sasa_data_science.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0cfc1c1b",
   "metadata": {},
   "source": [
    "Most of the datasets about ImDb Review on Kaggle were pre-processed for machine learning. This made it difficult to get a dataset for descriptive analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c46a179c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import relevant libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "fd61a9ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import pathlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "fa94688b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read .csv file for analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "acfbb8c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = pathlib.Path('../Saa/review/data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "7908a0cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df = pd.read_csv(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "120eb1f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# view dataframe object -- 1st ten rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "4aa20101",
   "metadata": {},
   "outputs": [],
   "source": [
    "#data_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "9ea9c8f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a filter table of relevant data features: movie_name, reviews and ratings/stars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "67f738c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df_filtered = data_df[['Movie_N', 'Stars', 'Review']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "dc00a86d",
   "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>Movie_N</th>\n",
       "      <th>Stars</th>\n",
       "      <th>Review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Old Guard</td>\n",
       "      <td>7</td>\n",
       "      <td>Love the concept, execution is not bad (there ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Old Guard</td>\n",
       "      <td>5</td>\n",
       "      <td>There were just so many clichés, cringe, and n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Old Guard</td>\n",
       "      <td>5</td>\n",
       "      <td>The actors are all good. Well that's it. The s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Old Guard</td>\n",
       "      <td>9</td>\n",
       "      <td>My mind kept referring back to Austin Powers. ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The Old Guard</td>\n",
       "      <td>5</td>\n",
       "      <td>The London scenes at the end were just bizarre...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5676</th>\n",
       "      <td>Greyhound</td>\n",
       "      <td>5</td>\n",
       "      <td>My only complaint is that the movie could have...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5677</th>\n",
       "      <td>Greyhound</td>\n",
       "      <td>8</td>\n",
       "      <td>Though the movie might have been right on tech...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5678</th>\n",
       "      <td>Greyhound</td>\n",
       "      <td>1</td>\n",
       "      <td>I felt the movie could have been longer with a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5679</th>\n",
       "      <td>Greyhound</td>\n",
       "      <td>9</td>\n",
       "      <td>The climax (if any) was not up to the mark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5680</th>\n",
       "      <td>Greyhound</td>\n",
       "      <td>5</td>\n",
       "      <td>U571 and Das Boot had done a better job long t...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5681 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Movie_N  Stars                                             Review\n",
       "0     The Old Guard      7  Love the concept, execution is not bad (there ...\n",
       "1     The Old Guard      5  There were just so many clichés, cringe, and n...\n",
       "2     The Old Guard      5  The actors are all good. Well that's it. The s...\n",
       "3     The Old Guard      9  My mind kept referring back to Austin Powers. ...\n",
       "4     The Old Guard      5  The London scenes at the end were just bizarre...\n",
       "...             ...    ...                                                ...\n",
       "5676      Greyhound      5  My only complaint is that the movie could have...\n",
       "5677      Greyhound      8  Though the movie might have been right on tech...\n",
       "5678      Greyhound      1  I felt the movie could have been longer with a...\n",
       "5679      Greyhound      9         The climax (if any) was not up to the mark\n",
       "5680      Greyhound      5  U571 and Das Boot had done a better job long t...\n",
       "\n",
       "[5681 rows x 3 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df_filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "b46c8482",
   "metadata": {},
   "outputs": [],
   "source": [
    "# choosing 10 as the highest rating\n",
    "data_df_high = data_df_filtered[data_df_filtered.Stars == 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "3db24044",
   "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>Movie_N</th>\n",
       "      <th>Stars</th>\n",
       "      <th>Review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>The Old Guard</td>\n",
       "      <td>10</td>\n",
       "      <td>Why why why so many haters?! First thing I tho...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>The Kissing Booth</td>\n",
       "      <td>10</td>\n",
       "      <td>This is not a old people's movie. The adventur...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>The Kissing Booth</td>\n",
       "      <td>10</td>\n",
       "      <td>I can't say that i did not enjoy this film. It...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Clueless</td>\n",
       "      <td>10</td>\n",
       "      <td>When romantic high-school comedy Clueless (199...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Three Billboards Outside Ebbing, Missouri</td>\n",
       "      <td>10</td>\n",
       "      <td>\"Three Billboards outside Ebbing, Missouri\" ma...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5579</th>\n",
       "      <td>The Kissing Booth 2</td>\n",
       "      <td>10</td>\n",
       "      <td>I wasn't sure if I was going to like this one....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5587</th>\n",
       "      <td>Knives Out</td>\n",
       "      <td>10</td>\n",
       "      <td>Daniel Craig is downright terrific as Benoit B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5615</th>\n",
       "      <td>Palm Springs</td>\n",
       "      <td>10</td>\n",
       "      <td>This is a greatly enjoyable and fun movie, tha...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5643</th>\n",
       "      <td>Extraction</td>\n",
       "      <td>10</td>\n",
       "      <td>The action scenes are great, and some of the c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5666</th>\n",
       "      <td>Greyhound</td>\n",
       "      <td>10</td>\n",
       "      <td>Honestly, I decided to write a review to count...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1677 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Movie_N  Stars  \\\n",
       "18                                The Old Guard     10   \n",
       "42                            The Kissing Booth     10   \n",
       "43                            The Kissing Booth     10   \n",
       "50                                     Clueless     10   \n",
       "68    Three Billboards Outside Ebbing, Missouri     10   \n",
       "...                                         ...    ...   \n",
       "5579                        The Kissing Booth 2     10   \n",
       "5587                                 Knives Out     10   \n",
       "5615                               Palm Springs     10   \n",
       "5643                                 Extraction     10   \n",
       "5666                                  Greyhound     10   \n",
       "\n",
       "                                                 Review  \n",
       "18    Why why why so many haters?! First thing I tho...  \n",
       "42    This is not a old people's movie. The adventur...  \n",
       "43    I can't say that i did not enjoy this film. It...  \n",
       "50    When romantic high-school comedy Clueless (199...  \n",
       "68    \"Three Billboards outside Ebbing, Missouri\" ma...  \n",
       "...                                                 ...  \n",
       "5579  I wasn't sure if I was going to like this one....  \n",
       "5587  Daniel Craig is downright terrific as Benoit B...  \n",
       "5615  This is a greatly enjoyable and fun movie, tha...  \n",
       "5643  The action scenes are great, and some of the c...  \n",
       "5666  Honestly, I decided to write a review to count...  \n",
       "\n",
       "[1677 rows x 3 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df_high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "f5a30d1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Movie_N    1677\n",
       "Stars      1677\n",
       "Review     1677\n",
       "dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df_high.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "024eb789",
   "metadata": {},
   "outputs": [],
   "source": [
    "# using a small sample for visualization - unbiased\n",
    "small_high_df = data_df_high.sample(frac=.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "290e10aa",
   "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>Movie_N</th>\n",
       "      <th>Stars</th>\n",
       "      <th>Review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3468</th>\n",
       "      <td>The Departed</td>\n",
       "      <td>10</td>\n",
       "      <td>This is one of the most entertaining and suspe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3313</th>\n",
       "      <td>The Prestige</td>\n",
       "      <td>10</td>\n",
       "      <td>I won't say it's an underrated movie, but it h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5351</th>\n",
       "      <td>The Godfather</td>\n",
       "      <td>10</td>\n",
       "      <td>This is one of those films that made me wonder...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2412</th>\n",
       "      <td>Léon: The Professional</td>\n",
       "      <td>10</td>\n",
       "      <td>We need a follow-up to the original and intrig...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>Yesterday</td>\n",
       "      <td>10</td>\n",
       "      <td>Story-wise, 'Yesterday' captures the feel-good...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>A Clockwork Orange</td>\n",
       "      <td>10</td>\n",
       "      <td>Is this the world's most stylish and interesti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4407</th>\n",
       "      <td>The Lord of the Rings: The Fellowship of the Ring</td>\n",
       "      <td>10</td>\n",
       "      <td>Breathtaking.  Unique.  Captivating.  Enchanting.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4022</th>\n",
       "      <td>Platoon</td>\n",
       "      <td>10</td>\n",
       "      <td>Those of us who did make it have an obligation...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5341</th>\n",
       "      <td>Interstellar</td>\n",
       "      <td>10</td>\n",
       "      <td>And finally, the scenario/story. I won't spoil...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>The Twilight Saga: Breaking Dawn - Part 2</td>\n",
       "      <td>10</td>\n",
       "      <td>I am planning to buy the DVD so that I can wat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3846</th>\n",
       "      <td>The Goonies</td>\n",
       "      <td>10</td>\n",
       "      <td>Just prior to graduating with a degree in Film...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1738</th>\n",
       "      <td>The Truman Show</td>\n",
       "      <td>10</td>\n",
       "      <td>If we go further, we must ask ourselves, what ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2439</th>\n",
       "      <td>Heat</td>\n",
       "      <td>10</td>\n",
       "      <td>Sound like a bold statement? Devotees of class...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3023</th>\n",
       "      <td>The Big Lebowski</td>\n",
       "      <td>10</td>\n",
       "      <td>The story is actually kind of simple. You see,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2636</th>\n",
       "      <td>The Notebook</td>\n",
       "      <td>10</td>\n",
       "      <td>Best known for his acting credits, Nick Cassav...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4718</th>\n",
       "      <td>The Green Mile</td>\n",
       "      <td>10</td>\n",
       "      <td>\"The Green Mile\" is one of my favorite Stephen...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3513</th>\n",
       "      <td>Django Unchained</td>\n",
       "      <td>10</td>\n",
       "      <td>The film threatens to go totally off the rails...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Movie_N  Stars  \\\n",
       "3468                                       The Departed     10   \n",
       "3313                                       The Prestige     10   \n",
       "5351                                      The Godfather     10   \n",
       "2412                             Léon: The Professional     10   \n",
       "550                                           Yesterday     10   \n",
       "479                                  A Clockwork Orange     10   \n",
       "4407  The Lord of the Rings: The Fellowship of the Ring     10   \n",
       "4022                                            Platoon     10   \n",
       "5341                                       Interstellar     10   \n",
       "408           The Twilight Saga: Breaking Dawn - Part 2     10   \n",
       "3846                                        The Goonies     10   \n",
       "1738                                    The Truman Show     10   \n",
       "2439                                               Heat     10   \n",
       "3023                                   The Big Lebowski     10   \n",
       "2636                                       The Notebook     10   \n",
       "4718                                     The Green Mile     10   \n",
       "3513                                   Django Unchained     10   \n",
       "\n",
       "                                                 Review  \n",
       "3468  This is one of the most entertaining and suspe...  \n",
       "3313  I won't say it's an underrated movie, but it h...  \n",
       "5351  This is one of those films that made me wonder...  \n",
       "2412  We need a follow-up to the original and intrig...  \n",
       "550   Story-wise, 'Yesterday' captures the feel-good...  \n",
       "479   Is this the world's most stylish and interesti...  \n",
       "4407  Breathtaking.  Unique.  Captivating.  Enchanting.  \n",
       "4022  Those of us who did make it have an obligation...  \n",
       "5341  And finally, the scenario/story. I won't spoil...  \n",
       "408   I am planning to buy the DVD so that I can wat...  \n",
       "3846  Just prior to graduating with a degree in Film...  \n",
       "1738  If we go further, we must ask ourselves, what ...  \n",
       "2439  Sound like a bold statement? Devotees of class...  \n",
       "3023  The story is actually kind of simple. You see,...  \n",
       "2636  Best known for his acting credits, Nick Cassav...  \n",
       "4718  \"The Green Mile\" is one of my favorite Stephen...  \n",
       "3513  The film threatens to go totally off the rails...  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "small_high_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "b280d8cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot of small_high_df\n",
    "sns.barplot(x='Stars', data=small_high_df, y='Movie_N', orient='h')\n",
    "plt.title('Highest Movie Rating from Data Sample')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "8afe827c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# choosing 1 as the lowest rating\n",
    "data_df_low = data_df_filtered[data_df_filtered.Stars == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5493a81b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "7f89374f",
   "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>Movie_N</th>\n",
       "      <th>Stars</th>\n",
       "      <th>Review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>The Old Guard</td>\n",
       "      <td>1</td>\n",
       "      <td>I was hoping for a more layered villain, just ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>The Old Guard</td>\n",
       "      <td>1</td>\n",
       "      <td>The cinematography n editing r top notch. Ther...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>The Kissing Booth</td>\n",
       "      <td>1</td>\n",
       "      <td>This wasfun to watch. The setting is fun and o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>The Kissing Booth</td>\n",
       "      <td>1</td>\n",
       "      <td>SPOILER: Yes, at first glance the movie is jus...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Relic</td>\n",
       "      <td>1</td>\n",
       "      <td>My retrospective on this one: sometimes you ha...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5567</th>\n",
       "      <td>The Kissing Booth 2</td>\n",
       "      <td>1</td>\n",
       "      <td>The plot was meh. The love triangle or square ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5569</th>\n",
       "      <td>The Kissing Booth 2</td>\n",
       "      <td>1</td>\n",
       "      <td>The script was terrible, it was a waste of tim...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5580</th>\n",
       "      <td>The Kissing Booth 2</td>\n",
       "      <td>1</td>\n",
       "      <td>I didn't like the first one very much, but wat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5674</th>\n",
       "      <td>Greyhound</td>\n",
       "      <td>1</td>\n",
       "      <td>Still, if you are a fan of WWII movies I sugge...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5678</th>\n",
       "      <td>Greyhound</td>\n",
       "      <td>1</td>\n",
       "      <td>I felt the movie could have been longer with a...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>845 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Movie_N  Stars  \\\n",
       "16          The Old Guard      1   \n",
       "21          The Old Guard      1   \n",
       "37      The Kissing Booth      1   \n",
       "41      The Kissing Booth      1   \n",
       "126                 Relic      1   \n",
       "...                   ...    ...   \n",
       "5567  The Kissing Booth 2      1   \n",
       "5569  The Kissing Booth 2      1   \n",
       "5580  The Kissing Booth 2      1   \n",
       "5674            Greyhound      1   \n",
       "5678            Greyhound      1   \n",
       "\n",
       "                                                 Review  \n",
       "16    I was hoping for a more layered villain, just ...  \n",
       "21    The cinematography n editing r top notch. Ther...  \n",
       "37    This wasfun to watch. The setting is fun and o...  \n",
       "41    SPOILER: Yes, at first glance the movie is jus...  \n",
       "126   My retrospective on this one: sometimes you ha...  \n",
       "...                                                 ...  \n",
       "5567  The plot was meh. The love triangle or square ...  \n",
       "5569  The script was terrible, it was a waste of tim...  \n",
       "5580  I didn't like the first one very much, but wat...  \n",
       "5674  Still, if you are a fan of WWII movies I sugge...  \n",
       "5678  I felt the movie could have been longer with a...  \n",
       "\n",
       "[845 rows x 3 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df_low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "9fe601a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Movie_N    845\n",
       "Stars      845\n",
       "Review     845\n",
       "dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df_low.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "1a1f4274",
   "metadata": {},
   "outputs": [],
   "source": [
    "# using a small sample for visualization - unbiased\n",
    "small_low_df = data_df_low.sample(frac=.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "0892d0e6",
   "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>Movie_N</th>\n",
       "      <th>Stars</th>\n",
       "      <th>Review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>The F**k-It List</td>\n",
       "      <td>1</td>\n",
       "      <td>What an interesting time to release this movie...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3701</th>\n",
       "      <td>The Lion King</td>\n",
       "      <td>1</td>\n",
       "      <td>Missed important scenes from the first movie l...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>The Vast of Night</td>\n",
       "      <td>1</td>\n",
       "      <td>\"The Vast of Night\" was recently purchased by ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3527</th>\n",
       "      <td>Django Unchained</td>\n",
       "      <td>1</td>\n",
       "      <td>The only gripe here was that this film was a l...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2721</th>\n",
       "      <td>Solo: A Star Wars Story</td>\n",
       "      <td>1</td>\n",
       "      <td>Moreover, the story really doesn't aim to tel...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4300</th>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>1</td>\n",
       "      <td>At a running time of almost 3 hours, the film ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3798</th>\n",
       "      <td>Artemis Fowl</td>\n",
       "      <td>1</td>\n",
       "      <td>Holly is wrecked. Root is wrecked. Foaley is n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2917</th>\n",
       "      <td>Da 5 Bloods</td>\n",
       "      <td>1</td>\n",
       "      <td>This film is an absolute mess. It starts out w...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2956</th>\n",
       "      <td>No Country for Old Men</td>\n",
       "      <td>1</td>\n",
       "      <td>Overall casting and acting are impressive. I e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3746</th>\n",
       "      <td>Gone Girl</td>\n",
       "      <td>1</td>\n",
       "      <td>And it only gets better from there.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4171</th>\n",
       "      <td>Bad Boys for Life</td>\n",
       "      <td>1</td>\n",
       "      <td>I love these films and this is nowhere close t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3246</th>\n",
       "      <td>Deadpool</td>\n",
       "      <td>1</td>\n",
       "      <td>Sitting in the cinema, the inner critic in me ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5674</th>\n",
       "      <td>Greyhound</td>\n",
       "      <td>1</td>\n",
       "      <td>Still, if you are a fan of WWII movies I sugge...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1827</th>\n",
       "      <td>X-Men: Dark Phoenix</td>\n",
       "      <td>1</td>\n",
       "      <td>If it wasn't for James McAvoy and Michael Fass...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2784</th>\n",
       "      <td>Aladdin</td>\n",
       "      <td>1</td>\n",
       "      <td>You know how sometimes you hear someone try an...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4259</th>\n",
       "      <td>The Wrong Missy</td>\n",
       "      <td>1</td>\n",
       "      <td>If you seen previous Happy Madison production ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>Shutter Island</td>\n",
       "      <td>1</td>\n",
       "      <td>Shutter Island represents exactly what one sho...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Movie_N  Stars  \\\n",
       "148          The F**k-It List      1   \n",
       "3701            The Lion King      1   \n",
       "1929        The Vast of Night      1   \n",
       "3527         Django Unchained      1   \n",
       "2721  Solo: A Star Wars Story      1   \n",
       "4300    The Dark Knight Rises      1   \n",
       "3798             Artemis Fowl      1   \n",
       "2917              Da 5 Bloods      1   \n",
       "2956   No Country for Old Men      1   \n",
       "3746                Gone Girl      1   \n",
       "4171        Bad Boys for Life      1   \n",
       "3246                 Deadpool      1   \n",
       "5674                Greyhound      1   \n",
       "1827      X-Men: Dark Phoenix      1   \n",
       "2784                  Aladdin      1   \n",
       "4259          The Wrong Missy      1   \n",
       "830            Shutter Island      1   \n",
       "\n",
       "                                                 Review  \n",
       "148   What an interesting time to release this movie...  \n",
       "3701  Missed important scenes from the first movie l...  \n",
       "1929  \"The Vast of Night\" was recently purchased by ...  \n",
       "3527  The only gripe here was that this film was a l...  \n",
       "2721   Moreover, the story really doesn't aim to tel...  \n",
       "4300  At a running time of almost 3 hours, the film ...  \n",
       "3798  Holly is wrecked. Root is wrecked. Foaley is n...  \n",
       "2917  This film is an absolute mess. It starts out w...  \n",
       "2956  Overall casting and acting are impressive. I e...  \n",
       "3746                And it only gets better from there.  \n",
       "4171  I love these films and this is nowhere close t...  \n",
       "3246  Sitting in the cinema, the inner critic in me ...  \n",
       "5674  Still, if you are a fan of WWII movies I sugge...  \n",
       "1827  If it wasn't for James McAvoy and Michael Fass...  \n",
       "2784  You know how sometimes you hear someone try an...  \n",
       "4259  If you seen previous Happy Madison production ...  \n",
       "830   Shutter Island represents exactly what one sho...  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "small_low_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "955bfb65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot of small_low_df\n",
    "plt.Figure(figsize=(200, 100))\n",
    "sns.barplot(x='Stars', data=small_low_df, y='Movie_N', orient='h')\n",
    "plt.title('Lowest Movie Rating from Data Sample')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25438a87",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "2f167044",
   "metadata": {},
   "outputs": [],
   "source": [
    "# merged dataframe of highest and lowest rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "2a27a1d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "joined_df = pd.merge(on=['Movie_N', 'Stars', 'Review'], right=small_high_df, left=small_low_df, how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "8fe53803",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Movie_N\n",
       "A Clockwork Orange                                   10\n",
       "Aladdin                                               1\n",
       "Artemis Fowl                                          1\n",
       "Bad Boys for Life                                     1\n",
       "Da 5 Bloods                                           1\n",
       "Deadpool                                              1\n",
       "Django Unchained                                     11\n",
       "Gone Girl                                             1\n",
       "Greyhound                                             1\n",
       "Heat                                                 10\n",
       "Interstellar                                         10\n",
       "Léon: The Professional                               10\n",
       "No Country for Old Men                                1\n",
       "Platoon                                              10\n",
       "Shutter Island                                        1\n",
       "Solo: A Star Wars Story                               1\n",
       "The Big Lebowski                                     10\n",
       "The Dark Knight Rises                                 1\n",
       "The Departed                                         10\n",
       "The F**k-It List                                      1\n",
       "The Godfather                                        10\n",
       "The Goonies                                          10\n",
       "The Green Mile                                       10\n",
       "The Lion King                                         1\n",
       "The Lord of the Rings: The Fellowship of the Ring    10\n",
       "The Notebook                                         10\n",
       "The Prestige                                         10\n",
       "The Truman Show                                      10\n",
       "The Twilight Saga: Breaking Dawn - Part 2            10\n",
       "The Vast of Night                                     1\n",
       "The Wrong Missy                                       1\n",
       "X-Men: Dark Phoenix                                   1\n",
       "Yesterday                                            10\n",
       "Name: Stars, dtype: int64"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total number of low and high ratings by 0.1% sample size\n",
    "joined_df.groupby(by='Movie_N')['Stars'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "e207a1ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Answers to skilled-based test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "01a0021c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# in accordance to the data i downloaded from kaggle,\n",
    "# there are many movies with high ratings and many\n",
    "# with low ratings\n",
    "# a sample data of size o.1% of population was used to \n",
    "# do exploratory data analysis as seen in this notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "711ab773",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' There are 1677 movies with a high rating of 10 '"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# questions and answers\n",
    "# 1 - which movie has the highest rating?\n",
    "''' There are 1677 movies with a high rating of 10 '''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "2b3baf84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' 1677 reviewers gave the movies the rating'"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2 - How many reviewers gave it the highest rating?\n",
    "''' 1677 reviewers gave the movies the rating'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "3c8941ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' there is 845 low rating of 1 by 845 reviewers'"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3 - what's its least rating and review details?\n",
    "''' there is 845 low rating of 1 by 845 reviewers'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "eda2e79b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=data_df_filtered.sample(20), x='Stars', y='Movie_N', orient='h')\n",
    "plt.title('Ratings vs Movie Title by 20 unbiased sample data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1263ccb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stars == Ratings!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}