv22muviMjvfXW+li9fHscdd1w0aNAg+zepJEni97//fey0005RWloaW265ZRx22GHx73//O2fd1bfCe/rpp6N3795Rr169OPHEE9e6n4ceeigymUxMmzYtO+9vf/tbZDKZGDhwYM6yPXr0iB/96EfZx9/+vLL65xQRccIJJ2Q/M6z5OeRf//pXDBgwIBo0aBDt27ePn//853n1G5588sno27dvNG3aNEpLS6NDhw7xox/9KOfvXsuWLYuLL74420Np3rx5nHDCCfHpp5/mbOvOO++M/fffP1q3bp39u9+wYcNi0aJF2WWOP/747Oe2b99e69u3HKT60hinWujVq1c8//zzcfbZZ8fzzz8fy5cvz3vdJUuWRL9+/eJPf/pTnHPOOfHQQw/FMcccE2PHjo1BgwZll9t3333jzTffzAbfihUrYvLkyVFaWhoTJ07MLvf4449HUVHReu/Nt2LFijjwwAPj17/+dRx00EFx7733xoQJE6J3797xwQcfRMQ3QXbooYfGb37zmzj22GPjoYceinPOOSduueWW2GeffXIC4bTTTovzzz8/9ttvv3jggQfi17/+dTzyyCPRu3fvdV6GKyLi6quvjh//+Mfxy1/+Mm688cYoKiqKp556KvbYY4/48ssv4/rrr4/7778/dtpppzjiiCPW2iw58cQTo7i4OP785z/HX//6Vx+6gGpnYzPklVdeieHDh8f5558f99xzTzRu3DgGDRoUF110Udx4440xZsyYuO2222L+/Plx0EEHxeLFi7Przpo1K7p27Rrjxo2LRx99NC6//PKYPXt27Lbbbms9Xq95jD3ttNPirLPOioiIe+65J+fyveuzfPnyGDBgQPTv3z/uu+++OPPMM+OGG26II444YhN+Yus2Z86cOPLII+MnP/lJ3H///XHYYYfFxRdfHD/96U/LdT8A1c19990X1113XVx44YXx6KOPxl577RVvvvlm7LbbbvH666/HlVdeGQ8++GAMHDgwzj777M26JN8vf/nLeP/99+PGG2+MP/zhD/Hee+/FwQcfnP2S0r333hsnnXRSREQ88sgjMXXq1Dj55JPXub1Zs2bFkCFD4q677op77rknBg0aFGeddVb8+te/zi5z7733xlZbbRU777xzNptWX1L+008/jd69e8djjz0Wv/71r+OBBx6IfffdN37xi1/EmWeeWWZ///u//xtPPvlk/OY3v4l//OMfsd12223yzwIgrU466aRYtmxZ3H777WWeGz9+fETEOpsc65NPMyDiv/eCffvtt+OAAw6I+vXrR+vWreOyyy6LiIjnnnsu9txzz6hfv35su+222S+Erbb6i8ATJ06ME044IZo0aRL169ePgw8+uEwDB6CqWH3m97dPTnjqqf/X3n3HSVWf+wN/ll1Y+qqgLE1AwBaxxRJRQRQlgkbjTaISjSZRsUvsXDQiVwEhCkZjvYgYu8YWNQoqYhSjWLDFqEkAiUIwogsogsD5/eGPuQ51gNmd2bPv9+t1XjpnTnm+u8N8duY5ZVL07Nkz9t577ygpKYk///nPWc/tuuuuUVFRERG5/V2di88//zz69OkTEyZMiMmTJ8chhxwSEREDBgyIgQMHRu/eveOhhx6K6667Lt55553o3r17/Pvf/87axuzZs+OYY46J/v37x+OPPx6nnnrqavfVs2fPqF+/ftatl5566qlo1KhRTJ48OfMd2ty5c+Ptt9+O3r17r3Y7u+66ayafLrroosxnhm9/Dvn666/jBz/4QRxwwAHx8MMPxy9+8YsYPXp0XHHFFWv9ecyYMSP69esXDRo0iFtuuSWeeOKJGDFiRDRp0iSWLFkSEd+cWHjYYYfFiBEjon///vHYY4/FiBEjYuLEibHffvtlfXf3wQcfRN++fWPs2LHxxBNPxMCBA+Pee+/NugrLxRdfHD/60Y8iIvvWW61bt15rrdQSCdQC//nPf5J99tkniYgkIpL69esn3bt3T4YPH54sWLAga9mePXsmPXv2zDy+4YYbkohI7r333qzlrrjiiiQikgkTJiRJkiR///vfk4hIbrvttiRJkuT5559PIiI5//zzk06dOmXWO/DAA5Pu3buvtd7bbrstiYjk5ptvXuMyTzzxRBIRyciRI7Pm33PPPUlEJDfddFOSJEny7rvvJhGRnHrqqVnLvfTSS0lEJP/93/+dNfbvfOc7ybJly5LTTz89adCgQXL77bdnrbftttsmu+yyS/L1119nzT/kkEOS1q1bJ8uWLUuSJEnGjRuXRETys5/9bK1jBSh265MhHTp0SBo1apT861//ysybNm1aEhFJ69atky+++CIz/6GHHkoiInnkkUfWuO+lS5cmCxcuTJo0aZJcffXVmflre48dNWpUEhHJ9OnTcxrfcccdl0RE1vaTJEkuv/zyJCKS559/PkmSJJk+fXoSEcm4ceNW2UZEJJdccskq9X27hp49eyYRkTz88MNZ65544olJvXr1kpkzZ+ZUL0CxO+6445ImTZqs8bkOHTpkzYuIpKKiIpk3b17W/D59+iTt2rVLqqqqsuaffvrpScOGDVdZfmUdOnRIjjvuuMzjSZMmJRGR9O3bN2u5e++9N4mI5MUXX8zMu+SSS5KISD755JOsZVf+rLSyZcuWJV9//XUydOjQpEWLFsny5cszz33nO99Z7boXXnhhEhHJSy+9lDX/lFNOSUpKSpL33nsvSZL/y6HOnTsnS5YsWevYAdJmTe/LK6z8Hrts2bKkQ4cOyc4775y13NKlS5PWrVsn3/ve93Le57f9z//8TzJ69OjkscceS5599tnkhhtuSDp16pT06tUra7njjjsuadCgQbLddtslV199dTJx4sTk5z//eRIRyaBBg5Ktt946GTt2bPLkk08mhxxySBIRySuvvJJZf8Xnifbt2ye/+MUvkj/96U/JTTfdlGyxxRZJ+/btk88++2yd9QPUtHnz5iX16tVLTjrppCRJvvk+qaSkJHniiSeSJEmSPfbYIzn33HOTJEmSDz/8MNM7WJ21/V298t/kK/5OHjVqVDJ9+vRk++23T7bffvtkxowZmWVefPHFJCKSK6+8Mms/s2bNSho1apRVx4rvb55++umcxr3PPvsk+++/f+Zxly5dkvPOOy+pV69eMnny5CRJkuSOO+5IIiJ5//33M8ut/Hll6tSpa/zeacV3Vyv3aPr27Ztss802a63v/vvvTyIimTZt2hqXueuuu5KISP7whz9kzV9R03XXXbfa9ZYvX558/fXXyeTJk5OISN54443Mc6eddtoqOUo6OGOcWqFFixbx5z//OaZOnRojRoyIww47LN5///0YNGhQdOvWba1nTT/zzDPRpEmTzBE+K6y4zMfTTz8dERGdO3eOjh07Zo6OmjhxYnTr1i2OOeaYmD59evzjH/+IxYsXx/PPP7/GI6NW+NOf/hQNGzZc69G7zzzzTFYdK/z4xz+OJk2aZOpacWnBlZfbY489Yrvttssst8JXX30Vhx9+eNxxxx0xYcKE+OlPf5p57u9//3v87W9/y8xbunRpZurbt2/Mnj076zKMEZF1eRSA2mh9M2TnnXeOtm3bZh5vt912EfHNpai+fT+mFfO/fZn0hQsXxgUXXBBdunSJsrKyKCsri6ZNm8YXX3wR77777iq15fM99tvv9xER/fv3j4jI6yVqmzVrFj/4wQ9W2c/y5cvjueeey9t+AGqb/fffPzbddNPM46+++iqefvrp+OEPfxiNGzde5e/ur776Kv7yl79s0L5Wfh/ecccdIyJWuW1Hrp555pno3bt3VFRURGlpadSvXz9+/etfx6effhpz587Naf3tt98+9thjj6z5xx9/fCRJkvnc8+36XYUKYO3q1asXxx9/fEybNi1ef/31zPw//elPMXv27MyVQdbXRRddFAMHDoy+fftGjx494pe//GXceuutMWnSpHjzzTezll1xSdozzzwzevfuHTfddFNsvvnmMXz48Lj77rvjF7/4RRx00EExfvz4KC0tXe3Z7bvttluMHTs2vv/978eJJ54YDz74YMyaNSvn24oA1KRNN900dtppp8wZ45MnT47S0tLYe++9I+Kbs6tXfMeyuvuLb+zf1a+99lp873vfi1atWsULL7wQHTp0yDz36KOPRklJSRxzzDFZny0qKyuzav72WPbff/+cxn3AAQfECy+8EIsWLYqZM2fG3//+9zjqqKNi5513zlxJ96mnnoott9wyunbtmtM2V6ekpCTrrOyIbz7LrOtzzM477xwNGjSIk046KcaPH7/aK488+uijsckmm8Shhx6a9fPZeeedo7KyMuvn889//jP69+8flZWVmd9Tz549IyJW+90d6aMxTq2y2267xQUXXBD33XdffPzxx/GrX/0qZsyYESNHjlzjOp9++mlUVlZGSUlJ1vwtttgiysrKsu7XdMABB2QazU899VQceOCB0a1bt2jVqlU89dRTmYBYV2P8k08+iTZt2qz1/ueffvpplJWVxeabb541v6SkJCorKzN1rfjv6i7T0aZNm1XuNzV37tx48sknY6+99oru3btnPbfikirnnntu1K9fP2tacTmVlRtELg8CpEWuGbLZZptlPW7QoMFa53/11VeZef37949rr702TjjhhHjyySfj5ZdfjqlTp8bmm2+eddmmFfL1HltWVhYtWrTImldZWRkRsdr7Em6oVq1arTKvOvYDUNus/H7+6aefxtKlS+Oaa65Z5e/uvn37RsSqf3fnauX3+/Ly8oiI1ebMurz88stx0EEHRUTEzTffHC+88EJMnTo1Bg8enPM2P/300zV+Vlnx/Lf5fAGQm5///OdRr169uOWWWzLzxo0bF02aNNngWyatTzOgpKQkk1kR33zm6NKlS7Ru3Tp22WWXzPzNNtsstthii9U2NlY+eLd79+7RoUOHvB68C5BPvXr1ivfffz8+/vjjmDRpUnz3u9+Npk2bRsQ3jfHXX389qqqqYtKkSVFWVhb77LNPROTn7+qJEyfGv//97zjhhBNik002yXru3//+dyRJEq1atVrl88Vf/vKXjfpOv3fv3pkTAidOnBgtW7aMXXbZJXr37p05ifDpp59eZ09kXRo3bhwNGzbMmldeXp71vdrqdO7cOZ566qnYYost4rTTTovOnTtH586d4+qrr84s8+9//zs+//zzaNCgwSo/nzlz5mR+PgsXLox99903Xnrppbjsssvi2WefjalTp8YDDzwQERv2mYrap6zQBcCGql+/flxyySUxevToePvtt9e4XIsWLeKll16KJEmymuNz586NpUuXRsuWLTPzDjjggBg7dmy8/PLL8dJLL8VFF10UEd+cATJx4sSYOXNmNG3aNL73ve+ttbbNN988nn/++Vi+fPkam+MtWrSIpUuXxieffJLVHE+SJObMmRO77757ZrmIb+4L0q5du6xtfPzxx1n1R0RsueWWcdVVV8UPf/jDOOKII+K+++7LBM6KZQcNGpR1f/Vv22abbbIer3xAAUAa5Joh66OqqioeffTRuOSSS+LCCy/MzF+8eHHMmzdvtevk6z126dKl8emnn2Y1S+bMmRMR/5cjK7Jg8eLFWeuuT0N75XtWrW4/AHXRyu/nm266aZSWlsaxxx4bp5122mrX6dSpU02UtlZ333131K9fPx599NGsL6keeuihnLfRokWLmD179irzP/7444iIVT6v+HwB1EVlZd98Bbts2bLVPr906dJVrqbRoUOHOOCAA+LOO++M3/zmN7FgwYJ49NFHo3///tGsWbP1rmFFM6Bhw4Zx2WWXxdZbbx2NGzeOWbNmxRFHHLFKM2B1DYwGDRqscsDwivmra2ysOIh25XkOqgWKVa9eveKqq66KZ599Np599tmsA4RWNMGfe+65ePbZZ2P33XfPNM3z8Xf1eeedF//4xz/iZz/7WSxdujR+9rOfZZ5r2bJl5h7nKw6M/baV563P39x77rlnNG3aNJ566qmYMWNGHHDAAVFSUhIHHHBAXHnllTF16tT48MMPN7oxvjH23Xff2HfffWPZsmXxyiuvxDXXXBMDBw6MVq1axVFHHRUtW7aMFi1axBNPPLHa9Vfk5jPPPBMff/xxPPvss5kDwyK+ua87dYczxqkVVvdFS8T/Hc264myE1TnggANi4cKFq4TQbbfdlnn+28uWlJTExRdfHPXq1YsePXpExDdHTU2aNCkmTpwYPXr0WOel/w4++OD46quv4tZbb11rXRERt99+e9b8P/zhD/HFF19knl9xyZOVl5s6dWq8++67WfWvcNBBB8WTTz4Zzz33XBxyyCHxxRdfRMQ3Te+uXbvGG2+8Ebvttttqpw35cAVQzDYmQ9ZHSUlJJEmyyoeR//3f/13jF2Crs6Fn/t1xxx1Zj1dcynC//faLiG/O9m7YsOEql0h8+OGHc97HggUL4pFHHlllP9/OTAC+aSb06tUrXn/99dhxxx1X+3d3MRxQVFJSEmVlZVFaWpqZt2jRovj973+/yrLl5eWrzaYDDjgg/vrXv8Zrr72WNf+2226LkpKSrMtLAtRVK6689NFHH63yXJIkMXv27NVenemXv/xlzJs3Lx5++OG4/fbbY8mSJRt8GfUVzYBbbrklTjjhhOjRo0e1fw+04iDalecVQwYCrE6PHj2itLQ07r///njnnXcy36lERFRUVMTOO+8c48ePjxkzZmT9nbs+f1evSb169eLGG2+Ms846K44//vi4/vrrM88dcsghkSRJfPTRR6v9bNGtW7cNHnP9+vWjR48eMXHixHjmmWfiwAMPjIhvmtFlZWVx0UUXZRrla7MxV7LKVWlpaey5556ZW3Ks+AxyyCGHxKeffhrLli1b7c9nxcmAKw4YWPm7uxtvvLEg46EwnDFOrdCnT59o165dHHroobHtttvG8uXLY9q0aXHllVdG06ZN46yzzlrjuj/72c/id7/7XRx33HExY8aM6NatWzz//PMxbNiw6Nu3b9aRTltssUXssMMOMWHChOjVq1fmXrK9e/eOefPmxbx58+Kqq65aZ71HH310jBs3Lk4++eR47733olevXrF8+fJ46aWXYrvttoujjjoqDjzwwOjTp09ccMEFMX/+/Nh7773jzTffjEsuuSR22WWXOPbYYyPim2b2SSedFNdcc03Uq1cvDj744JgxY0ZcfPHF0b59+/jVr3612hr22WefePrpp+P73/9+HHTQQfH4449HRUVF3HjjjXHwwQdHnz594vjjj4+2bdvGvHnz4t13343XXnst7rvvvvX51QAUvY3JkPXRvHnz6NGjR4waNSpatmwZHTt2jMmTJ8fYsWNXuQTW2qz4MHP11VfHcccdF/Xr149tttlmrV9YNWjQIK688spYuHBh7L777jFlypS47LLL4uCDD84c0bziXlS33HJLdO7cOXbaaad4+eWXV3svwDVp0aJFnHLKKfHhhx/G1ltvHY8//njcfPPNccopp8SWW26Z83YA6oKrr7469tlnn9h3333jlFNOiY4dO8aCBQvi73//e/zxj39c5d7bhdCvX7+46qqron///nHSSSfFp59+Gr/5zW9WexZKt27d4u6774577rknttpqq2jYsGF069YtfvWrX8Vtt90W/fr1i6FDh0aHDh3isccei+uuuy5OOeWU2HrrrQswMoDisv/++0dJSUncc889seuuu2Y998QTT8T8+fNXeybe4YcfHi1atIhbbrklZs+eHVtvvXXm7/v1tT7NgHy544474r/+678yj6dMmRIzZ86ME044odr2CbAxmjdvHrvuums89NBDUa9evcz9xVfo2bNnjBkzJiKy7y++Pn9Xr8uVV14ZzZo1i1NPPTUWLlwY5513Xuy9995x0kknxc9//vN45ZVXokePHtGkSZOYPXt2PP/889GtW7c45ZRTNnjcBxxwQJxzzjkREZk8atSoUXTv3j0mTJgQO+64Y2yxxRZr3Ubnzp2jUaNGcccdd8R2220XTZs2jTZt2mz0CSk33HBDPPPMM9GvX7/Ycsst46uvvsrcZmRFrUcddVTccccd0bdv3zjrrLNijz32iPr168e//vWvmDRpUhx22GHxwx/+MLp37x6bbrppnHzyyXHJJZdE/fr144477og33nhjlf2u+H7uiiuuiIMPPjhKS0tjxx13zNxakdpLY5xa4aKLLoqHH344Ro8eHbNnz47FixdH69ato3fv3jFo0KDYbrvt1rhuw4YNY9KkSTF48OAYNWpUfPLJJ9G2bds499xz45JLLlll+d69e8dbb72V9YFkyy23jK5du8YHH3yQ0yVDysrK4vHHH4/hw4fHXXfdFWPGjIlmzZrFTjvtFN///vcj4psPJA899FAMGTIkxo0bF5dffnm0bNkyjj322Bg2bFhWaF5//fXRuXPnGDt2bPzud7+LioqK+P73vx/Dhw9f61G2u+22W0yePDl69+4d+++/fzz55JPRq1evePnll+Pyyy+PgQMHxmeffRYtWrSI7bffPn7yk5+sc2wAtc3GZMj6uvPOO+Oss86K888/P5YuXRp77713TJw4Mfr165fzNvbbb78YNGhQjB8/Pm6++eZYvnx5TJo0Keso5ZWtuFzXmWeeGZdddlk0atQoTjzxxBg1alTWcldeeWVERIwcOTIWLlwY+++/fzz66KPRsWPHnGqrrKyM3/3ud3HuuefGW2+9FZtttln893//d1x66aU5jw+grth+++3jtddei//5n/+Jiy66KObOnRubbLJJdO3aNeuSjIW0//77xy233BJXXHFFHHroodG2bds48cQTY4sttljljMRLL700Zs+eHSeeeGIsWLAgOnToEDNmzIjNN988pkyZEoMGDYpBgwbF/PnzY6uttoqRI0fG2WefXaCRARSXzp07x+mnnx6jRo2Kzz//PPr27RuNGjWKqVOnxogRI2K33XaL/v37r7JeeXl5/PSnP41rrrkmkiSJESNGbHAN69MMyJdXXnklTjjhhPjxj38cs2bNisGDB0fbtm3j1FNPrbZ9AmysXr16xdSpU2OXXXaJ5s2bZz3Xs2fPGD16dDRo0CC6d++emb8+f1fnYsiQIdG0adM477zzYuHChXHppZfGjTfeGN/73vfixhtvjOuuuy6WL18ebdq0ib333jv22GOPjRrzip5H165do0OHDlnzJ02alFNPpHHjxnH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      "text/plain": [
       "<Figure size 2000x1000 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 5, figsize=(20, 10))\n",
    "\n",
    "# Biased items: \"Car seat\", \"Leaf Blower\", \"Rash cream\", \"Slow cooker\", \"Tire inflator\"\n",
    "\n",
    "counter = 0\n",
    "\n",
    "labels1 = [\"T_A\", \"C_A\", \"A_A\"]\n",
    "labels2 = [\"T_B\", \"C_B\", \"A_B\"]\n",
    "\n",
    "n = Att_items_targets_A.shape[0]\n",
    "\n",
    "for x in range(2):\n",
    "    \n",
    "    for xi in range(5):\n",
    "        \n",
    "        if (counter == (n)):\n",
    "            counter = 0\n",
    "        \n",
    "        y1 = np.array([Att_items_targets_A[counter], Att_items_competitors_A[counter], Att_items_decoys_A[counter]])\n",
    "        y2 = np.array([Att_items_targets_B[counter], Att_items_competitors_B[counter], Att_items_decoys_B[counter]])\n",
    "        ax[x][xi].bar(labels1, y1, 0.5, capsize=8, label=\"Target A\")\n",
    "        ax[x][xi].bar(labels2, y2, 0.5, capsize=8, label=\"Target B\")\n",
    "        ax[x][xi].set_ylim(0, 105)\n",
    "        ax[x][xi].set_title(item_list[counter])\n",
    "        ax[x][xi].legend()\n",
    "        counter = counter + 1\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f19dd61d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/1548328079.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_A_Items_Overall_df = decoy_A_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/1548328079.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_A_Items_Overall_df = decoy_A_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/1548328079.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_A_Items_Overall_df = decoy_A_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/1548328079.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_A_Items_Overall_df = decoy_A_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/1548328079.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_A_Items_Overall_df = decoy_A_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/1548328079.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_A_Items_Overall_df = decoy_A_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/1548328079.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_A_Items_Overall_df = decoy_A_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/1548328079.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_A_Items_Overall_df = decoy_A_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/1548328079.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_A_Items_Overall_df = decoy_A_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/1548328079.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_A_Items_Overall_df = decoy_A_Items_Overall_df.append(current_item, ignore_index = True)\n"
     ]
    },
    {
     "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>Item</th>\n",
       "      <th>Attraction Effect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Car seat</td>\n",
       "      <td>0.482143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cloth diapers</td>\n",
       "      <td>0.610619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Clothes Steamer</td>\n",
       "      <td>0.469027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Leaf Blower</td>\n",
       "      <td>0.578261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rash cream</td>\n",
       "      <td>0.790179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Slow cooker</td>\n",
       "      <td>0.851852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Smart bulb</td>\n",
       "      <td>0.636771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tire inflator</td>\n",
       "      <td>0.955556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>UV lamp</td>\n",
       "      <td>0.760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Walker with seat</td>\n",
       "      <td>0.452915</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Item  Attraction Effect\n",
       "0          Car seat           0.482143\n",
       "1     Cloth diapers           0.610619\n",
       "2   Clothes Steamer           0.469027\n",
       "3       Leaf Blower           0.578261\n",
       "4        Rash cream           0.790179\n",
       "5       Slow cooker           0.851852\n",
       "6        Smart bulb           0.636771\n",
       "7     Tire inflator           0.955556\n",
       "8           UV lamp           0.760000\n",
       "9  Walker with seat           0.452915"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoy_A_Items_Overall_df =  pd.DataFrame(columns=['Item', 'Attraction Effect'])\n",
    "\n",
    "item_list = decoy_A_TargetA_df[\"Item\"].unique()\n",
    "item_list = np.sort(item_list)\n",
    "   \n",
    "for x in range(10):\n",
    "    \n",
    "    current_item_name = item_list[x]\n",
    "    \n",
    "    current_target_A = decoy_A_Items_TargetA_df.loc[x, \"Target(num)\"]\n",
    "    current_target_B = decoy_A_Items_TargetB_df.loc[x, \"Target(num)\"]\n",
    "    \n",
    "    current_competitor_A = decoy_A_Items_TargetA_df.loc[x, \"Competitor(num)\"]\n",
    "    current_competitor_B = decoy_A_Items_TargetB_df.loc[x, \"Competitor(num)\"]\n",
    "    \n",
    "    total_effect = (current_target_A+current_target_B)/(current_target_A+current_target_B+current_competitor_A+current_competitor_B)\n",
    "    \n",
    "    current_item = pd.DataFrame(data={'Item': current_item_name, 'Attraction Effect': total_effect}, index=range(1))\n",
    "    \n",
    "    decoy_A_Items_Overall_df = decoy_A_Items_Overall_df.append(current_item, ignore_index = True)\n",
    "    \n",
    "decoy_A_Items_Overall_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c18bf22",
   "metadata": {},
   "source": [
    "### Phantom Decoy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8e2c4a59",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:414: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_participant_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:414: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_participant_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:414: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_participant_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:414: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_participant_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:414: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_participant_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:414: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_participant_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:414: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_participant_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:414: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_participant_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:414: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_participant_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:414: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_participant_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:414: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_participant_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:414: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_participant_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:414: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_participant_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:414: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_participant_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:414: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_participant_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:414: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_participant_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:414: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_participant_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:414: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_participant_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:414: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_participant_freq, ignore_index = True)\n",
      "/Users/luisalvarez/Documents/Decoy/22AY_01_Code/Useful_functions.py:414: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  freq_df = freq_df.append(current_participant_freq, ignore_index = True)\n"
     ]
    }
   ],
   "source": [
    "decoy_P_TargetA_df = decoy_P_df.loc[decoy_P_df['Option_1'].str.contains('P_A') | decoy_P_df['Option_2'].str.contains('P_A') | decoy_P_df['Option_3'].str.contains('P_A')]\n",
    "decoy_P_TargetB_df = decoy_P_df.loc[decoy_P_df['Option_1'].str.contains('P_B') | decoy_P_df['Option_2'].str.contains('P_B') | decoy_P_df['Option_3'].str.contains('P_B')]\n",
    "\n",
    "# Only looking at \"Weakly_desirable\" condition, as it is sort of a control group.\n",
    "#decoy_P_TargetA_df = decoy_P_TargetA_df[decoy_P_TargetA_df[\"PhantomCondition\"] == \"Weakly_desirable\"]\n",
    "#decoy_P_TargetB_df = decoy_P_TargetB_df[decoy_P_TargetB_df[\"PhantomCondition\"] == \"Weakly_desirable\"]\n",
    "\n",
    "# \"Weakly desirable\" -> \"Dehumidifier\", \"Organic fertilizer\" \"Wifi extender\"\n",
    "# \"Highly desirable\" -> \"Baby stroller\", \"Dehumidifier\", \"Electric mop\", \"Organic fertilizer\", \"Paint sprayer\"\n",
    "# \"Undesirable\" ->\n",
    "\n",
    "decoy_P_Items_TargetA_df = uf.itemFreq_PhantomDecoy(decoy_P_TargetA_df)\n",
    "decoy_P_Items_TargetB_df = uf.itemFreq_PhantomDecoy(decoy_P_TargetB_df)\n",
    "\n",
    "item_list = decoy_P_TargetA_df[\"Item\"].unique()\n",
    "item_list = np.sort(item_list)\n",
    "\n",
    "Phan_items_targets_A = np.array([])\n",
    "Phan_items_competitors_A = np.array([])\n",
    "\n",
    "Phan_items_targets_B = np.array([])\n",
    "Phan_items_competitors_B = np.array([])\n",
    "\n",
    "for item in item_list:\n",
    "    \n",
    "    current_item_A = decoy_P_Items_TargetA_df[decoy_P_Items_TargetA_df[\"Item\"] == item]\n",
    "    current_item_B = decoy_P_Items_TargetB_df[decoy_P_Items_TargetB_df[\"Item\"] == item]\n",
    "    \n",
    "    Phan_items_targets_A = np.append(Phan_items_targets_A, current_item_A[\"Target(%)\"])\n",
    "    Phan_items_competitors_A = np.append(Phan_items_competitors_A, current_item_A[\"Competitor(%)\"])\n",
    "    \n",
    "    Phan_items_targets_B = np.append(Phan_items_targets_B, current_item_B[\"Target(%)\"])\n",
    "    Phan_items_competitors_B = np.append(Phan_items_competitors_B, current_item_B[\"Competitor(%)\"])\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "016e8151",
   "metadata": {},
   "outputs": [],
   "source": [
    "decoy_P_Items_TargetA_df.sort_values(by=['Item'], inplace=True) \n",
    "#decoy_P_Items_TargetA_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5799c72a",
   "metadata": {},
   "outputs": [],
   "source": [
    "decoy_P_Items_TargetB_df.sort_values(by=['Item'], inplace=True) \n",
    "#decoy_P_Items_TargetB_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "14efd815",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 5, figsize=(20, 10))\n",
    "\n",
    "counter = 0\n",
    "\n",
    "labels1 = [\"T_A\", \"C_A\"]\n",
    "labels2 = [\"T_B\", \"C_B\"]\n",
    "\n",
    "n = item_list.shape[0]\n",
    "\n",
    "for x in range(2):\n",
    "    \n",
    "    for xi in range(5):\n",
    "        \n",
    "        if (counter == (n)):\n",
    "            counter = 0\n",
    "        \n",
    "        y1 = np.array([Phan_items_targets_A[counter], Phan_items_competitors_A[counter]])\n",
    "        y2 = np.array([Phan_items_targets_B[counter], Phan_items_competitors_B[counter]])\n",
    "        ax[x][xi].bar(labels1, y1, 0.5, capsize=8, label=\"Target A\")\n",
    "        ax[x][xi].bar(labels2, y2, 0.5, capsize=8, label=\"Target B\")\n",
    "        ax[x][xi].set_ylim(0, 105)\n",
    "        ax[x][xi].set_title(item_list[counter])\n",
    "        ax[x][xi].legend()\n",
    "        counter = counter + 1\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b12430f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/1057711742.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_P_Items_Overall_df = decoy_P_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/1057711742.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_P_Items_Overall_df = decoy_P_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/1057711742.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_P_Items_Overall_df = decoy_P_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/1057711742.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_P_Items_Overall_df = decoy_P_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/1057711742.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_P_Items_Overall_df = decoy_P_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/1057711742.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_P_Items_Overall_df = decoy_P_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/1057711742.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_P_Items_Overall_df = decoy_P_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/1057711742.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_P_Items_Overall_df = decoy_P_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/1057711742.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_P_Items_Overall_df = decoy_P_Items_Overall_df.append(current_item, ignore_index = True)\n",
      "/var/folders/0z/1yt2h6410kb7_mgghf4q28z00000gn/T/ipykernel_18712/1057711742.py:20: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  decoy_P_Items_Overall_df = decoy_P_Items_Overall_df.append(current_item, ignore_index = True)\n"
     ]
    },
    {
     "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>Item</th>\n",
       "      <th>Phantom Effect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Air purifier</td>\n",
       "      <td>0.472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Baby stroller</td>\n",
       "      <td>0.568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Countertop blender</td>\n",
       "      <td>0.328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dehumidifier</td>\n",
       "      <td>0.404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Electric mop</td>\n",
       "      <td>0.752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Organic fertilizer</td>\n",
       "      <td>0.684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Outdoor solar lights</td>\n",
       "      <td>0.624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Paint sprayer</td>\n",
       "      <td>0.444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Solar panel system</td>\n",
       "      <td>0.632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Wifi extender</td>\n",
       "      <td>0.564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Item  Phantom Effect\n",
       "0          Air purifier           0.472\n",
       "1         Baby stroller           0.568\n",
       "2    Countertop blender           0.328\n",
       "3          Dehumidifier           0.404\n",
       "4          Electric mop           0.752\n",
       "5    Organic fertilizer           0.684\n",
       "6  Outdoor solar lights           0.624\n",
       "7         Paint sprayer           0.444\n",
       "8    Solar panel system           0.632\n",
       "9         Wifi extender           0.564"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoy_P_Items_Overall_df =  pd.DataFrame(columns=['Item', 'Phantom Effect'])\n",
    "\n",
    "item_list = decoy_P_Items_TargetA_df[\"Item\"].unique()\n",
    "item_list = np.sort(item_list)\n",
    "   \n",
    "for x in range(10):\n",
    "    \n",
    "    current_item_name = item_list[x]\n",
    "    \n",
    "    current_target_A = decoy_P_Items_TargetA_df.loc[x, \"Target(num)\"]\n",
    "    current_target_B = decoy_P_Items_TargetB_df.loc[x, \"Target(num)\"]\n",
    "    \n",
    "    current_competitor_A = decoy_P_Items_TargetA_df.loc[x, \"Competitor(num)\"]\n",
    "    current_competitor_B = decoy_P_Items_TargetB_df.loc[x, \"Competitor(num)\"]\n",
    "    \n",
    "    total_effect = (current_target_A+current_target_B)/(current_target_A+current_target_B+current_competitor_A+current_competitor_B)\n",
    "    \n",
    "    current_item = pd.DataFrame(data={'Item': current_item_name, 'Phantom Effect': total_effect}, index=range(1))\n",
    "    \n",
    "    decoy_P_Items_Overall_df = decoy_P_Items_Overall_df.append(current_item, ignore_index = True)\n",
    "    \n",
    "decoy_P_Items_Overall_df.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4db65078",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "# Combined Scores (Target A & B) for all scenarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "89de9edc",
   "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>Item</th>\n",
       "      <th>Attraction Effect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Car seat</td>\n",
       "      <td>0.482143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cloth diapers</td>\n",
       "      <td>0.610619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Clothes Steamer</td>\n",
       "      <td>0.469027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Leaf Blower</td>\n",
       "      <td>0.578261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rash cream</td>\n",
       "      <td>0.790179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Slow cooker</td>\n",
       "      <td>0.851852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Smart bulb</td>\n",
       "      <td>0.636771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tire inflator</td>\n",
       "      <td>0.955556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>UV lamp</td>\n",
       "      <td>0.760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Walker with seat</td>\n",
       "      <td>0.452915</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Item  Attraction Effect\n",
       "0          Car seat           0.482143\n",
       "1     Cloth diapers           0.610619\n",
       "2   Clothes Steamer           0.469027\n",
       "3       Leaf Blower           0.578261\n",
       "4        Rash cream           0.790179\n",
       "5       Slow cooker           0.851852\n",
       "6        Smart bulb           0.636771\n",
       "7     Tire inflator           0.955556\n",
       "8           UV lamp           0.760000\n",
       "9  Walker with seat           0.452915"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoy_A_Items_Overall_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e6784bdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Item</th>\n",
       "      <th>Compromise Effect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Baby crib</td>\n",
       "      <td>0.840659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Car vacuum</td>\n",
       "      <td>0.775330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Insect repellent</td>\n",
       "      <td>0.726496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lawn mower</td>\n",
       "      <td>0.604167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mobility scooter</td>\n",
       "      <td>0.697778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pesticide</td>\n",
       "      <td>0.767327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Puree machine</td>\n",
       "      <td>0.595652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Smart lock</td>\n",
       "      <td>0.794118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Water heater</td>\n",
       "      <td>0.504587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>White noise machine</td>\n",
       "      <td>0.789157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Item  Compromise Effect\n",
       "0            Baby crib           0.840659\n",
       "1           Car vacuum           0.775330\n",
       "2     Insect repellent           0.726496\n",
       "3           Lawn mower           0.604167\n",
       "4     Mobility scooter           0.697778\n",
       "5            Pesticide           0.767327\n",
       "6        Puree machine           0.595652\n",
       "7           Smart lock           0.794118\n",
       "8         Water heater           0.504587\n",
       "9  White noise machine           0.789157"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoy_C_Items_Overall_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6dfa003b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Item</th>\n",
       "      <th>Similarity Effect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Air Fryer Oven</td>\n",
       "      <td>0.318841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air mattress</td>\n",
       "      <td>0.365714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Baby high chair</td>\n",
       "      <td>0.549708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Headlight bulbs</td>\n",
       "      <td>0.215116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Metal detector</td>\n",
       "      <td>0.427807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Night light</td>\n",
       "      <td>0.350254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Odor neutralizer</td>\n",
       "      <td>0.284264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Pressure washer</td>\n",
       "      <td>0.253968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Security camera</td>\n",
       "      <td>0.272277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Vacuum</td>\n",
       "      <td>0.380597</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Item  Similarity Effect\n",
       "0    Air Fryer Oven           0.318841\n",
       "1      Air mattress           0.365714\n",
       "2   Baby high chair           0.549708\n",
       "3   Headlight bulbs           0.215116\n",
       "4    Metal detector           0.427807\n",
       "5       Night light           0.350254\n",
       "6  Odor neutralizer           0.284264\n",
       "7   Pressure washer           0.253968\n",
       "8   Security camera           0.272277\n",
       "9            Vacuum           0.380597"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoy_S_Items_Overall_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "78be7b1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item</th>\n",
       "      <th>Phantom Effect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Air purifier</td>\n",
       "      <td>0.472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Baby stroller</td>\n",
       "      <td>0.568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Countertop blender</td>\n",
       "      <td>0.328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dehumidifier</td>\n",
       "      <td>0.404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Electric mop</td>\n",
       "      <td>0.752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Organic fertilizer</td>\n",
       "      <td>0.684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Outdoor solar lights</td>\n",
       "      <td>0.624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Paint sprayer</td>\n",
       "      <td>0.444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Solar panel system</td>\n",
       "      <td>0.632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Wifi extender</td>\n",
       "      <td>0.564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Item  Phantom Effect\n",
       "0          Air purifier           0.472\n",
       "1         Baby stroller           0.568\n",
       "2    Countertop blender           0.328\n",
       "3          Dehumidifier           0.404\n",
       "4          Electric mop           0.752\n",
       "5    Organic fertilizer           0.684\n",
       "6  Outdoor solar lights           0.624\n",
       "7         Paint sprayer           0.444\n",
       "8    Solar panel system           0.632\n",
       "9         Wifi extender           0.564"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoy_P_Items_Overall_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25dc66c4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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