EvolentHealth_BestBeer.ipynb

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    "# Evolent Health: Beer Data Analysis\n",
    "#### The project descriptions, questions and datasets were all downloaded from Strata Scratch. According to Strata Scratch, this data project has been used as a take-home assignment in the recruitment process for the data science positions at Evolent Health. \n",
    "#### !! All of the followings are done solely for the purpose of self-practice. !!\n",
    "\n",
    "### Assignment: \n",
    "- Rank the top 3 breweries which produce the strongest beers.\n",
    "- Which year did beers enjoy the highest ratings?\n",
    "- Based on the users' ratings, which factors are important among taste, aroma, appearance, and palette?\n",
    "- If you were to recommend 3 beers to your friends based on this data, which ones would you recommend?\n",
    "- Which beer style seems to be the favourite based on the reviews written by users? How does written reviews compare to overall review score for the beer style?\n",
    "\n",
    "### About the dataset(s): \n",
    "- Source: https://platform.stratascratch.com/data-projects/beer-data-analysis\n",
    "- Description: The provided compressed file `EvolentHealth_data_beer.tar.bz2` contains data about beers and their reviews\n",
    "\n",
    "### Data Dictionary:\n",
    "\n",
    "**EvolentHealth_data_beer.tar.bz2**\n",
    "\n",
    "| variable               | description                            |\n",
    "|:-----------------------|:---------------------------------------|\n",
    "| beer_ABV               | alcohol by volume                      |\n",
    "| beer_beerId            | beer ID                                |\n",
    "| beer_brewerId          | beer brewer ID                         |\n",
    "| beer_name              | beer name                              |\n",
    "| beer_style             | beer style                             |\n",
    "| review_appearance      | review on the beer's appearance        |\n",
    "| review_palette         | review on the beer's palette (colours) |\n",
    "| review_overall         | overall beer review                    |\n",
    "| review_taste           | review on the beer's taste             |\n",
    "| review_profileName     | profile name of the reviewer           |\n",
    "| review_aroma           | review on the beer's aroma             |\n",
    "| review_text            | the full text of the review            |\n",
    "| review_time            | timestamp when the review was made     |"
   ]
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   "source": [
    "---\n",
    "## EDA\n",
    "---"
   ]
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 528870 entries, 0 to 528869\n",
      "Data columns (total 13 columns):\n",
      " #   Column              Non-Null Count   Dtype  \n",
      "---  ------              --------------   -----  \n",
      " 0   beer_ABV            508590 non-null  float64\n",
      " 1   beer_beerId         528870 non-null  int64  \n",
      " 2   beer_brewerId       528870 non-null  int64  \n",
      " 3   beer_name           528870 non-null  object \n",
      " 4   beer_style          528870 non-null  object \n",
      " 5   review_appearance   528870 non-null  float64\n",
      " 6   review_palette      528870 non-null  float64\n",
      " 7   review_overall      528870 non-null  float64\n",
      " 8   review_taste        528870 non-null  float64\n",
      " 9   review_profileName  528755 non-null  object \n",
      " 10  review_aroma        528870 non-null  float64\n",
      " 11  review_text         528751 non-null  object \n",
      " 12  review_time         528870 non-null  int64  \n",
      "dtypes: float64(6), int64(3), object(4)\n",
      "memory usage: 52.5+ MB\n"
     ]
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       "None"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beer_ABV</th>\n",
       "      <th>beer_beerId</th>\n",
       "      <th>beer_brewerId</th>\n",
       "      <th>beer_name</th>\n",
       "      <th>beer_style</th>\n",
       "      <th>review_appearance</th>\n",
       "      <th>review_palette</th>\n",
       "      <th>review_overall</th>\n",
       "      <th>review_taste</th>\n",
       "      <th>review_profileName</th>\n",
       "      <th>review_aroma</th>\n",
       "      <th>review_text</th>\n",
       "      <th>review_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>47986</td>\n",
       "      <td>10325</td>\n",
       "      <td>Sausa Weizen</td>\n",
       "      <td>Hefeweizen</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>stcules</td>\n",
       "      <td>1.5</td>\n",
       "      <td>A lot of foam. But a lot. In the smell some ba...</td>\n",
       "      <td>1234817823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.2</td>\n",
       "      <td>48213</td>\n",
       "      <td>10325</td>\n",
       "      <td>Red Moon</td>\n",
       "      <td>English Strong Ale</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>stcules</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Dark red color, light beige foam, average. In ...</td>\n",
       "      <td>1235915097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.5</td>\n",
       "      <td>48215</td>\n",
       "      <td>10325</td>\n",
       "      <td>Black Horse Black Beer</td>\n",
       "      <td>Foreign / Export Stout</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>stcules</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Almost totally black. Beige foam, quite compac...</td>\n",
       "      <td>1235916604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>47969</td>\n",
       "      <td>10325</td>\n",
       "      <td>Sausa Pils</td>\n",
       "      <td>German Pilsener</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>stcules</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Golden yellow color. White, compact foam, quit...</td>\n",
       "      <td>1234725145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.7</td>\n",
       "      <td>64883</td>\n",
       "      <td>1075</td>\n",
       "      <td>Cauldron DIPA</td>\n",
       "      <td>American Double / Imperial IPA</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>johnmichaelsen</td>\n",
       "      <td>4.5</td>\n",
       "      <td>According to the website, the style for the Ca...</td>\n",
       "      <td>1293735206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   beer_ABV  beer_beerId  beer_brewerId               beer_name  \\\n",
       "0       5.0        47986          10325            Sausa Weizen   \n",
       "1       6.2        48213          10325                Red Moon   \n",
       "2       6.5        48215          10325  Black Horse Black Beer   \n",
       "3       5.0        47969          10325              Sausa Pils   \n",
       "4       7.7        64883           1075           Cauldron DIPA   \n",
       "\n",
       "                       beer_style  review_appearance  review_palette  \\\n",
       "0                      Hefeweizen                2.5             2.0   \n",
       "1              English Strong Ale                3.0             2.5   \n",
       "2          Foreign / Export Stout                3.0             2.5   \n",
       "3                 German Pilsener                3.5             3.0   \n",
       "4  American Double / Imperial IPA                4.0             4.5   \n",
       "\n",
       "   review_overall  review_taste review_profileName  review_aroma  \\\n",
       "0             1.5           1.5            stcules           1.5   \n",
       "1             3.0           3.0            stcules           3.0   \n",
       "2             3.0           3.0            stcules           3.0   \n",
       "3             3.0           2.5            stcules           3.0   \n",
       "4             4.0           4.0     johnmichaelsen           4.5   \n",
       "\n",
       "                                         review_text  review_time  \n",
       "0  A lot of foam. But a lot. In the smell some ba...   1234817823  \n",
       "1  Dark red color, light beige foam, average. In ...   1235915097  \n",
       "2  Almost totally black. Beige foam, quite compac...   1235916604  \n",
       "3  Golden yellow color. White, compact foam, quit...   1234725145  \n",
       "4  According to the website, the style for the Ca...   1293735206  "
      ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beer_ABV</th>\n",
       "      <th>beer_beerId</th>\n",
       "      <th>beer_brewerId</th>\n",
       "      <th>review_appearance</th>\n",
       "      <th>review_palette</th>\n",
       "      <th>review_overall</th>\n",
       "      <th>review_taste</th>\n",
       "      <th>review_aroma</th>\n",
       "      <th>review_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>508590.000000</td>\n",
       "      <td>528870.000000</td>\n",
       "      <td>528870.000000</td>\n",
       "      <td>528870.000000</td>\n",
       "      <td>528870.000000</td>\n",
       "      <td>528870.000000</td>\n",
       "      <td>528870.000000</td>\n",
       "      <td>528870.000000</td>\n",
       "      <td>5.288700e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.017442</td>\n",
       "      <td>22098.466016</td>\n",
       "      <td>2598.423429</td>\n",
       "      <td>3.864522</td>\n",
       "      <td>3.758926</td>\n",
       "      <td>3.833197</td>\n",
       "      <td>3.765993</td>\n",
       "      <td>3.817350</td>\n",
       "      <td>1.224885e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.204460</td>\n",
       "      <td>22158.284352</td>\n",
       "      <td>5281.805350</td>\n",
       "      <td>0.604010</td>\n",
       "      <td>0.685335</td>\n",
       "      <td>0.709962</td>\n",
       "      <td>0.669018</td>\n",
       "      <td>0.718903</td>\n",
       "      <td>7.605600e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.010000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.843904e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.300000</td>\n",
       "      <td>1745.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>1.174613e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>14368.000000</td>\n",
       "      <td>394.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.240366e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.500000</td>\n",
       "      <td>40528.000000</td>\n",
       "      <td>1475.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.288560e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>57.700000</td>\n",
       "      <td>77310.000000</td>\n",
       "      <td>27980.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.326277e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            beer_ABV    beer_beerId  beer_brewerId  review_appearance  \\\n",
       "count  508590.000000  528870.000000  528870.000000      528870.000000   \n",
       "mean        7.017442   22098.466016    2598.423429           3.864522   \n",
       "std         2.204460   22158.284352    5281.805350           0.604010   \n",
       "min         0.010000       3.000000       1.000000           0.000000   \n",
       "25%         5.300000    1745.000000     132.000000           3.500000   \n",
       "50%         6.500000   14368.000000     394.000000           4.000000   \n",
       "75%         8.500000   40528.000000    1475.000000           4.000000   \n",
       "max        57.700000   77310.000000   27980.000000           5.000000   \n",
       "\n",
       "       review_palette  review_overall   review_taste   review_aroma  \\\n",
       "count   528870.000000   528870.000000  528870.000000  528870.000000   \n",
       "mean         3.758926        3.833197       3.765993       3.817350   \n",
       "std          0.685335        0.709962       0.669018       0.718903   \n",
       "min          1.000000        0.000000       1.000000       1.000000   \n",
       "25%          3.500000        3.500000       3.500000       3.500000   \n",
       "50%          4.000000        4.000000       4.000000       4.000000   \n",
       "75%          4.000000        4.500000       4.000000       4.500000   \n",
       "max          5.000000        5.000000       5.000000       5.000000   \n",
       "\n",
       "        review_time  \n",
       "count  5.288700e+05  \n",
       "mean   1.224885e+09  \n",
       "std    7.605600e+07  \n",
       "min    8.843904e+08  \n",
       "25%    1.174613e+09  \n",
       "50%    1.240366e+09  \n",
       "75%    1.288560e+09  \n",
       "max    1.326277e+09  "
      ]
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   ],
   "source": [
    "import os \n",
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "\n",
    "os.chdir(path=\"/Users/noel/Desktop/2023_projects/datasets\")\n",
    "df = pd.read_csv(\"EvolentHealth_data_beer.tar.bz2\", compression=\"bz2\")\n",
    "\n",
    "display(df.info())\n",
    "display(df.head())\n",
    "display(df.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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     "name": "stdout",
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     "text": [
      "(528870, 13)\n"
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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(508358, 13)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remove NAs\n",
    "print(df.shape)\n",
    "pre_na = sns.heatmap(df.isna()); plt.show()\n",
    "df = df.dropna()\n",
    "\n",
    "print(df.shape)\n",
    "post_na = sns.heatmap(df.isna()); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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_profileName</th>\n",
       "      <th>beer_beerId</th>\n",
       "      <th>review_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18245</th>\n",
       "      <td>northyorksammy</td>\n",
       "      <td>1732</td>\n",
       "      <td>1732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17419</th>\n",
       "      <td>mikesgroove</td>\n",
       "      <td>1320</td>\n",
       "      <td>1320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1646</th>\n",
       "      <td>BuckeyeNation</td>\n",
       "      <td>1212</td>\n",
       "      <td>1212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6620</th>\n",
       "      <td>Phyl21ca</td>\n",
       "      <td>1152</td>\n",
       "      <td>1152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22236</th>\n",
       "      <td>womencantsail</td>\n",
       "      <td>1113</td>\n",
       "      <td>1113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>ChainGangGuy</td>\n",
       "      <td>1045</td>\n",
       "      <td>1045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8452</th>\n",
       "      <td>Thorpe429</td>\n",
       "      <td>980</td>\n",
       "      <td>980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10905</th>\n",
       "      <td>brentk56</td>\n",
       "      <td>972</td>\n",
       "      <td>972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13513</th>\n",
       "      <td>feloniousmonk</td>\n",
       "      <td>919</td>\n",
       "      <td>919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6109</th>\n",
       "      <td>NeroFiddled</td>\n",
       "      <td>907</td>\n",
       "      <td>907</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      review_profileName  beer_beerId  review_time\n",
       "18245     northyorksammy         1732         1732\n",
       "17419        mikesgroove         1320         1320\n",
       "1646       BuckeyeNation         1212         1212\n",
       "6620            Phyl21ca         1152         1152\n",
       "22236      womencantsail         1113         1113\n",
       "1916        ChainGangGuy         1045         1045\n",
       "8452           Thorpe429          980          980\n",
       "10905           brentk56          972          972\n",
       "13513      feloniousmonk          919          919\n",
       "6109         NeroFiddled          907          907"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(503700, 13)\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>beer_ABV</th>\n",
       "      <th>beer_beerId</th>\n",
       "      <th>beer_brewerId</th>\n",
       "      <th>beer_name</th>\n",
       "      <th>beer_style</th>\n",
       "      <th>review_appearance</th>\n",
       "      <th>review_palette</th>\n",
       "      <th>review_overall</th>\n",
       "      <th>review_taste</th>\n",
       "      <th>review_profileName</th>\n",
       "      <th>review_aroma</th>\n",
       "      <th>review_text</th>\n",
       "      <th>review_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>47986</td>\n",
       "      <td>10325</td>\n",
       "      <td>Sausa Weizen</td>\n",
       "      <td>Hefeweizen</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>stcules</td>\n",
       "      <td>1.5</td>\n",
       "      <td>A lot of foam. But a lot. In the smell some ba...</td>\n",
       "      <td>1234817823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.2</td>\n",
       "      <td>48213</td>\n",
       "      <td>10325</td>\n",
       "      <td>Red Moon</td>\n",
       "      <td>English Strong Ale</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>stcules</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Dark red color, light beige foam, average. In ...</td>\n",
       "      <td>1235915097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.5</td>\n",
       "      <td>48215</td>\n",
       "      <td>10325</td>\n",
       "      <td>Black Horse Black Beer</td>\n",
       "      <td>Foreign / Export Stout</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>stcules</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Almost totally black. Beige foam, quite compac...</td>\n",
       "      <td>1235916604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>47969</td>\n",
       "      <td>10325</td>\n",
       "      <td>Sausa Pils</td>\n",
       "      <td>German Pilsener</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>stcules</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Golden yellow color. White, compact foam, quit...</td>\n",
       "      <td>1234725145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.7</td>\n",
       "      <td>64883</td>\n",
       "      <td>1075</td>\n",
       "      <td>Cauldron DIPA</td>\n",
       "      <td>American Double / Imperial IPA</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>johnmichaelsen</td>\n",
       "      <td>4.5</td>\n",
       "      <td>According to the website, the style for the Ca...</td>\n",
       "      <td>1293735206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   beer_ABV  beer_beerId  beer_brewerId               beer_name  \\\n",
       "0       5.0        47986          10325            Sausa Weizen   \n",
       "1       6.2        48213          10325                Red Moon   \n",
       "2       6.5        48215          10325  Black Horse Black Beer   \n",
       "3       5.0        47969          10325              Sausa Pils   \n",
       "4       7.7        64883           1075           Cauldron DIPA   \n",
       "\n",
       "                       beer_style  review_appearance  review_palette  \\\n",
       "0                      Hefeweizen                2.5             2.0   \n",
       "1              English Strong Ale                3.0             2.5   \n",
       "2          Foreign / Export Stout                3.0             2.5   \n",
       "3                 German Pilsener                3.5             3.0   \n",
       "4  American Double / Imperial IPA                4.0             4.5   \n",
       "\n",
       "   review_overall  review_taste review_profileName  review_aroma  \\\n",
       "0             1.5           1.5            stcules           1.5   \n",
       "1             3.0           3.0            stcules           3.0   \n",
       "2             3.0           3.0            stcules           3.0   \n",
       "3             3.0           2.5            stcules           3.0   \n",
       "4             4.0           4.0     johnmichaelsen           4.5   \n",
       "\n",
       "                                         review_text  review_time  \n",
       "0  A lot of foam. But a lot. In the smell some ba...   1234817823  \n",
       "1  Dark red color, light beige foam, average. In ...   1235915097  \n",
       "2  Almost totally black. Beige foam, quite compac...   1235916604  \n",
       "3  Golden yellow color. White, compact foam, quit...   1234725145  \n",
       "4  According to the website, the style for the Ca...   1293735206  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove duplicates\n",
    "df_reviews_of_each_profile = pd.DataFrame(df.groupby(\"review_profileName\")[[\"beer_beerId\", \"review_time\"]].count()).reset_index()\n",
    "df_reviews_of_each_profile = df_reviews_of_each_profile.sort_values(by=\"beer_beerId\", ascending=False)\n",
    "display(df_reviews_of_each_profile.head(10))\n",
    "\n",
    "df = df.drop_duplicates(subset=[\"review_profileName\", \"beer_beerId\"], keep=\"first\").reset_index(drop=True)\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Q1: Rank the top 3 breweries which produce the strongest beers.\n",
    "`Apprach: compare the mean/median of overall review by brewer`\n",
    "- Check skewness of alcohol by volumne (col: beer_ABV) before deciding whether to use mean/median\n",
    "- Group the data by beer (col: beer_brewerID) and calculate the mean/median\n",
    "- Rank by mean/median\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SkewtestResult(statistic=318.3167088772542, pvalue=0.0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fba983096a0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import skewtest\n",
    "print(skewtest(df.beer_ABV, nan_policy=\"omit\"))\n",
    "\n",
    "q1_sample_skewtest = df.sample(frac=0.3, random_state=2023)\n",
    "sns.displot(data=q1_sample_skewtest, x=\"beer_ABV\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>beer_brewerId</th>\n",
       "      <th>median_ABV</th>\n",
       "      <th>RANK_by_median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>736</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>5562</td>\n",
       "      <td>13.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>36</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>6513</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    beer_brewerId  median_ABV RANK_by_median\n",
       "165           736        14.0            1.0\n",
       "636          5562        13.2            2.0\n",
       "8              36        13.0            3.0\n",
       "699          6513        13.0            3.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
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        "title": {
         "text": "<b>Question 1: Rank the top 3 breweries which produce the strongest beers"
        },
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          1
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         "tickmode": "linear",
         "title": {
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     "text": [
      "![ANS to Q1]: \n",
      "Brewer 736 \n",
      "Brewer 5562 \n",
      "Brewer 36 & 6513\n"
     ]
    }
   ],
   "source": [
    "ans_q1_df = df.groupby(\"beer_brewerId\").agg(median_ABV=(\"beer_ABV\", \"median\")).reset_index()\n",
    "ans_q1_df = ans_q1_df.sort_values(by=\"median_ABV\")\n",
    "\n",
    "ans_q1_df[\"RANK_by_median\"] = ans_q1_df[\"median_ABV\"].rank(method=\"min\", ascending=False)\n",
    "ans_q1_df = ans_q1_df.sort_values(by=\"RANK_by_median\", ascending=True)\n",
    "\n",
    "ans_q1_df = ans_q1_df[ans_q1_df[\"RANK_by_median\"]<=3]\n",
    "ans_q1_df[\"beer_brewerId\"] = ans_q1_df[\"beer_brewerId\"].astype(str)\n",
    "ans_q1_df[\"RANK_by_median\"] = ans_q1_df[\"RANK_by_median\"].astype(\"category\")\n",
    "\n",
    "display(ans_q1_df)\n",
    "\n",
    "ans_q1_plot = px.bar(ans_q1_df, x=\"beer_brewerId\", y=\"median_ABV\", text=\"RANK_by_median\", color=\"RANK_by_median\", template=\"simple_white\")\n",
    "ans_q1_plot.update_xaxes(tickmode=\"linear\")\n",
    "ans_q1_plot.update_layout(\n",
    "    xaxis_title = \"Beer brewer id\", yaxis_title = \"Beer ABV (median)\",\n",
    "    title=\"<b>Question 1: Rank the top 3 breweries which produce the strongest beers\")\n",
    "\n",
    "ans_q1_plot.show()\n",
    "print(\"![ANS to Q1]:\",\n",
    "      \"\\nBrewer 736\",\n",
    "      \"\\nBrewer 5562\",\n",
    "      \"\\nBrewer 36 & 6513\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Q2: Which year did beers enjoy the highest ratings?\n",
    "`Aapprach: compare the mean/median of overall review by year`\n",
    "\n",
    "- Convert the review time to standard datetime format -> col: review_time_converted\n",
    "- Extract the review year from the converted time -> col: review_yr\n",
    "- Check skewness of overall review scores (col: review_overall) before deciding whether to use mean/median\n",
    "- Group the data by year of reviews (col: review_yr) and calculate the mean/median\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SkewtestResult(statistic=-250.20887110577434, pvalue=0.0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fbaadf98460>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import skewtest\n",
    "print(skewtest(df.review_overall, nan_policy=\"omit\"))\n",
    "\n",
    "q2_ssample_skewtest = df.sample(frac=0.3, random_state=2023)\n",
    "sns.displot(data=q2_ssample_skewtest, x=\"review_overall\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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_yr</th>\n",
       "      <th>mean_review_overall</th>\n",
       "      <th>RANK_by_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>4.241379</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1998</td>\n",
       "      <td>4.045455</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1999</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001</td>\n",
       "      <td>3.959357</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010</td>\n",
       "      <td>3.867784</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2009</td>\n",
       "      <td>3.866853</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2005</td>\n",
       "      <td>3.843692</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2008</td>\n",
       "      <td>3.838512</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2012</td>\n",
       "      <td>3.838173</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2011</td>\n",
       "      <td>3.832429</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2007</td>\n",
       "      <td>3.817254</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2002</td>\n",
       "      <td>3.817035</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2006</td>\n",
       "      <td>3.807381</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2004</td>\n",
       "      <td>3.803884</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2003</td>\n",
       "      <td>3.771235</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   review_yr  mean_review_overall RANK_by_mean\n",
       "0       2000             4.241379          1.0\n",
       "1       1998             4.045455          2.0\n",
       "2       1999             4.000000          3.0\n",
       "3       2001             3.959357          4.0\n",
       "4       2010             3.867784          5.0\n",
       "5       2009             3.866853          6.0\n",
       "6       2005             3.843692          7.0\n",
       "7       2008             3.838512          8.0\n",
       "8       2012             3.838173          9.0\n",
       "9       2011             3.832429         10.0\n",
       "10      2007             3.817254         11.0\n",
       "11      2002             3.817035         12.0\n",
       "12      2006             3.807381         13.0\n",
       "13      2004             3.803884         14.0\n",
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            "showline": true,
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          },
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           "linecolor": "rgb(36,36,36)",
           "showgrid": false,
           "showline": true,
           "ticks": "outside",
           "title": {
            "standoff": 15
           },
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           "title": {
            "standoff": 15
           },
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         }
        },
        "title": {
         "text": "<b>Q2: Which year did beers enjoy the highest ratings?"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
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         "tickmode": "linear",
         "title": {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "![ANS to Q2]: \n",
      "2000\n"
     ]
    }
   ],
   "source": [
    "df[\"review_time_converted\"] = pd.to_datetime(df[\"review_time\"], unit=\"s\")\n",
    "df[\"review_yr\"] = df[\"review_time_converted\"] .dt.year\n",
    "\n",
    "ans_q2_df = df.groupby(\"review_yr\").agg(mean_review_overall=(\"review_overall\", \"mean\")).reset_index()\n",
    "ans_q2_df = ans_q2_df.sort_values(by=\"mean_review_overall\", ascending=False).reset_index(drop=True)\n",
    "ans_q2_df[\"review_yr\"] = ans_q2_df[\"review_yr\"].astype(str)\n",
    "\n",
    "ans_q2_df[\"RANK_by_mean\"] = ans_q2_df[\"mean_review_overall\"].rank(method=\"min\", ascending=False)\n",
    "ans_q2_df[\"review_yr\"] = ans_q2_df[\"review_yr\"].astype(str)\n",
    "ans_q2_df[\"RANK_by_mean\"] = ans_q2_df[\"RANK_by_mean\"].astype(\"category\")\n",
    "\n",
    "display(ans_q2_df)\n",
    "\n",
    "ans_q2_plot = px.bar(ans_q2_df, x=\"review_yr\", y=\"mean_review_overall\", text=\"RANK_by_mean\", color=\"RANK_by_mean\", template=\"simple_white\")\n",
    "ans_q2_plot.update_xaxes(tickmode=\"linear\")\n",
    "ans_q2_plot.update_layout(\n",
    "    xaxis_title = \"Review year\", yaxis_title = \"Overall review (mean)\",\n",
    "    title = \"<b>Q2: Which year did beers enjoy the highest ratings?\")\n",
    "\n",
    "ans_q2_plot.show()\n",
    "print(\"![ANS to Q2]: \\n2000\")"
   ]
  },
  {
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Q3: Based on the users' ratings, which factors are important among taste, aroma, appearance, and palette?\n",
    "\n",
    "`Aapprach: compare the correlation coefficients (between each factor and overall review)`\n",
    "- Group the data by reviewed dimensions, and calcualte the mean for each\n",
    "- Find the correlations between each and overall review (col: review_overall)\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
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        },
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     "text": [
      "![ANS to Q3]: \n",
      "Aroma\n"
     ]
    }
   ],
   "source": [
    "factors = [\"review_taste\", \"review_aroma\", \"review_appearance\", \"review_palette\", \"review_overall\"]\n",
    "ans_q3_df = df.groupby(\"beer_name\")[factors].mean().reset_index()\n",
    "ans_q3_df = ans_q3_df.corr()\n",
    "\n",
    "display(ans_q3_df)\n",
    "\n",
    "import plotly.figure_factory as ff\n",
    "ans_q3_plot = ff.create_annotated_heatmap(\n",
    "    z=ans_q3_df.to_numpy().round(2),\n",
    "    x=list(ans_q3_df.index.values),\n",
    "    y=list(ans_q3_df.columns.values),       \n",
    "    colorscale='Viridis'\n",
    ")\n",
    "\n",
    "ans_q3_plot.update_layout(\n",
    "    template = \"simple_white\",\n",
    "    title = \"<b>Q3: Based on the users' ratings, which factors are important<br>among taste, aroma, appearance, and palette?\"\n",
    ")\n",
    "\n",
    "ans_q3_plot.update_xaxes(side=\"bottom\")\n",
    "\n",
    "ans_q3_plot.show()\n",
    "print(\"![ANS to Q3]: \\nAroma\")"
   ]
  },
  {
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Q4: If you were to recommend 3 beers to your friends based on this data, which ones would you recommend?\n",
    "`Aapprach: find the ones with highest overall reviews, and meanwhile being reviewed most often `\n",
    "- group by beers (col: beer_beerId)\n",
    "- calcuate the mean and the count the number of reviews (col: review_overall)\n",
    "- find the beer with highest scores, and meanwhile mostly reviewed\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beer_beerId</th>\n",
       "      <th>mean_overall_review</th>\n",
       "      <th>count_overall_review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14990.000000</td>\n",
       "      <td>14990.000000</td>\n",
       "      <td>14990.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40740.290527</td>\n",
       "      <td>3.650533</td>\n",
       "      <td>33.602402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>22634.524770</td>\n",
       "      <td>0.625193</td>\n",
       "      <td>140.888114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20957.250000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>42341.500000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>60703.250000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77310.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2928.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        beer_beerId  mean_overall_review  count_overall_review\n",
       "count  14990.000000         14990.000000          14990.000000\n",
       "mean   40740.290527             3.650533             33.602402\n",
       "std    22634.524770             0.625193            140.888114\n",
       "min        5.000000             1.000000              1.000000\n",
       "25%    20957.250000             3.400000              1.000000\n",
       "50%    42341.500000             3.750000              3.000000\n",
       "75%    60703.250000             4.000000             10.000000\n",
       "max    77310.000000             5.000000           2928.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'count_overall_review'}>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ans_q4_df = df.groupby(\"beer_beerId\")[\"review_overall\"].agg([\"mean\", \"count\"]).reset_index()\n",
    "ans_q4_df = ans_q4_df.sort_values(by=[\"mean\", \"count\"], ascending=False)\n",
    "ans_q4_df.columns = [\"beer_beerId\", \"mean_overall_review\", \"count_overall_review\"]\n",
    "\n",
    "display(ans_q4_df.describe())\n",
    "ans_q4_df.hist(column=\"count_overall_review\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>beer_beerId</th>\n",
       "      <th>mean_overall_review</th>\n",
       "      <th>count_overall_review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10317</th>\n",
       "      <td>56082</td>\n",
       "      <td>4.628049</td>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3014</th>\n",
       "      <td>16814</td>\n",
       "      <td>4.618510</td>\n",
       "      <td>443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8547</th>\n",
       "      <td>47658</td>\n",
       "      <td>4.592233</td>\n",
       "      <td>618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1171</th>\n",
       "      <td>6368</td>\n",
       "      <td>4.482456</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>2899</td>\n",
       "      <td>4.435943</td>\n",
       "      <td>281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       beer_beerId  mean_overall_review  count_overall_review\n",
       "10317        56082             4.628049                   246\n",
       "3014         16814             4.618510                   443\n",
       "8547         47658             4.592233                   618\n",
       "1171          6368             4.482456                   627\n",
       "565           2899             4.435943                   281"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'count_overall_review'}>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ans_q4_df = ans_q4_df[ans_q4_df[\"count_overall_review\"] > 200]\n",
    "ans_q4_df = ans_q4_df.sort_values(by=[\"mean_overall_review\", \"count_overall_review\"], ascending=False)\n",
    "\n",
    "display(ans_q4_df.head(5))\n",
    "ans_q4_df.hist(column=\"count_overall_review\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>beer_ABV</th>\n",
       "      <th>beer_beerId</th>\n",
       "      <th>beer_brewerId</th>\n",
       "      <th>beer_name</th>\n",
       "      <th>beer_style</th>\n",
       "      <th>review_appearance</th>\n",
       "      <th>review_palette</th>\n",
       "      <th>review_overall</th>\n",
       "      <th>review_taste</th>\n",
       "      <th>review_profileName</th>\n",
       "      <th>review_aroma</th>\n",
       "      <th>review_text</th>\n",
       "      <th>review_time</th>\n",
       "      <th>review_time_converted</th>\n",
       "      <th>review_yr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>340355</th>\n",
       "      <td>5.9</td>\n",
       "      <td>6368</td>\n",
       "      <td>1177</td>\n",
       "      <td>Masala Mama India Pale Ale</td>\n",
       "      <td>American IPA</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>rye726</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Happy to have the chance to try this. Enjoyed ...</td>\n",
       "      <td>1298345522</td>\n",
       "      <td>2011-02-22 03:32:02</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14751</th>\n",
       "      <td>10.6</td>\n",
       "      <td>47658</td>\n",
       "      <td>1199</td>\n",
       "      <td>Founders CBS Imperial Stout</td>\n",
       "      <td>American Double / Imperial Stout</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>ColForbinBC</td>\n",
       "      <td>4.5</td>\n",
       "      <td>750mL bottle poured into a snifter Pours an in...</td>\n",
       "      <td>1321818225</td>\n",
       "      <td>2011-11-20 19:43:45</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320151</th>\n",
       "      <td>8.0</td>\n",
       "      <td>56082</td>\n",
       "      <td>14064</td>\n",
       "      <td>Citra DIPA</td>\n",
       "      <td>American Double / Imperial IPA</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>IdrinkGas</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Poured from a bomber into a Lost Abbey tulip. ...</td>\n",
       "      <td>1314513820</td>\n",
       "      <td>2011-08-28 06:43:40</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361664</th>\n",
       "      <td>8.0</td>\n",
       "      <td>16814</td>\n",
       "      <td>27039</td>\n",
       "      <td>Heady Topper</td>\n",
       "      <td>American Double / Imperial IPA</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>onefmatt</td>\n",
       "      <td>4.5</td>\n",
       "      <td>A+ in my book, just a fantastic and well round...</td>\n",
       "      <td>1321078004</td>\n",
       "      <td>2011-11-12 06:06:44</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411515</th>\n",
       "      <td>7.1</td>\n",
       "      <td>2899</td>\n",
       "      <td>911</td>\n",
       "      <td>Andechser Doppelbock Dunkel</td>\n",
       "      <td>Doppelbock</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>CrazyDavros</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Pours ruby with a large but fading head. Nose ...</td>\n",
       "      <td>1263519393</td>\n",
       "      <td>2010-01-15 01:36:33</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        beer_ABV  beer_beerId  beer_brewerId                    beer_name  \\\n",
       "340355       5.9         6368           1177   Masala Mama India Pale Ale   \n",
       "14751       10.6        47658           1199  Founders CBS Imperial Stout   \n",
       "320151       8.0        56082          14064                   Citra DIPA   \n",
       "361664       8.0        16814          27039                 Heady Topper   \n",
       "411515       7.1         2899            911  Andechser Doppelbock Dunkel   \n",
       "\n",
       "                              beer_style  review_appearance  review_palette  \\\n",
       "340355                      American IPA                5.0             4.5   \n",
       "14751   American Double / Imperial Stout                4.5             4.5   \n",
       "320151    American Double / Imperial IPA                4.0             4.5   \n",
       "361664    American Double / Imperial IPA                4.5             5.0   \n",
       "411515                        Doppelbock                4.0             4.0   \n",
       "\n",
       "        review_overall  review_taste review_profileName  review_aroma  \\\n",
       "340355             5.0           4.5             rye726           4.5   \n",
       "14751              4.5           5.0        ColForbinBC           4.5   \n",
       "320151             4.5           4.0          IdrinkGas           4.5   \n",
       "361664             4.5           4.5           onefmatt           4.5   \n",
       "411515             3.5           4.0        CrazyDavros           3.5   \n",
       "\n",
       "                                              review_text  review_time  \\\n",
       "340355  Happy to have the chance to try this. Enjoyed ...   1298345522   \n",
       "14751   750mL bottle poured into a snifter Pours an in...   1321818225   \n",
       "320151  Poured from a bomber into a Lost Abbey tulip. ...   1314513820   \n",
       "361664  A+ in my book, just a fantastic and well round...   1321078004   \n",
       "411515  Pours ruby with a large but fading head. Nose ...   1263519393   \n",
       "\n",
       "       review_time_converted  review_yr  \n",
       "340355   2011-02-22 03:32:02       2011  \n",
       "14751    2011-11-20 19:43:45       2011  \n",
       "320151   2011-08-28 06:43:40       2011  \n",
       "361664   2011-11-12 06:06:44       2011  \n",
       "411515   2010-01-15 01:36:33       2010  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ans_q4_df = df[(df[\"beer_beerId\"] == 56082) | (df[\"beer_beerId\"] == 16814) | (df[\"beer_beerId\"] == 47658) | \n",
    "               (df[\"beer_beerId\"] == 6368) | (df[\"beer_beerId\"] == 2899) ]\n",
    "\n",
    "ans_q4_df = ans_q4_df.drop_duplicates(subset=\"beer_beerId\")\n",
    "ans_q4_df = ans_q4_df.sort_values(by=\"review_overall\", ascending=False)\n",
    "\n",
    "ans_q4_df"
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          "title": {
           "x": 0.05
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         "text": "<b>Q4: If you were to recommend 3 beers to your friends based on this data, <br>which ones would you recommend?<b>"
        },
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          0,
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     "text": [
      "![ANS to Q4]:\n",
      "Masala Mama India Pale Ale \n",
      "Founders CBS Imperial Stout OR Citra DIPA OR Heady Topper\n",
      "Andechser Doppelbock Dunkel\n"
     ]
    }
   ],
   "source": [
    "ans_q4_plot = px.bar(ans_q4_df, x=\"beer_name\", y=\"review_overall\", text=\"review_overall\", template=\"simple_white\")\n",
    "ans_q4_plot.update_xaxes(tickmode=\"linear\", tickangle= 15)\n",
    "ans_q4_plot.update_layout(\n",
    "    xaxis_title = \"Beer name\", yaxis_title = \"Review socre (overall)\",\n",
    "    title = \"<b>Q4: If you were to recommend 3 beers to your friends based on this data, <br>which ones would you recommend?<b>\"\n",
    ")\n",
    "\n",
    "ans_q4_plot.show()\n",
    "print(\"![ANS to Q4]:\" \n",
    "      \"\\nMasala Mama India Pale Ale\",\n",
    "      \"\\nFounders CBS Imperial Stout OR Citra DIPA OR Heady Topper\" \n",
    "      \"\\nAndechser Doppelbock Dunkel\")"
   ]
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   "source": [
    "---\n",
    "## Q5: Which beer style seems to be the favourite based on the reviews written by users? How does written reviews compare to overall review score for that beer style?\n",
    "`Aapprach: Compare the scores of overall review and sentiment scores coverted from written reviews `\n",
    "- convert the written reviews (col: review_text) to quantiative scores, based on their sentiment\n",
    "- group the data by beer style, calcualte sentiment mean and count the number of reviews (col: beer_style, review_sentiment)\n",
    "- find the beer style with highest sentiment, and meanwhile mostly reviewed\n",
    "- compare the converted scores and overall review scores (col: review_overall)\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0.1879\n",
       "1         0.5927\n",
       "2         0.3021\n",
       "3         0.9772\n",
       "4         0.9538\n",
       "           ...  \n",
       "503695    0.5723\n",
       "503696    0.8666\n",
       "503697    0.7003\n",
       "503698    0.8768\n",
       "503699   -0.6428\n",
       "Name: review_sentiment, Length: 503700, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download(\"vader_lexicon\", quiet=True)\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer \n",
    "\n",
    "sia = SentimentIntensityAnalyzer()\n",
    "df[\"review_sentiment\"] = df[\"review_text\"].apply(lambda text_one_line: sia.polarity_scores(text_one_line)[\"compound\"])\n",
    "df[\"review_sentiment\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
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       "      <th></th>\n",
       "      <th>beer_style</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>RANK_by_sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>86</th>\n",
       "      <td>Quadrupel (Quad)</td>\n",
       "      <td>0.857</td>\n",
       "      <td>4808</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>38</th>\n",
       "      <td>Dortmunder / Export Lager</td>\n",
       "      <td>0.855</td>\n",
       "      <td>1709</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Flanders Red Ale</td>\n",
       "      <td>0.851</td>\n",
       "      <td>2751</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Roggenbier</td>\n",
       "      <td>0.851</td>\n",
       "      <td>137</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Braggot</td>\n",
       "      <td>0.850</td>\n",
       "      <td>197</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
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       "                   beer_style   mean  count  RANK_by_sentiment\n",
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           "ticks": "outside",
           "title": {
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         "text": "<b>Q5: Which beer style seems to be the favourite based on the reviews written by users?"
        },
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         "anchor": "y",
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         "tickmode": "linear",
         "title": {
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         "domain": [
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   ],
   "source": [
    "ans_q5_df = df.groupby(\"beer_style\")[\"review_sentiment\"].agg([\"mean\", \"count\"]).reset_index()\n",
    "ans_q5_df = ans_q5_df.sort_values(by=[\"mean\", \"count\"], ascending=False)\n",
    "ans_q5_df[\"RANK_by_sentiment\"] = ans_q5_df[\"mean\"].rank(method=\"min\", ascending=False)\n",
    "\n",
    "ans_q5_df = round(ans_q5_df.head(), 3)\n",
    "display(ans_q5_df.head())\n",
    "\n",
    "ans_q5_plot = px.bar(ans_q5_df, x=\"beer_style\", y=\"mean\", template=\"simple_white\",\n",
    "                     text= \"Rank: \" + ans_q5_df[\"RANK_by_sentiment\"].astype(int).astype(str) + \n",
    "                     \"<br>Sentiment: \" + ans_q5_df[\"mean\"].astype(str))\n",
    "\n",
    "ans_q5_plot.update_xaxes(tickmode=\"linear\")\n",
    "ans_q5_plot.update_layout(\n",
    "    xaxis_title = \"Beer style\", yaxis_title = \"Sentiment (mean)\", \n",
    "    title = \"<b>Q5: Which beer style seems to be the favourite based on the reviews written by users?\" \n",
    ")\n",
    "\n",
    "ans_q5_plot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>beer_ABV</th>\n",
       "      <th>beer_beerId</th>\n",
       "      <th>beer_brewerId</th>\n",
       "      <th>beer_name</th>\n",
       "      <th>beer_style</th>\n",
       "      <th>review_appearance</th>\n",
       "      <th>review_palette</th>\n",
       "      <th>review_overall</th>\n",
       "      <th>review_taste</th>\n",
       "      <th>review_profileName</th>\n",
       "      <th>review_aroma</th>\n",
       "      <th>review_text</th>\n",
       "      <th>review_time</th>\n",
       "      <th>review_time_converted</th>\n",
       "      <th>review_yr</th>\n",
       "      <th>review_sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8057</th>\n",
       "      <td>11.5</td>\n",
       "      <td>69510</td>\n",
       "      <td>395</td>\n",
       "      <td>Bourbon Barrel Quad</td>\n",
       "      <td>Quadrupel (Quad)</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>DrDemento456</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Well I guess I will be to review this. Here go...</td>\n",
       "      <td>1306625330</td>\n",
       "      <td>2011-05-28 23:28:50</td>\n",
       "      <td>2011</td>\n",
       "      <td>-0.1566</td>\n",
       "    </tr>\n",
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       "      <th>13082</th>\n",
       "      <td>12.8</td>\n",
       "      <td>65526</td>\n",
       "      <td>5500</td>\n",
       "      <td>The Lost Fugawi Ale</td>\n",
       "      <td>Quadrupel (Quad)</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Hazer115</td>\n",
       "      <td>4.5</td>\n",
       "      <td>A: Red with about a 1/2\" head that stuck aroun...</td>\n",
       "      <td>1308752937</td>\n",
       "      <td>2011-06-22 14:28:57</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.8892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13083</th>\n",
       "      <td>12.8</td>\n",
       "      <td>65526</td>\n",
       "      <td>5500</td>\n",
       "      <td>The Lost Fugawi Ale</td>\n",
       "      <td>Quadrupel (Quad)</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>StoutHunter</td>\n",
       "      <td>4.5</td>\n",
       "      <td>I picked up a growler of this last week at the...</td>\n",
       "      <td>1295752667</td>\n",
       "      <td>2011-01-23 03:17:47</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.9926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28327</th>\n",
       "      <td>9.8</td>\n",
       "      <td>16073</td>\n",
       "      <td>1199</td>\n",
       "      <td>Founders Bad Habit Belgian Quad</td>\n",
       "      <td>Quadrupel (Quad)</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>dirtylou</td>\n",
       "      <td>3.5</td>\n",
       "      <td>12oz single, $2.49, Riley's...rewatching the 2...</td>\n",
       "      <td>1156550410</td>\n",
       "      <td>2006-08-26 00:00:10</td>\n",
       "      <td>2006</td>\n",
       "      <td>0.8773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28328</th>\n",
       "      <td>9.8</td>\n",
       "      <td>16073</td>\n",
       "      <td>1199</td>\n",
       "      <td>Founders Bad Habit Belgian Quad</td>\n",
       "      <td>Quadrupel (Quad)</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>DaveFL1976</td>\n",
       "      <td>3.5</td>\n",
       "      <td>I was a bit disappointed with the looks of thi...</td>\n",
       "      <td>1156522839</td>\n",
       "      <td>2006-08-25 16:20:39</td>\n",
       "      <td>2006</td>\n",
       "      <td>0.9332</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       beer_ABV  beer_beerId  beer_brewerId                        beer_name  \\\n",
       "8057       11.5        69510            395              Bourbon Barrel Quad   \n",
       "13082      12.8        65526           5500              The Lost Fugawi Ale   \n",
       "13083      12.8        65526           5500              The Lost Fugawi Ale   \n",
       "28327       9.8        16073           1199  Founders Bad Habit Belgian Quad   \n",
       "28328       9.8        16073           1199  Founders Bad Habit Belgian Quad   \n",
       "\n",
       "             beer_style  review_appearance  review_palette  review_overall  \\\n",
       "8057   Quadrupel (Quad)                4.0             4.0             3.0   \n",
       "13082  Quadrupel (Quad)                4.5             4.0             4.5   \n",
       "13083  Quadrupel (Quad)                3.5             4.0             4.0   \n",
       "28327  Quadrupel (Quad)                4.5             4.0             3.5   \n",
       "28328  Quadrupel (Quad)                3.5             4.0             3.5   \n",
       "\n",
       "       review_taste review_profileName  review_aroma  \\\n",
       "8057            3.0       DrDemento456           3.0   \n",
       "13082           4.0           Hazer115           4.5   \n",
       "13083           3.5        StoutHunter           4.5   \n",
       "28327           3.5           dirtylou           3.5   \n",
       "28328           4.0         DaveFL1976           3.5   \n",
       "\n",
       "                                             review_text  review_time  \\\n",
       "8057   Well I guess I will be to review this. Here go...   1306625330   \n",
       "13082  A: Red with about a 1/2\" head that stuck aroun...   1308752937   \n",
       "13083  I picked up a growler of this last week at the...   1295752667   \n",
       "28327  12oz single, $2.49, Riley's...rewatching the 2...   1156550410   \n",
       "28328  I was a bit disappointed with the looks of thi...   1156522839   \n",
       "\n",
       "      review_time_converted  review_yr  review_sentiment  \n",
       "8057    2011-05-28 23:28:50       2011           -0.1566  \n",
       "13082   2011-06-22 14:28:57       2011            0.8892  \n",
       "13083   2011-01-23 03:17:47       2011            0.9926  \n",
       "28327   2006-08-26 00:00:10       2006            0.8773  \n",
       "28328   2006-08-25 16:20:39       2006            0.9332  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "![ANS to Q5]: \n",
      "Most favored beer style: Quadrupel (Quad) \n",
      "Overall, written reviews postively correlate with overall review scores\n"
     ]
    }
   ],
   "source": [
    "ans_q5_df = df[df[\"beer_style\"] == \"Quadrupel (Quad)\"]\n",
    "display(ans_q5_df.head())\n",
    "\n",
    "ans_q5_df[[\"review_sentiment\", \"review_overall\"]].corr()\n",
    "\n",
    "print(\"![ANS to Q5]:\",\n",
    "      \"\\nMost favored beer style: Quadrupel (Quad)\", \n",
    "      \"\\nOverall, written reviews postively correlate with overall review scores\")"
   ]
  }
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