ramen-rating-eda-29Nov2019.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the relevant packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns \n",
    "import scipy as sp\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy import array\n",
    "from numpy import argmax\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "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 #</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Variety</th>\n",
       "      <th>Style</th>\n",
       "      <th>Country</th>\n",
       "      <th>Stars</th>\n",
       "      <th>Top Ten</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2580</td>\n",
       "      <td>New Touch</td>\n",
       "      <td>T's Restaurant Tantanmen</td>\n",
       "      <td>Cup</td>\n",
       "      <td>Japan</td>\n",
       "      <td>3.75</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2579</td>\n",
       "      <td>Just Way</td>\n",
       "      <td>Noodles Spicy Hot Sesame Spicy Hot Sesame Guan...</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2578</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Cup Noodles Chicken Vegetable</td>\n",
       "      <td>Cup</td>\n",
       "      <td>USA</td>\n",
       "      <td>2.25</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2577</td>\n",
       "      <td>Wei Lih</td>\n",
       "      <td>GGE Ramen Snack Tomato Flavor</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>2.75</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2576</td>\n",
       "      <td>Ching's Secret</td>\n",
       "      <td>Singapore Curry</td>\n",
       "      <td>Pack</td>\n",
       "      <td>India</td>\n",
       "      <td>3.75</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Review #           Brand  \\\n",
       "0      2580       New Touch   \n",
       "1      2579        Just Way   \n",
       "2      2578          Nissin   \n",
       "3      2577         Wei Lih   \n",
       "4      2576  Ching's Secret   \n",
       "\n",
       "                                             Variety Style Country Stars  \\\n",
       "0                          T's Restaurant Tantanmen    Cup   Japan  3.75   \n",
       "1  Noodles Spicy Hot Sesame Spicy Hot Sesame Guan...  Pack  Taiwan     1   \n",
       "2                      Cup Noodles Chicken Vegetable   Cup     USA  2.25   \n",
       "3                      GGE Ramen Snack Tomato Flavor  Pack  Taiwan  2.75   \n",
       "4                                    Singapore Curry  Pack   India  3.75   \n",
       "\n",
       "  Top Ten  \n",
       "0     NaN  \n",
       "1     NaN  \n",
       "2     NaN  \n",
       "3     NaN  \n",
       "4     NaN  "
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read dataframe\n",
    "ramen_df = pd.read_csv('https://raw.githubusercontent.com/leekahhow/ramen-rating-eda/master/ramen-ratings.csv')\n",
    "ramen_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2580, 7)"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ramen_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2580 entries, 0 to 2579\n",
      "Data columns (total 7 columns):\n",
      "Review #    2580 non-null int64\n",
      "Brand       2580 non-null object\n",
      "Variety     2580 non-null object\n",
      "Style       2578 non-null object\n",
      "Country     2580 non-null object\n",
      "Stars       2580 non-null object\n",
      "Top Ten     41 non-null object\n",
      "dtypes: int64(1), object(6)\n",
      "memory usage: 141.2+ KB\n"
     ]
    }
   ],
   "source": [
    "ramen_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4          384\n",
       "5          369\n",
       "3.75       350\n",
       "3.5        326\n",
       "3          173\n",
       "3.25       170\n",
       "4.25       143\n",
       "4.5        132\n",
       "2.75        85\n",
       "2           68\n",
       "2.5         67\n",
       "4.75        64\n",
       "1.5         37\n",
       "1.75        27\n",
       "1           26\n",
       "0           26\n",
       "2.25        21\n",
       "0.5         14\n",
       "0.25        11\n",
       "5.0         10\n",
       "1.25        10\n",
       "3.50         9\n",
       "5.00         7\n",
       "4.00         6\n",
       "4.3          4\n",
       "3.8          3\n",
       "Unrated      3\n",
       "4.50         3\n",
       "4.0          3\n",
       "1.1          2\n",
       "2.3          2\n",
       "3.0          2\n",
       "2.8          2\n",
       "2.9          2\n",
       "3.1          2\n",
       "4.125        2\n",
       "0.75         1\n",
       "3.3          1\n",
       "0.1          1\n",
       "0.9          1\n",
       "3.125        1\n",
       "3.7          1\n",
       "2.1          1\n",
       "2.125        1\n",
       "3.4          1\n",
       "3.65         1\n",
       "3.6          1\n",
       "3.00         1\n",
       "1.8          1\n",
       "2.85         1\n",
       "3.2          1\n",
       "Name: Stars, dtype: int64"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ramen_df['Stars'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1447\n",
       "1    1130\n",
       "Name: good, dtype: int64"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def good(x):\n",
    "    if x>3.75:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "ramen_df=ramen_df[~ramen_df.Stars.str.contains(\"Unrated\")]\n",
    "ramen_df['good']=ramen_df['Stars']\n",
    "ramen_df['good']=ramen_df['good'].astype(float)\n",
    "ramen_df['good']=ramen_df['good'].apply(good)\n",
    "ramen_df['good'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2db514e0>"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,5))\n",
    "\n",
    "sns.heatmap(ramen_df.isnull(),cbar=False,yticklabels=False,cmap = 'viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "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 #</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Variety</th>\n",
       "      <th>Style</th>\n",
       "      <th>Country</th>\n",
       "      <th>Stars</th>\n",
       "      <th>Top Ten</th>\n",
       "      <th>good</th>\n",
       "      <th>Bar</th>\n",
       "      <th>Bowl</th>\n",
       "      <th>Box</th>\n",
       "      <th>Can</th>\n",
       "      <th>Cup</th>\n",
       "      <th>Pack</th>\n",
       "      <th>Tray</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2580</td>\n",
       "      <td>New Touch</td>\n",
       "      <td>T's Restaurant Tantanmen</td>\n",
       "      <td>Cup</td>\n",
       "      <td>Japan</td>\n",
       "      <td>3.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2579</td>\n",
       "      <td>Just Way</td>\n",
       "      <td>Noodles Spicy Hot Sesame Spicy Hot Sesame Guan...</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2578</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Cup Noodles Chicken Vegetable</td>\n",
       "      <td>Cup</td>\n",
       "      <td>USA</td>\n",
       "      <td>2.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2577</td>\n",
       "      <td>Wei Lih</td>\n",
       "      <td>GGE Ramen Snack Tomato Flavor</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>2.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2576</td>\n",
       "      <td>Ching's Secret</td>\n",
       "      <td>Singapore Curry</td>\n",
       "      <td>Pack</td>\n",
       "      <td>India</td>\n",
       "      <td>3.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2575</td>\n",
       "      <td>Samyang Foods</td>\n",
       "      <td>Kimchi song Song Ramen</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>4.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2574</td>\n",
       "      <td>Acecook</td>\n",
       "      <td>Spice Deli Tantan Men With Cilantro</td>\n",
       "      <td>Cup</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2573</td>\n",
       "      <td>Ikeda Shoku</td>\n",
       "      <td>Nabeyaki Kitsune Udon</td>\n",
       "      <td>Tray</td>\n",
       "      <td>Japan</td>\n",
       "      <td>3.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2572</td>\n",
       "      <td>Ripe'n'Dry</td>\n",
       "      <td>Hokkaido Soy Sauce Ramen</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>0.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2571</td>\n",
       "      <td>KOKA</td>\n",
       "      <td>The Original Spicy Stir-Fried Noodles</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2570</td>\n",
       "      <td>Tao Kae Noi</td>\n",
       "      <td>Creamy tom Yum Kung Flavour</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2569</td>\n",
       "      <td>Yamachan</td>\n",
       "      <td>Yokohama Tonkotsu Shoyu</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2568</td>\n",
       "      <td>Nongshim</td>\n",
       "      <td>Mr. Bibim Stir-Fried Kimchi Flavor</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>4.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2567</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Deka Buto Kimchi Pork Flavor</td>\n",
       "      <td>Bowl</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2566</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Demae Ramen Bar Noodle Aka Tonkotsu Flavour In...</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2565</td>\n",
       "      <td>KOKA</td>\n",
       "      <td>Mushroom Flavour Instant Noodles</td>\n",
       "      <td>Cup</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2564</td>\n",
       "      <td>TRDP</td>\n",
       "      <td>Mario Masala Noodles</td>\n",
       "      <td>Pack</td>\n",
       "      <td>India</td>\n",
       "      <td>3.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2563</td>\n",
       "      <td>Yamachan</td>\n",
       "      <td>Tokyo Shoyu Ramen</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2562</td>\n",
       "      <td>Binh Tay</td>\n",
       "      <td>Mi Hai Cua</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2561</td>\n",
       "      <td>Paldo</td>\n",
       "      <td>Premium Gomtang</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2560</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Cup Noodles Laksa Flavour</td>\n",
       "      <td>Cup</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>4.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2559</td>\n",
       "      <td>Jackpot Teriyaki</td>\n",
       "      <td>Beef Ramen</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2558</td>\n",
       "      <td>KOKA</td>\n",
       "      <td>Creamy Soup With Crushed Noodles Hot &amp; Sour Fi...</td>\n",
       "      <td>Cup</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2557</td>\n",
       "      <td>Yamachan</td>\n",
       "      <td>Sapporo Miso Ramen</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>4.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2556</td>\n",
       "      <td>Ching's Secret</td>\n",
       "      <td>Hot Garlic Instant Noodles</td>\n",
       "      <td>Pack</td>\n",
       "      <td>India</td>\n",
       "      <td>4.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2555</td>\n",
       "      <td>Samyang Foods</td>\n",
       "      <td>Song Song Kimchi Big Bowl</td>\n",
       "      <td>Bowl</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>4.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2554</td>\n",
       "      <td>Yum-Mie</td>\n",
       "      <td>Instant Noodles Beef In Light Soup Flavour</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Ghana</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2553</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Hakata Ramen Noodle White Tonkotsu</td>\n",
       "      <td>Bowl</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2552</td>\n",
       "      <td>MyKuali</td>\n",
       "      <td>Penang White Curry Rice Vermicelli Soup</td>\n",
       "      <td>Bowl</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2551</td>\n",
       "      <td>KOKA</td>\n",
       "      <td>Signature Tom Yum Flavor Instant Noodles</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2550</th>\n",
       "      <td>30</td>\n",
       "      <td>Myojo</td>\n",
       "      <td>Special Duck</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2551</th>\n",
       "      <td>29</td>\n",
       "      <td>Mee Jang</td>\n",
       "      <td>Tom Yum Shrimp</td>\n",
       "      <td>Bowl</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2552</th>\n",
       "      <td>28</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Chikin Ramen</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2553</th>\n",
       "      <td>27</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Demae Ramen Curry Flavor</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2554</th>\n",
       "      <td>26</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Soba Noodles With Mayo Mustard Squirt</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2555</th>\n",
       "      <td>25</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Demae Ramen Spicy Flavor</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2556</th>\n",
       "      <td>24</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Demae Ramen Spicy Seafood With Chili Pepper</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2557</th>\n",
       "      <td>23</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Top Ramen Creamy Chicken</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>4.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2558</th>\n",
       "      <td>22</td>\n",
       "      <td>Nongshim</td>\n",
       "      <td>Ansungtangmyun Noodle Soup</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>3.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2559</th>\n",
       "      <td>21</td>\n",
       "      <td>Nongshim</td>\n",
       "      <td>Champong Oriental Noodles</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2560</th>\n",
       "      <td>20</td>\n",
       "      <td>Nongshim</td>\n",
       "      <td>Neoguri (Seafood'n'Spicy)</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2561</th>\n",
       "      <td>19</td>\n",
       "      <td>Nongshim</td>\n",
       "      <td>Shin Ramyun</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2562</th>\n",
       "      <td>18</td>\n",
       "      <td>Ottogi</td>\n",
       "      <td>Jin Ramen (Hot Taste)</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2563</th>\n",
       "      <td>17</td>\n",
       "      <td>Quickchow</td>\n",
       "      <td>Pancit Palabok</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2564</th>\n",
       "      <td>16</td>\n",
       "      <td>Samyang</td>\n",
       "      <td>Kalgug-Su (Spicy)</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2565</th>\n",
       "      <td>15</td>\n",
       "      <td>Samyang</td>\n",
       "      <td>Pojangmacha U-dong</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2566</th>\n",
       "      <td>14</td>\n",
       "      <td>Samyang</td>\n",
       "      <td>Hot</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2567</th>\n",
       "      <td>13</td>\n",
       "      <td>Sapporo Ichiban</td>\n",
       "      <td>Chow Mein</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>5</td>\n",
       "      <td>2012 #4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2568</th>\n",
       "      <td>12</td>\n",
       "      <td>Sapporo Ichiban</td>\n",
       "      <td>Shrimp Flavor</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2569</th>\n",
       "      <td>11</td>\n",
       "      <td>Six Fortune</td>\n",
       "      <td>Chicken Flavor Instant Soup Noodle</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2570</th>\n",
       "      <td>10</td>\n",
       "      <td>Smack</td>\n",
       "      <td>Vegetable Beef</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2571</th>\n",
       "      <td>9</td>\n",
       "      <td>Sutah</td>\n",
       "      <td>Cup Noodle</td>\n",
       "      <td>Cup</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2572</th>\n",
       "      <td>8</td>\n",
       "      <td>Tung-I</td>\n",
       "      <td>Chinese Beef Instant Rice Noodle</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2573</th>\n",
       "      <td>7</td>\n",
       "      <td>Ve Wong</td>\n",
       "      <td>Mushroom Pork</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2574</th>\n",
       "      <td>6</td>\n",
       "      <td>Vifon</td>\n",
       "      <td>Nam Vang</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2575</th>\n",
       "      <td>5</td>\n",
       "      <td>Vifon</td>\n",
       "      <td>Hu Tiu Nam Vang [\"Phnom Penh\" style] Asian Sty...</td>\n",
       "      <td>Bowl</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2576</th>\n",
       "      <td>4</td>\n",
       "      <td>Wai Wai</td>\n",
       "      <td>Oriental Style Instant Noodles</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2577</th>\n",
       "      <td>3</td>\n",
       "      <td>Wai Wai</td>\n",
       "      <td>Tom Yum Shrimp</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2578</th>\n",
       "      <td>2</td>\n",
       "      <td>Wai Wai</td>\n",
       "      <td>Tom Yum Chili Flavor</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2579</th>\n",
       "      <td>1</td>\n",
       "      <td>Westbrae</td>\n",
       "      <td>Miso Ramen</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>0.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2577 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Review #             Brand  \\\n",
       "0         2580         New Touch   \n",
       "1         2579          Just Way   \n",
       "2         2578            Nissin   \n",
       "3         2577           Wei Lih   \n",
       "4         2576    Ching's Secret   \n",
       "5         2575     Samyang Foods   \n",
       "6         2574           Acecook   \n",
       "7         2573       Ikeda Shoku   \n",
       "8         2572        Ripe'n'Dry   \n",
       "9         2571              KOKA   \n",
       "10        2570       Tao Kae Noi   \n",
       "11        2569          Yamachan   \n",
       "12        2568          Nongshim   \n",
       "13        2567            Nissin   \n",
       "14        2566            Nissin   \n",
       "15        2565              KOKA   \n",
       "16        2564              TRDP   \n",
       "17        2563          Yamachan   \n",
       "18        2562          Binh Tay   \n",
       "19        2561             Paldo   \n",
       "20        2560            Nissin   \n",
       "21        2559  Jackpot Teriyaki   \n",
       "22        2558              KOKA   \n",
       "23        2557          Yamachan   \n",
       "24        2556    Ching's Secret   \n",
       "25        2555     Samyang Foods   \n",
       "26        2554           Yum-Mie   \n",
       "27        2553            Nissin   \n",
       "28        2552           MyKuali   \n",
       "29        2551              KOKA   \n",
       "...        ...               ...   \n",
       "2550        30             Myojo   \n",
       "2551        29          Mee Jang   \n",
       "2552        28            Nissin   \n",
       "2553        27            Nissin   \n",
       "2554        26            Nissin   \n",
       "2555        25            Nissin   \n",
       "2556        24            Nissin   \n",
       "2557        23            Nissin   \n",
       "2558        22          Nongshim   \n",
       "2559        21          Nongshim   \n",
       "2560        20          Nongshim   \n",
       "2561        19          Nongshim   \n",
       "2562        18            Ottogi   \n",
       "2563        17         Quickchow   \n",
       "2564        16           Samyang   \n",
       "2565        15           Samyang   \n",
       "2566        14           Samyang   \n",
       "2567        13   Sapporo Ichiban   \n",
       "2568        12   Sapporo Ichiban   \n",
       "2569        11       Six Fortune   \n",
       "2570        10             Smack   \n",
       "2571         9             Sutah   \n",
       "2572         8            Tung-I   \n",
       "2573         7           Ve Wong   \n",
       "2574         6             Vifon   \n",
       "2575         5             Vifon   \n",
       "2576         4           Wai Wai   \n",
       "2577         3           Wai Wai   \n",
       "2578         2           Wai Wai   \n",
       "2579         1          Westbrae   \n",
       "\n",
       "                                                Variety Style      Country  \\\n",
       "0                             T's Restaurant Tantanmen    Cup        Japan   \n",
       "1     Noodles Spicy Hot Sesame Spicy Hot Sesame Guan...  Pack       Taiwan   \n",
       "2                         Cup Noodles Chicken Vegetable   Cup          USA   \n",
       "3                         GGE Ramen Snack Tomato Flavor  Pack       Taiwan   \n",
       "4                                       Singapore Curry  Pack        India   \n",
       "5                                Kimchi song Song Ramen  Pack  South Korea   \n",
       "6                   Spice Deli Tantan Men With Cilantro   Cup        Japan   \n",
       "7                                 Nabeyaki Kitsune Udon  Tray        Japan   \n",
       "8                              Hokkaido Soy Sauce Ramen  Pack        Japan   \n",
       "9                 The Original Spicy Stir-Fried Noodles  Pack    Singapore   \n",
       "10                          Creamy tom Yum Kung Flavour  Pack     Thailand   \n",
       "11                              Yokohama Tonkotsu Shoyu  Pack          USA   \n",
       "12                   Mr. Bibim Stir-Fried Kimchi Flavor  Pack  South Korea   \n",
       "13                         Deka Buto Kimchi Pork Flavor  Bowl        Japan   \n",
       "14    Demae Ramen Bar Noodle Aka Tonkotsu Flavour In...  Pack    Hong Kong   \n",
       "15                     Mushroom Flavour Instant Noodles   Cup    Singapore   \n",
       "16                                 Mario Masala Noodles  Pack        India   \n",
       "17                                    Tokyo Shoyu Ramen  Pack          USA   \n",
       "18                                           Mi Hai Cua  Pack      Vietnam   \n",
       "19                                      Premium Gomtang  Pack  South Korea   \n",
       "20                            Cup Noodles Laksa Flavour   Cup    Hong Kong   \n",
       "21                                           Beef Ramen  Pack          USA   \n",
       "22    Creamy Soup With Crushed Noodles Hot & Sour Fi...   Cup    Singapore   \n",
       "23                                   Sapporo Miso Ramen  Pack          USA   \n",
       "24                           Hot Garlic Instant Noodles  Pack        India   \n",
       "25                            Song Song Kimchi Big Bowl  Bowl  South Korea   \n",
       "26           Instant Noodles Beef In Light Soup Flavour  Pack        Ghana   \n",
       "27                   Hakata Ramen Noodle White Tonkotsu  Bowl        Japan   \n",
       "28              Penang White Curry Rice Vermicelli Soup  Bowl     Malaysia   \n",
       "29             Signature Tom Yum Flavor Instant Noodles  Pack    Singapore   \n",
       "...                                                 ...   ...          ...   \n",
       "2550                                       Special Duck  Pack        Japan   \n",
       "2551                                     Tom Yum Shrimp  Bowl     Thailand   \n",
       "2552                                       Chikin Ramen  Pack        Japan   \n",
       "2553                           Demae Ramen Curry Flavor  Pack        Japan   \n",
       "2554              Soba Noodles With Mayo Mustard Squirt  Pack        Japan   \n",
       "2555                           Demae Ramen Spicy Flavor  Pack        Japan   \n",
       "2556        Demae Ramen Spicy Seafood With Chili Pepper  Pack        Japan   \n",
       "2557                           Top Ramen Creamy Chicken  Pack          USA   \n",
       "2558                         Ansungtangmyun Noodle Soup  Pack  South Korea   \n",
       "2559                          Champong Oriental Noodles  Pack  South Korea   \n",
       "2560                          Neoguri (Seafood'n'Spicy)  Pack  South Korea   \n",
       "2561                                        Shin Ramyun  Pack  South Korea   \n",
       "2562                              Jin Ramen (Hot Taste)  Pack  South Korea   \n",
       "2563                                     Pancit Palabok  Pack  Philippines   \n",
       "2564                                  Kalgug-Su (Spicy)  Pack  South Korea   \n",
       "2565                                 Pojangmacha U-dong  Pack  South Korea   \n",
       "2566                                                Hot  Pack  South Korea   \n",
       "2567                                          Chow Mein  Pack        Japan   \n",
       "2568                                      Shrimp Flavor  Pack        Japan   \n",
       "2569                 Chicken Flavor Instant Soup Noodle  Pack  South Korea   \n",
       "2570                                     Vegetable Beef  Pack          USA   \n",
       "2571                                         Cup Noodle   Cup  South Korea   \n",
       "2572                   Chinese Beef Instant Rice Noodle  Pack       Taiwan   \n",
       "2573                                      Mushroom Pork  Pack      Vietnam   \n",
       "2574                                           Nam Vang  Pack      Vietnam   \n",
       "2575  Hu Tiu Nam Vang [\"Phnom Penh\" style] Asian Sty...  Bowl      Vietnam   \n",
       "2576                     Oriental Style Instant Noodles  Pack     Thailand   \n",
       "2577                                     Tom Yum Shrimp  Pack     Thailand   \n",
       "2578                               Tom Yum Chili Flavor  Pack     Thailand   \n",
       "2579                                         Miso Ramen  Pack          USA   \n",
       "\n",
       "     Stars  Top Ten  good  Bar  Bowl  Box  Can  Cup  Pack  Tray  \n",
       "0     3.75      NaN     0    0     0    0    0    1     0     0  \n",
       "1        1      NaN     0    0     0    0    0    0     1     0  \n",
       "2     2.25      NaN     0    0     0    0    0    1     0     0  \n",
       "3     2.75      NaN     0    0     0    0    0    0     1     0  \n",
       "4     3.75      NaN     0    0     0    0    0    0     1     0  \n",
       "5     4.75      NaN     1    0     0    0    0    0     1     0  \n",
       "6        4      NaN     1    0     0    0    0    1     0     0  \n",
       "7     3.75      NaN     0    0     0    0    0    0     0     1  \n",
       "8     0.25      NaN     0    0     0    0    0    0     1     0  \n",
       "9      2.5      NaN     0    0     0    0    0    0     1     0  \n",
       "10       5      NaN     1    0     0    0    0    0     1     0  \n",
       "11       5      NaN     1    0     0    0    0    0     1     0  \n",
       "12    4.25      NaN     1    0     0    0    0    0     1     0  \n",
       "13     4.5      NaN     1    0     1    0    0    0     0     0  \n",
       "14       5      NaN     1    0     0    0    0    0     1     0  \n",
       "15     3.5      NaN     0    0     0    0    0    1     0     0  \n",
       "16    3.75      NaN     0    0     0    0    0    0     1     0  \n",
       "17       5      NaN     1    0     0    0    0    0     1     0  \n",
       "18       4      NaN     1    0     0    0    0    0     1     0  \n",
       "19       4      NaN     1    0     0    0    0    0     1     0  \n",
       "20    4.25      NaN     1    0     0    0    0    1     0     0  \n",
       "21       5      NaN     1    0     0    0    0    0     1     0  \n",
       "22       5      NaN     1    0     0    0    0    1     0     0  \n",
       "23    4.75      NaN     1    0     0    0    0    0     1     0  \n",
       "24    4.25      NaN     1    0     0    0    0    0     1     0  \n",
       "25    4.25      NaN     1    0     1    0    0    0     0     0  \n",
       "26     3.5      NaN     0    0     0    0    0    0     1     0  \n",
       "27    4.75      NaN     1    0     1    0    0    0     0     0  \n",
       "28       5      NaN     1    0     1    0    0    0     0     0  \n",
       "29       4      NaN     1    0     0    0    0    0     1     0  \n",
       "...    ...      ...   ...  ...   ...  ...  ...  ...   ...   ...  \n",
       "2550     1      NaN     0    0     0    0    0    0     1     0  \n",
       "2551   3.5      NaN     0    0     1    0    0    0     0     0  \n",
       "2552     5      NaN     1    0     0    0    0    0     1     0  \n",
       "2553     4      NaN     1    0     0    0    0    0     1     0  \n",
       "2554     4      NaN     1    0     0    0    0    0     1     0  \n",
       "2555     3      NaN     0    0     0    0    0    0     1     0  \n",
       "2556     2      NaN     0    0     0    0    0    0     1     0  \n",
       "2557   4.5      NaN     1    0     0    0    0    0     1     0  \n",
       "2558  3.75      NaN     0    0     0    0    0    0     1     0  \n",
       "2559     4      NaN     1    0     0    0    0    0     1     0  \n",
       "2560   3.5      NaN     0    0     0    0    0    0     1     0  \n",
       "2561     4      NaN     1    0     0    0    0    0     1     0  \n",
       "2562   3.5      NaN     0    0     0    0    0    0     1     0  \n",
       "2563   2.5      NaN     0    0     0    0    0    0     1     0  \n",
       "2564   3.5      NaN     0    0     0    0    0    0     1     0  \n",
       "2565   2.5      NaN     0    0     0    0    0    0     1     0  \n",
       "2566   3.5      NaN     0    0     0    0    0    0     1     0  \n",
       "2567     5  2012 #4     1    0     0    0    0    0     1     0  \n",
       "2568   2.5      NaN     0    0     0    0    0    0     1     0  \n",
       "2569     2      NaN     0    0     0    0    0    0     1     0  \n",
       "2570   1.5      NaN     0    0     0    0    0    0     1     0  \n",
       "2571     2      NaN     0    0     0    0    0    1     0     0  \n",
       "2572     3      NaN     0    0     0    0    0    0     1     0  \n",
       "2573     1      NaN     0    0     0    0    0    0     1     0  \n",
       "2574   2.5      NaN     0    0     0    0    0    0     1     0  \n",
       "2575   3.5      NaN     0    0     1    0    0    0     0     0  \n",
       "2576     1      NaN     0    0     0    0    0    0     1     0  \n",
       "2577     2      NaN     0    0     0    0    0    0     1     0  \n",
       "2578     2      NaN     0    0     0    0    0    0     1     0  \n",
       "2579   0.5      NaN     0    0     0    0    0    0     1     0  \n",
       "\n",
       "[2577 rows x 15 columns]"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(ramen_df['Style'], drop_first=True)\n",
    "#add binary variables for style\n",
    "ramen_df2 = pd.concat([ramen_df, pd.get_dummies(ramen_df['Style'])], axis=1); ramen_df\n",
    "ramen_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Review #</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Variety</th>\n",
       "      <th>Style</th>\n",
       "      <th>Country</th>\n",
       "      <th>Stars</th>\n",
       "      <th>Top Ten</th>\n",
       "      <th>good</th>\n",
       "      <th>Bar</th>\n",
       "      <th>Bowl</th>\n",
       "      <th>...</th>\n",
       "      <th>Sarawak</th>\n",
       "      <th>Singapore</th>\n",
       "      <th>South Korea</th>\n",
       "      <th>Sweden</th>\n",
       "      <th>Taiwan</th>\n",
       "      <th>Thailand</th>\n",
       "      <th>UK</th>\n",
       "      <th>USA</th>\n",
       "      <th>United States</th>\n",
       "      <th>Vietnam</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2580</td>\n",
       "      <td>New Touch</td>\n",
       "      <td>T's Restaurant Tantanmen</td>\n",
       "      <td>Cup</td>\n",
       "      <td>Japan</td>\n",
       "      <td>3.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2579</td>\n",
       "      <td>Just Way</td>\n",
       "      <td>Noodles Spicy Hot Sesame Spicy Hot Sesame Guan...</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2578</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Cup Noodles Chicken Vegetable</td>\n",
       "      <td>Cup</td>\n",
       "      <td>USA</td>\n",
       "      <td>2.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2577</td>\n",
       "      <td>Wei Lih</td>\n",
       "      <td>GGE Ramen Snack Tomato Flavor</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>2.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2576</td>\n",
       "      <td>Ching's Secret</td>\n",
       "      <td>Singapore Curry</td>\n",
       "      <td>Pack</td>\n",
       "      <td>India</td>\n",
       "      <td>3.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2575</td>\n",
       "      <td>Samyang Foods</td>\n",
       "      <td>Kimchi song Song Ramen</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>4.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>6</th>\n",
       "      <td>2574</td>\n",
       "      <td>Acecook</td>\n",
       "      <td>Spice Deli Tantan Men With Cilantro</td>\n",
       "      <td>Cup</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2573</td>\n",
       "      <td>Ikeda Shoku</td>\n",
       "      <td>Nabeyaki Kitsune Udon</td>\n",
       "      <td>Tray</td>\n",
       "      <td>Japan</td>\n",
       "      <td>3.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2572</td>\n",
       "      <td>Ripe'n'Dry</td>\n",
       "      <td>Hokkaido Soy Sauce Ramen</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>0.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2571</td>\n",
       "      <td>KOKA</td>\n",
       "      <td>The Original Spicy Stir-Fried Noodles</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2570</td>\n",
       "      <td>Tao Kae Noi</td>\n",
       "      <td>Creamy tom Yum Kung Flavour</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2569</td>\n",
       "      <td>Yamachan</td>\n",
       "      <td>Yokohama Tonkotsu Shoyu</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2568</td>\n",
       "      <td>Nongshim</td>\n",
       "      <td>Mr. Bibim Stir-Fried Kimchi Flavor</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>4.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2567</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Deka Buto Kimchi Pork Flavor</td>\n",
       "      <td>Bowl</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2566</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Demae Ramen Bar Noodle Aka Tonkotsu Flavour In...</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2565</td>\n",
       "      <td>KOKA</td>\n",
       "      <td>Mushroom Flavour Instant Noodles</td>\n",
       "      <td>Cup</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2564</td>\n",
       "      <td>TRDP</td>\n",
       "      <td>Mario Masala Noodles</td>\n",
       "      <td>Pack</td>\n",
       "      <td>India</td>\n",
       "      <td>3.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2563</td>\n",
       "      <td>Yamachan</td>\n",
       "      <td>Tokyo Shoyu Ramen</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2562</td>\n",
       "      <td>Binh Tay</td>\n",
       "      <td>Mi Hai Cua</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2561</td>\n",
       "      <td>Paldo</td>\n",
       "      <td>Premium Gomtang</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2560</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Cup Noodles Laksa Flavour</td>\n",
       "      <td>Cup</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>4.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2559</td>\n",
       "      <td>Jackpot Teriyaki</td>\n",
       "      <td>Beef Ramen</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2558</td>\n",
       "      <td>KOKA</td>\n",
       "      <td>Creamy Soup With Crushed Noodles Hot &amp; Sour Fi...</td>\n",
       "      <td>Cup</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2557</td>\n",
       "      <td>Yamachan</td>\n",
       "      <td>Sapporo Miso Ramen</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>4.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2556</td>\n",
       "      <td>Ching's Secret</td>\n",
       "      <td>Hot Garlic Instant Noodles</td>\n",
       "      <td>Pack</td>\n",
       "      <td>India</td>\n",
       "      <td>4.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2555</td>\n",
       "      <td>Samyang Foods</td>\n",
       "      <td>Song Song Kimchi Big Bowl</td>\n",
       "      <td>Bowl</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>4.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2554</td>\n",
       "      <td>Yum-Mie</td>\n",
       "      <td>Instant Noodles Beef In Light Soup Flavour</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Ghana</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2553</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Hakata Ramen Noodle White Tonkotsu</td>\n",
       "      <td>Bowl</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2552</td>\n",
       "      <td>MyKuali</td>\n",
       "      <td>Penang White Curry Rice Vermicelli Soup</td>\n",
       "      <td>Bowl</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2551</td>\n",
       "      <td>KOKA</td>\n",
       "      <td>Signature Tom Yum Flavor Instant Noodles</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2550</th>\n",
       "      <td>30</td>\n",
       "      <td>Myojo</td>\n",
       "      <td>Special Duck</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2551</th>\n",
       "      <td>29</td>\n",
       "      <td>Mee Jang</td>\n",
       "      <td>Tom Yum Shrimp</td>\n",
       "      <td>Bowl</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2552</th>\n",
       "      <td>28</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Chikin Ramen</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2553</th>\n",
       "      <td>27</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Demae Ramen Curry Flavor</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2554</th>\n",
       "      <td>26</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Soba Noodles With Mayo Mustard Squirt</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2555</th>\n",
       "      <td>25</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Demae Ramen Spicy Flavor</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2556</th>\n",
       "      <td>24</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Demae Ramen Spicy Seafood With Chili Pepper</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2557</th>\n",
       "      <td>23</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Top Ramen Creamy Chicken</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>4.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2558</th>\n",
       "      <td>22</td>\n",
       "      <td>Nongshim</td>\n",
       "      <td>Ansungtangmyun Noodle Soup</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>3.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2559</th>\n",
       "      <td>21</td>\n",
       "      <td>Nongshim</td>\n",
       "      <td>Champong Oriental Noodles</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2560</th>\n",
       "      <td>20</td>\n",
       "      <td>Nongshim</td>\n",
       "      <td>Neoguri (Seafood'n'Spicy)</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2561</th>\n",
       "      <td>19</td>\n",
       "      <td>Nongshim</td>\n",
       "      <td>Shin Ramyun</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2562</th>\n",
       "      <td>18</td>\n",
       "      <td>Ottogi</td>\n",
       "      <td>Jin Ramen (Hot Taste)</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2563</th>\n",
       "      <td>17</td>\n",
       "      <td>Quickchow</td>\n",
       "      <td>Pancit Palabok</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2564</th>\n",
       "      <td>16</td>\n",
       "      <td>Samyang</td>\n",
       "      <td>Kalgug-Su (Spicy)</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2565</th>\n",
       "      <td>15</td>\n",
       "      <td>Samyang</td>\n",
       "      <td>Pojangmacha U-dong</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2566</th>\n",
       "      <td>14</td>\n",
       "      <td>Samyang</td>\n",
       "      <td>Hot</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2567</th>\n",
       "      <td>13</td>\n",
       "      <td>Sapporo Ichiban</td>\n",
       "      <td>Chow Mein</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>5</td>\n",
       "      <td>2012 #4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2568</th>\n",
       "      <td>12</td>\n",
       "      <td>Sapporo Ichiban</td>\n",
       "      <td>Shrimp Flavor</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2569</th>\n",
       "      <td>11</td>\n",
       "      <td>Six Fortune</td>\n",
       "      <td>Chicken Flavor Instant Soup Noodle</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2570</th>\n",
       "      <td>10</td>\n",
       "      <td>Smack</td>\n",
       "      <td>Vegetable Beef</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2571</th>\n",
       "      <td>9</td>\n",
       "      <td>Sutah</td>\n",
       "      <td>Cup Noodle</td>\n",
       "      <td>Cup</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2572</th>\n",
       "      <td>8</td>\n",
       "      <td>Tung-I</td>\n",
       "      <td>Chinese Beef Instant Rice Noodle</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2573</th>\n",
       "      <td>7</td>\n",
       "      <td>Ve Wong</td>\n",
       "      <td>Mushroom Pork</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2574</th>\n",
       "      <td>6</td>\n",
       "      <td>Vifon</td>\n",
       "      <td>Nam Vang</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2575</th>\n",
       "      <td>5</td>\n",
       "      <td>Vifon</td>\n",
       "      <td>Hu Tiu Nam Vang [\"Phnom Penh\" style] Asian Sty...</td>\n",
       "      <td>Bowl</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2576</th>\n",
       "      <td>4</td>\n",
       "      <td>Wai Wai</td>\n",
       "      <td>Oriental Style Instant Noodles</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2577</th>\n",
       "      <td>3</td>\n",
       "      <td>Wai Wai</td>\n",
       "      <td>Tom Yum Shrimp</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2578</th>\n",
       "      <td>2</td>\n",
       "      <td>Wai Wai</td>\n",
       "      <td>Tom Yum Chili Flavor</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2579</th>\n",
       "      <td>1</td>\n",
       "      <td>Westbrae</td>\n",
       "      <td>Miso Ramen</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>0.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2577 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Review #             Brand  \\\n",
       "0         2580         New Touch   \n",
       "1         2579          Just Way   \n",
       "2         2578            Nissin   \n",
       "3         2577           Wei Lih   \n",
       "4         2576    Ching's Secret   \n",
       "5         2575     Samyang Foods   \n",
       "6         2574           Acecook   \n",
       "7         2573       Ikeda Shoku   \n",
       "8         2572        Ripe'n'Dry   \n",
       "9         2571              KOKA   \n",
       "10        2570       Tao Kae Noi   \n",
       "11        2569          Yamachan   \n",
       "12        2568          Nongshim   \n",
       "13        2567            Nissin   \n",
       "14        2566            Nissin   \n",
       "15        2565              KOKA   \n",
       "16        2564              TRDP   \n",
       "17        2563          Yamachan   \n",
       "18        2562          Binh Tay   \n",
       "19        2561             Paldo   \n",
       "20        2560            Nissin   \n",
       "21        2559  Jackpot Teriyaki   \n",
       "22        2558              KOKA   \n",
       "23        2557          Yamachan   \n",
       "24        2556    Ching's Secret   \n",
       "25        2555     Samyang Foods   \n",
       "26        2554           Yum-Mie   \n",
       "27        2553            Nissin   \n",
       "28        2552           MyKuali   \n",
       "29        2551              KOKA   \n",
       "...        ...               ...   \n",
       "2550        30             Myojo   \n",
       "2551        29          Mee Jang   \n",
       "2552        28            Nissin   \n",
       "2553        27            Nissin   \n",
       "2554        26            Nissin   \n",
       "2555        25            Nissin   \n",
       "2556        24            Nissin   \n",
       "2557        23            Nissin   \n",
       "2558        22          Nongshim   \n",
       "2559        21          Nongshim   \n",
       "2560        20          Nongshim   \n",
       "2561        19          Nongshim   \n",
       "2562        18            Ottogi   \n",
       "2563        17         Quickchow   \n",
       "2564        16           Samyang   \n",
       "2565        15           Samyang   \n",
       "2566        14           Samyang   \n",
       "2567        13   Sapporo Ichiban   \n",
       "2568        12   Sapporo Ichiban   \n",
       "2569        11       Six Fortune   \n",
       "2570        10             Smack   \n",
       "2571         9             Sutah   \n",
       "2572         8            Tung-I   \n",
       "2573         7           Ve Wong   \n",
       "2574         6             Vifon   \n",
       "2575         5             Vifon   \n",
       "2576         4           Wai Wai   \n",
       "2577         3           Wai Wai   \n",
       "2578         2           Wai Wai   \n",
       "2579         1          Westbrae   \n",
       "\n",
       "                                                Variety Style      Country  \\\n",
       "0                             T's Restaurant Tantanmen    Cup        Japan   \n",
       "1     Noodles Spicy Hot Sesame Spicy Hot Sesame Guan...  Pack       Taiwan   \n",
       "2                         Cup Noodles Chicken Vegetable   Cup          USA   \n",
       "3                         GGE Ramen Snack Tomato Flavor  Pack       Taiwan   \n",
       "4                                       Singapore Curry  Pack        India   \n",
       "5                                Kimchi song Song Ramen  Pack  South Korea   \n",
       "6                   Spice Deli Tantan Men With Cilantro   Cup        Japan   \n",
       "7                                 Nabeyaki Kitsune Udon  Tray        Japan   \n",
       "8                              Hokkaido Soy Sauce Ramen  Pack        Japan   \n",
       "9                 The Original Spicy Stir-Fried Noodles  Pack    Singapore   \n",
       "10                          Creamy tom Yum Kung Flavour  Pack     Thailand   \n",
       "11                              Yokohama Tonkotsu Shoyu  Pack          USA   \n",
       "12                   Mr. Bibim Stir-Fried Kimchi Flavor  Pack  South Korea   \n",
       "13                         Deka Buto Kimchi Pork Flavor  Bowl        Japan   \n",
       "14    Demae Ramen Bar Noodle Aka Tonkotsu Flavour In...  Pack    Hong Kong   \n",
       "15                     Mushroom Flavour Instant Noodles   Cup    Singapore   \n",
       "16                                 Mario Masala Noodles  Pack        India   \n",
       "17                                    Tokyo Shoyu Ramen  Pack          USA   \n",
       "18                                           Mi Hai Cua  Pack      Vietnam   \n",
       "19                                      Premium Gomtang  Pack  South Korea   \n",
       "20                            Cup Noodles Laksa Flavour   Cup    Hong Kong   \n",
       "21                                           Beef Ramen  Pack          USA   \n",
       "22    Creamy Soup With Crushed Noodles Hot & Sour Fi...   Cup    Singapore   \n",
       "23                                   Sapporo Miso Ramen  Pack          USA   \n",
       "24                           Hot Garlic Instant Noodles  Pack        India   \n",
       "25                            Song Song Kimchi Big Bowl  Bowl  South Korea   \n",
       "26           Instant Noodles Beef In Light Soup Flavour  Pack        Ghana   \n",
       "27                   Hakata Ramen Noodle White Tonkotsu  Bowl        Japan   \n",
       "28              Penang White Curry Rice Vermicelli Soup  Bowl     Malaysia   \n",
       "29             Signature Tom Yum Flavor Instant Noodles  Pack    Singapore   \n",
       "...                                                 ...   ...          ...   \n",
       "2550                                       Special Duck  Pack        Japan   \n",
       "2551                                     Tom Yum Shrimp  Bowl     Thailand   \n",
       "2552                                       Chikin Ramen  Pack        Japan   \n",
       "2553                           Demae Ramen Curry Flavor  Pack        Japan   \n",
       "2554              Soba Noodles With Mayo Mustard Squirt  Pack        Japan   \n",
       "2555                           Demae Ramen Spicy Flavor  Pack        Japan   \n",
       "2556        Demae Ramen Spicy Seafood With Chili Pepper  Pack        Japan   \n",
       "2557                           Top Ramen Creamy Chicken  Pack          USA   \n",
       "2558                         Ansungtangmyun Noodle Soup  Pack  South Korea   \n",
       "2559                          Champong Oriental Noodles  Pack  South Korea   \n",
       "2560                          Neoguri (Seafood'n'Spicy)  Pack  South Korea   \n",
       "2561                                        Shin Ramyun  Pack  South Korea   \n",
       "2562                              Jin Ramen (Hot Taste)  Pack  South Korea   \n",
       "2563                                     Pancit Palabok  Pack  Philippines   \n",
       "2564                                  Kalgug-Su (Spicy)  Pack  South Korea   \n",
       "2565                                 Pojangmacha U-dong  Pack  South Korea   \n",
       "2566                                                Hot  Pack  South Korea   \n",
       "2567                                          Chow Mein  Pack        Japan   \n",
       "2568                                      Shrimp Flavor  Pack        Japan   \n",
       "2569                 Chicken Flavor Instant Soup Noodle  Pack  South Korea   \n",
       "2570                                     Vegetable Beef  Pack          USA   \n",
       "2571                                         Cup Noodle   Cup  South Korea   \n",
       "2572                   Chinese Beef Instant Rice Noodle  Pack       Taiwan   \n",
       "2573                                      Mushroom Pork  Pack      Vietnam   \n",
       "2574                                           Nam Vang  Pack      Vietnam   \n",
       "2575  Hu Tiu Nam Vang [\"Phnom Penh\" style] Asian Sty...  Bowl      Vietnam   \n",
       "2576                     Oriental Style Instant Noodles  Pack     Thailand   \n",
       "2577                                     Tom Yum Shrimp  Pack     Thailand   \n",
       "2578                               Tom Yum Chili Flavor  Pack     Thailand   \n",
       "2579                                         Miso Ramen  Pack          USA   \n",
       "\n",
       "     Stars  Top Ten  good  Bar  Bowl  ...  Sarawak  Singapore  South Korea  \\\n",
       "0     3.75      NaN     0    0     0  ...        0          0            0   \n",
       "1        1      NaN     0    0     0  ...        0          0            0   \n",
       "2     2.25      NaN     0    0     0  ...        0          0            0   \n",
       "3     2.75      NaN     0    0     0  ...        0          0            0   \n",
       "4     3.75      NaN     0    0     0  ...        0          0            0   \n",
       "5     4.75      NaN     1    0     0  ...        0          0            1   \n",
       "6        4      NaN     1    0     0  ...        0          0            0   \n",
       "7     3.75      NaN     0    0     0  ...        0          0            0   \n",
       "8     0.25      NaN     0    0     0  ...        0          0            0   \n",
       "9      2.5      NaN     0    0     0  ...        0          1            0   \n",
       "10       5      NaN     1    0     0  ...        0          0            0   \n",
       "11       5      NaN     1    0     0  ...        0          0            0   \n",
       "12    4.25      NaN     1    0     0  ...        0          0            1   \n",
       "13     4.5      NaN     1    0     1  ...        0          0            0   \n",
       "14       5      NaN     1    0     0  ...        0          0            0   \n",
       "15     3.5      NaN     0    0     0  ...        0          1            0   \n",
       "16    3.75      NaN     0    0     0  ...        0          0            0   \n",
       "17       5      NaN     1    0     0  ...        0          0            0   \n",
       "18       4      NaN     1    0     0  ...        0          0            0   \n",
       "19       4      NaN     1    0     0  ...        0          0            1   \n",
       "20    4.25      NaN     1    0     0  ...        0          0            0   \n",
       "21       5      NaN     1    0     0  ...        0          0            0   \n",
       "22       5      NaN     1    0     0  ...        0          1            0   \n",
       "23    4.75      NaN     1    0     0  ...        0          0            0   \n",
       "24    4.25      NaN     1    0     0  ...        0          0            0   \n",
       "25    4.25      NaN     1    0     1  ...        0          0            1   \n",
       "26     3.5      NaN     0    0     0  ...        0          0            0   \n",
       "27    4.75      NaN     1    0     1  ...        0          0            0   \n",
       "28       5      NaN     1    0     1  ...        0          0            0   \n",
       "29       4      NaN     1    0     0  ...        0          1            0   \n",
       "...    ...      ...   ...  ...   ...  ...      ...        ...          ...   \n",
       "2550     1      NaN     0    0     0  ...        0          0            0   \n",
       "2551   3.5      NaN     0    0     1  ...        0          0            0   \n",
       "2552     5      NaN     1    0     0  ...        0          0            0   \n",
       "2553     4      NaN     1    0     0  ...        0          0            0   \n",
       "2554     4      NaN     1    0     0  ...        0          0            0   \n",
       "2555     3      NaN     0    0     0  ...        0          0            0   \n",
       "2556     2      NaN     0    0     0  ...        0          0            0   \n",
       "2557   4.5      NaN     1    0     0  ...        0          0            0   \n",
       "2558  3.75      NaN     0    0     0  ...        0          0            1   \n",
       "2559     4      NaN     1    0     0  ...        0          0            1   \n",
       "2560   3.5      NaN     0    0     0  ...        0          0            1   \n",
       "2561     4      NaN     1    0     0  ...        0          0            1   \n",
       "2562   3.5      NaN     0    0     0  ...        0          0            1   \n",
       "2563   2.5      NaN     0    0     0  ...        0          0            0   \n",
       "2564   3.5      NaN     0    0     0  ...        0          0            1   \n",
       "2565   2.5      NaN     0    0     0  ...        0          0            1   \n",
       "2566   3.5      NaN     0    0     0  ...        0          0            1   \n",
       "2567     5  2012 #4     1    0     0  ...        0          0            0   \n",
       "2568   2.5      NaN     0    0     0  ...        0          0            0   \n",
       "2569     2      NaN     0    0     0  ...        0          0            1   \n",
       "2570   1.5      NaN     0    0     0  ...        0          0            0   \n",
       "2571     2      NaN     0    0     0  ...        0          0            1   \n",
       "2572     3      NaN     0    0     0  ...        0          0            0   \n",
       "2573     1      NaN     0    0     0  ...        0          0            0   \n",
       "2574   2.5      NaN     0    0     0  ...        0          0            0   \n",
       "2575   3.5      NaN     0    0     1  ...        0          0            0   \n",
       "2576     1      NaN     0    0     0  ...        0          0            0   \n",
       "2577     2      NaN     0    0     0  ...        0          0            0   \n",
       "2578     2      NaN     0    0     0  ...        0          0            0   \n",
       "2579   0.5      NaN     0    0     0  ...        0          0            0   \n",
       "\n",
       "      Sweden  Taiwan  Thailand  UK  USA  United States  Vietnam  \n",
       "0          0       0         0   0    0              0        0  \n",
       "1          0       1         0   0    0              0        0  \n",
       "2          0       0         0   0    1              0        0  \n",
       "3          0       1         0   0    0              0        0  \n",
       "4          0       0         0   0    0              0        0  \n",
       "5          0       0         0   0    0              0        0  \n",
       "6          0       0         0   0    0              0        0  \n",
       "7          0       0         0   0    0              0        0  \n",
       "8          0       0         0   0    0              0        0  \n",
       "9          0       0         0   0    0              0        0  \n",
       "10         0       0         1   0    0              0        0  \n",
       "11         0       0         0   0    1              0        0  \n",
       "12         0       0         0   0    0              0        0  \n",
       "13         0       0         0   0    0              0        0  \n",
       "14         0       0         0   0    0              0        0  \n",
       "15         0       0         0   0    0              0        0  \n",
       "16         0       0         0   0    0              0        0  \n",
       "17         0       0         0   0    1              0        0  \n",
       "18         0       0         0   0    0              0        1  \n",
       "19         0       0         0   0    0              0        0  \n",
       "20         0       0         0   0    0              0        0  \n",
       "21         0       0         0   0    1              0        0  \n",
       "22         0       0         0   0    0              0        0  \n",
       "23         0       0         0   0    1              0        0  \n",
       "24         0       0         0   0    0              0        0  \n",
       "25         0       0         0   0    0              0        0  \n",
       "26         0       0         0   0    0              0        0  \n",
       "27         0       0         0   0    0              0        0  \n",
       "28         0       0         0   0    0              0        0  \n",
       "29         0       0         0   0    0              0        0  \n",
       "...      ...     ...       ...  ..  ...            ...      ...  \n",
       "2550       0       0         0   0    0              0        0  \n",
       "2551       0       0         1   0    0              0        0  \n",
       "2552       0       0         0   0    0              0        0  \n",
       "2553       0       0         0   0    0              0        0  \n",
       "2554       0       0         0   0    0              0        0  \n",
       "2555       0       0         0   0    0              0        0  \n",
       "2556       0       0         0   0    0              0        0  \n",
       "2557       0       0         0   0    1              0        0  \n",
       "2558       0       0         0   0    0              0        0  \n",
       "2559       0       0         0   0    0              0        0  \n",
       "2560       0       0         0   0    0              0        0  \n",
       "2561       0       0         0   0    0              0        0  \n",
       "2562       0       0         0   0    0              0        0  \n",
       "2563       0       0         0   0    0              0        0  \n",
       "2564       0       0         0   0    0              0        0  \n",
       "2565       0       0         0   0    0              0        0  \n",
       "2566       0       0         0   0    0              0        0  \n",
       "2567       0       0         0   0    0              0        0  \n",
       "2568       0       0         0   0    0              0        0  \n",
       "2569       0       0         0   0    0              0        0  \n",
       "2570       0       0         0   0    1              0        0  \n",
       "2571       0       0         0   0    0              0        0  \n",
       "2572       0       1         0   0    0              0        0  \n",
       "2573       0       0         0   0    0              0        1  \n",
       "2574       0       0         0   0    0              0        1  \n",
       "2575       0       0         0   0    0              0        1  \n",
       "2576       0       0         1   0    0              0        0  \n",
       "2577       0       0         1   0    0              0        0  \n",
       "2578       0       0         1   0    0              0        0  \n",
       "2579       0       0         0   0    1              0        0  \n",
       "\n",
       "[2577 rows x 53 columns]"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#add binary variables for country\n",
    "ramen_df3 = pd.concat([ramen_df2, pd.get_dummies(ramen_df2['Country'])], axis=1); ramen_df2\n",
    "ramen_df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Review #</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Variety</th>\n",
       "      <th>Style</th>\n",
       "      <th>Country</th>\n",
       "      <th>Stars</th>\n",
       "      <th>Top Ten</th>\n",
       "      <th>good</th>\n",
       "      <th>Bar</th>\n",
       "      <th>Bowl</th>\n",
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       "      <th>Xiuhe</th>\n",
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       "      <th>Yum Yum</th>\n",
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       "      <td>2580</td>\n",
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       "      <td>Taiwan</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2571</td>\n",
       "      <td>KOKA</td>\n",
       "      <td>The Original Spicy Stir-Fried Noodles</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2570</td>\n",
       "      <td>Tao Kae Noi</td>\n",
       "      <td>Creamy tom Yum Kung Flavour</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2569</td>\n",
       "      <td>Yamachan</td>\n",
       "      <td>Yokohama Tonkotsu Shoyu</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2568</td>\n",
       "      <td>Nongshim</td>\n",
       "      <td>Mr. Bibim Stir-Fried Kimchi Flavor</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>4.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2567</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Deka Buto Kimchi Pork Flavor</td>\n",
       "      <td>Bowl</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2566</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Demae Ramen Bar Noodle Aka Tonkotsu Flavour In...</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2565</td>\n",
       "      <td>KOKA</td>\n",
       "      <td>Mushroom Flavour Instant Noodles</td>\n",
       "      <td>Cup</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2564</td>\n",
       "      <td>TRDP</td>\n",
       "      <td>Mario Masala Noodles</td>\n",
       "      <td>Pack</td>\n",
       "      <td>India</td>\n",
       "      <td>3.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2563</td>\n",
       "      <td>Yamachan</td>\n",
       "      <td>Tokyo Shoyu Ramen</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2562</td>\n",
       "      <td>Binh Tay</td>\n",
       "      <td>Mi Hai Cua</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2561</td>\n",
       "      <td>Paldo</td>\n",
       "      <td>Premium Gomtang</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2560</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Cup Noodles Laksa Flavour</td>\n",
       "      <td>Cup</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>4.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2559</td>\n",
       "      <td>Jackpot Teriyaki</td>\n",
       "      <td>Beef Ramen</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2558</td>\n",
       "      <td>KOKA</td>\n",
       "      <td>Creamy Soup With Crushed Noodles Hot &amp; Sour Fi...</td>\n",
       "      <td>Cup</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2557</td>\n",
       "      <td>Yamachan</td>\n",
       "      <td>Sapporo Miso Ramen</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>4.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2556</td>\n",
       "      <td>Ching's Secret</td>\n",
       "      <td>Hot Garlic Instant Noodles</td>\n",
       "      <td>Pack</td>\n",
       "      <td>India</td>\n",
       "      <td>4.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2555</td>\n",
       "      <td>Samyang Foods</td>\n",
       "      <td>Song Song Kimchi Big Bowl</td>\n",
       "      <td>Bowl</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>4.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2554</td>\n",
       "      <td>Yum-Mie</td>\n",
       "      <td>Instant Noodles Beef In Light Soup Flavour</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Ghana</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2553</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Hakata Ramen Noodle White Tonkotsu</td>\n",
       "      <td>Bowl</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2552</td>\n",
       "      <td>MyKuali</td>\n",
       "      <td>Penang White Curry Rice Vermicelli Soup</td>\n",
       "      <td>Bowl</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2551</td>\n",
       "      <td>KOKA</td>\n",
       "      <td>Signature Tom Yum Flavor Instant Noodles</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2550</th>\n",
       "      <td>30</td>\n",
       "      <td>Myojo</td>\n",
       "      <td>Special Duck</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2551</th>\n",
       "      <td>29</td>\n",
       "      <td>Mee Jang</td>\n",
       "      <td>Tom Yum Shrimp</td>\n",
       "      <td>Bowl</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2552</th>\n",
       "      <td>28</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Chikin Ramen</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2553</th>\n",
       "      <td>27</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Demae Ramen Curry Flavor</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2554</th>\n",
       "      <td>26</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Soba Noodles With Mayo Mustard Squirt</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2555</th>\n",
       "      <td>25</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Demae Ramen Spicy Flavor</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2556</th>\n",
       "      <td>24</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Demae Ramen Spicy Seafood With Chili Pepper</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2557</th>\n",
       "      <td>23</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Top Ramen Creamy Chicken</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>4.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2558</th>\n",
       "      <td>22</td>\n",
       "      <td>Nongshim</td>\n",
       "      <td>Ansungtangmyun Noodle Soup</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>3.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2559</th>\n",
       "      <td>21</td>\n",
       "      <td>Nongshim</td>\n",
       "      <td>Champong Oriental Noodles</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2560</th>\n",
       "      <td>20</td>\n",
       "      <td>Nongshim</td>\n",
       "      <td>Neoguri (Seafood'n'Spicy)</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2561</th>\n",
       "      <td>19</td>\n",
       "      <td>Nongshim</td>\n",
       "      <td>Shin Ramyun</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2562</th>\n",
       "      <td>18</td>\n",
       "      <td>Ottogi</td>\n",
       "      <td>Jin Ramen (Hot Taste)</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2563</th>\n",
       "      <td>17</td>\n",
       "      <td>Quickchow</td>\n",
       "      <td>Pancit Palabok</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2564</th>\n",
       "      <td>16</td>\n",
       "      <td>Samyang</td>\n",
       "      <td>Kalgug-Su (Spicy)</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2565</th>\n",
       "      <td>15</td>\n",
       "      <td>Samyang</td>\n",
       "      <td>Pojangmacha U-dong</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2566</th>\n",
       "      <td>14</td>\n",
       "      <td>Samyang</td>\n",
       "      <td>Hot</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2567</th>\n",
       "      <td>13</td>\n",
       "      <td>Sapporo Ichiban</td>\n",
       "      <td>Chow Mein</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>5</td>\n",
       "      <td>2012 #4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2568</th>\n",
       "      <td>12</td>\n",
       "      <td>Sapporo Ichiban</td>\n",
       "      <td>Shrimp Flavor</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2569</th>\n",
       "      <td>11</td>\n",
       "      <td>Six Fortune</td>\n",
       "      <td>Chicken Flavor Instant Soup Noodle</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2570</th>\n",
       "      <td>10</td>\n",
       "      <td>Smack</td>\n",
       "      <td>Vegetable Beef</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2571</th>\n",
       "      <td>9</td>\n",
       "      <td>Sutah</td>\n",
       "      <td>Cup Noodle</td>\n",
       "      <td>Cup</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2572</th>\n",
       "      <td>8</td>\n",
       "      <td>Tung-I</td>\n",
       "      <td>Chinese Beef Instant Rice Noodle</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2573</th>\n",
       "      <td>7</td>\n",
       "      <td>Ve Wong</td>\n",
       "      <td>Mushroom Pork</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2574</th>\n",
       "      <td>6</td>\n",
       "      <td>Vifon</td>\n",
       "      <td>Nam Vang</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2575</th>\n",
       "      <td>5</td>\n",
       "      <td>Vifon</td>\n",
       "      <td>Hu Tiu Nam Vang [\"Phnom Penh\" style] Asian Sty...</td>\n",
       "      <td>Bowl</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2576</th>\n",
       "      <td>4</td>\n",
       "      <td>Wai Wai</td>\n",
       "      <td>Oriental Style Instant Noodles</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2577</th>\n",
       "      <td>3</td>\n",
       "      <td>Wai Wai</td>\n",
       "      <td>Tom Yum Shrimp</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2578</th>\n",
       "      <td>2</td>\n",
       "      <td>Wai Wai</td>\n",
       "      <td>Tom Yum Chili Flavor</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2579</th>\n",
       "      <td>1</td>\n",
       "      <td>Westbrae</td>\n",
       "      <td>Miso Ramen</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>0.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2577 rows × 408 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Review #             Brand  \\\n",
       "0         2580         New Touch   \n",
       "1         2579          Just Way   \n",
       "2         2578            Nissin   \n",
       "3         2577           Wei Lih   \n",
       "4         2576    Ching's Secret   \n",
       "5         2575     Samyang Foods   \n",
       "6         2574           Acecook   \n",
       "7         2573       Ikeda Shoku   \n",
       "8         2572        Ripe'n'Dry   \n",
       "9         2571              KOKA   \n",
       "10        2570       Tao Kae Noi   \n",
       "11        2569          Yamachan   \n",
       "12        2568          Nongshim   \n",
       "13        2567            Nissin   \n",
       "14        2566            Nissin   \n",
       "15        2565              KOKA   \n",
       "16        2564              TRDP   \n",
       "17        2563          Yamachan   \n",
       "18        2562          Binh Tay   \n",
       "19        2561             Paldo   \n",
       "20        2560            Nissin   \n",
       "21        2559  Jackpot Teriyaki   \n",
       "22        2558              KOKA   \n",
       "23        2557          Yamachan   \n",
       "24        2556    Ching's Secret   \n",
       "25        2555     Samyang Foods   \n",
       "26        2554           Yum-Mie   \n",
       "27        2553            Nissin   \n",
       "28        2552           MyKuali   \n",
       "29        2551              KOKA   \n",
       "...        ...               ...   \n",
       "2550        30             Myojo   \n",
       "2551        29          Mee Jang   \n",
       "2552        28            Nissin   \n",
       "2553        27            Nissin   \n",
       "2554        26            Nissin   \n",
       "2555        25            Nissin   \n",
       "2556        24            Nissin   \n",
       "2557        23            Nissin   \n",
       "2558        22          Nongshim   \n",
       "2559        21          Nongshim   \n",
       "2560        20          Nongshim   \n",
       "2561        19          Nongshim   \n",
       "2562        18            Ottogi   \n",
       "2563        17         Quickchow   \n",
       "2564        16           Samyang   \n",
       "2565        15           Samyang   \n",
       "2566        14           Samyang   \n",
       "2567        13   Sapporo Ichiban   \n",
       "2568        12   Sapporo Ichiban   \n",
       "2569        11       Six Fortune   \n",
       "2570        10             Smack   \n",
       "2571         9             Sutah   \n",
       "2572         8            Tung-I   \n",
       "2573         7           Ve Wong   \n",
       "2574         6             Vifon   \n",
       "2575         5             Vifon   \n",
       "2576         4           Wai Wai   \n",
       "2577         3           Wai Wai   \n",
       "2578         2           Wai Wai   \n",
       "2579         1          Westbrae   \n",
       "\n",
       "                                                Variety Style      Country  \\\n",
       "0                             T's Restaurant Tantanmen    Cup        Japan   \n",
       "1     Noodles Spicy Hot Sesame Spicy Hot Sesame Guan...  Pack       Taiwan   \n",
       "2                         Cup Noodles Chicken Vegetable   Cup          USA   \n",
       "3                         GGE Ramen Snack Tomato Flavor  Pack       Taiwan   \n",
       "4                                       Singapore Curry  Pack        India   \n",
       "5                                Kimchi song Song Ramen  Pack  South Korea   \n",
       "6                   Spice Deli Tantan Men With Cilantro   Cup        Japan   \n",
       "7                                 Nabeyaki Kitsune Udon  Tray        Japan   \n",
       "8                              Hokkaido Soy Sauce Ramen  Pack        Japan   \n",
       "9                 The Original Spicy Stir-Fried Noodles  Pack    Singapore   \n",
       "10                          Creamy tom Yum Kung Flavour  Pack     Thailand   \n",
       "11                              Yokohama Tonkotsu Shoyu  Pack          USA   \n",
       "12                   Mr. Bibim Stir-Fried Kimchi Flavor  Pack  South Korea   \n",
       "13                         Deka Buto Kimchi Pork Flavor  Bowl        Japan   \n",
       "14    Demae Ramen Bar Noodle Aka Tonkotsu Flavour In...  Pack    Hong Kong   \n",
       "15                     Mushroom Flavour Instant Noodles   Cup    Singapore   \n",
       "16                                 Mario Masala Noodles  Pack        India   \n",
       "17                                    Tokyo Shoyu Ramen  Pack          USA   \n",
       "18                                           Mi Hai Cua  Pack      Vietnam   \n",
       "19                                      Premium Gomtang  Pack  South Korea   \n",
       "20                            Cup Noodles Laksa Flavour   Cup    Hong Kong   \n",
       "21                                           Beef Ramen  Pack          USA   \n",
       "22    Creamy Soup With Crushed Noodles Hot & Sour Fi...   Cup    Singapore   \n",
       "23                                   Sapporo Miso Ramen  Pack          USA   \n",
       "24                           Hot Garlic Instant Noodles  Pack        India   \n",
       "25                            Song Song Kimchi Big Bowl  Bowl  South Korea   \n",
       "26           Instant Noodles Beef In Light Soup Flavour  Pack        Ghana   \n",
       "27                   Hakata Ramen Noodle White Tonkotsu  Bowl        Japan   \n",
       "28              Penang White Curry Rice Vermicelli Soup  Bowl     Malaysia   \n",
       "29             Signature Tom Yum Flavor Instant Noodles  Pack    Singapore   \n",
       "...                                                 ...   ...          ...   \n",
       "2550                                       Special Duck  Pack        Japan   \n",
       "2551                                     Tom Yum Shrimp  Bowl     Thailand   \n",
       "2552                                       Chikin Ramen  Pack        Japan   \n",
       "2553                           Demae Ramen Curry Flavor  Pack        Japan   \n",
       "2554              Soba Noodles With Mayo Mustard Squirt  Pack        Japan   \n",
       "2555                           Demae Ramen Spicy Flavor  Pack        Japan   \n",
       "2556        Demae Ramen Spicy Seafood With Chili Pepper  Pack        Japan   \n",
       "2557                           Top Ramen Creamy Chicken  Pack          USA   \n",
       "2558                         Ansungtangmyun Noodle Soup  Pack  South Korea   \n",
       "2559                          Champong Oriental Noodles  Pack  South Korea   \n",
       "2560                          Neoguri (Seafood'n'Spicy)  Pack  South Korea   \n",
       "2561                                        Shin Ramyun  Pack  South Korea   \n",
       "2562                              Jin Ramen (Hot Taste)  Pack  South Korea   \n",
       "2563                                     Pancit Palabok  Pack  Philippines   \n",
       "2564                                  Kalgug-Su (Spicy)  Pack  South Korea   \n",
       "2565                                 Pojangmacha U-dong  Pack  South Korea   \n",
       "2566                                                Hot  Pack  South Korea   \n",
       "2567                                          Chow Mein  Pack        Japan   \n",
       "2568                                      Shrimp Flavor  Pack        Japan   \n",
       "2569                 Chicken Flavor Instant Soup Noodle  Pack  South Korea   \n",
       "2570                                     Vegetable Beef  Pack          USA   \n",
       "2571                                         Cup Noodle   Cup  South Korea   \n",
       "2572                   Chinese Beef Instant Rice Noodle  Pack       Taiwan   \n",
       "2573                                      Mushroom Pork  Pack      Vietnam   \n",
       "2574                                           Nam Vang  Pack      Vietnam   \n",
       "2575  Hu Tiu Nam Vang [\"Phnom Penh\" style] Asian Sty...  Bowl      Vietnam   \n",
       "2576                     Oriental Style Instant Noodles  Pack     Thailand   \n",
       "2577                                     Tom Yum Shrimp  Pack     Thailand   \n",
       "2578                               Tom Yum Chili Flavor  Pack     Thailand   \n",
       "2579                                         Miso Ramen  Pack          USA   \n",
       "\n",
       "     Stars  Top Ten  good  Bar  Bowl  ...  Xiuhe  Yamachan  Yamadai  Yamamori  \\\n",
       "0     3.75      NaN     0    0     0  ...      0         0        0         0   \n",
       "1        1      NaN     0    0     0  ...      0         0        0         0   \n",
       "2     2.25      NaN     0    0     0  ...      0         0        0         0   \n",
       "3     2.75      NaN     0    0     0  ...      0         0        0         0   \n",
       "4     3.75      NaN     0    0     0  ...      0         0        0         0   \n",
       "5     4.75      NaN     1    0     0  ...      0         0        0         0   \n",
       "6        4      NaN     1    0     0  ...      0         0        0         0   \n",
       "7     3.75      NaN     0    0     0  ...      0         0        0         0   \n",
       "8     0.25      NaN     0    0     0  ...      0         0        0         0   \n",
       "9      2.5      NaN     0    0     0  ...      0         0        0         0   \n",
       "10       5      NaN     1    0     0  ...      0         0        0         0   \n",
       "11       5      NaN     1    0     0  ...      0         1        0         0   \n",
       "12    4.25      NaN     1    0     0  ...      0         0        0         0   \n",
       "13     4.5      NaN     1    0     1  ...      0         0        0         0   \n",
       "14       5      NaN     1    0     0  ...      0         0        0         0   \n",
       "15     3.5      NaN     0    0     0  ...      0         0        0         0   \n",
       "16    3.75      NaN     0    0     0  ...      0         0        0         0   \n",
       "17       5      NaN     1    0     0  ...      0         1        0         0   \n",
       "18       4      NaN     1    0     0  ...      0         0        0         0   \n",
       "19       4      NaN     1    0     0  ...      0         0        0         0   \n",
       "20    4.25      NaN     1    0     0  ...      0         0        0         0   \n",
       "21       5      NaN     1    0     0  ...      0         0        0         0   \n",
       "22       5      NaN     1    0     0  ...      0         0        0         0   \n",
       "23    4.75      NaN     1    0     0  ...      0         1        0         0   \n",
       "24    4.25      NaN     1    0     0  ...      0         0        0         0   \n",
       "25    4.25      NaN     1    0     1  ...      0         0        0         0   \n",
       "26     3.5      NaN     0    0     0  ...      0         0        0         0   \n",
       "27    4.75      NaN     1    0     1  ...      0         0        0         0   \n",
       "28       5      NaN     1    0     1  ...      0         0        0         0   \n",
       "29       4      NaN     1    0     0  ...      0         0        0         0   \n",
       "...    ...      ...   ...  ...   ...  ...    ...       ...      ...       ...   \n",
       "2550     1      NaN     0    0     0  ...      0         0        0         0   \n",
       "2551   3.5      NaN     0    0     1  ...      0         0        0         0   \n",
       "2552     5      NaN     1    0     0  ...      0         0        0         0   \n",
       "2553     4      NaN     1    0     0  ...      0         0        0         0   \n",
       "2554     4      NaN     1    0     0  ...      0         0        0         0   \n",
       "2555     3      NaN     0    0     0  ...      0         0        0         0   \n",
       "2556     2      NaN     0    0     0  ...      0         0        0         0   \n",
       "2557   4.5      NaN     1    0     0  ...      0         0        0         0   \n",
       "2558  3.75      NaN     0    0     0  ...      0         0        0         0   \n",
       "2559     4      NaN     1    0     0  ...      0         0        0         0   \n",
       "2560   3.5      NaN     0    0     0  ...      0         0        0         0   \n",
       "2561     4      NaN     1    0     0  ...      0         0        0         0   \n",
       "2562   3.5      NaN     0    0     0  ...      0         0        0         0   \n",
       "2563   2.5      NaN     0    0     0  ...      0         0        0         0   \n",
       "2564   3.5      NaN     0    0     0  ...      0         0        0         0   \n",
       "2565   2.5      NaN     0    0     0  ...      0         0        0         0   \n",
       "2566   3.5      NaN     0    0     0  ...      0         0        0         0   \n",
       "2567     5  2012 #4     1    0     0  ...      0         0        0         0   \n",
       "2568   2.5      NaN     0    0     0  ...      0         0        0         0   \n",
       "2569     2      NaN     0    0     0  ...      0         0        0         0   \n",
       "2570   1.5      NaN     0    0     0  ...      0         0        0         0   \n",
       "2571     2      NaN     0    0     0  ...      0         0        0         0   \n",
       "2572     3      NaN     0    0     0  ...      0         0        0         0   \n",
       "2573     1      NaN     0    0     0  ...      0         0        0         0   \n",
       "2574   2.5      NaN     0    0     0  ...      0         0        0         0   \n",
       "2575   3.5      NaN     0    0     1  ...      0         0        0         0   \n",
       "2576     1      NaN     0    0     0  ...      0         0        0         0   \n",
       "2577     2      NaN     0    0     0  ...      0         0        0         0   \n",
       "2578     2      NaN     0    0     0  ...      0         0        0         0   \n",
       "2579   0.5      NaN     0    0     0  ...      0         0        0         0   \n",
       "\n",
       "      Yamamoto  Yum Yum  Yum-Mie  Zow Zow  iMee  iNoodle  \n",
       "0            0        0        0        0     0        0  \n",
       "1            0        0        0        0     0        0  \n",
       "2            0        0        0        0     0        0  \n",
       "3            0        0        0        0     0        0  \n",
       "4            0        0        0        0     0        0  \n",
       "5            0        0        0        0     0        0  \n",
       "6            0        0        0        0     0        0  \n",
       "7            0        0        0        0     0        0  \n",
       "8            0        0        0        0     0        0  \n",
       "9            0        0        0        0     0        0  \n",
       "10           0        0        0        0     0        0  \n",
       "11           0        0        0        0     0        0  \n",
       "12           0        0        0        0     0        0  \n",
       "13           0        0        0        0     0        0  \n",
       "14           0        0        0        0     0        0  \n",
       "15           0        0        0        0     0        0  \n",
       "16           0        0        0        0     0        0  \n",
       "17           0        0        0        0     0        0  \n",
       "18           0        0        0        0     0        0  \n",
       "19           0        0        0        0     0        0  \n",
       "20           0        0        0        0     0        0  \n",
       "21           0        0        0        0     0        0  \n",
       "22           0        0        0        0     0        0  \n",
       "23           0        0        0        0     0        0  \n",
       "24           0        0        0        0     0        0  \n",
       "25           0        0        0        0     0        0  \n",
       "26           0        0        1        0     0        0  \n",
       "27           0        0        0        0     0        0  \n",
       "28           0        0        0        0     0        0  \n",
       "29           0        0        0        0     0        0  \n",
       "...        ...      ...      ...      ...   ...      ...  \n",
       "2550         0        0        0        0     0        0  \n",
       "2551         0        0        0        0     0        0  \n",
       "2552         0        0        0        0     0        0  \n",
       "2553         0        0        0        0     0        0  \n",
       "2554         0        0        0        0     0        0  \n",
       "2555         0        0        0        0     0        0  \n",
       "2556         0        0        0        0     0        0  \n",
       "2557         0        0        0        0     0        0  \n",
       "2558         0        0        0        0     0        0  \n",
       "2559         0        0        0        0     0        0  \n",
       "2560         0        0        0        0     0        0  \n",
       "2561         0        0        0        0     0        0  \n",
       "2562         0        0        0        0     0        0  \n",
       "2563         0        0        0        0     0        0  \n",
       "2564         0        0        0        0     0        0  \n",
       "2565         0        0        0        0     0        0  \n",
       "2566         0        0        0        0     0        0  \n",
       "2567         0        0        0        0     0        0  \n",
       "2568         0        0        0        0     0        0  \n",
       "2569         0        0        0        0     0        0  \n",
       "2570         0        0        0        0     0        0  \n",
       "2571         0        0        0        0     0        0  \n",
       "2572         0        0        0        0     0        0  \n",
       "2573         0        0        0        0     0        0  \n",
       "2574         0        0        0        0     0        0  \n",
       "2575         0        0        0        0     0        0  \n",
       "2576         0        0        0        0     0        0  \n",
       "2577         0        0        0        0     0        0  \n",
       "2578         0        0        0        0     0        0  \n",
       "2579         0        0        0        0     0        0  \n",
       "\n",
       "[2577 rows x 408 columns]"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#add binary variables for brand\n",
    "ramen_df4 = pd.concat([ramen_df3, pd.get_dummies(ramen_df3['Brand'])], axis=1); ramen_df3\n",
    "ramen_df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Review #</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Variety</th>\n",
       "      <th>Style</th>\n",
       "      <th>Country</th>\n",
       "      <th>Stars</th>\n",
       "      <th>Top Ten</th>\n",
       "      <th>good</th>\n",
       "      <th>Bar</th>\n",
       "      <th>Bowl</th>\n",
       "      <th>...</th>\n",
       "      <th>Xiuhe</th>\n",
       "      <th>Yamachan</th>\n",
       "      <th>Yamadai</th>\n",
       "      <th>Yamamori</th>\n",
       "      <th>Yamamoto</th>\n",
       "      <th>Yum Yum</th>\n",
       "      <th>Yum-Mie</th>\n",
       "      <th>Zow Zow</th>\n",
       "      <th>iMee</th>\n",
       "      <th>iNoodle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2580</td>\n",
       "      <td>New Touch</td>\n",
       "      <td>T's Restaurant Tantanmen</td>\n",
       "      <td>Cup</td>\n",
       "      <td>Japan</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2579</td>\n",
       "      <td>Just Way</td>\n",
       "      <td>Noodles Spicy Hot Sesame Spicy Hot Sesame Guan...</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2578</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Cup Noodles Chicken Vegetable</td>\n",
       "      <td>Cup</td>\n",
       "      <td>USA</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2577</td>\n",
       "      <td>Wei Lih</td>\n",
       "      <td>GGE Ramen Snack Tomato Flavor</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>2.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2576</td>\n",
       "      <td>Ching's Secret</td>\n",
       "      <td>Singapore Curry</td>\n",
       "      <td>Pack</td>\n",
       "      <td>India</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2575</td>\n",
       "      <td>Samyang Foods</td>\n",
       "      <td>Kimchi song Song Ramen</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>4.75</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2574</td>\n",
       "      <td>Acecook</td>\n",
       "      <td>Spice Deli Tantan Men With Cilantro</td>\n",
       "      <td>Cup</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2573</td>\n",
       "      <td>Ikeda Shoku</td>\n",
       "      <td>Nabeyaki Kitsune Udon</td>\n",
       "      <td>Tray</td>\n",
       "      <td>Japan</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2572</td>\n",
       "      <td>Ripe'n'Dry</td>\n",
       "      <td>Hokkaido Soy Sauce Ramen</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2571</td>\n",
       "      <td>KOKA</td>\n",
       "      <td>The Original Spicy Stir-Fried Noodles</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2570</td>\n",
       "      <td>Tao Kae Noi</td>\n",
       "      <td>Creamy tom Yum Kung Flavour</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2569</td>\n",
       "      <td>Yamachan</td>\n",
       "      <td>Yokohama Tonkotsu Shoyu</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2568</td>\n",
       "      <td>Nongshim</td>\n",
       "      <td>Mr. Bibim Stir-Fried Kimchi Flavor</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>4.25</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2567</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Deka Buto Kimchi Pork Flavor</td>\n",
       "      <td>Bowl</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2566</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Demae Ramen Bar Noodle Aka Tonkotsu Flavour In...</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2565</td>\n",
       "      <td>KOKA</td>\n",
       "      <td>Mushroom Flavour Instant Noodles</td>\n",
       "      <td>Cup</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2564</td>\n",
       "      <td>TRDP</td>\n",
       "      <td>Mario Masala Noodles</td>\n",
       "      <td>Pack</td>\n",
       "      <td>India</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2563</td>\n",
       "      <td>Yamachan</td>\n",
       "      <td>Tokyo Shoyu Ramen</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2562</td>\n",
       "      <td>Binh Tay</td>\n",
       "      <td>Mi Hai Cua</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2561</td>\n",
       "      <td>Paldo</td>\n",
       "      <td>Premium Gomtang</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2560</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Cup Noodles Laksa Flavour</td>\n",
       "      <td>Cup</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>4.25</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2559</td>\n",
       "      <td>Jackpot Teriyaki</td>\n",
       "      <td>Beef Ramen</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2558</td>\n",
       "      <td>KOKA</td>\n",
       "      <td>Creamy Soup With Crushed Noodles Hot &amp; Sour Fi...</td>\n",
       "      <td>Cup</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2557</td>\n",
       "      <td>Yamachan</td>\n",
       "      <td>Sapporo Miso Ramen</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>4.75</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2556</td>\n",
       "      <td>Ching's Secret</td>\n",
       "      <td>Hot Garlic Instant Noodles</td>\n",
       "      <td>Pack</td>\n",
       "      <td>India</td>\n",
       "      <td>4.25</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2555</td>\n",
       "      <td>Samyang Foods</td>\n",
       "      <td>Song Song Kimchi Big Bowl</td>\n",
       "      <td>Bowl</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>4.25</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2554</td>\n",
       "      <td>Yum-Mie</td>\n",
       "      <td>Instant Noodles Beef In Light Soup Flavour</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Ghana</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2553</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Hakata Ramen Noodle White Tonkotsu</td>\n",
       "      <td>Bowl</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4.75</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2552</td>\n",
       "      <td>MyKuali</td>\n",
       "      <td>Penang White Curry Rice Vermicelli Soup</td>\n",
       "      <td>Bowl</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2551</td>\n",
       "      <td>KOKA</td>\n",
       "      <td>Signature Tom Yum Flavor Instant Noodles</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2550</th>\n",
       "      <td>30</td>\n",
       "      <td>Myojo</td>\n",
       "      <td>Special Duck</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2551</th>\n",
       "      <td>29</td>\n",
       "      <td>Mee Jang</td>\n",
       "      <td>Tom Yum Shrimp</td>\n",
       "      <td>Bowl</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2552</th>\n",
       "      <td>28</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Chikin Ramen</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2553</th>\n",
       "      <td>27</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Demae Ramen Curry Flavor</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2554</th>\n",
       "      <td>26</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Soba Noodles With Mayo Mustard Squirt</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2555</th>\n",
       "      <td>25</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Demae Ramen Spicy Flavor</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2556</th>\n",
       "      <td>24</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Demae Ramen Spicy Seafood With Chili Pepper</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2557</th>\n",
       "      <td>23</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Top Ramen Creamy Chicken</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2558</th>\n",
       "      <td>22</td>\n",
       "      <td>Nongshim</td>\n",
       "      <td>Ansungtangmyun Noodle Soup</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2559</th>\n",
       "      <td>21</td>\n",
       "      <td>Nongshim</td>\n",
       "      <td>Champong Oriental Noodles</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2560</th>\n",
       "      <td>20</td>\n",
       "      <td>Nongshim</td>\n",
       "      <td>Neoguri (Seafood'n'Spicy)</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2561</th>\n",
       "      <td>19</td>\n",
       "      <td>Nongshim</td>\n",
       "      <td>Shin Ramyun</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2562</th>\n",
       "      <td>18</td>\n",
       "      <td>Ottogi</td>\n",
       "      <td>Jin Ramen (Hot Taste)</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2563</th>\n",
       "      <td>17</td>\n",
       "      <td>Quickchow</td>\n",
       "      <td>Pancit Palabok</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2564</th>\n",
       "      <td>16</td>\n",
       "      <td>Samyang</td>\n",
       "      <td>Kalgug-Su (Spicy)</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2565</th>\n",
       "      <td>15</td>\n",
       "      <td>Samyang</td>\n",
       "      <td>Pojangmacha U-dong</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2566</th>\n",
       "      <td>14</td>\n",
       "      <td>Samyang</td>\n",
       "      <td>Hot</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2567</th>\n",
       "      <td>13</td>\n",
       "      <td>Sapporo Ichiban</td>\n",
       "      <td>Chow Mein</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>5</td>\n",
       "      <td>2012 #4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2568</th>\n",
       "      <td>12</td>\n",
       "      <td>Sapporo Ichiban</td>\n",
       "      <td>Shrimp Flavor</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2569</th>\n",
       "      <td>11</td>\n",
       "      <td>Six Fortune</td>\n",
       "      <td>Chicken Flavor Instant Soup Noodle</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2570</th>\n",
       "      <td>10</td>\n",
       "      <td>Smack</td>\n",
       "      <td>Vegetable Beef</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2571</th>\n",
       "      <td>9</td>\n",
       "      <td>Sutah</td>\n",
       "      <td>Cup Noodle</td>\n",
       "      <td>Cup</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2572</th>\n",
       "      <td>8</td>\n",
       "      <td>Tung-I</td>\n",
       "      <td>Chinese Beef Instant Rice Noodle</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2573</th>\n",
       "      <td>7</td>\n",
       "      <td>Ve Wong</td>\n",
       "      <td>Mushroom Pork</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2574</th>\n",
       "      <td>6</td>\n",
       "      <td>Vifon</td>\n",
       "      <td>Nam Vang</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2575</th>\n",
       "      <td>5</td>\n",
       "      <td>Vifon</td>\n",
       "      <td>Hu Tiu Nam Vang [\"Phnom Penh\" style] Asian Sty...</td>\n",
       "      <td>Bowl</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2576</th>\n",
       "      <td>4</td>\n",
       "      <td>Wai Wai</td>\n",
       "      <td>Oriental Style Instant Noodles</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2577</th>\n",
       "      <td>3</td>\n",
       "      <td>Wai Wai</td>\n",
       "      <td>Tom Yum Shrimp</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2578</th>\n",
       "      <td>2</td>\n",
       "      <td>Wai Wai</td>\n",
       "      <td>Tom Yum Chili Flavor</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2579</th>\n",
       "      <td>1</td>\n",
       "      <td>Westbrae</td>\n",
       "      <td>Miso Ramen</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2577 rows × 408 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Review #             Brand  \\\n",
       "0         2580         New Touch   \n",
       "1         2579          Just Way   \n",
       "2         2578            Nissin   \n",
       "3         2577           Wei Lih   \n",
       "4         2576    Ching's Secret   \n",
       "5         2575     Samyang Foods   \n",
       "6         2574           Acecook   \n",
       "7         2573       Ikeda Shoku   \n",
       "8         2572        Ripe'n'Dry   \n",
       "9         2571              KOKA   \n",
       "10        2570       Tao Kae Noi   \n",
       "11        2569          Yamachan   \n",
       "12        2568          Nongshim   \n",
       "13        2567            Nissin   \n",
       "14        2566            Nissin   \n",
       "15        2565              KOKA   \n",
       "16        2564              TRDP   \n",
       "17        2563          Yamachan   \n",
       "18        2562          Binh Tay   \n",
       "19        2561             Paldo   \n",
       "20        2560            Nissin   \n",
       "21        2559  Jackpot Teriyaki   \n",
       "22        2558              KOKA   \n",
       "23        2557          Yamachan   \n",
       "24        2556    Ching's Secret   \n",
       "25        2555     Samyang Foods   \n",
       "26        2554           Yum-Mie   \n",
       "27        2553            Nissin   \n",
       "28        2552           MyKuali   \n",
       "29        2551              KOKA   \n",
       "...        ...               ...   \n",
       "2550        30             Myojo   \n",
       "2551        29          Mee Jang   \n",
       "2552        28            Nissin   \n",
       "2553        27            Nissin   \n",
       "2554        26            Nissin   \n",
       "2555        25            Nissin   \n",
       "2556        24            Nissin   \n",
       "2557        23            Nissin   \n",
       "2558        22          Nongshim   \n",
       "2559        21          Nongshim   \n",
       "2560        20          Nongshim   \n",
       "2561        19          Nongshim   \n",
       "2562        18            Ottogi   \n",
       "2563        17         Quickchow   \n",
       "2564        16           Samyang   \n",
       "2565        15           Samyang   \n",
       "2566        14           Samyang   \n",
       "2567        13   Sapporo Ichiban   \n",
       "2568        12   Sapporo Ichiban   \n",
       "2569        11       Six Fortune   \n",
       "2570        10             Smack   \n",
       "2571         9             Sutah   \n",
       "2572         8            Tung-I   \n",
       "2573         7           Ve Wong   \n",
       "2574         6             Vifon   \n",
       "2575         5             Vifon   \n",
       "2576         4           Wai Wai   \n",
       "2577         3           Wai Wai   \n",
       "2578         2           Wai Wai   \n",
       "2579         1          Westbrae   \n",
       "\n",
       "                                                Variety Style      Country  \\\n",
       "0                             T's Restaurant Tantanmen    Cup        Japan   \n",
       "1     Noodles Spicy Hot Sesame Spicy Hot Sesame Guan...  Pack       Taiwan   \n",
       "2                         Cup Noodles Chicken Vegetable   Cup          USA   \n",
       "3                         GGE Ramen Snack Tomato Flavor  Pack       Taiwan   \n",
       "4                                       Singapore Curry  Pack        India   \n",
       "5                                Kimchi song Song Ramen  Pack  South Korea   \n",
       "6                   Spice Deli Tantan Men With Cilantro   Cup        Japan   \n",
       "7                                 Nabeyaki Kitsune Udon  Tray        Japan   \n",
       "8                              Hokkaido Soy Sauce Ramen  Pack        Japan   \n",
       "9                 The Original Spicy Stir-Fried Noodles  Pack    Singapore   \n",
       "10                          Creamy tom Yum Kung Flavour  Pack     Thailand   \n",
       "11                              Yokohama Tonkotsu Shoyu  Pack          USA   \n",
       "12                   Mr. Bibim Stir-Fried Kimchi Flavor  Pack  South Korea   \n",
       "13                         Deka Buto Kimchi Pork Flavor  Bowl        Japan   \n",
       "14    Demae Ramen Bar Noodle Aka Tonkotsu Flavour In...  Pack    Hong Kong   \n",
       "15                     Mushroom Flavour Instant Noodles   Cup    Singapore   \n",
       "16                                 Mario Masala Noodles  Pack        India   \n",
       "17                                    Tokyo Shoyu Ramen  Pack          USA   \n",
       "18                                           Mi Hai Cua  Pack      Vietnam   \n",
       "19                                      Premium Gomtang  Pack  South Korea   \n",
       "20                            Cup Noodles Laksa Flavour   Cup    Hong Kong   \n",
       "21                                           Beef Ramen  Pack          USA   \n",
       "22    Creamy Soup With Crushed Noodles Hot & Sour Fi...   Cup    Singapore   \n",
       "23                                   Sapporo Miso Ramen  Pack          USA   \n",
       "24                           Hot Garlic Instant Noodles  Pack        India   \n",
       "25                            Song Song Kimchi Big Bowl  Bowl  South Korea   \n",
       "26           Instant Noodles Beef In Light Soup Flavour  Pack        Ghana   \n",
       "27                   Hakata Ramen Noodle White Tonkotsu  Bowl        Japan   \n",
       "28              Penang White Curry Rice Vermicelli Soup  Bowl     Malaysia   \n",
       "29             Signature Tom Yum Flavor Instant Noodles  Pack    Singapore   \n",
       "...                                                 ...   ...          ...   \n",
       "2550                                       Special Duck  Pack        Japan   \n",
       "2551                                     Tom Yum Shrimp  Bowl     Thailand   \n",
       "2552                                       Chikin Ramen  Pack        Japan   \n",
       "2553                           Demae Ramen Curry Flavor  Pack        Japan   \n",
       "2554              Soba Noodles With Mayo Mustard Squirt  Pack        Japan   \n",
       "2555                           Demae Ramen Spicy Flavor  Pack        Japan   \n",
       "2556        Demae Ramen Spicy Seafood With Chili Pepper  Pack        Japan   \n",
       "2557                           Top Ramen Creamy Chicken  Pack          USA   \n",
       "2558                         Ansungtangmyun Noodle Soup  Pack  South Korea   \n",
       "2559                          Champong Oriental Noodles  Pack  South Korea   \n",
       "2560                          Neoguri (Seafood'n'Spicy)  Pack  South Korea   \n",
       "2561                                        Shin Ramyun  Pack  South Korea   \n",
       "2562                              Jin Ramen (Hot Taste)  Pack  South Korea   \n",
       "2563                                     Pancit Palabok  Pack  Philippines   \n",
       "2564                                  Kalgug-Su (Spicy)  Pack  South Korea   \n",
       "2565                                 Pojangmacha U-dong  Pack  South Korea   \n",
       "2566                                                Hot  Pack  South Korea   \n",
       "2567                                          Chow Mein  Pack        Japan   \n",
       "2568                                      Shrimp Flavor  Pack        Japan   \n",
       "2569                 Chicken Flavor Instant Soup Noodle  Pack  South Korea   \n",
       "2570                                     Vegetable Beef  Pack          USA   \n",
       "2571                                         Cup Noodle   Cup  South Korea   \n",
       "2572                   Chinese Beef Instant Rice Noodle  Pack       Taiwan   \n",
       "2573                                      Mushroom Pork  Pack      Vietnam   \n",
       "2574                                           Nam Vang  Pack      Vietnam   \n",
       "2575  Hu Tiu Nam Vang [\"Phnom Penh\" style] Asian Sty...  Bowl      Vietnam   \n",
       "2576                     Oriental Style Instant Noodles  Pack     Thailand   \n",
       "2577                                     Tom Yum Shrimp  Pack     Thailand   \n",
       "2578                               Tom Yum Chili Flavor  Pack     Thailand   \n",
       "2579                                         Miso Ramen  Pack          USA   \n",
       "\n",
       "     Stars  Top Ten  good  Bar  Bowl  ...  Xiuhe  Yamachan  Yamadai  Yamamori  \\\n",
       "0     3.75        0     0    0     0  ...      0         0        0         0   \n",
       "1        1        0     0    0     0  ...      0         0        0         0   \n",
       "2     2.25        0     0    0     0  ...      0         0        0         0   \n",
       "3     2.75        0     0    0     0  ...      0         0        0         0   \n",
       "4     3.75        0     0    0     0  ...      0         0        0         0   \n",
       "5     4.75        0     1    0     0  ...      0         0        0         0   \n",
       "6        4        0     1    0     0  ...      0         0        0         0   \n",
       "7     3.75        0     0    0     0  ...      0         0        0         0   \n",
       "8     0.25        0     0    0     0  ...      0         0        0         0   \n",
       "9      2.5        0     0    0     0  ...      0         0        0         0   \n",
       "10       5        0     1    0     0  ...      0         0        0         0   \n",
       "11       5        0     1    0     0  ...      0         1        0         0   \n",
       "12    4.25        0     1    0     0  ...      0         0        0         0   \n",
       "13     4.5        0     1    0     1  ...      0         0        0         0   \n",
       "14       5        0     1    0     0  ...      0         0        0         0   \n",
       "15     3.5        0     0    0     0  ...      0         0        0         0   \n",
       "16    3.75        0     0    0     0  ...      0         0        0         0   \n",
       "17       5        0     1    0     0  ...      0         1        0         0   \n",
       "18       4        0     1    0     0  ...      0         0        0         0   \n",
       "19       4        0     1    0     0  ...      0         0        0         0   \n",
       "20    4.25        0     1    0     0  ...      0         0        0         0   \n",
       "21       5        0     1    0     0  ...      0         0        0         0   \n",
       "22       5        0     1    0     0  ...      0         0        0         0   \n",
       "23    4.75        0     1    0     0  ...      0         1        0         0   \n",
       "24    4.25        0     1    0     0  ...      0         0        0         0   \n",
       "25    4.25        0     1    0     1  ...      0         0        0         0   \n",
       "26     3.5        0     0    0     0  ...      0         0        0         0   \n",
       "27    4.75        0     1    0     1  ...      0         0        0         0   \n",
       "28       5        0     1    0     1  ...      0         0        0         0   \n",
       "29       4        0     1    0     0  ...      0         0        0         0   \n",
       "...    ...      ...   ...  ...   ...  ...    ...       ...      ...       ...   \n",
       "2550     1        0     0    0     0  ...      0         0        0         0   \n",
       "2551   3.5        0     0    0     1  ...      0         0        0         0   \n",
       "2552     5        0     1    0     0  ...      0         0        0         0   \n",
       "2553     4        0     1    0     0  ...      0         0        0         0   \n",
       "2554     4        0     1    0     0  ...      0         0        0         0   \n",
       "2555     3        0     0    0     0  ...      0         0        0         0   \n",
       "2556     2        0     0    0     0  ...      0         0        0         0   \n",
       "2557   4.5        0     1    0     0  ...      0         0        0         0   \n",
       "2558  3.75        0     0    0     0  ...      0         0        0         0   \n",
       "2559     4        0     1    0     0  ...      0         0        0         0   \n",
       "2560   3.5        0     0    0     0  ...      0         0        0         0   \n",
       "2561     4        0     1    0     0  ...      0         0        0         0   \n",
       "2562   3.5        0     0    0     0  ...      0         0        0         0   \n",
       "2563   2.5        0     0    0     0  ...      0         0        0         0   \n",
       "2564   3.5        0     0    0     0  ...      0         0        0         0   \n",
       "2565   2.5        0     0    0     0  ...      0         0        0         0   \n",
       "2566   3.5        0     0    0     0  ...      0         0        0         0   \n",
       "2567     5  2012 #4     1    0     0  ...      0         0        0         0   \n",
       "2568   2.5        0     0    0     0  ...      0         0        0         0   \n",
       "2569     2        0     0    0     0  ...      0         0        0         0   \n",
       "2570   1.5        0     0    0     0  ...      0         0        0         0   \n",
       "2571     2        0     0    0     0  ...      0         0        0         0   \n",
       "2572     3        0     0    0     0  ...      0         0        0         0   \n",
       "2573     1        0     0    0     0  ...      0         0        0         0   \n",
       "2574   2.5        0     0    0     0  ...      0         0        0         0   \n",
       "2575   3.5        0     0    0     1  ...      0         0        0         0   \n",
       "2576     1        0     0    0     0  ...      0         0        0         0   \n",
       "2577     2        0     0    0     0  ...      0         0        0         0   \n",
       "2578     2        0     0    0     0  ...      0         0        0         0   \n",
       "2579   0.5        0     0    0     0  ...      0         0        0         0   \n",
       "\n",
       "      Yamamoto  Yum Yum  Yum-Mie  Zow Zow  iMee  iNoodle  \n",
       "0            0        0        0        0     0        0  \n",
       "1            0        0        0        0     0        0  \n",
       "2            0        0        0        0     0        0  \n",
       "3            0        0        0        0     0        0  \n",
       "4            0        0        0        0     0        0  \n",
       "5            0        0        0        0     0        0  \n",
       "6            0        0        0        0     0        0  \n",
       "7            0        0        0        0     0        0  \n",
       "8            0        0        0        0     0        0  \n",
       "9            0        0        0        0     0        0  \n",
       "10           0        0        0        0     0        0  \n",
       "11           0        0        0        0     0        0  \n",
       "12           0        0        0        0     0        0  \n",
       "13           0        0        0        0     0        0  \n",
       "14           0        0        0        0     0        0  \n",
       "15           0        0        0        0     0        0  \n",
       "16           0        0        0        0     0        0  \n",
       "17           0        0        0        0     0        0  \n",
       "18           0        0        0        0     0        0  \n",
       "19           0        0        0        0     0        0  \n",
       "20           0        0        0        0     0        0  \n",
       "21           0        0        0        0     0        0  \n",
       "22           0        0        0        0     0        0  \n",
       "23           0        0        0        0     0        0  \n",
       "24           0        0        0        0     0        0  \n",
       "25           0        0        0        0     0        0  \n",
       "26           0        0        1        0     0        0  \n",
       "27           0        0        0        0     0        0  \n",
       "28           0        0        0        0     0        0  \n",
       "29           0        0        0        0     0        0  \n",
       "...        ...      ...      ...      ...   ...      ...  \n",
       "2550         0        0        0        0     0        0  \n",
       "2551         0        0        0        0     0        0  \n",
       "2552         0        0        0        0     0        0  \n",
       "2553         0        0        0        0     0        0  \n",
       "2554         0        0        0        0     0        0  \n",
       "2555         0        0        0        0     0        0  \n",
       "2556         0        0        0        0     0        0  \n",
       "2557         0        0        0        0     0        0  \n",
       "2558         0        0        0        0     0        0  \n",
       "2559         0        0        0        0     0        0  \n",
       "2560         0        0        0        0     0        0  \n",
       "2561         0        0        0        0     0        0  \n",
       "2562         0        0        0        0     0        0  \n",
       "2563         0        0        0        0     0        0  \n",
       "2564         0        0        0        0     0        0  \n",
       "2565         0        0        0        0     0        0  \n",
       "2566         0        0        0        0     0        0  \n",
       "2567         0        0        0        0     0        0  \n",
       "2568         0        0        0        0     0        0  \n",
       "2569         0        0        0        0     0        0  \n",
       "2570         0        0        0        0     0        0  \n",
       "2571         0        0        0        0     0        0  \n",
       "2572         0        0        0        0     0        0  \n",
       "2573         0        0        0        0     0        0  \n",
       "2574         0        0        0        0     0        0  \n",
       "2575         0        0        0        0     0        0  \n",
       "2576         0        0        0        0     0        0  \n",
       "2577         0        0        0        0     0        0  \n",
       "2578         0        0        0        0     0        0  \n",
       "2579         0        0        0        0     0        0  \n",
       "\n",
       "[2577 rows x 408 columns]"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ramen_df4=ramen_df4.fillna(0)\n",
    "ramen_df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "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 #</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Variety</th>\n",
       "      <th>Style</th>\n",
       "      <th>Country</th>\n",
       "      <th>Stars</th>\n",
       "      <th>Top Ten</th>\n",
       "      <th>good</th>\n",
       "      <th>Bar</th>\n",
       "      <th>Bowl</th>\n",
       "      <th>...</th>\n",
       "      <th>miso</th>\n",
       "      <th>fried</th>\n",
       "      <th>artificial</th>\n",
       "      <th>vegetarian</th>\n",
       "      <th>pork</th>\n",
       "      <th>chicken</th>\n",
       "      <th>beef</th>\n",
       "      <th>big</th>\n",
       "      <th>cream</th>\n",
       "      <th>diff_noodle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2580</td>\n",
       "      <td>New Touch</td>\n",
       "      <td>T's Restaurant Tantanmen</td>\n",
       "      <td>Cup</td>\n",
       "      <td>Japan</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2579</td>\n",
       "      <td>Just Way</td>\n",
       "      <td>Noodles Spicy Hot Sesame Spicy Hot Sesame Guan...</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2578</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Cup Noodles Chicken Vegetable</td>\n",
       "      <td>Cup</td>\n",
       "      <td>USA</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2577</td>\n",
       "      <td>Wei Lih</td>\n",
       "      <td>GGE Ramen Snack Tomato Flavor</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>2.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2576</td>\n",
       "      <td>Ching's Secret</td>\n",
       "      <td>Singapore Curry</td>\n",
       "      <td>Pack</td>\n",
       "      <td>India</td>\n",
       "      <td>3.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 423 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Review #           Brand  \\\n",
       "0      2580       New Touch   \n",
       "1      2579        Just Way   \n",
       "2      2578          Nissin   \n",
       "3      2577         Wei Lih   \n",
       "4      2576  Ching's Secret   \n",
       "\n",
       "                                             Variety Style Country Stars  \\\n",
       "0                          T's Restaurant Tantanmen    Cup   Japan  3.75   \n",
       "1  Noodles Spicy Hot Sesame Spicy Hot Sesame Guan...  Pack  Taiwan     1   \n",
       "2                      Cup Noodles Chicken Vegetable   Cup     USA  2.25   \n",
       "3                      GGE Ramen Snack Tomato Flavor  Pack  Taiwan  2.75   \n",
       "4                                    Singapore Curry  Pack   India  3.75   \n",
       "\n",
       "  Top Ten  good  Bar  Bowl  ...  miso  fried  artificial  vegetarian  pork  \\\n",
       "0       0     0    0     0  ...     0      0           0           0     0   \n",
       "1       0     0    0     0  ...     0      0           0           0     0   \n",
       "2       0     0    0     0  ...     0      0           0           0     0   \n",
       "3       0     0    0     0  ...     0      0           0           0     0   \n",
       "4       0     0    0     0  ...     0      0           0           0     0   \n",
       "\n",
       "   chicken  beef  big  cream  diff_noodle  \n",
       "0        0     0    0      0            0  \n",
       "1        0     0    0      0            0  \n",
       "2        1     0    0      0            0  \n",
       "3        0     0    0      0            0  \n",
       "4        0     0    0      0            0  \n",
       "\n",
       "[5 rows x 423 columns]"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#more feature engineering\n",
    "#in addition to creating binary variables of country and type, I suspect length of brand name and variety might have an effect on rating\n",
    "#the intuition is that the longer the name, the harder it is to remember\n",
    "\n",
    "ramen_df4['brand_length']  = ramen_df4['Brand'].str.len()\n",
    "ramen_df4['style_length']  = ramen_df4['Variety'].str.len()\n",
    "ramen_df4['spicy']= ramen_df4['Variety'].copy()\n",
    "ramen_df4['spicy'] = ramen_df4['spicy'].astype(str)\n",
    "\n",
    "def spicy_replace(x):\n",
    "    if ('Spicy' in x or 'hot' in x or 'Kimchi' in x or 'Hot' in x or 'Tom Yum' in x or 'Curry' in x or 'Chili' in x or 'Tantanmen' in x or 'Pedas' in x or 'Laksa' in x):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "ramen_df4['spicy'] = ramen_df4['spicy'].apply(spicy_replace)\n",
    "\n",
    "ramen_df4['premium']= ramen_df4['Variety'].copy()\n",
    "def premium_replace(x):\n",
    "    if ('Premium' in x or 'Special' in x or 'Signature' in x or 'Supreme' in x):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "ramen_df4['premium'] = ramen_df4['premium'].apply(premium_replace)\n",
    "\n",
    "ramen_df4['seafood']= ramen_df4['Variety'].copy()\n",
    "def seafood_replace(x):\n",
    "    if ('Seafood' in x or 'Shrimp' in x or 'Prawn' in x or 'Crab' in x or 'Palabok' in x):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "ramen_df4['seafood'] = ramen_df4['seafood'].apply(seafood_replace)\n",
    "\n",
    "ramen_df4['miso']= ramen_df4['Variety'].copy()\n",
    "def miso_replace(x):\n",
    "    if ('Miso' in x):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "ramen_df4['miso'] = ramen_df4['miso'].apply(miso_replace)\n",
    "\n",
    "#fried\n",
    "ramen_df4['fried']= ramen_df4['Variety'].copy()\n",
    "def fried_replace(x):\n",
    "    if ('Goreng' in x or 'Yakisoba' in x or 'Stir Fry' in x or 'Stir Fried' in x or 'Dry' in x or 'Chow Mein' in x):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "ramen_df4['fried'] = ramen_df4['fried'].apply(fried_replace)\n",
    "\n",
    "#artificial\n",
    "ramen_df4['artificial']= ramen_df4['Variety'].copy()\n",
    "def artificial_replace(x):\n",
    "    if ('Artificial' in x):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "ramen_df4['artificial'] = ramen_df4['artificial'].apply(artificial_replace)\n",
    "\n",
    "#vegetarian\n",
    "ramen_df4['vegetarian']= ramen_df4['Variety'].copy()\n",
    "def veg_replace(x):\n",
    "    if ('Vegetarian' in x or 'Imitation' in x):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "ramen_df4['vegetarian'] = ramen_df4['vegetarian'].apply(veg_replace)\n",
    "\n",
    "#pork\n",
    "ramen_df4['pork']= ramen_df4['Variety'].copy()\n",
    "def pork_replace(x):\n",
    "    if ('Pork' in x or 'Buta' in x or 'Tonkotsu' in x):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "ramen_df4['pork'] = ramen_df4['pork'].apply(pork_replace)\n",
    "\n",
    "#chicken\n",
    "ramen_df4['chicken']= ramen_df4['Variety'].copy()\n",
    "def chicken_replace(x):\n",
    "    if ('Chicken' in x or 'Ayam' in x):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "ramen_df4['chicken'] = ramen_df4['chicken'].apply(chicken_replace)\n",
    "\n",
    "#beef\n",
    "ramen_df4['beef']= ramen_df4['Variety'].copy()\n",
    "def beef_replace(x):\n",
    "    if ('Beef' in x or 'Gyu' in x):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "ramen_df4['beef'] = ramen_df4['beef'].apply(beef_replace)\n",
    "\n",
    "\n",
    "#big or hearty\n",
    "ramen_df4['big']= ramen_df4['Variety'].copy()\n",
    "def big_replace(x):\n",
    "    if ('Big' in x or 'Hearty' in x):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "ramen_df4['big'] = ramen_df4['big'].apply(big_replace)\n",
    "\n",
    "#cream\n",
    "ramen_df4['cream']= ramen_df4['Variety'].copy()\n",
    "def cream_replace(x):\n",
    "    if ('Cream' in x or 'Creamy' in x or 'Carbonara' in x):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "ramen_df4['cream'] = ramen_df4['cream'].apply(cream_replace)\n",
    "\n",
    "#diff_noodle\n",
    "ramen_df4['diff_noodle']= ramen_df4['Variety'].copy()\n",
    "def noodle_replace(x):\n",
    "    if ('Udon' in x or 'Soba' in x or 'Vermicelli ' in x):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "ramen_df4['diff_noodle'] = ramen_df4['diff_noodle'].apply(noodle_replace)\n",
    "ramen_df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data cleaning\n",
    "ramen_df4=ramen_df4[~ramen_df4.Stars.str.contains(\"Unrated\")]\n",
    "ramen_df4=ramen_df4.drop(['Top Ten'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a25cd1be0>"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#I want to know if the stars follow a normal distribution\n",
    "plt.figure(figsize=(16,10))\n",
    "ramen_df4=ramen_df4[~ramen_df4.Stars.str.contains(\"Unrated\")]\n",
    "ramen_df4['Stars'] = ramen_df4['Stars'].astype(float)\n",
    "sns.distplot(ramen_df4['Stars'])\n",
    "#okay guess not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spiciness\n",
      "non-spicy    3.298309\n",
      "spicy        3.500000\n",
      "Name: Stars, dtype: float64\n",
      "Spiciness\n",
      "non-spicy    3\n",
      "spicy        3\n",
      "Name: Stars, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#to create spiciness pivot table\n",
    "ramen_df5=ramen_df4\n",
    "ramen_df5['Spiciness']= ramen_df4['spicy'].copy()\n",
    "def sp_replace(x):\n",
    "    if x == 1:\n",
    "        return 'spicy'\n",
    "    else:\n",
    "        return 'non-spicy'\n",
    "ramen_df5['Spiciness'] = ramen_df4['Spiciness'].apply(sp_replace)\n",
    "\n",
    "classtype_gb = ramen_df5.groupby('Spiciness')\n",
    "classtype_gb\n",
    "print(classtype_gb['Stars'].mean())\n",
    "print(classtype_gb['Stars'].median())\n",
    "#print(classtype_gb['Stars'].mode())\n",
    "#spicy ramen, on average, have a higher rating than non-spicy ramen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['Top Ten'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-306-a77c738b2575>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mramen_df4\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mramen_df4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Top Ten'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mramen_df4\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Stars'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mramen_df4\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Stars'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   3938\u001b[0m                                            \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3939\u001b[0m                                            \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3940\u001b[0;31m                                            errors=errors)\n\u001b[0m\u001b[1;32m   3941\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3942\u001b[0m     @rewrite_axis_style_signature('mapper', [('copy', True),\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   3778\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3779\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3780\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3781\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3782\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[1;32m   3810\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3811\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3812\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3813\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3814\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   4963\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'ignore'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4964\u001b[0m                 raise KeyError(\n\u001b[0;32m-> 4965\u001b[0;31m                     '{} not found in axis'.format(labels[mask]))\n\u001b[0m\u001b[1;32m   4966\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4967\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"['Top Ten'] not found in axis\""
     ]
    }
   ],
   "source": [
    "ramen_df4=ramen_df4.drop(['Top Ten'], axis=1)\n",
    "ramen_df4['Stars'] = ramen_df4['Stars'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['Spiciness'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-307-3bdd04c98d64>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mramen_df4\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mramen_df4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Spiciness'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   3938\u001b[0m                                            \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3939\u001b[0m                                            \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3940\u001b[0;31m                                            errors=errors)\n\u001b[0m\u001b[1;32m   3941\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3942\u001b[0m     @rewrite_axis_style_signature('mapper', [('copy', True),\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   3778\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3779\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3780\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3781\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3782\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[1;32m   3810\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3811\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3812\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3813\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3814\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   4963\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'ignore'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4964\u001b[0m                 raise KeyError(\n\u001b[0;32m-> 4965\u001b[0;31m                     '{} not found in axis'.format(labels[mask]))\n\u001b[0m\u001b[1;32m   4966\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4967\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"['Spiciness'] not found in axis\""
     ]
    }
   ],
   "source": [
    "ramen_df4=ramen_df4.drop(['Spiciness'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [],
   "source": [
    "#attempting regression using style and country alone\n",
    "X = ramen_df4.iloc[:, 6:].values\n",
    "y = ramen_df4.iloc[:, 5].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Unknown label type: (array([3.75, 1.  , 2.25, ..., 2.  , 2.  , 0.5 ]),)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-309-62185a9f44d3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m#apply SelectKBest class to extract top 10 best features\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mbestfeatures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSelectKBest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscore_func\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mchi2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mfit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbestfeatures\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mdfscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mdfcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mramen_df4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/feature_selection/univariate_selection.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    347\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    348\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 349\u001b[0;31m         \u001b[0mscore_func_ret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    350\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscore_func_ret\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    351\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpvalues_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscore_func_ret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/feature_selection/univariate_selection.py\u001b[0m in \u001b[0;36mchi2\u001b[0;34m(X, y)\u001b[0m\n\u001b[1;32m    216\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Input X must be non-negative.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 218\u001b[0;31m     \u001b[0mY\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLabelBinarizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    219\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m         \u001b[0mY\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/label.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, y)\u001b[0m\n\u001b[1;32m    437\u001b[0m             \u001b[0mShape\u001b[0m \u001b[0mwill\u001b[0m \u001b[0mbe\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mbinary\u001b[0m \u001b[0mproblems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m         \"\"\"\n\u001b[0;32m--> 439\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/label.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, y)\u001b[0m\n\u001b[1;32m    414\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    415\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msparse_input_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0missparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 416\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munique_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    417\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    418\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/utils/multiclass.py\u001b[0m in \u001b[0;36munique_labels\u001b[0;34m(*ys)\u001b[0m\n\u001b[1;32m     92\u001b[0m     \u001b[0m_unique_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_FN_UNIQUE_LABELS\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_unique_labels\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 94\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Unknown label type: %s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mrepr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m     \u001b[0mys_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_iterable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_unique_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0my\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Unknown label type: (array([3.75, 1.  , 2.25, ..., 2.  , 2.  , 0.5 ]),)"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "\n",
    "#apply SelectKBest class to extract top 10 best features\n",
    "bestfeatures = SelectKBest(score_func=chi2, k=10)\n",
    "fit = bestfeatures.fit(X,y)\n",
    "dfscores = pd.DataFrame(fit.scores_)\n",
    "dfcolumns = pd.DataFrame(ramen_df4.columns)\n",
    "#concat two dataframes for better visualization \n",
    "featureScores = pd.concat([dfcolumns,dfscores],axis=1)\n",
    "featureScores.columns = ['Specs','Score']  #naming the dataframe columns\n",
    "print(featureScores.nlargest(35,'Score'))  #print 35 best features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "#attempting regression using style and country alone\n",
    "X = ramen_df4.iloc[:, 6:51].values\n",
    "y = ramen_df4.iloc[:, 5].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: 0.391975 (0.084305)\n",
      "LDA: 0.384210 (0.075587)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN: 0.344927 (0.034589)\n",
      "CART: 0.375669 (0.075260)\n",
      "NB: 0.061676 (0.045695)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/Users/kahhow/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM: 0.414110 (0.089854)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Compare Algorithms\n",
    "#found one by Jason Brownlee, you can refer to it at the following link:\n",
    "#https://machinelearningmastery.com/compare-machine-learning-algorithms-python-scikit-learn/\n",
    "plt.figure(figsize=(16,10))\n",
    "import pandas\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import model_selection\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "dataframe = ramen_df4\n",
    "Y = ramen_df4.iloc[:, 5].values# prepare configuration for cross validation test harness\n",
    "seed = 7\n",
    "# prepare models\n",
    "models = []\n",
    "models.append(('LR', LogisticRegression()))\n",
    "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('CART', DecisionTreeClassifier()))\n",
    "models.append(('NB', GaussianNB()))\n",
    "models.append(('SVM', SVC()))\n",
    "# evaluate each model in turn\n",
    "results = []\n",
    "names = []\n",
    "scoring = 'accuracy'\n",
    "for name, model in models:\n",
    "    kfold = model_selection.KFold(n_splits=10, random_state=seed)\n",
    "    cv_results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
    "    print(msg)\n",
    "# boxplot algorithm comparison\n",
    "fig = plt.figure(figsize=(16,10))\n",
    "fig.suptitle('Algorithm Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(results)\n",
    "ax.set_xticklabels(names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "LR: 0.395851 (0.084318)\n",
    "LDA: 0.384210 (0.075587)\n",
    "KNN: 0.346477 (0.034643)\n",
    "CART: 0.376056 (0.075450)\n",
    "NB: 0.061676 (0.045695)\n",
    "SVM: 0.416436 (0.091530)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.8207501777780941\n",
      "Mean Squared Error: 1.1303899581864205\n",
      "Root Mean Squared Error: 1.0631979863536332\n",
      "Out-of-bag R-2 score estimate: 0.108\n",
      "Test data R-2 score: 0.0524\n",
      "Test data Spearman correlation: 0.289\n",
      "Test data Pearson correlation: 0.275\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 3. 3. 1. 4. 4. 5. 3. 3. 3. 3. 5. 5. 5. 3. 3. 3. 3. 3. 3. 3. 3. 4. 3.\n",
      " 4. 3. 4. 3. 3. 3. 3. 3. 5. 3. 4. 5. 3. 3. 3. 5. 3. 4. 3. 3. 3. 3. 3. 5.\n",
      " 3. 3. 3. 3. 5. 3. 5. 5. 3. 3. 3. 4. 2. 2. 3. 4. 4. 3. 5. 4. 5. 3. 4. 4.\n",
      " 4. 5. 4. 5. 5. 2. 4. 3. 4. 2. 3. 5. 4. 4. 4. 4. 4. 4. 2. 3. 3. 3. 4. 4.\n",
      " 2. 4. 3. 5. 5. 4. 4. 4. 3. 1. 4. 0. 3. 4. 3. 3. 0. 4. 3. 3. 3. 4. 5. 3.\n",
      " 5. 4. 3. 2. 3. 2. 4. 4. 4. 1. 3. 4. 2. 5. 4. 3. 3. 2. 3. 3. 3. 5. 3. 3.\n",
      " 4. 3. 3. 5. 4. 5. 5. 3. 3. 3. 5. 5. 2. 2. 3. 4. 4. 3. 5. 5. 3. 5. 2. 1.\n",
      " 4. 4. 2. 2. 3. 4. 3. 3. 3. 3. 2. 4. 1. 3. 5. 3. 1. 3. 5. 3. 3. 2. 3. 4.\n",
      " 4. 5. 4. 3. 4. 4. 3. 3. 5. 5. 1. 3. 3. 2. 1. 1. 4. 4. 4. 1. 3. 3. 2. 4.\n",
      " 5. 3. 5. 4. 4. 2. 3. 5. 3. 4. 3. 4. 4. 2. 4. 3. 3. 3. 3. 3. 4. 3. 4. 4.\n",
      " 3. 3. 2. 2. 2. 5. 3. 3. 3. 3. 3. 3. 2. 0. 3. 3. 5. 4. 4. 4. 3. 4. 4. 5.\n",
      " 4. 3. 4. 3. 4. 3. 5. 3. 3. 3. 5. 5. 3. 3. 3. 4. 3. 2. 2. 4. 3. 2. 4. 4.\n",
      " 3. 1. 3. 3. 3. 3. 5. 3. 2. 3. 4. 4. 2. 2. 2. 3. 4. 3. 3. 3. 3. 4. 4. 4.\n",
      " 5. 1. 4. 4. 3. 0. 3. 3. 4. 2. 3. 3. 3. 4. 4. 4. 3. 2. 5. 3. 3. 5. 5. 4.\n",
      " 1. 2. 2. 5. 4. 3. 5. 4. 5. 4. 4. 5. 1. 3. 3. 4. 4. 5. 2. 2. 4. 4. 5. 4.\n",
      " 4. 5. 3. 4. 4. 4. 3. 4. 4. 0. 3. 5. 5. 4. 4. 4. 4. 1. 3. 1. 3. 3. 1. 3.\n",
      " 5. 3. 3. 5. 4. 3. 2. 5. 4. 4. 3. 2. 4. 4. 3. 3. 4. 2. 5. 2. 3. 1. 1. 3.\n",
      " 3. 1. 5. 4. 3. 2. 4. 3. 4. 4. 3. 3. 3. 4. 2. 4. 2. 4. 3. 3. 5. 3. 4. 3.\n",
      " 4. 3. 4. 5. 2. 5. 3. 3. 3. 4. 3. 4. 3. 4. 3. 3. 4. 3. 3. 3. 4. 3. 3. 2.\n",
      " 5. 4. 3. 3. 3. 3. 5. 2. 2. 3. 2. 4. 4. 1. 5. 4. 3. 3. 0. 4. 3. 4. 3. 5.\n",
      " 3. 3. 4. 5. 4. 4. 0. 4. 3. 3. 3. 3. 4. 5. 5. 4. 3. 0. 2. 3. 3. 3. 3. 4.\n",
      " 4. 3. 4. 0. 3. 5. 5. 5. 4. 3. 5. 4.]\n"
     ]
    }
   ],
   "source": [
    "#RF was not compared so I will try that\n",
    "#attempt RF regressor and evaluate model\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "#RF fitting\n",
    "regressor = RandomForestRegressor(n_estimators=20, oob_score=True, random_state=0)\n",
    "regressor.fit(X_train, y_train)\n",
    "y_predict = regressor.predict(X_test)\n",
    "\n",
    "#model evaluation\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_predict))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_predict))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_predict)))\n",
    "\n",
    "# Use the forest's predict method on the test data\n",
    "y_predict = regressor.predict(X_test)\n",
    "y_test=y_test.astype(float)\n",
    "residuals = y_predict - y_test\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.distplot((residuals),bins=50);\n",
    "\n",
    "#further evaluation metrics\n",
    "from sklearn.metrics import r2_score\n",
    "from scipy.stats import spearmanr, pearsonr\n",
    "from sklearn.ensemble import RandomForestRegressor as rf\n",
    "predicted_train = regressor.predict(X_train)\n",
    "predicted_test = regressor.predict(X_test)\n",
    "test_score = r2_score(y_test, predicted_test)\n",
    "spearman = spearmanr(y_test, predicted_test)\n",
    "pearson = pearsonr(y_test, predicted_test)\n",
    "print(f'Out-of-bag R-2 score estimate: {regressor.oob_score_:>5.3}')\n",
    "print(f'Test data R-2 score: {test_score:>5.3}')\n",
    "print(f'Test data Spearman correlation: {spearman[0]:.3}')\n",
    "print(f'Test data Pearson correlation: {pearson[0]:.3}')\n",
    "#for both PEARSON and SPEARMAN correlation:\n",
    "#correlation coefficients between .10 and .29 represent a small association\n",
    "#coefficients between .30 and .49 represent a medium association\n",
    "#coefficients of .50 and above represent a large association or relationship.\n",
    "\n",
    "# running the model against the test data\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(y_test, y_predict, edgecolors=(0, 0, 0))\n",
    "ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=4)\n",
    "ax.set_xlabel('Actual Stars')\n",
    "ax.set_ylabel('Predicted Stars')\n",
    "ax.set_title(\"True Stars vs Predicted\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "Train score:\n",
      "0.14265074098919317\n",
      "Test score:\n",
      "0.04682126502192063\n"
     ]
    },
    {
     "data": {
      "image/png": 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//9Euvrr9wKgLzlt2+J+oWhvCOOLQ2hCmNRZiy47DdRzx3FOVskURCeEH86+r6rer8ZzGzNRMt5a1dErtTJUGO9qfZGVrbNTvNMeCHOmz/d2no+KALiICfAnYqar/UPmQjKmMbS1bH/u6E3z3ief41aF+QLlgdStvuGAlHe3xUWkwYOTrtj09dLTHWdESYyDp0toQHnm+gaQ75kK1mVw1Ui6XADcCl4vIE/k/r6nC8xozI5Y+mX37uhPcuXUvj+7tJRp0iIQCPLa3lzt+vpd93Ykp02DXbVxFXzJL33AGTz36hjP0JbNct3FVPQ5nzqpGlcs2QKowFjNP1aM7vaVPame8apT9J5P0DGVY1BAmmt/+QBqE3qEM2/b0TJkGK1SzbNlxmCN9fgnpzZessyqXabKl/6amZlJCaE4/hSD+zJEBTgylWb2ogY72RgaSfjXK2kUx0tncqJRJNOivoD3en+JNL1k5ZRrs0g1LLYBXyJb+m5qy7vRzX3FJ4XAmhyAcOpnkxGB6pBplz4khIqEA6aw38nsp1yMcdFjWErU02CyxGbqpqXJWYJr6KScdVlxSmHJzNIQDZD3lQO8wS5tjNMeCdA+maGsMs7d7iNZYCBUYGM6ybknjSC2/pcFqzwK6KdtMcuHWnf70U0if7O0eJO0qm9a2cv6qRROmw4pLCqPhANmcEgr4u0yCX43S0R7nlkvPHFXlctGZi0eqXMzssIBuyjLTXLiVEJ5eCumT1liIgOOgnst/7emlIRzk7GXNwKlSwoLiksK1ixrYeWyQrAvRsDNSjXLzJevoaI/zgas21OvQDJZDN2WaaS7ccqezY7JVmMWK0yfprEdTNERDKEDngT5g/BW1xSWF7c0RVi+K+g0+IkHi0SAfuvJsu5h5mrAZuilLJblwy53Wxjd/cYC7tx/k6EASQXjp2lYuOXvppJ+eStMnbk6JhgP0D0/cpLq0pHDtkjgfvvocC+KnIQvopiyWCz+9fPMXB7j9gd3EwwEiAYdkxmPbsz3EQgE2dSwBxqZOYHT6pD0e4WDvMLmMN2WTaispnBss5WLKYrsRnl7u3n6QeDhAcyyM60FjJEg4GGD7vpPAxJuRFadPGiMBFsVCZHLK+vZGS4fNAzZDN2UpbW68rCXK1ecus//8dXIikaa9KQJAKOCQUyUSdBhKu8DEn57GW5H5vsvPstn3PGEB3ZTNcuGnjyXxCEMpl+ZYmKZokN6hDFnXoyE8eeoELH0yn1lAN2aWVaMzz00Xr+H2B3YDGRqjQcIBIZnxeP6KFhqjQfv0tEBZQDdmFhXXga9sjY3shQJMK6i/+cK1gJ9LP5FI09YY4f2XnzVyu1mYLKAbM4uK68CBka9bdhye9iz9zReutQBuRrGAbkyFCvXgJxJplsQj3HTxmgkDrXXmMbVkAd2YaSjsZ/PTp4+y89gQfcMpMjlobQhxRmuMoZSbz20zblC3zjymlqwO3ZgyFfazeeR3XfzyQB+pbI6MCyj0J7P0DWVojoWJhwPcvf3guM9hnXlMLdkM3Zi82+7ZwX/85hhufkvveBj+6rXnjcy0C/vZ3HdkgHAwQCwUoD+VRQBHhBNDGdriURqjQU4k0uO+hnXmMbVkAd0sWN/8xQH+5eG9HB1IkXa9MfcnMvCRbz8F+OmTwn42iZRLPOK3WXMEPA8CjpDNKQBDKZe2xsiEr2t14KZWLOViFqQvPLSLT9z3NAd7h/G80cFcONUk14WR9ElhP5t4NEja9YN3LOigAhnXIyDKQDJDIpPjpovXzN7BGJNXlYAuIl8WkS4Reaoaz2dMLe3rTvDlRw/ieh6hoBB0Rv830JLH9wz56ZPCfjYvOqOZjJtjKO0SCQVoDgcQgWgoSEMkyG1XnW3lhFVW7vbAC121Ui53Af8M/GuVns+Ymtm2p4dUNocCjkw9pymkT4r3s0m7OXYeG8L1PFa3NU5aqmimr3g1bXM0SCwU5IWrWq3R+BSqEtBV9ecisq4az2VMrR3vT9EUDZFxc3iqOCJjHlOYpQdhVPqksJ/NjRdb8K62Qj3/oZNDZFxl9aIY56xoZufRAYZSLsuawjTHWka2cB5ve+CFbtYuiorILcAtAGvWWH7R1M+yligXrGzm4d2ZfO4bQg5kS66Llla5mOop7U87OJTiru2HiIcDqAqqfhPqxnAQEaExGqTzYB9nL28BrNH4RGYtoKvqncCdAJs2bSpNU9bcTBocm/lp8/o2njuZ5JXAY/tP0p/MIgJnL4nxidedZxUoNXLDHdt4ZF8/hffNdYui/MllZ5FIuXz5kYNEg0JzLMyxwTSRoIPrKXt7hljb1kjWzTGQzI48lzVXGd+CKFucaYNjMz8V58LXL2u2N/hZcMMd29i2r3/UbftPprhr6x7++BVnkXJzhIN+KiUUEDxVggEhk/VY2hRh17EBGiMhPFVrND6JBRHQixscA5aDO43U65OT7e1efV94aBd3btvPYMrFEWF9ewMfe80LuHTDUh7JB/OAgKenrlHs6UkC0BQJMpzxm3MsbghzbCCNqkc4ILg5JR4J8dJ1i6y5yhSqEtBF5N+AVwJLROQw8Feq+qVqPHc1VNLg2FRXcfVCUyRIPBLkvJVWvTDXfeGhXfyvh57FUwg44Kmy6/gQt33rN9z+phcydtmWrxDYX7ymlZ/v7mEgmSEeDdKcdulLZVncGCEeDXLzJdaUuhzVqnL5w2o8T61Yg+P6KgTx3x0dpCuRYnVrjI6lcXYeHSSRdmlvitAca7ZPTqexT9/3JPc+foShtEuu6AqYA/z+OW38+sgQACFHEMchADji0TuUYcuOwzgwKqgLp4L5QDLLmrY471nRxP2/7fLb6zVH+bPff55dkJ6mBZFy2by+jS2dhwF/Zm45uNlT3NBhMJ1FEA71pWiMhhAgHg7QeaCPs5c1A/bJ6XT06fue5CuPHsIRHRXMwQ/S//lMDwBBh1NLbPHTK2lXOdaf4uUdLWzb1z/m91e3REZ1WHrPFRtqeizz3YII6NbguH6KGzqkXY+GSJBszuPgyWGWxCNkc55VL5xmSq9r/Fvnc4QCkg/GExeoSeHufFDPKQQDwvKWKP/81peMqnJxgJd3tPC1WzfX+nAWlAUR0MEugtVLcUOHWNgP5iFHSGZytMcj/O74II2RoFUvzLJCGqxzbzd9KT8ZsqghzDXnLSUYCo2qCBvO5GgIO2SzY4N5ceoEIOspAXJ4CjkPljVHRrYGtuBdewsmoJvKzaQipbihw9rFMZ4+NkjWhVg4QM5TGiMBLrLqhVlRWIm5/0SC4Yw3ktMuZEoG01nu2n6Il5+5iFedvxLwK8LCQb980HGEXEnOZGRFrQN/esVZo6pcNiw7VeViZocFdFOWmdbyX7dx1UgT5CVNEdakXQ72DtMQcohHg3zskufbf/gaKK4mWtESY3k8xH1PHsdBGcp4o2bVCgQCQi6/DfATh/pHAjrAxtUtPLqvj6BOnG658nltvOeKDZYDrzML6KYsM63lL23osKatkdt+f4MF8Sq74n8+wJ7ezMjPAeDS57WzstX/hPT1ncdpjgRJ5jxEICCC6/kBWgQ8T8nhEQ0FSGXdUc/9ig3LAXjqaAJvgiqXL7794lofoimDBXRTlkpq+a2hQ2286BM/oD8z/n054LE93Vx9/kpaG8LkPI+k6+F6hc3ITkVlwV/sE3Yc1FNCgQADyeyoirDPvPFFlgqbA6zBhSlLoZa/mFWk1M9kwbwgmTv1fTQYIJ3NEQoIQQeKsye5/MrNgAOuwh9uWklj/s26MRq0hV5ziM3QTVmslv/0MlUwL7W8JcqB3mFiQYd0Nue3zitagh8KCE2REDdetMry4DVQek3juo2ravKp1QK6KYvV8s9NnnoMJF0aIiFuvHA12/f3kXY9cgqtsSAvXL2oZsHF+IoX1xWuaRQKBar9924B3ZTNavmrb+uuLj7+3Sc51JtCgVjI4a0vXcXHrz2/4ueOOnCkz7/2cfMl6yxo10nx4jpg5OuWHYctoBsz1912zw6+91QXKffU7iaFWvDhrMeXH/WbUk8W1FvCk6dd1i8O89CHr6rSiE0lihfXFTTHghzpq/4WFxbQjamxwoKeE4k0Q6k0gxl/n5PiWhMPP4/tqOJ5cO/jRyYN6L/+1DVjLoy2hP3bzemleHFdwUDSHVM1Vg0W0I2poW/+4gC3P7CbeDhAe1OEYwNpABxH8Ep3qgIEQVGS2dyY+0pZ8J4bihfXNceCDCT9goKbL1lX9deygG7MDBW2lB0oKueMBh1ed95Sbr9+IwB3bz9IPBygORYe9btuThEZXT4IoCgCxEKBWg/fzJLSxXW1vKZhAd2Yabj0b3/EoQF3wvtTrse/P3EM2MHt12/09/Zuiox5nOI3ps4UTcRzOfV3IhR4y0vOqPrYTf3M1uI6W1hkzBT2dSf46vYDPP9jP5g0mBc4At97qguAJfEIQ0Uz+JbIqf9ywqjtw1GgIeTwxy9bU5UqF7Pw2AzdmLwvPLSLf/rpHlJufo8T4Opz2li3rBUv541aeTmZoAOZnF/BctPFa7j9gd1AhsZokLZ4lOHMMCqQ9SBSkqIxphIW0M2CNVX6RPG78Zzfn6ZjaXPZz+t6EA74M/FCC7VClUtbY4S//W/nWWs1UxMW0M2CdPHf/JBjQxO1Lh7t6WMJ2ppjUz8wz1N43Xmn8qVvvnCtBXAzK6oS0EXkVcD/xt+18/+p6t9V43mNqYYvPLSLrz52mIFUluZoiGvOW1p2MAd/86rmaIhoAFJTpF1Kq1xMdcykucpCVHFAF5EA8C/AVcBh4Jcicp+qPl3pcxszU4Ug3j2QwgWiAVgcj5DM5Lj7sUPTeq6AwPKmCC89cwm/2HOC9DjvBbaop3Zm2lxlIarGDP1C4FlV3QsgIvcArwcsoJtZsa87wX//Wic7jw+N6sSzKBaiMKFO5WAw5dISC5PMuGTHe6IJXLWhjdVLGhnK5Gg5bwWLYiGet6LZZolVVryidkk8wk0Xr+HNF66dcXOVhagaAX0lUDzlOQxcVPogEbkFuAVgzZo1VXhZY/xg/o6vPMb+3rH7Ygyns8VN6P2AHINwyCGVm7pkRYA/uGC5pU9qoDSFMjiU4q7th0ZW1A6l3Hx1UGXNVRaaagR0Gee2MWuaVfVO4E6ATZs2TdycsEYsBzc/bdvTw4F8MA/k/yUWVtSnPX+hhUf+a9G/usZwAPFyJEqKXF6wNMb9H7y81sNekG64YxuP7OsfaU69uiXMrb/3PBIply8/cpBo2BlZUet/zXD39oNcd+EaEil3ZGYO1lxlItUI6IeB1UU/rwKOVOF5q8ZycPPX8f7UqFl4qYawQyLf4V6AobRLNgd/9NKVrF3abG/ws+SGO7axbV//qNsO9Wf40s9/xzsv20DK9QgHR5/FxmiQE4m0NVeZhmoE9F8CZ4tIB/AccD3w1io8b9VYDu70VZw3jUeCnL+ymZWLGssOsstaoozukDlacyxEJpcmk/NbrEVDAd65ea115amB0qAdDcDfvN6vuX8kf3tATn1SUmBfvrF1UyTIcDoLTaeebyjl0tYYseYq01BxQFdVV0TeC/wIv2zxy6r624pHVkWWgzt9FJcQBhwh67qsaGmgKRqkayDNj5/u4uoXLCUeCZb1KWrz+jbWLo6yvzdF6eaFYQcGUi5tjVFrrVZj483AUzn4828/BcBURaIXrGpm254eBpL+itqhlEsik+Pdl50JWHOVclWlDl1V7wfur8Zz1UKhwbHl4OpjvEU8Z7REONafxsNPg4SCARrCQbI5j+37TrKpYwkw9aeojvY4X3nHRaOqXALAVee08cW3X1y7g1pgNn7yB/RMMP9pi8LJCe7z8FfJFq5lAGN2mRxIZlnb3sT5K5u5/7ddIytq333ZmbYga5oWxEpRy8HVz0QrMo/0p3EEHIWe4QyLGyOEHCESckik/SuV5X6K6miPc/+fvrLaQzd5kwVzYNL7AHqG0ry8o4Vt+/rHfIpaEQ/SGA2OpFDsU1RlFkRAtxxc/Uy2IjMgQk6VXH7vk5ynZF2PxrD/z9I+RdVHaT34VAF7Km2NEb526+ZRVS4O8PKOFr526+ZqDNnkLYiADpaDOx01RgL0JV0CQCzs0DWQxvWUV5zdxsqFtQ8AABUzSURBVEAya5+iZsHWXV1s2XGYo/1JVrTEWB4Pcd+Tx0fVg1fCwd9xErDgPQsWTEA3lSvdE6XSC42CEHKgNRYkkXZpb4pw/spmzljUOOpjuKmOd921nQd/14Nb8qFpaWOQi89qZyDp8vWnj9MSC5XUgw+X9fyb82mVguIqFzM7LKCbsnzhoV18/uF9RIMOLbEgyUyOzz+8D2DSoL680Zkw7WIlhLVTOvM+0jPI488lxn1s15DLr/b3sLGjnZwqw5npz8rbojYDPx1YQDdl+epjh4kGHRoj/j+ZxogDuHz1scOTBuTtf/nqMRdGlzc6bP/LV9d6yAvKbffs4HtPdZF2PQIONIUDvGjNYla2+h3nJwrmBc/1Z9iI/yabLmlQfWZbA/t6hies9W+Lwo5P2sZkpwML6Atc6Uzuuo2rxu19OJDK0hIb/c8lFnYYSE29zZUF79p40Sd+QH9m9G2RAKRzcDKVY0/XABvXLaG1ITz+E+QJp0oKlzWFOdCbHFMP/j/faKmTucB6ii5gW3d18bkHd5NIuaxsjZFIuXzuwd1s3dU15rHN0RDJzOjUSTLj0RQJjXmsqb3xgjn4wbywgP65vnRZz6X4gaBvOENjNMzbLlpNQ8Rfdt8QCXLbVWdbMJ8jbIa+gG3Z4e9vU5jBFb5u2XF4zCz9xotW5XPmLrGwQzLjkXI93rnZ/qPXw3jBvKCwcKf47XdxLEDvJE1RW2MB4tEgN1+ybla605vasIC+gB3tT7KydXRrteZYkCN9YwuPC3nyQpVLUyRkFzRPU36T6nwqRT0Gki5rljSxznP5zdHEqCoX67A0v1hAX8BWtPgXzIpzrANJd8y+NwXvuWKDBfA5IJcP2Ctbwhzp8/cxspn3wmABfQG7buMqPveg30SgORZkIOlviXDzJevqO7B5rhr1/C3hidMuoYDDG23WvSBZQF/ACjO2LTsO20yuhoqbqzx95CT/taeXhlBwWvX8pX79qWvGXBi1vqZGVGe9eRCbNm3Szs7OWX9dY2ZbcXOVeDTIZ//zGbJZl9bGCEHHLzIbSrtEQwEe/egVdR6tOd2JyA5V3TTR/TZDN2aGCqmT7sEUbn5e1BwN8paXnMHHrz0fGNtcJZ3NEQ44pLIe8Ygf0Mut5zdmKhbQjZmGN37+Z5OuukykXL7yqN8z/ePXnj+muUo0FCCb85CiBqdWz2+qxRYWGVOmqYI5+LXfoYBw7+N+W91Cc5WCi9a1kM0prufhqcdQ2iXletx40apaDt0sEDZDr5LiC1/WdHhu+sJDu7jr0YP0JbOEAg4XdbTyl689b+Q8ThXMC8JBIZnfD6W0ucrGde2cHHY52JtkIOVaPb+pKgvoVVB84Wt5fkZWTj9MU1+33bODbz9xjNL1k/GwQ06Vn+/u5bZvPsHtb75gWucx4yqxUAAYv7nKdJ/PmHJZQK+C0gtfha9T9cM09XPbPTv45hPHxr0v43o0REJAjl1dQ9M6jw6QzSk3XHjGyG3WXMXMlooCuoi8Gfgk8HzgQlVdkLWIpRe+oPx+mKY+vvfU2A3ICjIeNODnwlPZHMfz5/ElK+NTpl3iJVUupjrK3RV0oat0hv4U8EbgjiqMZc4qXPgqzMzB+mHW21TXNNKlbXvGkc0pkVBg5Dx++32XjXthtLRU0VSuOIAHRegeTLOmrXFkf/fCCmcL6qNVFNBVdSeAiEz10Hmt9MJXIuVaP8xZVtzYuCkaZGVLlIvXt094TSMSdEhNEtTTbo5sTjl3RSOb17eN3P7t911W82NZqArn8NDJITKusnpRjHNWNPPonh5S2RzLmqM4DeFJdwVd6GatbFFEbhGRThHp7O7unq2XHbF1Vxfv/cbjvOn//Bfv/cbj4+75PVOFC1+N+TRLYzRoF0Rn0Td/cYDbH9jNcL4vae9Qll8e6ON3x/pxRGiOhWiNhdi2p2fkd1533uSBwBHhFWcvtguYs6T4HKoKqnCgd5hDPcPkPCUaCnDg5Kneps0xS2mOZ8oZuog8CCwf566Pqep3y30hVb0TuBP8pf9lj7AKCo0cWmOhmn1kswtf9XP39oPEw4GRxsYBR4gGHbbvO8mmjiXA2Gsa/sZVo6tcIg68/4qzrISwRl7zDz/h6a7kyM8vWBrj/g9eDow+h8cG00SCDq6n7O0ZIh4JknFzpDKn6pEm2xV0IZsyoKvqlbMxkFqaTiMHM/ecSKRpb4qM/BwKOIgoQ+lTC3rGu6Zx+/Ubuf36WRvmglYazAGe7krymn/4Cfd/8PJR5zAUEDxVggEhk/VYu6KBJ5/rJxoKjOzvbruCjm9BlC1Op5GDmV2fvu9J7n38CMOZHA3hwIwuLi6JRxhKuSMz9KZokKN9WaLhAJ6qXdOYJd/8xQHu3LaP4wNpwkGHzesX8/4rN9DRHh8TzAsKtxefw8UNYY4NpFH1CAeEcDDA0qYobfGQ7Qo6hUrLFv8b8HmgHfiBiDyhqldXZWTjmOlqzOk2cjC1U3zxMpnJMpD2iAQcGsIOGdcbtQ9KuW66eA23P7Ab8BsbZ7I5AgHhZR2LRhbzXH3uMkuJVUnxOVwSj3DTxWsA+P9+tItcziMWCuB6ygM7uzk5nOWT15435XMWn8N4NEhz2qUvlWVxY4R4NMgnX/cCC+BlqLTK5T+A/6jSWCZVyWpMa+RQX4U34p8+fZRfHuijpSFEe1OEJ5/zmxg7IjjiEA0B5Lj38SPTCuiFBsaFINPWGOHdl51pjY2rqPBJaiC/L40ATZEAJ1S5/YHdBEQRoCESIiCCv1A2xzPHE6MuRk+k9By2N0f5s99/np3DaZozKZdKVmNaI4f6KX4j3nl8iKDjkHGVbO7UdfG0myMc9AuuivdBmY43X7jW/vPXyKfve5KvPHoIh1PnTIHhTI6sBy3RAN2JDItifjAviASFRNpfmPWCpbFx0y4vWHoqFWrnsHJzJqBXuhrz0g1LLYDXQfEb8VDaJR4JoAiDKRcHf3fCop1kR+2DYk4P9z5+hFBA/ObSRQ1xXIWYIwznq088IKc6EtTTrhIL+wuz7v/g5ZNWuZjqmDMB3VZjzk3Fb8TxaJB01iMWcsjmPFpjAXqTORS/O31h5l68D4qprXKW1PsXrB2y6bELsQKOv5/70qYoKTfHcDpLNJ9DT2U9Np3ROrIwy4J37c2ZgG6rMetvJvtpFL8RX7yulQd2duN5Sizk0NgQIZEexnEckln/YtoNF9oS+lopnL+93YM44hAPBzjcl+KM1uik6zMawgEyruevCC9pWZnK+kH+T684C2BUlctVz28fqXIxs2POBPTxtiG1yoXZM9PFWcVvxC9Zt4RkxuOXB/vIesriSJDPvOE8y5vOgsL5CztCxlU8z+XZrgStsRAnh7M0R0MTrs94y0vOGMmhC4xk0iX/54aLVo+cQzuX9TVnAjrYasxaKLcUdKaLs0rfiF/+vKV8+NXPt/NYZeOVEhYH18L56x3OEAkGCAUcjvQnGc66LI5H6EqkiUdD467PKHxiuvfxIyRSLgEHFjeEuPDMJbbr4WlmTgV0U13TKQWtZHGWvRHXxg13bOORff0UMtvRAJy9vJmhlJuv6T41Yy6cvyN9yZGLzpFQgHTWG9kmGCZen/Hxa8+3VNgcYD1FF7DiCpSJNrEqKCzOKmaLs+rnhju2sa0omAOkcv6bdHMsTDwc4O7tB0fuK5y/aNi/YAnQGA4gAoPJLOGAQ99whr5klus2Wn/TucoC+gJ2vD9FPDr6Q1o8Ghxp6FDsuo2r6Etm6RvO4Kln//nr7JF9/QAESnauTmT8EN8YDdIzlB65vXD+ogGHtJtjIJlBRHjp2lbE8Xc3jEeDfOjKsy2FModZymUBm04pqC3OOr1M1Z5jKOXS1nhqw7Li8zeUcRERzm9vZFNHmzU0n0csoC9g0y0FtcVZ1TfT/YkKi7LGM5DMkMjkePdlZ4663c7f/GcBfQGzUtD6uPRvf8ShgVPXI+JB+IvXnjet/Yle3tHCtn395Eo6C4QcaIgEbS+bBUpUZ7XXBOA3uOjsXJD9pM0CVxrMC5rD8BfXnM9AMktjNMiNF08djIurXBz8IP+1WzdXf9DmtCEiO1R100T32wzdmFk0XjAHGMj4X6ezP5EFb1PKAroxM7CvO8F3n3iOXx3qB5QLVrfyhgtWVpyusv2JTCUsoBszDfu6E3znief4j8cPc2Iog4PQmC/1PD6Q5tZXnDnjoD6QzNr+RKYiVoduTJkKK2u37u6mazANKiDguh5H+lP87vjAlM0cVjePP4eKOX7teDkXRI2ZiM3QjcmbqoSwsLL20MkkIkI46KAoOYVI0OFQb3LcRVnFtn706jEXRlc3B9n60Zp1bjQLiAV0s2Bt3dXF3/zgafaeGMJTCAbgorWLufr8M8YtISzs7e7mPIIBQVEEIecp0ZBDMpsrK/9twdvUiqVczIK0dVcXt33z1+zuGkIAFDIuPLK3l589c3TcfW0KK2uXNEUQBTenuDkPESWZydEcDY40czCmHiqaoYvI7cDrgAywB3iHqvZVY2DGVFNpOuWhncfoTWUJOhAMBFA8HBTPg8f29/N7zz9jTAlhYWXti1e18rOhbtI5j3TWw3EcokGH//F7Z1n+29RVpSmXB4CPqKorIp8FPgL8eeXDMqZ6xtsm+Jkjg7iuEg76u1uJgHp+84Z0fivZ0hLC4pW16WyOPSeGQWFde6PtC25OCxUFdFX9cdGP24E/qGw4xlRf8TbBgJ9OaQzTnUiT85RgAAIiZPO9eCKhwIQlhIW93ctZyWnMbKtmDv2PgR9OdKeI3CIinSLS2d3dXcWXNWZy420TfHHHIiJBv5O9m8uR8zxUwRE4Z2mjlRCaOWnKGbqIPAgsH+euj6nqd/OP+RjgAl+f6HlU9U7gTvD3cpnRaI2ZgfG2CV7e0sCbN67m0b097D0xhCq0NAR55yXreM8VG+o4WmNmbsqArqpXTna/iNwEvBa4Quux05cxU5hom+C3X9LBX7/B2qqZ+aPSKpdX4V8EvUxVh6szJLOQzHQ/8OmwbYLNQlFplcs/AxHgAREB2K6q76p4VGZBmE6T6kpZo2qzEFRa5XJWtQZi5q+JZuHjVZ+AX5ViwdeY6bOVoqamCrPwoZTL8pYoQ/lZ+L7uxLSaVBtjpmZ7uZiammwWPp0m1caYqdkM3dTUZLPwzevb6EtmGUhm8VRHFvPYfijGzIwFdFNThVl4scIsvFB90pjfM8UW8xhTGUu5mJqaqAa8sKTeqk+MqR6boZuaslm4MbPHZuim5mwWbszssBm6McbMExbQjTFmnrCAbowx84QFdGOMmScsoBtjzDxhAd0YY+YJC+jGGDNPWEA3xph5wgK6McbMExbQjTFmnrCAbowx84QFdGOMmScsoBtjzDxRUUAXkb8Rkd+IyBMi8mMROaNaAzPGGDM9lc7Qb1fVF6rqBcD3gU9UYUzGGGNmoKKArqoDRT82AlrZcIwxxsxUxQ0uROQzwNuAfuD3JnncLcAtAGvWrKn0ZSuyrzvBtj09HO9Psawlyub1bdaAwRgz54nq5JNqEXkQWD7OXR9T1e8WPe4jQFRV/2qqF920aZN2dnZOd6xVsa87wZbOw7TGQqN6XFpbNGPM6U5Edqjqponun3KGrqpXlvla3wB+AEwZ0Otp254eWmMhmmMhgJGv2/b0WEA3xsxplVa5nF3047XAM5UNp/aO96eIR0e/j8WjQY73p+o0ImOMqY5Kc+h/JyIbAA84ALyr8iHV1rKWKImUOzIzB0ikXJa1ROs4KmOMqVxFAV1V31StgcyWzevb2NJ5GGBUDv3qc5fVeWTGGFOZBbdStKM9znWbVtEYDXKsP0VjNGgXRI0x80LFZYtzUUd73AK4MWbeWXAzdGOMma8soBtjzDxhAd0YY+YJC+jGGDNPWEA3xph5Ysq9XGryoiLd+AuR5oolwIl6D6LG7Bjnvvl+fGDHuFZV2yf6xboE9LlGRDon2xBnPrBjnPvm+/GBHeNULOVijDHzhAV0Y4yZJyygl+fOeg9gFtgxzn3z/fjAjnFSlkM3xph5wmboxhgzT1hAN8aYecICep6IrBaRn4rIThH5rYi8f5zHiIj8k4g8KyK/EZGX1GOsM1XmMb5SRPpF5In8n0/UY6wzISJREfmFiPw6f3x/Pc5jIiKyJX8OHxORdbM/0pkr8xjfLiLdRefwT+ox1kqJSEBEfiUi3x/nvjl9HmHK45vROVyQ2+dOwAU+pKqPi0gTsENEHlDVp4se82rg7Pyfi4D/k/86V5RzjABbVfW1dRhfpdLA5aqaEJEQsE1Efqiq24seczNwUlXPEpHrgc8C19VjsDNUzjECbFHV99ZhfNX0fmAn0DzOfXP9PMLkxwczOIc2Q89T1aOq+nj++0H8v+iVJQ97PfCv6tsOtIrIilke6oyVeYxzVv68JPI/hvJ/Sq/6vx64O//9vwNXiIjM0hArVuYxznkisgq4Bvh/EzxkTp/HMo5vRiygjyP/8e3FwGMld60EDhX9fJg5GhAnOUaAl+U/0v9QRM6d1YFVKP8x9gmgC3hAVSc8h6rqAv1A2+yOsjJlHCPAm/JpwX8XkdWzPMRq+Efgw/j9iscz18/jVMcHMziHFtBLiEgc+BbwAVUdKL17nF+Zc7OjKY7xcfz9Il4EfB74zmyPrxKqmlPVC4BVwIUicl7JQ+b8OSzjGL8HrFPVFwIPcmomOyeIyGuBLlXdMdnDxrltTpzHMo9vRufQAnqRfE7yW8DXVfXb4zzkMFD8TrkKODIbY6uWqY5RVQcKH+lV9X4gJCJLZnmYFVPVPuBh4FUld42cQxEJAi1A76wOrkomOkZV7VHVdP7H/wtsnOWhVeoS4FoR2Q/cA1wuIl8recxcPo9THt9Mz6EF9Lx8/u1LwE5V/YcJHnYf8LZ8tcvFQL+qHp21QVaonGMUkeWFXKSIXIj/b6Rn9kY5cyLSLiKt+e9jwJXAMyUPuw+4Kf/9HwA/0Tm0uq6cYyy5rnMt/rWSOUNVP6Kqq1R1HXA9/jm6oeRhc/Y8lnN8Mz2HVuVyyiXAjcCT+fwkwEeBNQCq+kXgfuA1wLPAMPCOOoyzEuUc4x8A7xYRF0gC18+V/yjACuBuEQngvxHdq6rfF5FPAZ2qeh/+G9pXReRZ/Bnd9fUb7oyUc4z/Q0Suxa9q6gXeXrfRVtE8O49jVOMc2tJ/Y4yZJyzlYowx84QFdGOMmScsoBtjzDxhAd0YY+YJC+jGGDNPWEA3xph5wgK6McbME/8/n4Ue7wJrGHYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5 4 5 5 3 3 5 3 5 3 3 3 4 4 4 3 4 4 4 3 4 3 3 4 4 3 3 4 3 4 2 3 3 5 5 4 4\n",
      " 5 5 3 3 3 4 3 3 5 3 2 4 2 5 3 4 3 3 3 4 4 4 2 3 0 3 1 1 3 3 4 4 3 0 4 2 5\n",
      " 3 0 3 5 3 3 2 3 5 3 4 5 4 3 4 3 5 0 3 3 4 4 5 4 4 3 2 0 3 3 5 3 4 3 2 5 4\n",
      " 4 5 3 3 5 3 3 3 4 4 4 4 3 3 4 3 4 2 4 4 3 3 4 3 3 3 2 3 4 3 2 3 5 4 3 3 3\n",
      " 5 2 3 5 5 3 4 3 5 4 4 3 5 3 3 3 5 3 5 4 3 4 2 2 4 5 4 3 5 3 3 0 2 5 3 2 3\n",
      " 5 3 3 3 3 4 4 2 4 3 1 3 2 3 3 4 5 4 3 4 4 3 5 3 0 4 5 5 4 3 3 4 4 4 5 4 3\n",
      " 4 3 5 3 4 4 3 4 4 3 3 3 4 5 4 2 3 4 3 3 3 5 3 5 2 3 3 2 3 3 3 3 5 2 4 3 2\n",
      " 3 5 1 5 3 3 5 4 3 3 4 3 4 4 3 3 4 3 3 2 4 3 3 2 5 0 4 4 3 4 5 5 4 3 2 4 4\n",
      " 3 2 3 3 3 5 2 4 3 3 4 3 3 4 5 5 3 4 3 3 3 5 2 5 2 4 3 2 5 3 0 4 4 2 4 2 1\n",
      " 3 5 4 3 3 4 4 4 2 2 4 4 3 3 4 2 1 3 4 3 0 4 4 4 4 4 3 3 3 4 3 5 4 3 4 3 2\n",
      " 1 3 4 4 1 3 4 4 3 4 4 3 4 4 5 3 5 3 5 3 4 4 5 1 4 3 2 4 3 4 2 0 3 4 5 3 3\n",
      " 3 5 3 3 4 3 2 3 3 5 3 3 2 4 4 4 3 3 4 5 4 3 3 3 3 4 4 3 3 1 3 4 4 3 3 3 3\n",
      " 4 3 3 3 5 5 5 3 1 2 3 3 3 2 3 2 5 4 1 4 0 4 3 3 4 3 4 3 4 1 3 4 3 4 3 4 4\n",
      " 4 4 3 5 4 3 5 5 4 5 5 4 4 4 3 4 4 3 5 2 5 3 3 3 4 3 3 1 2 3 4 3 3 4 2]\n"
     ]
    }
   ],
   "source": [
    "#while we are at it, let's just try LM\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "lm = LinearRegression()\n",
    "\n",
    "lm.fit(X_train, y_train)\n",
    "y_predict = lm.predict(X_test)\n",
    "\n",
    "def x_round(x):\n",
    "    round(x*4)/4\n",
    "print (x_round(y_predict[0]))\n",
    "#print(predictions)\n",
    "\n",
    "print(\"Train score:\")\n",
    "print(lm.score(X_train,y_train))\n",
    "\n",
    "print(\"Test score:\")\n",
    "print(lm.score(X_test,y_test))\n",
    "\n",
    "residuals = y_predict - y_test\n",
    "plt.scatter(y_predict, residuals, alpha=0.4)\n",
    "plt.title('Residual Analysis')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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": [
    "predictions = lm.predict(x_test)\n",
    "sns.distplot((y_test-predictions),bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score:\n",
      "0.14265074098919317\n",
      "Test score:\n",
      "0.04682126502192063\n"
     ]
    },
    {
     "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": [
    "model = lm.fit(X_train, y_train)\n",
    "\n",
    "y_predict= lm.predict(X_test)\n",
    "\n",
    "print(\"Train score:\")\n",
    "print(lm.score(X_train, y_train))\n",
    "\n",
    "print(\"Test score:\")\n",
    "print(lm.score(X_test, y_test))\n",
    "\n",
    "plt.scatter(y_test, y_predict, alpha=0.1)\n",
    "plt.plot(range(5), range(5))\n",
    "\n",
    "plt.xlabel(\"Stars: $Y_i$\")\n",
    "plt.ylabel(\"Predicted Stars: $\\hat{Y}_i$\")\n",
    "plt.title(\"Stars vs Predicted Stars using MLR\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNR training set score: 0.16848742301642305\n",
      "KNR test set score: 0.03985791871927291\n"
     ]
    },
    {
     "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": [
    "#try KNR as well\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "kn_regressor = KNeighborsRegressor(n_neighbors = 50, weights = \"distance\")\n",
    "kn_regressor.fit(X_train, y_train)\n",
    "\n",
    "knr_training_score = kn_regressor.score(X_train, y_train)\n",
    "\n",
    "print(\"KNR training set score: %s\" %(knr_training_score))\n",
    "\n",
    "knr_test_score = kn_regressor.score(X_test, y_test)\n",
    "\n",
    "print(\"KNR test set score: %s\" %(knr_test_score))\n",
    "\n",
    "#generate plot showing difference between predicted age and actual age using K Neighbors Regressor\n",
    "\n",
    "knr_predictions = kn_regressor.predict(X_test)\n",
    "\n",
    "plt.scatter(y_test, knr_predictions,  alpha=0.1)\n",
    "plt.title(\"Actual star versus predicted star using K Neighbors Regression\")\n",
    "plt.xlabel(\"Actual star\")\n",
    "plt.ylabel(\"Predicted star\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "ramen_df4=ramen_df4.rename(columns={\"Annie Chun's\": \"Annie Chun\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select features and scale data for regression\n",
    "\n",
    "from sklearn.preprocessing import scale\n",
    "\n",
    "list_of_features_classification = [\n",
    "    'USA',\n",
    "    'Indonesia',\n",
    "    'Japan',\n",
    "    'Yum Yum',\n",
    "    'Morre',\n",
    "    'Qin Zong',\n",
    "    'Pack',\n",
    "    'Miliket',\n",
    "    'Batchelors',\n",
    "    'Holland',\n",
    "    'Sempio',\n",
    "    'Chorip Dong',\n",
    "    'Daifuku',\n",
    "    'Healtimie',\n",
    "    'Kabuto Noodles',\n",
    "    'Mi E-Zee',\n",
    "    'MyKuali',\n",
    "    'spicy'\n",
    "]\n",
    "features_classification = ramen_df4[list_of_features_classification]\n",
    "scaled_features_classification = scale(features_classification, axis=0)\n",
    "labels = ramen_df4['good']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate training and test sets\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_data, test_data, train_labels, test_labels = train_test_split(scaled_features_classification, labels, test_size=0.2, random_state=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN training set score: 0.6191169335274139\n",
      "KNN test set score: 0.6453488372093024\n"
     ]
    }
   ],
   "source": [
    "# build K Nearest Neighbors model to predict drug use\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn_classifier = KNeighborsClassifier(n_neighbors=76)\n",
    "knn_classifier.fit(train_data, train_labels)\n",
    "\n",
    "knn_training_score = knn_classifier.score(train_data, train_labels)\n",
    "\n",
    "print(\"KNN training set score: %s\" %(knn_training_score))\n",
    "\n",
    "knn_test_score = knn_classifier.score(test_data, test_labels)\n",
    "\n",
    "print(\"KNN test set score: %s\" %(knn_test_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[248  61]\n",
      " [122  85]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      0.80      0.73       309\n",
      "           1       0.58      0.41      0.48       207\n",
      "\n",
      "    accuracy                           0.65       516\n",
      "   macro avg       0.63      0.61      0.61       516\n",
      "weighted avg       0.63      0.65      0.63       516\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# generate classification report and confusion matrix for K Nearest Neighbors classifier\n",
    "\n",
    "from sklearn.metrics import classification_report, confusion_matrix \n",
    "\n",
    "knn_predictions = knn_classifier.predict(test_data)\n",
    "print(confusion_matrix(test_labels, knn_predictions))\n",
    "print(classification_report(test_labels, knn_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'k': 68, 'score': 0.6511627906976745}\n"
     ]
    }
   ],
   "source": [
    "# generate plot showing K Nearest Neighbors classifier score based on different n_neighbors values \n",
    "# and print out n_neighbors value that results in highest score\n",
    "\n",
    "scores = []\n",
    "\n",
    "highest_score = {\n",
    "    'k': 0,\n",
    "    'score': 0\n",
    "}\n",
    "\n",
    "for k in range(1, 101):\n",
    "    classifier = KNeighborsClassifier(n_neighbors=k)\n",
    "    classifier.fit(train_data, train_labels)\n",
    "    score = classifier.score(test_data, test_labels)\n",
    "    scores.append(score)\n",
    "    if score > highest_score['score']:\n",
    "        highest_score = {\n",
    "            'k': k,\n",
    "            'score': score\n",
    "        }\n",
    "\n",
    "x_values = range(1, 101)\n",
    "\n",
    "y_values = scores\n",
    "\n",
    "plt.plot(x_values, y_values)\n",
    "plt.title('Accuracy of K Nearest Neighbours classifier based on different k values')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('k value')\n",
    "plt.show()\n",
    "print(highest_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC training set score: 0.6244541484716157\n",
      "SVC test set score: 0.6434108527131783\n"
     ]
    }
   ],
   "source": [
    "#Use of support vector machine classification\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "svc_classifier = SVC(kernel='rbf', gamma=10)\n",
    "svc_classifier.fit(train_data, train_labels)\n",
    "\n",
    "svc_training_score = svc_classifier.score(train_data, train_labels)\n",
    "\n",
    "print(\"SVC training set score: %s\" %(svc_training_score))\n",
    "\n",
    "svc_test_score = classifier.score(test_data, test_labels)\n",
    "\n",
    "print(\"SVC test set score: %s\" %(svc_test_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[247  62]\n",
      " [124  83]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      0.80      0.73       309\n",
      "           1       0.57      0.40      0.47       207\n",
      "\n",
      "    accuracy                           0.64       516\n",
      "   macro avg       0.62      0.60      0.60       516\n",
      "weighted avg       0.63      0.64      0.62       516\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# generate classification report and confusion matrix for Support Vector Machines classifier\n",
    "\n",
    "from sklearn.metrics import classification_report, confusion_matrix \n",
    "\n",
    "svc_predictions = svc_classifier.predict(test_data)\n",
    "print(confusion_matrix(test_labels, svc_predictions))\n",
    "print(classification_report(test_labels, svc_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'gamma': 0.1, 'score': 0.6434108527131783}\n"
     ]
    }
   ],
   "source": [
    "# generate plot showing SVC Classifier score based on different gamma values \n",
    "# and print out gamma value that results in highest score\n",
    "\n",
    "scores = []\n",
    "\n",
    "highest_score = {\n",
    "    'gamma': 0,\n",
    "    'score': 0\n",
    "}\n",
    "\n",
    "gamma_values = np.arange(0.1, 1.0, 0.1)\n",
    "\n",
    "for val in gamma_values:\n",
    "    svc_classifier = SVC(kernel='rbf', gamma=val)\n",
    "    svc_classifier.fit(train_data, train_labels)\n",
    "    score = svc_classifier.score(test_data, test_labels)\n",
    "    scores.append(score)\n",
    "    if score > highest_score['score']:\n",
    "        highest_score = {\n",
    "            'gamma': val,\n",
    "            'score': score\n",
    "        }\n",
    "\n",
    "x_values = gamma_values\n",
    "\n",
    "y_values = scores\n",
    "\n",
    "plt.plot(x_values, y_values)\n",
    "plt.title('Accuracy of Support Vector Machines classifier based on different gamma values')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Gamma value')\n",
    "plt.show()\n",
    "print(highest_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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