Vine_Review_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "72916f21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Connect to libraries and set global variables\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "\n",
    "url = 'Resources/vine_table.csv'\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d0524889",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>star_rating</th>\n",
       "      <th>helpful_votes</th>\n",
       "      <th>total_votes</th>\n",
       "      <th>vine</th>\n",
       "      <th>verified_purchase</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>review_id</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>R3EQSTM9PWRAL</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>4.0</td>\n",
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       "      <th>R10ITC6DGYFD3J</th>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>46.0</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>N</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>N</td>\n",
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       "<p>5331449 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "                star_rating  helpful_votes  total_votes vine verified_purchase\n",
       "review_id                                                                     \n",
       "R3EQSTM9PWRAL           3.0            0.0          0.0    N                 Y\n",
       "RBWPRK17XKIXD           5.0            0.0          0.0    N                 Y\n",
       "RRSLOAF273XFC           5.0            1.0          2.0    N                 Y\n",
       "R3S8W9Q6SWIT8O          4.0            0.0          0.0    N                 Y\n",
       "R3QQ6NSLRVBFJC          4.0            0.0          0.0    N                 Y\n",
       "...                     ...            ...          ...  ...               ...\n",
       "R10ITC6DGYFD3J          1.0           11.0         46.0    N                 N\n",
       "R16BFEO1ITQUP9          4.0            3.0          4.0    N                 N\n",
       "RJGC0F0GOD6QN           5.0            8.0         13.0    N                 N\n",
       "R2RTO9RPW3E7GE          5.0            1.0          8.0    N                 N\n",
       "R1F2ST77BCB5DZ          5.0            0.0          2.0    N                 N\n",
       "\n",
       "[5331449 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in the vine_table.csv file as a DataFrame\n",
    "\n",
    "vine_df = pd.read_csv(url, index_col=False, header=None)\n",
    "vine_df.rename({0: \"review_id\", 1:\"star_rating\", 2:\"helpful_votes\", \n",
    "                3:\"total_votes\", 4:\"vine\", 5:\"verified_purchase\"}, axis=1, inplace=True)\n",
    "\n",
    "vine_df.set_index(\"review_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "be320c29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32026 5299423\n"
     ]
    }
   ],
   "source": [
    "vine_votes = (vine_df['vine'].values == 'Y').sum()\n",
    "non_vine_votes = (vine_df['vine'].values != 'Y').sum()\n",
    "print(vine_votes, non_vine_votes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c21ab136",
   "metadata": {},
   "outputs": [
    {
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       "      <th>helpful_votes</th>\n",
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       "      <th>57</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>37.0</td>\n",
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       "      <th>178</th>\n",
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       "      <td>5.0</td>\n",
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       "      <td>RZ40FSRB4CJWS</td>\n",
       "      <td>5.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
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       "      <td>R10ITC6DGYFD3J</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
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       "<p>125304 rows × 6 columns</p>\n",
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      ],
      "text/plain": [
       "              review_id  star_rating  helpful_votes  total_votes vine  \\\n",
       "57       R2984F9VD9WDJB          1.0           37.0         46.0    N   \n",
       "178      R218TGWCIAZYNY          5.0          191.0        198.0    N   \n",
       "244       RGC2TLSBHLPZA          5.0           49.0         50.0    N   \n",
       "292      R2YYVOW2RYGRM8          5.0           27.0         28.0    N   \n",
       "353      R2X4WF89XICSB2          1.0           21.0         21.0    N   \n",
       "...                 ...          ...            ...          ...  ...   \n",
       "5331434  R2E9I59R1AUR7O          5.0           33.0         35.0    N   \n",
       "5331437  R32YWVU6WFM1DD          5.0           34.0         34.0    N   \n",
       "5331438  R14K2O125RAO00          5.0           26.0         28.0    N   \n",
       "5331439   RZ40FSRB4CJWS          5.0           47.0         47.0    N   \n",
       "5331444  R10ITC6DGYFD3J          1.0           11.0         46.0    N   \n",
       "\n",
       "        verified_purchase  \n",
       "57                      Y  \n",
       "178                     Y  \n",
       "244                     Y  \n",
       "292                     Y  \n",
       "353                     Y  \n",
       "...                   ...  \n",
       "5331434                 Y  \n",
       "5331437                 N  \n",
       "5331438                 N  \n",
       "5331439                 N  \n",
       "5331444                 N  \n",
       "\n",
       "[125304 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter the data and create a new DataFrame or table to retrieve all the rows where \n",
    "# the total_votes count is equal to or greater than 20 to pick reviews that are more \n",
    "# likely to be helpful and to avoid having division by zero errors later on.\n",
    "\n",
    "review_df = vine_df[vine_df[\"total_votes\"]>20]\n",
    "review_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3b10ce6c",
   "metadata": {},
   "outputs": [
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       "      <td>5.0</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>N</td>\n",
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      ],
      "text/plain": [
       "              review_id  star_rating  helpful_votes  total_votes vine  \\\n",
       "57       R2984F9VD9WDJB          1.0           37.0         46.0    N   \n",
       "178      R218TGWCIAZYNY          5.0          191.0        198.0    N   \n",
       "244       RGC2TLSBHLPZA          5.0           49.0         50.0    N   \n",
       "292      R2YYVOW2RYGRM8          5.0           27.0         28.0    N   \n",
       "353      R2X4WF89XICSB2          1.0           21.0         21.0    N   \n",
       "...                 ...          ...            ...          ...  ...   \n",
       "5331434  R2E9I59R1AUR7O          5.0           33.0         35.0    N   \n",
       "5331437  R32YWVU6WFM1DD          5.0           34.0         34.0    N   \n",
       "5331438  R14K2O125RAO00          5.0           26.0         28.0    N   \n",
       "5331439   RZ40FSRB4CJWS          5.0           47.0         47.0    N   \n",
       "5331444  R10ITC6DGYFD3J          1.0           11.0         46.0    N   \n",
       "\n",
       "        verified_purchase  helpful_ratio  \n",
       "57                      Y       0.804348  \n",
       "178                     Y       0.964646  \n",
       "244                     Y       0.980000  \n",
       "292                     Y       0.964286  \n",
       "353                     Y       1.000000  \n",
       "...                   ...            ...  \n",
       "5331434                 Y       0.942857  \n",
       "5331437                 N       1.000000  \n",
       "5331438                 N       0.928571  \n",
       "5331439                 N       1.000000  \n",
       "5331444                 N       0.239130  \n",
       "\n",
       "[125304 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter the new DataFrame or table created in Step 1 and create a new DataFrame \n",
    "# or table to retrieve all the rows where the number of helpful_votes divided by \n",
    "# total_votes is equal to or greater than 50%.\n",
    "\n",
    "ratio_df = review_df\n",
    "ratio_df[\"helpful_ratio\"] = ratio_df.helpful_votes / ratio_df.total_votes\n",
    "\n",
    "ratio_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "791b257f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>review_id</th>\n",
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       "      <th>helpful_votes</th>\n",
       "      <th>total_votes</th>\n",
       "      <th>vine</th>\n",
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       "      <th>helpful_ratio</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38366</th>\n",
       "      <td>RG70K1HMY4LXX</td>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>44.0</td>\n",
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       "    <tr>\n",
       "      <th>145857</th>\n",
       "      <td>R1DHGTNXDXJ0GB</td>\n",
       "      <td>5.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>93.0</td>\n",
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       "      <td>R3B5S3OSA8N6AO</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>43.0</td>\n",
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       "      <td>27.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
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       "      <td>R3GSPG69UD13JN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>0.958678</td>\n",
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       "    <tr>\n",
       "      <th>5278147</th>\n",
       "      <td>R19KPT1VUJH0ZH</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>0.965517</td>\n",
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       "    <tr>\n",
       "      <th>5278790</th>\n",
       "      <td>R1LTHP49NUQMFH</td>\n",
       "      <td>4.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5278803</th>\n",
       "      <td>R19I5HOSQ73JPY</td>\n",
       "      <td>4.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>358.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>0.988827</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>480 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              review_id  star_rating  helpful_votes  total_votes vine  \\\n",
       "38366     RG70K1HMY4LXX          4.0           37.0         44.0    Y   \n",
       "145857   R1DHGTNXDXJ0GB          5.0           78.0         93.0    Y   \n",
       "150655   R3B5S3OSA8N6AO          5.0           38.0         43.0    Y   \n",
       "173012   R29X0YBS0SBIUP          5.0           56.0         74.0    Y   \n",
       "223093   R1ZEDMCU6NYC8Q          3.0           26.0         27.0    Y   \n",
       "...                 ...          ...            ...          ...  ...   \n",
       "5277568  R36RASFZVN2Q8N          3.0           23.0         24.0    Y   \n",
       "5278013  R3GSPG69UD13JN          3.0          116.0        121.0    Y   \n",
       "5278147  R19KPT1VUJH0ZH          5.0           28.0         29.0    Y   \n",
       "5278790  R1LTHP49NUQMFH          4.0           33.0         33.0    Y   \n",
       "5278803  R19I5HOSQ73JPY          4.0          354.0        358.0    Y   \n",
       "\n",
       "        verified_purchase  helpful_ratio  \n",
       "38366                   N       0.840909  \n",
       "145857                  N       0.838710  \n",
       "150655                  N       0.883721  \n",
       "173012                  N       0.756757  \n",
       "223093                  N       0.962963  \n",
       "...                   ...            ...  \n",
       "5277568                 N       0.958333  \n",
       "5278013                 N       0.958678  \n",
       "5278147                 N       0.965517  \n",
       "5278790                 N       1.000000  \n",
       "5278803                 N       0.988827  \n",
       "\n",
       "[480 rows x 7 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter the DataFrame or table created in Step 2, and create a new DataFrame or \n",
    "# table that retrieves all the rows where a review was written as part of the \n",
    "# Vine program\n",
    "\n",
    "yes_df = ratio_df[ratio_df['vine'] == 'Y']\n",
    "yes_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "86d1327a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review_id</th>\n",
       "      <th>star_rating</th>\n",
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       "    <tr>\n",
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       "      <td>198.0</td>\n",
       "      <td>N</td>\n",
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       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>R2YYVOW2RYGRM8</td>\n",
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       "      <td>1.0</td>\n",
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       "      <th>...</th>\n",
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       "      <th>5331434</th>\n",
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       "      <td>5.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>R32YWVU6WFM1DD</td>\n",
       "      <td>5.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5331438</th>\n",
       "      <td>R14K2O125RAO00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
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       "    <tr>\n",
       "      <th>5331439</th>\n",
       "      <td>RZ40FSRB4CJWS</td>\n",
       "      <td>5.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
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       "    <tr>\n",
       "      <th>5331444</th>\n",
       "      <td>R10ITC6DGYFD3J</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0.239130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>124824 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              review_id  star_rating  helpful_votes  total_votes vine  \\\n",
       "57       R2984F9VD9WDJB          1.0           37.0         46.0    N   \n",
       "178      R218TGWCIAZYNY          5.0          191.0        198.0    N   \n",
       "244       RGC2TLSBHLPZA          5.0           49.0         50.0    N   \n",
       "292      R2YYVOW2RYGRM8          5.0           27.0         28.0    N   \n",
       "353      R2X4WF89XICSB2          1.0           21.0         21.0    N   \n",
       "...                 ...          ...            ...          ...  ...   \n",
       "5331434  R2E9I59R1AUR7O          5.0           33.0         35.0    N   \n",
       "5331437  R32YWVU6WFM1DD          5.0           34.0         34.0    N   \n",
       "5331438  R14K2O125RAO00          5.0           26.0         28.0    N   \n",
       "5331439   RZ40FSRB4CJWS          5.0           47.0         47.0    N   \n",
       "5331444  R10ITC6DGYFD3J          1.0           11.0         46.0    N   \n",
       "\n",
       "        verified_purchase  helpful_ratio  \n",
       "57                      Y       0.804348  \n",
       "178                     Y       0.964646  \n",
       "244                     Y       0.980000  \n",
       "292                     Y       0.964286  \n",
       "353                     Y       1.000000  \n",
       "...                   ...            ...  \n",
       "5331434                 Y       0.942857  \n",
       "5331437                 N       1.000000  \n",
       "5331438                 N       0.928571  \n",
       "5331439                 N       1.000000  \n",
       "5331444                 N       0.239130  \n",
       "\n",
       "[124824 rows x 7 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Repeat, but this time retrieve all the rows where the review was not part \n",
    "# of the Vine program (unpaid)\n",
    "\n",
    "no_df = ratio_df[ratio_df['vine'] == 'N']\n",
    "no_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2c21255c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Determine the total number of reviews, the number of 5-star reviews, and the\n",
    "# percentage of 5-star reviews for the two types of review (paid vs unpaid).\n",
    "\n",
    "# Number of reviews and 5* reviews for Y and N, and format output\n",
    "vine_votes = len(yes_df.index)\n",
    "yes_votes = (\"{:,}\").format(vine_votes)\n",
    "vine_stars = (yes_df['star_rating'].values == 5).sum()\n",
    "yes_stars = (\"{:,}\").format(vine_stars)\n",
    "vine_help = (vine_stars / vine_votes)\n",
    "yes_perc = \"{:.2%}\".format(vine_help)\n",
    "not_vine_votes = len(no_df.index)\n",
    "not_votes = (\"{:,}\").format(not_vine_votes)\n",
    "not_vine_stars = (no_df['star_rating'].values == 5).sum()\n",
    "not_stars = (\"{:,}\").format(not_vine_stars)\n",
    "not_vine_help = (not_vine_stars / not_vine_votes)\n",
    "not_vine_perc = \"{:.2%}\".format(not_vine_help)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "403980c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "480 214 44.58% 124,824 71,737 57.47%\n"
     ]
    }
   ],
   "source": [
    "print(yes_votes, yes_stars, yes_perc, not_votes, not_stars, not_vine_perc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b41e2ff7",
   "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>Subscriber</th>\n",
       "      <th>Non-subscriber</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comparison</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total Votes</th>\n",
       "      <td>480</td>\n",
       "      <td>124,824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total 5 Star</th>\n",
       "      <td>214</td>\n",
       "      <td>71,737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Five Star Percentage</th>\n",
       "      <td>44.58%</td>\n",
       "      <td>57.47%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Subscriber Non-subscriber\n",
       "Comparison                                    \n",
       "Total Votes                 480        124,824\n",
       "Total 5 Star                214         71,737\n",
       "Five Star Percentage     44.58%         57.47%"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create dataframe to hold results\n",
    "\n",
    "df = pd.DataFrame({'Comparison':['Total Votes','Total 5 Star','Five Star Percentage'], \n",
    "                   'Subscriber':[yes_votes, yes_stars, yes_perc], \n",
    "                   'Non-subscriber': [not_votes, not_stars, not_vine_perc]})\n",
    "df.set_index([\"Comparison\"], inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c83974ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "name": "5-Star",
         "type": "bar",
         "x": [
          "Vine Subscriber",
          "Non-subscriber"
         ],
         "y": [
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         ]
        },
        {
         "name": "< 5-Star",
         "type": "bar",
         "x": [
          "Vine Subscriber",
          "Non-subscriber"
         ],
         "y": [
          55.416666666666664,
          42.529481509966025
         ]
        }
       ],
       "layout": {
        "barmode": "stack",
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
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              "fillmode": "overlay",
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             }
            },
            "type": "bar"
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          ],
          "barpolar": [
           {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "cust_type=[\"Vine Subscriber\",\"Non-subscriber\"]\n",
    "\n",
    "fig = go.Figure(data=[\n",
    "    go.Bar(name='5-Star', x=cust_type, y=[vine_help*100, not_vine_help*100]),\n",
    "    go.Bar(name='< 5-Star', x=cust_type, y=[(100-(vine_help*100)), (100-(not_vine_help*100))])\n",
    "])\n",
    "# Change the bar mode\n",
    "fig.update_layout(barmode='stack', title=\"Subscribers vs Non-subscriber Percent of 5-star Reviews in Study\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5b6ba90b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.44583333333333336 0.5747051849003397\n"
     ]
    }
   ],
   "source": [
    "print(vine_help, not_vine_help)"
   ]
  }
 ],
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