Vine_Review_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3076ac71",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import dependencies\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9a268f60",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review_id</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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R1KKOXHNI8MSXU</td>\n",
       "      <td>4.0</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>5.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        review_id  star_rating  helpful_votes  total_votes vine  \\\n",
       "0  R1KKOXHNI8MSXU          4.0            0.0          0.0    N   \n",
       "1  R26SP2OPDK4HT7          5.0            1.0          2.0    N   \n",
       "2   RWQEDYAX373I1          5.0            0.0          0.0    N   \n",
       "3  R231YI7R4GPF6J          5.0            0.0          0.0    N   \n",
       "4  R3KO3W45DD0L1K          5.0            0.0          0.0    N   \n",
       "5  R1C4QH63NFL5NJ          5.0            0.0          0.0    N   \n",
       "6  R2GP65O1U9N7BP          5.0            0.0          0.0    N   \n",
       "7  R3O29CT5MQQ3XQ          4.0            0.0          0.0    N   \n",
       "8  R1ZECD2AA8QFF6          5.0            0.0          0.0    N   \n",
       "9  R2S79GCF6J89OA          3.0            0.0          0.0    N   \n",
       "\n",
       "  verified_purchase  \n",
       "0                 Y  \n",
       "1                 Y  \n",
       "2                 Y  \n",
       "3                 Y  \n",
       "4                 Y  \n",
       "5                 Y  \n",
       "6                 Y  \n",
       "7                 Y  \n",
       "8                 Y  \n",
       "9                 Y  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import file (path may be different than one below)\n",
    "df = pd.read_csv ('../vine_table.csv')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a57aa7f2",
   "metadata": {},
   "source": [
    "### 1. Create new dataframe where total_votes is equal or greater than 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8a74994f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review_id</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",
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       "      <td>150.0</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
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       "      <th>419</th>\n",
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       "      <td>4.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
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       "      <th>595</th>\n",
       "      <td>R3M02FSD3BLUPU</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
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       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>R1V6UU1EHW3Q12</td>\n",
       "      <td>5.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>N</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "          review_id  star_rating  helpful_votes  total_votes vine  \\\n",
       "211  R35PT06NWP7LDP          5.0           30.0         32.0    N   \n",
       "344  R2P76PJFUGXBHO          1.0            6.0         45.0    N   \n",
       "372   RQW4AFOG9MR4Z          5.0           51.0         52.0    N   \n",
       "406  R2SMUEBMGLAJQK          5.0           29.0         36.0    N   \n",
       "408  R1XVIZZALU5P6J          5.0          148.0        150.0    N   \n",
       "419  R11UFMG8M2488I          4.0           23.0         26.0    N   \n",
       "548  R2OSHKSPXU68W5          5.0          136.0        147.0    N   \n",
       "595  R3M02FSD3BLUPU          5.0           19.0         21.0    N   \n",
       "701  R1P0LEEJHH09L3          5.0           32.0         32.0    N   \n",
       "749  R1V6UU1EHW3Q12          5.0           57.0         62.0    N   \n",
       "\n",
       "    verified_purchase  \n",
       "211                 N  \n",
       "344                 N  \n",
       "372                 N  \n",
       "406                 Y  \n",
       "408                 Y  \n",
       "419                 N  \n",
       "548                 N  \n",
       "595                 N  \n",
       "701                 Y  \n",
       "749                 Y  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_votes_df = df.loc[df['total_votes'] >= 20]\n",
    "total_votes_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ef880a8",
   "metadata": {},
   "source": [
    "### 2. Create new dataframe from step 1 where helpful_votes divided by total_votes is equal to or greater than 50%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f2676b1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/23/zl9yqn5x13dgspnwvqxkrvz40000gp/T/ipykernel_12513/3137480267.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  total_votes_df['helpful_votes_percent'] = total_votes_df['helpful_votes'] / total_votes_df['total_votes']\n"
     ]
    },
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review_id</th>\n",
       "      <th>star_rating</th>\n",
       "      <th>helpful_votes</th>\n",
       "      <th>total_votes</th>\n",
       "      <th>vine</th>\n",
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       "      <td>5.0</td>\n",
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       "      <th>749</th>\n",
       "      <td>R1V6UU1EHW3Q12</td>\n",
       "      <td>5.0</td>\n",
       "      <td>57.0</td>\n",
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       "      <td>N</td>\n",
       "      <td>Y</td>\n",
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       "    <tr>\n",
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       "      <td>5.0</td>\n",
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      ],
      "text/plain": [
       "          review_id  star_rating  helpful_votes  total_votes vine  \\\n",
       "211  R35PT06NWP7LDP          5.0           30.0         32.0    N   \n",
       "372   RQW4AFOG9MR4Z          5.0           51.0         52.0    N   \n",
       "406  R2SMUEBMGLAJQK          5.0           29.0         36.0    N   \n",
       "408  R1XVIZZALU5P6J          5.0          148.0        150.0    N   \n",
       "419  R11UFMG8M2488I          4.0           23.0         26.0    N   \n",
       "548  R2OSHKSPXU68W5          5.0          136.0        147.0    N   \n",
       "595  R3M02FSD3BLUPU          5.0           19.0         21.0    N   \n",
       "701  R1P0LEEJHH09L3          5.0           32.0         32.0    N   \n",
       "749  R1V6UU1EHW3Q12          5.0           57.0         62.0    N   \n",
       "823  R230W87COFSTCL          5.0           21.0         21.0    N   \n",
       "\n",
       "    verified_purchase  helpful_votes_percent  \n",
       "211                 N               0.937500  \n",
       "372                 N               0.980769  \n",
       "406                 Y               0.805556  \n",
       "408                 Y               0.986667  \n",
       "419                 N               0.884615  \n",
       "548                 N               0.925170  \n",
       "595                 N               0.904762  \n",
       "701                 Y               1.000000  \n",
       "749                 Y               0.919355  \n",
       "823                 Y               1.000000  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_votes_df['helpful_votes_percent'] = total_votes_df['helpful_votes'] / total_votes_df['total_votes']\n",
    "high_helpful_votes_df = total_votes_df.loc[total_votes_df['helpful_votes_percent'] >= .5]\n",
    "high_helpful_votes_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a55b2aa6",
   "metadata": {},
   "source": [
    "### 3. Create new dataframe from step 2 where a review was written as part of the Vine program (paid), vine == 'Y'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "89aef946",
   "metadata": {},
   "outputs": [
    {
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       "    .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>review_id</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",
       "      <th>helpful_votes_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50483</th>\n",
       "      <td>R6U9701C3BGO6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>0.945578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50625</th>\n",
       "      <td>R1XK3ALB45D7N4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>0.970588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50635</th>\n",
       "      <td>R1IZCSTLX81D6C</td>\n",
       "      <td>5.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>0.939394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50656</th>\n",
       "      <td>R2C8NC8EQLH4JF</td>\n",
       "      <td>3.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>0.937500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50890</th>\n",
       "      <td>R1JJ1YOJMOML1P</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            review_id  star_rating  helpful_votes  total_votes vine  \\\n",
       "50483   R6U9701C3BGO6          3.0          139.0        147.0    Y   \n",
       "50625  R1XK3ALB45D7N4          5.0           33.0         34.0    Y   \n",
       "50635  R1IZCSTLX81D6C          5.0           31.0         33.0    Y   \n",
       "50656  R2C8NC8EQLH4JF          3.0           45.0         48.0    Y   \n",
       "50890  R1JJ1YOJMOML1P          5.0           18.0         21.0    Y   \n",
       "\n",
       "      verified_purchase  helpful_votes_percent  \n",
       "50483                 N               0.945578  \n",
       "50625                 N               0.970588  \n",
       "50635                 N               0.939394  \n",
       "50656                 N               0.937500  \n",
       "50890                 N               0.857143  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "paid_review_df = high_helpful_votes_df.loc[high_helpful_votes_df['vine'] == 'Y']\n",
    "paid_review_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37aca775",
   "metadata": {},
   "source": [
    "### 4. Create new dataframe from step 2 where the review was not part of the Vine program (unpaid), vine == 'N'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0656fc23",
   "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>review_id</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",
       "      <th>helpful_votes_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>R35PT06NWP7LDP</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0.937500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>RQW4AFOG9MR4Z</td>\n",
       "      <td>5.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0.980769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>R2SMUEBMGLAJQK</td>\n",
       "      <td>5.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.805556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>R1XVIZZALU5P6J</td>\n",
       "      <td>5.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.986667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>R11UFMG8M2488I</td>\n",
       "      <td>4.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0.884615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          review_id  star_rating  helpful_votes  total_votes vine  \\\n",
       "211  R35PT06NWP7LDP          5.0           30.0         32.0    N   \n",
       "372   RQW4AFOG9MR4Z          5.0           51.0         52.0    N   \n",
       "406  R2SMUEBMGLAJQK          5.0           29.0         36.0    N   \n",
       "408  R1XVIZZALU5P6J          5.0          148.0        150.0    N   \n",
       "419  R11UFMG8M2488I          4.0           23.0         26.0    N   \n",
       "\n",
       "    verified_purchase  helpful_votes_percent  \n",
       "211                 N               0.937500  \n",
       "372                 N               0.980769  \n",
       "406                 Y               0.805556  \n",
       "408                 Y               0.986667  \n",
       "419                 N               0.884615  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unpaid_review_df = high_helpful_votes_df.loc[high_helpful_votes_df['vine'] == 'N']\n",
    "unpaid_review_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4aa16c29",
   "metadata": {},
   "source": [
    "### 5. Determine the total number of reviews, the number of 5-star reviews, and the percentage of 5-star reviews for the two types of review (paid vs unpaid)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ae1ed67e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#paid: number of reviews\n",
    "len(paid_review_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4804a744",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#paid: number of 5 star reviews\n",
    "len(paid_review_df[paid_review_df['star_rating'] == 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f54dd457",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.45454545454545453"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#paid: percentage of 5 star reviews\n",
    "len(paid_review_df[paid_review_df['star_rating'] == 5]) / len(paid_review_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3949846f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45388"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#unpaid: number of reviews\n",
    "len(unpaid_review_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "187f2ddd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23733"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#unpaid: number of 5 star reviews\n",
    "len(unpaid_review_df[unpaid_review_df['star_rating'] == 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6090ab6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5228915131752886"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#unpaid: percentage of 5 star reviews\n",
    "len(unpaid_review_df[unpaid_review_df['star_rating'] == 5]) / len(unpaid_review_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae0ec10e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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