Vine_Review_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Determine Bias of Vine Reviews\n",
    "Using your knowledge of PySpark, Pandas, or SQL, you’ll determine if there is any bias towards reviews that were written as part of the Vine program. For this analysis, you'll determine if having a paid Vine review makes a difference in the percentage of 5-star reviews."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Data and Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import dependencies\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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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>RTIS3L2M1F5SM</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>R1ZV7R40OLHKD</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R3BH071QLH8QMC</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R127K9NTSXA2YH</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R32ZWUXDJPW27Q</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>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   RTIS3L2M1F5SM            5              0            0    N   \n",
       "1   R1ZV7R40OLHKD            5              0            0    N   \n",
       "2  R3BH071QLH8QMC            1              0            1    N   \n",
       "3  R127K9NTSXA2YH            3              0            0    N   \n",
       "4  R32ZWUXDJPW27Q            4              0            0    N   \n",
       "\n",
       "  verified_purchase  \n",
       "0                 Y  \n",
       "1                 Y  \n",
       "2                 Y  \n",
       "3                 Y  \n",
       "4                 Y  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read in vine table csv which was exported from PgAdmin\n",
    "import matplotlib.pyplot as plt\n",
    "vine_table_df = pd.read_csv('resources/vine_table.csv')\n",
    "vine_table_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preview Analysis - Examine Data Types & Basic Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.785997e+06</td>\n",
       "      <td>1.785997e+06</td>\n",
       "      <td>1.785997e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.059893e+00</td>\n",
       "      <td>2.259349e+00</td>\n",
       "      <td>3.757369e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.355812e+00</td>\n",
       "      <td>1.917129e+01</td>\n",
       "      <td>2.245439e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>1.049800e+04</td>\n",
       "      <td>1.078000e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        star_rating  helpful_votes   total_votes\n",
       "count  1.785997e+06   1.785997e+06  1.785997e+06\n",
       "mean   4.059893e+00   2.259349e+00  3.757369e+00\n",
       "std    1.355812e+00   1.917129e+01  2.245439e+01\n",
       "min    1.000000e+00   0.000000e+00  0.000000e+00\n",
       "25%    4.000000e+00   0.000000e+00  0.000000e+00\n",
       "50%    5.000000e+00   0.000000e+00  1.000000e+00\n",
       "75%    5.000000e+00   1.000000e+00  3.000000e+00\n",
       "max    5.000000e+00   1.049800e+04  1.078000e+04"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look for Patterns\n",
    "vine_table_df.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1785997 entries, 0 to 1785996\n",
      "Data columns (total 6 columns):\n",
      " #   Column             Dtype \n",
      "---  ------             ----- \n",
      " 0   review_id          object\n",
      " 1   star_rating        int64 \n",
      " 2   helpful_votes      int64 \n",
      " 3   total_votes        int64 \n",
      " 4   vine               object\n",
      " 5   verified_purchase  object\n",
      "dtypes: int64(3), object(3)\n",
      "memory usage: 81.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# Look at data types\n",
    "vine_table_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1785997"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count Rows\n",
    "vine_table_df[\"review_id\"].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Filter By Total Votes\n",
    "Filter the data and create a new DataFrame or table to retrieve all the rows where the total_votes count is equal to or greater than 20 to pick reviews that are more likely to be helpful and to avoid having division by zero errors later on.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
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       "      <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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       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>R4PKAZRQJJX14</td>\n",
       "      <td>1</td>\n",
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       "      <td>N</td>\n",
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       "      <th>74</th>\n",
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       "      <td>1</td>\n",
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       "      <td>N</td>\n",
       "      <td>Y</td>\n",
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       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>R127WEQY2FM1T3</td>\n",
       "      <td>1</td>\n",
       "      <td>147</td>\n",
       "      <td>175</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>R3EZ0EPYLDA34S</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>31</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
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       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>R2FJ94555FZH32</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>60</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
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      ],
      "text/plain": [
       "          review_id  star_rating  helpful_votes  total_votes vine  \\\n",
       "55    R4PKAZRQJJX14            1             21           34    N   \n",
       "74   R2CI0Y288CC7E2            1             21           35    N   \n",
       "209  R127WEQY2FM1T3            1            147          175    N   \n",
       "289  R3EZ0EPYLDA34S            1             14           31    N   \n",
       "483  R2FJ94555FZH32            2             55           60    N   \n",
       "\n",
       "    verified_purchase  \n",
       "55                  N  \n",
       "74                  Y  \n",
       "209                 Y  \n",
       "289                 Y  \n",
       "483                 N  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_votes_df = vine_table_df[vine_table_df['total_votes'] >= 20]\n",
    "total_votes_df.head(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65379"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_votes_df[\"review_id\"].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Filter By Most Helpful Votes\n",
    "Filter the new DataFrame or table created in Step 1 and create a new DataFrame or table to retrieve all the rows where the number of helpful_votes divided by total_votes is equal to or greater than 50%.\n",
    "\n",
    "- If you use the SQL option below, you’ll need to cast your columns as floats using WHERE CAST(helpful_votes AS FLOAT)/CAST(total_votes AS FLOAT) >=0.5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>review_id</th>\n",
       "      <th>star_rating</th>\n",
       "      <th>helpful_votes</th>\n",
       "      <th>total_votes</th>\n",
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       "      <th>55</th>\n",
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       "      <td>1</td>\n",
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       "      <th>209</th>\n",
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       "      <th>483</th>\n",
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       "      <td>2</td>\n",
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       "      <td>60</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>R1U3AR67RE273L</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>65</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "          review_id  star_rating  helpful_votes  total_votes vine  \\\n",
       "55    R4PKAZRQJJX14            1             21           34    N   \n",
       "74   R2CI0Y288CC7E2            1             21           35    N   \n",
       "209  R127WEQY2FM1T3            1            147          175    N   \n",
       "483  R2FJ94555FZH32            2             55           60    N   \n",
       "537  R1U3AR67RE273L            1             51           65    N   \n",
       "\n",
       "    verified_purchase  \n",
       "55                  N  \n",
       "74                  Y  \n",
       "209                 Y  \n",
       "483                 N  \n",
       "537                 Y  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "helpful_votes_df = total_votes_df.loc[total_votes_df.helpful_votes / total_votes_df.total_votes >= 0.5]\n",
    "helpful_votes_df.head(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40565"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "helpful_votes_df[\"review_id\"].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Filter By Paid Reviews\n",
    "Filter the DataFrame or table created in Step 2, and create a new DataFrame or table that retrieves all the rows where a review was written as part of the Vine program (paid), vine == 'Y'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32611</th>\n",
       "      <td>R3KKUSGFZWSUIY</td>\n",
       "      <td>5</td>\n",
       "      <td>56</td>\n",
       "      <td>63</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
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       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69680</th>\n",
       "      <td>RM4KSGEOR7MU1</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155361</th>\n",
       "      <td>RG7VRMYLEXD23</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>26</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239327</th>\n",
       "      <td>R11O4YSCPSNL6L</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>26</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             review_id  star_rating  helpful_votes  total_votes vine  \\\n",
       "32611   R3KKUSGFZWSUIY            5             56           63    Y   \n",
       "33112   R10FO5UKKVZBK2            3             23           23    Y   \n",
       "69680    RM4KSGEOR7MU1            5             19           24    Y   \n",
       "155361   RG7VRMYLEXD23            4             22           26    Y   \n",
       "239327  R11O4YSCPSNL6L            3             20           26    Y   \n",
       "\n",
       "       verified_purchase  \n",
       "32611                  N  \n",
       "33112                  N  \n",
       "69680                  N  \n",
       "155361                 N  \n",
       "239327                 N  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "paid_reviews_df = helpful_votes_df[helpful_votes_df['vine'] == 'Y']\n",
    "paid_reviews_df.head(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "94"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "paid_count = paid_reviews_df[\"review_id\"].count()\n",
    "paid_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Filter By Unpaid Reviews\n",
    "Repeat Step 3, but this time retrieve all the rows where the review was not part of the Vine program (unpaid), vine == 'N'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>R4PKAZRQJJX14</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>34</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>R2CI0Y288CC7E2</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>R127WEQY2FM1T3</td>\n",
       "      <td>1</td>\n",
       "      <td>147</td>\n",
       "      <td>175</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>R2FJ94555FZH32</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>60</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>R1U3AR67RE273L</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>65</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",
       "55    R4PKAZRQJJX14            1             21           34    N   \n",
       "74   R2CI0Y288CC7E2            1             21           35    N   \n",
       "209  R127WEQY2FM1T3            1            147          175    N   \n",
       "483  R2FJ94555FZH32            2             55           60    N   \n",
       "537  R1U3AR67RE273L            1             51           65    N   \n",
       "\n",
       "    verified_purchase  \n",
       "55                  N  \n",
       "74                  Y  \n",
       "209                 Y  \n",
       "483                 N  \n",
       "537                 Y  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unpaid_reviews_df = helpful_votes_df[helpful_votes_df['vine'] == 'N']\n",
    "unpaid_reviews_df.head(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40471"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unpaid_count = unpaid_reviews_df[\"review_id\"].count()\n",
    "unpaid_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 5-Star Review Analysis\n",
    "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).\n",
    "\n",
    "### Total Number of Paid vs Unpaid Reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Number of Reviews: 40565\n",
      "Number of Paid Views: 94\n",
      "Number of Unpaid Views: 40471\n",
      "Out of 40565 reviews, 94 were paid.\n"
     ]
    }
   ],
   "source": [
    "vine_count = paid_count + unpaid_count\n",
    "print(f\"Total Number of Reviews: {vine_count}\")\n",
    "print(f\"Number of Paid Views: {paid_count}\")\n",
    "print(f\"Number of Unpaid Views: {unpaid_count}\")\n",
    "print(f\"Out of {vine_count} reviews, {paid_count} were paid.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total Number of 5-Star Paid & Unpaid Reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "five_star_paid = len(paid_reviews_df[paid_reviews_df['star_rating']==5])\n",
    "\n",
    "five_star_unpaid = len(unpaid_reviews_df[unpaid_reviews_df['star_rating']==5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of 5-Star Paid Views: 48\n",
      "Number of Paid Views: 94\n",
      "Number of 5-Star UnPaid Views: 15663\n",
      "Number of Paid Views: 40471\n"
     ]
    }
   ],
   "source": [
    "print(f\"Number of 5-Star Paid Views: {five_star_paid}\")\n",
    "print(f\"Number of Paid Views: {paid_count}\")\n",
    "print(f\"Number of 5-Star UnPaid Views: {five_star_unpaid}\")\n",
    "print(f\"Number of Paid Views: {unpaid_count}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Percentage Of 5-Star Reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of 5-Star Paid Reviews: 0.51\n",
      "Percentage of 5-Star Unpaid Reviews: 0.39\n"
     ]
    }
   ],
   "source": [
    "print(f\"Percentage of 5-Star Paid Reviews: {round(five_star_paid / paid_count, 2)}\")\n",
    "print(f\"Percentage of 5-Star Unpaid Reviews: {round(five_star_unpaid / unpaid_count, 2)}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average 5-Star Rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "paid_avg = round(paid_reviews_df['star_rating'].mean(),2)\n",
    "\n",
    "unpaid_avg = round(unpaid_reviews_df['star_rating'].mean(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average 5-Star Paid Review: 4.2\n",
      "Average 5-Star Unpaid Review: 3.35\n"
     ]
    }
   ],
   "source": [
    "print(f\"Average 5-Star Paid Review: {paid_avg}\")\n",
    "print(f\"Average 5-Star Unpaid Review: {unpaid_avg}\")\n"
   ]
  }
 ],
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