ramen-ratings-eda.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exploratory Data Analysis**\n",
    "by [Iqrar Agalosi Nureyza](http://https://www.kaggle.com/iqrar99)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hello Everyone! I try my best to do data analysis. This Ramen Rating Dataset is a very good dataset for beginner and I hope you can understand my analysis. I am very open in accepting criticism and suggestions for perfecting this kernel.\n",
    "\n",
    "If you find this notebook useful, feel free to **Upvote**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "#importing all important packages\n",
    "import numpy as np #linear algebra\n",
    "import pandas as pd #data processing\n",
    "import matplotlib.pyplot as plt #data visualisation\n",
    "import seaborn as sns #data visualisation\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "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",
       "    <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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "5      2575   Samyang Foods   \n",
       "6      2574         Acecook   \n",
       "7      2573     Ikeda Shoku   \n",
       "8      2572      Ripe'n'Dry   \n",
       "9      2571            KOKA   \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",
       "5                             Kimchi song Song Ramen  Pack  South Korea  4.75   \n",
       "6                Spice Deli Tantan Men With Cilantro   Cup        Japan     4   \n",
       "7                              Nabeyaki Kitsune Udon  Tray        Japan  3.75   \n",
       "8                           Hokkaido Soy Sauce Ramen  Pack        Japan  0.25   \n",
       "9              The Original Spicy Stir-Fried Noodles  Pack    Singapore   2.5   \n",
       "\n",
       "  Top Ten  \n",
       "0     NaN  \n",
       "1     NaN  \n",
       "2     NaN  \n",
       "3     NaN  \n",
       "4     NaN  \n",
       "5     NaN  \n",
       "6     NaN  \n",
       "7     NaN  \n",
       "8     NaN  \n",
       "9     NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('../input/ramen-ratings/ramen-ratings.csv') #reading the data and save it into a variable\n",
    "data.head(10) #show the first 10 rows of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2580, 7)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking total rows and column in our data\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alright, we know that we got 7 columns in our data. Which are:\n",
    "1. *Review* : unique numbers that inform the review order from the latest\n",
    "2. *Brand*  : Ramen brand\n",
    "3. *Variety*: variation of ramen\n",
    "4. *Style*  : style of ramen\n",
    "5. *Country*: Where the ramen is available\n",
    "6. *Stars*  : Ramen ratings\n",
    "7. *Top Ten*: ramen achievement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning\n",
    "Let's check if there are data missing in our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Review #       0\n",
       "Brand          0\n",
       "Variety        0\n",
       "Style          2\n",
       "Country        0\n",
       "Stars          0\n",
       "Top Ten     2539\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We know that in the *Style* column we have 2 missing data. And *Top Ten* data tells us that if the ramen doesn't get 'Top Ten' achievement, then the data will be blank. Since we only have 2 missing data, we can drop it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "data = data.dropna(subset=['Style'])\n",
    "print(data[\"Style\"].isna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Frequency\n",
    "Let's see all styles in *Style* column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Cup', 'Pack', 'Tray', 'Bowl', 'Box', 'Can', 'Bar'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Style'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, now we use *value_counts()* to count each unique Ramen Style."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pack    1531\n",
       "Bowl     481\n",
       "Cup      450\n",
       "Tray     108\n",
       "Box        6\n",
       "Bar        1\n",
       "Can        1\n",
       "Name: Style, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Style'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pack Style is the most used style. Now let's see all countries in *Country* column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Japan' 'Taiwan' 'USA' 'India' 'South Korea' 'Singapore' 'Thailand'\n",
      " 'Hong Kong' 'Vietnam' 'Ghana' 'Malaysia' 'Indonesia' 'China' 'Nigeria'\n",
      " 'Germany' 'Hungary' 'Mexico' 'Fiji' 'Australia' 'Pakistan' 'Bangladesh'\n",
      " 'Canada' 'Nepal' 'Brazil' 'UK' 'Myanmar' 'Netherlands' 'United States'\n",
      " 'Cambodia' 'Finland' 'Sarawak' 'Philippines' 'Sweden' 'Colombia'\n",
      " 'Estonia' 'Holland' 'Poland' 'Dubai']\n",
      "38 Countries\n"
     ]
    }
   ],
   "source": [
    "print(data[\"Country\"].unique())\n",
    "print(len(data[\"Country\"].unique()), 'Countries')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And again, we use *value_counts()*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Japan            352\n",
       "USA              323\n",
       "South Korea      309\n",
       "Taiwan           223\n",
       "Thailand         191\n",
       "China            168\n",
       "Malaysia         156\n",
       "Hong Kong        137\n",
       "Indonesia        126\n",
       "Singapore        109\n",
       "Vietnam          108\n",
       "UK                69\n",
       "Philippines       47\n",
       "Canada            41\n",
       "India             31\n",
       "Germany           27\n",
       "Mexico            25\n",
       "Australia         22\n",
       "Netherlands       15\n",
       "Nepal             14\n",
       "Myanmar           14\n",
       "Hungary            9\n",
       "Pakistan           9\n",
       "Bangladesh         7\n",
       "Colombia           6\n",
       "Cambodia           5\n",
       "Brazil             5\n",
       "Holland            4\n",
       "Fiji               4\n",
       "Poland             4\n",
       "Sarawak            3\n",
       "Finland            3\n",
       "Dubai              3\n",
       "Sweden             3\n",
       "Estonia            2\n",
       "Ghana              2\n",
       "United States      1\n",
       "Nigeria            1\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Country'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since Ramen is very popular in Japan, there is no doubt Japan will be the top one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Top 10\n",
    "Let's see all top 10 ramen for each year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>616</th>\n",
       "      <td>1964</td>\n",
       "      <td>MAMA</td>\n",
       "      <td>Instant Noodles Coconut Milk Flavour</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Myanmar</td>\n",
       "      <td>5</td>\n",
       "      <td>2016 #10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>1947</td>\n",
       "      <td>Prima Taste</td>\n",
       "      <td>Singapore Laksa Wholegrain La Mian</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>5</td>\n",
       "      <td>2016 #1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>1925</td>\n",
       "      <td>Prima</td>\n",
       "      <td>Juzz's Mee Creamy Chicken Flavour</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>5</td>\n",
       "      <td>2016 #8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673</th>\n",
       "      <td>1907</td>\n",
       "      <td>Prima Taste</td>\n",
       "      <td>Singapore Curry Wholegrain La Mian</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>5</td>\n",
       "      <td>2016 #5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>1828</td>\n",
       "      <td>Tseng Noodles</td>\n",
       "      <td>Scallion With Sichuan Pepper  Flavor</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>5</td>\n",
       "      <td>2016 #9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891</th>\n",
       "      <td>1689</td>\n",
       "      <td>Wugudaochang</td>\n",
       "      <td>Tomato Beef Brisket Flavor Purple Potato Noodle</td>\n",
       "      <td>Pack</td>\n",
       "      <td>China</td>\n",
       "      <td>5</td>\n",
       "      <td>2016 #7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>1638</td>\n",
       "      <td>A-Sha Dry Noodle</td>\n",
       "      <td>Veggie Noodle Tomato Noodle With Vine Ripened ...</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>5</td>\n",
       "      <td>2015 #10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>963</th>\n",
       "      <td>1617</td>\n",
       "      <td>MyKuali</td>\n",
       "      <td>Penang Hokkien Prawn Noodle (New Improved Taste)</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>5</td>\n",
       "      <td>2015 #7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>1585</td>\n",
       "      <td>CarJEN</td>\n",
       "      <td>Nyonya Curry Laksa</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>5</td>\n",
       "      <td>2015 #4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1059</th>\n",
       "      <td>1521</td>\n",
       "      <td>Maruchan</td>\n",
       "      <td>Gotsumori Sauce Yakisoba</td>\n",
       "      <td>Tray</td>\n",
       "      <td>Japan</td>\n",
       "      <td>5</td>\n",
       "      <td>2015 #9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1072</th>\n",
       "      <td>1508</td>\n",
       "      <td>Mamee</td>\n",
       "      <td>Chef Gold Recipe Mi Kari Seribu Rasa</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>5</td>\n",
       "      <td>2015 #6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1080</th>\n",
       "      <td>1500</td>\n",
       "      <td>MyKuali</td>\n",
       "      <td>Penang Red Tom Yum Goong Noodle</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>5</td>\n",
       "      <td>2015 #1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1109</th>\n",
       "      <td>1471</td>\n",
       "      <td>Mama</td>\n",
       "      <td>Instant Noodles Shrimp Creamy Tom Yum Flavour ...</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>5</td>\n",
       "      <td>2013 #10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1114</th>\n",
       "      <td>1466</td>\n",
       "      <td>Mama</td>\n",
       "      <td>Oriental Style Instant Noodles Green Curry Fla...</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>5</td>\n",
       "      <td>2015 #8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1230</th>\n",
       "      <td>1350</td>\n",
       "      <td>Mamee</td>\n",
       "      <td>Chef Curry Laksa Flavour</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>5</td>\n",
       "      <td>2014 #7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>1330</td>\n",
       "      <td>Sapporo Ichiban</td>\n",
       "      <td>Otafuku Okonomi Sauce Yakisoba</td>\n",
       "      <td>Tray</td>\n",
       "      <td>Japan</td>\n",
       "      <td>5</td>\n",
       "      <td>2014 #4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1272</th>\n",
       "      <td>1308</td>\n",
       "      <td>Nongshim</td>\n",
       "      <td>Soon Veggie Noodle Soup</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>5</td>\n",
       "      <td>2014 #9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1278</th>\n",
       "      <td>1302</td>\n",
       "      <td>Mama</td>\n",
       "      <td>Instant Noodles Yentafo Tom Yum Mohfai Flavour</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>5</td>\n",
       "      <td>2014 #10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>1271</td>\n",
       "      <td>Prima Taste</td>\n",
       "      <td>Singapore Chilli Crab La Mian</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>5</td>\n",
       "      <td>2014 #8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1382</th>\n",
       "      <td>1198</td>\n",
       "      <td>Samyang Foods</td>\n",
       "      <td>Maesaengyitangmyun Baked Noodle</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>5</td>\n",
       "      <td>2014 #5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1397</th>\n",
       "      <td>1183</td>\n",
       "      <td>Paldo</td>\n",
       "      <td>Cheese Noodle</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>5</td>\n",
       "      <td>2014 #6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1493</th>\n",
       "      <td>1087</td>\n",
       "      <td>MyKuali</td>\n",
       "      <td>Penang White Curry Noodle</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>5</td>\n",
       "      <td>2014 #1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1588</th>\n",
       "      <td>992</td>\n",
       "      <td>Prima Taste</td>\n",
       "      <td>Singapore Laksa La Mian</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>5</td>\n",
       "      <td>2013 #1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1589</th>\n",
       "      <td>991</td>\n",
       "      <td>Prima Taste</td>\n",
       "      <td>Singapore Curry La Mian</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>5</td>\n",
       "      <td>2013 #2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1646</th>\n",
       "      <td>934</td>\n",
       "      <td>Nongshim</td>\n",
       "      <td>Jinjja Jinjja Flamin' Hot &amp; Nutty</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>5</td>\n",
       "      <td>2013 #4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1670</th>\n",
       "      <td>910</td>\n",
       "      <td>Paldo</td>\n",
       "      <td>Namja Ramyun</td>\n",
       "      <td>Bowl</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>4.25</td>\n",
       "      <td>\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1757</th>\n",
       "      <td>823</td>\n",
       "      <td>Paldo</td>\n",
       "      <td>Kokomen Spicy Chicken</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>5</td>\n",
       "      <td>2013 #9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1846</th>\n",
       "      <td>734</td>\n",
       "      <td>Indomie</td>\n",
       "      <td>Mi Goreng Rasa Ayam Panggang Jumbo (Local)</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>5</td>\n",
       "      <td>\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1865</th>\n",
       "      <td>715</td>\n",
       "      <td>Indomie</td>\n",
       "      <td>Mi Goreng Rendang (Import)</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>5</td>\n",
       "      <td>2013 #3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>608</td>\n",
       "      <td>Koka</td>\n",
       "      <td>Spicy Black Pepper</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>5</td>\n",
       "      <td>2012 #10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>578</td>\n",
       "      <td>Nongshim</td>\n",
       "      <td>Shin Ramyun Black</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>4.75</td>\n",
       "      <td>2012 #7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2146</th>\n",
       "      <td>434</td>\n",
       "      <td>Mi Sedaap</td>\n",
       "      <td>Kari Spesial</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2012 #5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2188</th>\n",
       "      <td>392</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Yakisoba Noodles Karashi</td>\n",
       "      <td>Tray</td>\n",
       "      <td>Japan</td>\n",
       "      <td>5</td>\n",
       "      <td>2012 #3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2189</th>\n",
       "      <td>391</td>\n",
       "      <td>Myojo</td>\n",
       "      <td>Hyoubanya No Chukasoba Oriental</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4.25</td>\n",
       "      <td>2012 #6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2248</th>\n",
       "      <td>332</td>\n",
       "      <td>Nongshim</td>\n",
       "      <td>Champong Oriental Noodles</td>\n",
       "      <td>Cup</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>3.75</td>\n",
       "      <td>\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2295</th>\n",
       "      <td>285</td>\n",
       "      <td>Doll</td>\n",
       "      <td>Artificial Chicken</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2012 #9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2475</th>\n",
       "      <td>105</td>\n",
       "      <td>Indomie</td>\n",
       "      <td>Special Fried Curly Noodle</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>5</td>\n",
       "      <td>2012 #1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2533</th>\n",
       "      <td>47</td>\n",
       "      <td>Indomie</td>\n",
       "      <td>Mi Goreng Jumbo Barbecue Chicken</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>5</td>\n",
       "      <td>2012 #2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2535</th>\n",
       "      <td>45</td>\n",
       "      <td>Indomie</td>\n",
       "      <td>Mi Goreng Sate</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>5</td>\n",
       "      <td>\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2549</th>\n",
       "      <td>31</td>\n",
       "      <td>Myojo</td>\n",
       "      <td>Ippeichan Yakisoba</td>\n",
       "      <td>Tray</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4</td>\n",
       "      <td>2013 #6</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Review #             Brand  \\\n",
       "616       1964              MAMA   \n",
       "633       1947       Prima Taste   \n",
       "655       1925             Prima   \n",
       "673       1907       Prima Taste   \n",
       "752       1828     Tseng Noodles   \n",
       "891       1689      Wugudaochang   \n",
       "942       1638  A-Sha Dry Noodle   \n",
       "963       1617           MyKuali   \n",
       "995       1585            CarJEN   \n",
       "1059      1521          Maruchan   \n",
       "1072      1508             Mamee   \n",
       "1080      1500           MyKuali   \n",
       "1109      1471              Mama   \n",
       "1114      1466              Mama   \n",
       "1230      1350             Mamee   \n",
       "1250      1330   Sapporo Ichiban   \n",
       "1272      1308          Nongshim   \n",
       "1278      1302              Mama   \n",
       "1309      1271       Prima Taste   \n",
       "1382      1198     Samyang Foods   \n",
       "1397      1183             Paldo   \n",
       "1493      1087           MyKuali   \n",
       "1588       992       Prima Taste   \n",
       "1589       991       Prima Taste   \n",
       "1646       934          Nongshim   \n",
       "1670       910             Paldo   \n",
       "1757       823             Paldo   \n",
       "1846       734           Indomie   \n",
       "1865       715           Indomie   \n",
       "1972       608              Koka   \n",
       "2002       578          Nongshim   \n",
       "2146       434         Mi Sedaap   \n",
       "2188       392            Nissin   \n",
       "2189       391             Myojo   \n",
       "2248       332          Nongshim   \n",
       "2295       285              Doll   \n",
       "2475       105           Indomie   \n",
       "2533        47           Indomie   \n",
       "2535        45           Indomie   \n",
       "2549        31             Myojo   \n",
       "2567        13   Sapporo Ichiban   \n",
       "\n",
       "                                                Variety Style      Country  \\\n",
       "616                Instant Noodles Coconut Milk Flavour  Pack      Myanmar   \n",
       "633                  Singapore Laksa Wholegrain La Mian  Pack    Singapore   \n",
       "655                   Juzz's Mee Creamy Chicken Flavour  Pack    Singapore   \n",
       "673                  Singapore Curry Wholegrain La Mian  Pack    Singapore   \n",
       "752                Scallion With Sichuan Pepper  Flavor  Pack       Taiwan   \n",
       "891     Tomato Beef Brisket Flavor Purple Potato Noodle  Pack        China   \n",
       "942   Veggie Noodle Tomato Noodle With Vine Ripened ...  Pack       Taiwan   \n",
       "963    Penang Hokkien Prawn Noodle (New Improved Taste)  Pack     Malaysia   \n",
       "995                                  Nyonya Curry Laksa  Pack     Malaysia   \n",
       "1059                           Gotsumori Sauce Yakisoba  Tray        Japan   \n",
       "1072               Chef Gold Recipe Mi Kari Seribu Rasa  Pack     Malaysia   \n",
       "1080                    Penang Red Tom Yum Goong Noodle  Pack     Malaysia   \n",
       "1109  Instant Noodles Shrimp Creamy Tom Yum Flavour ...  Pack     Thailand   \n",
       "1114  Oriental Style Instant Noodles Green Curry Fla...  Pack     Thailand   \n",
       "1230                           Chef Curry Laksa Flavour  Pack     Malaysia   \n",
       "1250                     Otafuku Okonomi Sauce Yakisoba  Tray        Japan   \n",
       "1272                            Soon Veggie Noodle Soup  Pack  South Korea   \n",
       "1278     Instant Noodles Yentafo Tom Yum Mohfai Flavour  Pack     Thailand   \n",
       "1309                      Singapore Chilli Crab La Mian  Pack    Singapore   \n",
       "1382                    Maesaengyitangmyun Baked Noodle  Pack  South Korea   \n",
       "1397                                      Cheese Noodle  Pack  South Korea   \n",
       "1493                          Penang White Curry Noodle  Pack     Malaysia   \n",
       "1588                            Singapore Laksa La Mian  Pack    Singapore   \n",
       "1589                            Singapore Curry La Mian  Pack    Singapore   \n",
       "1646                  Jinjja Jinjja Flamin' Hot & Nutty  Pack          USA   \n",
       "1670                                       Namja Ramyun  Bowl  South Korea   \n",
       "1757                              Kokomen Spicy Chicken  Pack  South Korea   \n",
       "1846         Mi Goreng Rasa Ayam Panggang Jumbo (Local)  Pack    Indonesia   \n",
       "1865                         Mi Goreng Rendang (Import)  Pack    Indonesia   \n",
       "1972                                 Spicy Black Pepper  Pack    Singapore   \n",
       "2002                                  Shin Ramyun Black  Pack  South Korea   \n",
       "2146                                       Kari Spesial  Pack    Indonesia   \n",
       "2188                           Yakisoba Noodles Karashi  Tray        Japan   \n",
       "2189                    Hyoubanya No Chukasoba Oriental  Pack        Japan   \n",
       "2248                          Champong Oriental Noodles   Cup  South Korea   \n",
       "2295                                 Artificial Chicken  Pack    Hong Kong   \n",
       "2475                         Special Fried Curly Noodle  Pack    Indonesia   \n",
       "2533                   Mi Goreng Jumbo Barbecue Chicken  Pack    Indonesia   \n",
       "2535                                     Mi Goreng Sate  Pack    Indonesia   \n",
       "2549                                 Ippeichan Yakisoba  Tray        Japan   \n",
       "2567                                          Chow Mein  Pack        Japan   \n",
       "\n",
       "     Stars   Top Ten  \n",
       "616      5  2016 #10  \n",
       "633      5   2016 #1  \n",
       "655      5   2016 #8  \n",
       "673      5   2016 #5  \n",
       "752      5   2016 #9  \n",
       "891      5   2016 #7  \n",
       "942      5  2015 #10  \n",
       "963      5   2015 #7  \n",
       "995      5   2015 #4  \n",
       "1059     5   2015 #9  \n",
       "1072     5   2015 #6  \n",
       "1080     5   2015 #1  \n",
       "1109     5  2013 #10  \n",
       "1114     5   2015 #8  \n",
       "1230     5   2014 #7  \n",
       "1250     5   2014 #4  \n",
       "1272     5   2014 #9  \n",
       "1278     5  2014 #10  \n",
       "1309     5   2014 #8  \n",
       "1382     5   2014 #5  \n",
       "1397     5   2014 #6  \n",
       "1493     5   2014 #1  \n",
       "1588     5   2013 #1  \n",
       "1589     5   2013 #2  \n",
       "1646     5   2013 #4  \n",
       "1670  4.25        \\n  \n",
       "1757     5   2013 #9  \n",
       "1846     5        \\n  \n",
       "1865     5   2013 #3  \n",
       "1972     5  2012 #10  \n",
       "2002  4.75   2012 #7  \n",
       "2146   4.5   2012 #5  \n",
       "2188     5   2012 #3  \n",
       "2189  4.25   2012 #6  \n",
       "2248  3.75        \\n  \n",
       "2295   4.5   2012 #9  \n",
       "2475     5   2012 #1  \n",
       "2533     5   2012 #2  \n",
       "2535     5        \\n  \n",
       "2549     4   2013 #6  \n",
       "2567     5   2012 #4  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top10 = data.dropna()\n",
    "top10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take a look at the data. We found '\\n' or *newline* in our data. Maybe it accidentally happened. So we easily filter the data using selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>2475</th>\n",
       "      <td>105</td>\n",
       "      <td>Indomie</td>\n",
       "      <td>Special Fried Curly Noodle</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>5</td>\n",
       "      <td>2012 #1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>608</td>\n",
       "      <td>Koka</td>\n",
       "      <td>Spicy Black Pepper</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>5</td>\n",
       "      <td>2012 #10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2533</th>\n",
       "      <td>47</td>\n",
       "      <td>Indomie</td>\n",
       "      <td>Mi Goreng Jumbo Barbecue Chicken</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>5</td>\n",
       "      <td>2012 #2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2188</th>\n",
       "      <td>392</td>\n",
       "      <td>Nissin</td>\n",
       "      <td>Yakisoba Noodles Karashi</td>\n",
       "      <td>Tray</td>\n",
       "      <td>Japan</td>\n",
       "      <td>5</td>\n",
       "      <td>2012 #3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2146</th>\n",
       "      <td>434</td>\n",
       "      <td>Mi Sedaap</td>\n",
       "      <td>Kari Spesial</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2012 #5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2189</th>\n",
       "      <td>391</td>\n",
       "      <td>Myojo</td>\n",
       "      <td>Hyoubanya No Chukasoba Oriental</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4.25</td>\n",
       "      <td>2012 #6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>578</td>\n",
       "      <td>Nongshim</td>\n",
       "      <td>Shin Ramyun Black</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>4.75</td>\n",
       "      <td>2012 #7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2295</th>\n",
       "      <td>285</td>\n",
       "      <td>Doll</td>\n",
       "      <td>Artificial Chicken</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2012 #9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1588</th>\n",
       "      <td>992</td>\n",
       "      <td>Prima Taste</td>\n",
       "      <td>Singapore Laksa La Mian</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>5</td>\n",
       "      <td>2013 #1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1109</th>\n",
       "      <td>1471</td>\n",
       "      <td>Mama</td>\n",
       "      <td>Instant Noodles Shrimp Creamy Tom Yum Flavour ...</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>5</td>\n",
       "      <td>2013 #10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1589</th>\n",
       "      <td>991</td>\n",
       "      <td>Prima Taste</td>\n",
       "      <td>Singapore Curry La Mian</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>5</td>\n",
       "      <td>2013 #2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1865</th>\n",
       "      <td>715</td>\n",
       "      <td>Indomie</td>\n",
       "      <td>Mi Goreng Rendang (Import)</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>5</td>\n",
       "      <td>2013 #3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1646</th>\n",
       "      <td>934</td>\n",
       "      <td>Nongshim</td>\n",
       "      <td>Jinjja Jinjja Flamin' Hot &amp; Nutty</td>\n",
       "      <td>Pack</td>\n",
       "      <td>USA</td>\n",
       "      <td>5</td>\n",
       "      <td>2013 #4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2549</th>\n",
       "      <td>31</td>\n",
       "      <td>Myojo</td>\n",
       "      <td>Ippeichan Yakisoba</td>\n",
       "      <td>Tray</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4</td>\n",
       "      <td>2013 #6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1757</th>\n",
       "      <td>823</td>\n",
       "      <td>Paldo</td>\n",
       "      <td>Kokomen Spicy Chicken</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>5</td>\n",
       "      <td>2013 #9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1493</th>\n",
       "      <td>1087</td>\n",
       "      <td>MyKuali</td>\n",
       "      <td>Penang White Curry Noodle</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>5</td>\n",
       "      <td>2014 #1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1278</th>\n",
       "      <td>1302</td>\n",
       "      <td>Mama</td>\n",
       "      <td>Instant Noodles Yentafo Tom Yum Mohfai Flavour</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>5</td>\n",
       "      <td>2014 #10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>1330</td>\n",
       "      <td>Sapporo Ichiban</td>\n",
       "      <td>Otafuku Okonomi Sauce Yakisoba</td>\n",
       "      <td>Tray</td>\n",
       "      <td>Japan</td>\n",
       "      <td>5</td>\n",
       "      <td>2014 #4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1382</th>\n",
       "      <td>1198</td>\n",
       "      <td>Samyang Foods</td>\n",
       "      <td>Maesaengyitangmyun Baked Noodle</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>5</td>\n",
       "      <td>2014 #5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1397</th>\n",
       "      <td>1183</td>\n",
       "      <td>Paldo</td>\n",
       "      <td>Cheese Noodle</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>5</td>\n",
       "      <td>2014 #6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1230</th>\n",
       "      <td>1350</td>\n",
       "      <td>Mamee</td>\n",
       "      <td>Chef Curry Laksa Flavour</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>5</td>\n",
       "      <td>2014 #7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>1271</td>\n",
       "      <td>Prima Taste</td>\n",
       "      <td>Singapore Chilli Crab La Mian</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>5</td>\n",
       "      <td>2014 #8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1272</th>\n",
       "      <td>1308</td>\n",
       "      <td>Nongshim</td>\n",
       "      <td>Soon Veggie Noodle Soup</td>\n",
       "      <td>Pack</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>5</td>\n",
       "      <td>2014 #9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1080</th>\n",
       "      <td>1500</td>\n",
       "      <td>MyKuali</td>\n",
       "      <td>Penang Red Tom Yum Goong Noodle</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>5</td>\n",
       "      <td>2015 #1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>1638</td>\n",
       "      <td>A-Sha Dry Noodle</td>\n",
       "      <td>Veggie Noodle Tomato Noodle With Vine Ripened ...</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>5</td>\n",
       "      <td>2015 #10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>1585</td>\n",
       "      <td>CarJEN</td>\n",
       "      <td>Nyonya Curry Laksa</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>5</td>\n",
       "      <td>2015 #4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1072</th>\n",
       "      <td>1508</td>\n",
       "      <td>Mamee</td>\n",
       "      <td>Chef Gold Recipe Mi Kari Seribu Rasa</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>5</td>\n",
       "      <td>2015 #6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>963</th>\n",
       "      <td>1617</td>\n",
       "      <td>MyKuali</td>\n",
       "      <td>Penang Hokkien Prawn Noodle (New Improved Taste)</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>5</td>\n",
       "      <td>2015 #7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1114</th>\n",
       "      <td>1466</td>\n",
       "      <td>Mama</td>\n",
       "      <td>Oriental Style Instant Noodles Green Curry Fla...</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>5</td>\n",
       "      <td>2015 #8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1059</th>\n",
       "      <td>1521</td>\n",
       "      <td>Maruchan</td>\n",
       "      <td>Gotsumori Sauce Yakisoba</td>\n",
       "      <td>Tray</td>\n",
       "      <td>Japan</td>\n",
       "      <td>5</td>\n",
       "      <td>2015 #9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>1947</td>\n",
       "      <td>Prima Taste</td>\n",
       "      <td>Singapore Laksa Wholegrain La Mian</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>5</td>\n",
       "      <td>2016 #1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>1964</td>\n",
       "      <td>MAMA</td>\n",
       "      <td>Instant Noodles Coconut Milk Flavour</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Myanmar</td>\n",
       "      <td>5</td>\n",
       "      <td>2016 #10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673</th>\n",
       "      <td>1907</td>\n",
       "      <td>Prima Taste</td>\n",
       "      <td>Singapore Curry Wholegrain La Mian</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>5</td>\n",
       "      <td>2016 #5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891</th>\n",
       "      <td>1689</td>\n",
       "      <td>Wugudaochang</td>\n",
       "      <td>Tomato Beef Brisket Flavor Purple Potato Noodle</td>\n",
       "      <td>Pack</td>\n",
       "      <td>China</td>\n",
       "      <td>5</td>\n",
       "      <td>2016 #7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>1925</td>\n",
       "      <td>Prima</td>\n",
       "      <td>Juzz's Mee Creamy Chicken Flavour</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>5</td>\n",
       "      <td>2016 #8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>1828</td>\n",
       "      <td>Tseng Noodles</td>\n",
       "      <td>Scallion With Sichuan Pepper  Flavor</td>\n",
       "      <td>Pack</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>5</td>\n",
       "      <td>2016 #9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Review #             Brand  \\\n",
       "2475       105           Indomie   \n",
       "1972       608              Koka   \n",
       "2533        47           Indomie   \n",
       "2188       392            Nissin   \n",
       "2567        13   Sapporo Ichiban   \n",
       "2146       434         Mi Sedaap   \n",
       "2189       391             Myojo   \n",
       "2002       578          Nongshim   \n",
       "2295       285              Doll   \n",
       "1588       992       Prima Taste   \n",
       "1109      1471              Mama   \n",
       "1589       991       Prima Taste   \n",
       "1865       715           Indomie   \n",
       "1646       934          Nongshim   \n",
       "2549        31             Myojo   \n",
       "1757       823             Paldo   \n",
       "1493      1087           MyKuali   \n",
       "1278      1302              Mama   \n",
       "1250      1330   Sapporo Ichiban   \n",
       "1382      1198     Samyang Foods   \n",
       "1397      1183             Paldo   \n",
       "1230      1350             Mamee   \n",
       "1309      1271       Prima Taste   \n",
       "1272      1308          Nongshim   \n",
       "1080      1500           MyKuali   \n",
       "942       1638  A-Sha Dry Noodle   \n",
       "995       1585            CarJEN   \n",
       "1072      1508             Mamee   \n",
       "963       1617           MyKuali   \n",
       "1114      1466              Mama   \n",
       "1059      1521          Maruchan   \n",
       "633       1947       Prima Taste   \n",
       "616       1964              MAMA   \n",
       "673       1907       Prima Taste   \n",
       "891       1689      Wugudaochang   \n",
       "655       1925             Prima   \n",
       "752       1828     Tseng Noodles   \n",
       "\n",
       "                                                Variety Style      Country  \\\n",
       "2475                         Special Fried Curly Noodle  Pack    Indonesia   \n",
       "1972                                 Spicy Black Pepper  Pack    Singapore   \n",
       "2533                   Mi Goreng Jumbo Barbecue Chicken  Pack    Indonesia   \n",
       "2188                           Yakisoba Noodles Karashi  Tray        Japan   \n",
       "2567                                          Chow Mein  Pack        Japan   \n",
       "2146                                       Kari Spesial  Pack    Indonesia   \n",
       "2189                    Hyoubanya No Chukasoba Oriental  Pack        Japan   \n",
       "2002                                  Shin Ramyun Black  Pack  South Korea   \n",
       "2295                                 Artificial Chicken  Pack    Hong Kong   \n",
       "1588                            Singapore Laksa La Mian  Pack    Singapore   \n",
       "1109  Instant Noodles Shrimp Creamy Tom Yum Flavour ...  Pack     Thailand   \n",
       "1589                            Singapore Curry La Mian  Pack    Singapore   \n",
       "1865                         Mi Goreng Rendang (Import)  Pack    Indonesia   \n",
       "1646                  Jinjja Jinjja Flamin' Hot & Nutty  Pack          USA   \n",
       "2549                                 Ippeichan Yakisoba  Tray        Japan   \n",
       "1757                              Kokomen Spicy Chicken  Pack  South Korea   \n",
       "1493                          Penang White Curry Noodle  Pack     Malaysia   \n",
       "1278     Instant Noodles Yentafo Tom Yum Mohfai Flavour  Pack     Thailand   \n",
       "1250                     Otafuku Okonomi Sauce Yakisoba  Tray        Japan   \n",
       "1382                    Maesaengyitangmyun Baked Noodle  Pack  South Korea   \n",
       "1397                                      Cheese Noodle  Pack  South Korea   \n",
       "1230                           Chef Curry Laksa Flavour  Pack     Malaysia   \n",
       "1309                      Singapore Chilli Crab La Mian  Pack    Singapore   \n",
       "1272                            Soon Veggie Noodle Soup  Pack  South Korea   \n",
       "1080                    Penang Red Tom Yum Goong Noodle  Pack     Malaysia   \n",
       "942   Veggie Noodle Tomato Noodle With Vine Ripened ...  Pack       Taiwan   \n",
       "995                                  Nyonya Curry Laksa  Pack     Malaysia   \n",
       "1072               Chef Gold Recipe Mi Kari Seribu Rasa  Pack     Malaysia   \n",
       "963    Penang Hokkien Prawn Noodle (New Improved Taste)  Pack     Malaysia   \n",
       "1114  Oriental Style Instant Noodles Green Curry Fla...  Pack     Thailand   \n",
       "1059                           Gotsumori Sauce Yakisoba  Tray        Japan   \n",
       "633                  Singapore Laksa Wholegrain La Mian  Pack    Singapore   \n",
       "616                Instant Noodles Coconut Milk Flavour  Pack      Myanmar   \n",
       "673                  Singapore Curry Wholegrain La Mian  Pack    Singapore   \n",
       "891     Tomato Beef Brisket Flavor Purple Potato Noodle  Pack        China   \n",
       "655                   Juzz's Mee Creamy Chicken Flavour  Pack    Singapore   \n",
       "752                Scallion With Sichuan Pepper  Flavor  Pack       Taiwan   \n",
       "\n",
       "     Stars   Top Ten  \n",
       "2475     5   2012 #1  \n",
       "1972     5  2012 #10  \n",
       "2533     5   2012 #2  \n",
       "2188     5   2012 #3  \n",
       "2567     5   2012 #4  \n",
       "2146   4.5   2012 #5  \n",
       "2189  4.25   2012 #6  \n",
       "2002  4.75   2012 #7  \n",
       "2295   4.5   2012 #9  \n",
       "1588     5   2013 #1  \n",
       "1109     5  2013 #10  \n",
       "1589     5   2013 #2  \n",
       "1865     5   2013 #3  \n",
       "1646     5   2013 #4  \n",
       "2549     4   2013 #6  \n",
       "1757     5   2013 #9  \n",
       "1493     5   2014 #1  \n",
       "1278     5  2014 #10  \n",
       "1250     5   2014 #4  \n",
       "1382     5   2014 #5  \n",
       "1397     5   2014 #6  \n",
       "1230     5   2014 #7  \n",
       "1309     5   2014 #8  \n",
       "1272     5   2014 #9  \n",
       "1080     5   2015 #1  \n",
       "942      5  2015 #10  \n",
       "995      5   2015 #4  \n",
       "1072     5   2015 #6  \n",
       "963      5   2015 #7  \n",
       "1114     5   2015 #8  \n",
       "1059     5   2015 #9  \n",
       "633      5   2016 #1  \n",
       "616      5  2016 #10  \n",
       "673      5   2016 #5  \n",
       "891      5   2016 #7  \n",
       "655      5   2016 #8  \n",
       "752      5   2016 #9  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top10 = top10[top10['Top Ten'] != '\\n'] #if the data in Top Ten column contains '\\n' we can ignore it\n",
    "top10 = top10.sort_values('Top Ten' ) #and we sort it by year\n",
    "top10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top 10 Ramen Brand by total products\n",
    "Top 10 Ramen brands that have the most products."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nissin           381\n",
       "Nongshim          98\n",
       "Maruchan          76\n",
       "Mama              71\n",
       "Paldo             66\n",
       "Myojo             63\n",
       "Indomie           53\n",
       "Samyang Foods     52\n",
       "Ottogi            46\n",
       "Vina Acecook      34\n",
       "Name: Brand, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Brand'].value_counts()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top 50 Ramen Ratings by brands\n",
    "We will calculate the average rating for each brand, and then we'll show who is in top 50."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "355\n"
     ]
    }
   ],
   "source": [
    "#First, let's see how many ramen brands are in our data\n",
    "print(len(data['Brand'].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see if *Stars* column has invalid value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unrated\n",
      "Unrated\n",
      "Unrated\n"
     ]
    }
   ],
   "source": [
    "for s in data['Stars']:\n",
    "    try:\n",
    "        s = float(s)\n",
    "    except:\n",
    "        print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, we found anomalies in our data. Some ramen don't have ratings. We can drop it from our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "data = data[data['Stars'] != 'Unrated']\n",
    "print(data[data['Stars'] == 'Unrated']['Stars'].sum()) #make sure if there are no 'Unrated'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, finally they're gone. It's time to have fun!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "brands = list(data['Brand'].unique())\n",
    "counter = [0.0]*355\n",
    "\n",
    "brands_cnt = dict(zip(brands, counter)) #create dictionary to count all ratings and then save the averages\n",
    "\n",
    "for brand in brands:\n",
    "    brands_data = data[data['Brand'] == brand]\n",
    "    for star in brands_data['Stars']:\n",
    "        brands_cnt[brand] += float(star) #count all ratings\n",
    "    brands_cnt[brand] /= len(brands_data) #average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#1  Tao Kae Noi               5.0\n",
      "#2  Jackpot Teriyaki          5.0\n",
      "#3  Prima                     5.0\n",
      "#4  Takamori                  5.0\n",
      "#5  The Ramen Rater Select    5.0\n",
      "#6  Kiki Noodle               5.0\n",
      "#7  Daifuku                   5.0\n",
      "#8  Patanjali                 5.0\n",
      "#9  Nyor Nyar                 5.0\n",
      "#10 ORee Garden               5.0\n",
      "#11 Prima Taste               5.0\n",
      "#12 MyOri                     5.0\n",
      "#13 Seven & I                 5.0\n",
      "#14 Torishi                   5.0\n",
      "#15 ChoripDong                5.0\n",
      "#16 The Bridge                5.0\n",
      "#17 Plats Du Chef             5.0\n",
      "#18 Kimura                    5.0\n",
      "#19 Peyang                    5.0\n",
      "#20 Komforte Chockolates      5.0\n",
      "#21 Daddy                     5.0\n",
      "#22 Song Hak                  5.0\n",
      "#23 Foodmon                   5.0\n",
      "#24 Higashi                   5.0\n",
      "#25 MyKuali                   4.95\n",
      "#26 CarJEN                    4.93\n",
      "#27 TTL                       4.92\n",
      "#28 Nanyang Chef              4.88\n",
      "#29 Mom's Dry Noodle          4.83\n",
      "#30 Tseng Noodles             4.82\n",
      "#31 Ogasawara                 4.75\n",
      "#32 World O' Noodle           4.75\n",
      "#33 Ah Lai                    4.75\n",
      "#34 Salam Mie                 4.62\n",
      "#35 Yamamori                  4.62\n",
      "#36 Yamachan                  4.57\n",
      "#37 Higashimaru               4.5\n",
      "#38 Ishimaru                  4.5\n",
      "#39 Aroi                      4.5\n",
      "#40 The Kitchen Food          4.5\n",
      "#41 President                 4.5\n",
      "#42 Dongwon                   4.5\n",
      "#43 Seven-Eleven              4.5\n",
      "#44 Chikara                   4.5\n",
      "#45 Sanyo Foods               4.5\n",
      "#46 Sun Noodle                4.46\n",
      "#47 Happy Cook                4.42\n",
      "#48 Tablemark                 4.42\n",
      "#49 Kuriki                    4.42\n",
      "#50 Jingqi                    4.38\n"
     ]
    }
   ],
   "source": [
    "top50ratings = [] #list for saving the brand name and its average rating\n",
    "for key, values in brands_cnt.items():\n",
    "    top50ratings.append([key,values])\n",
    "\n",
    "#print the top 50 ramen ratings by brand\n",
    "top50ratings = sorted(top50ratings, key = lambda x : x[1], reverse = True) #sorting values in descending order\n",
    "top50ratings\n",
    "for i in range(50):\n",
    "    print('#{:<3}{:25} {}'.format(i+1, top50ratings[i][0], round(top50ratings[i][1],2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_________________________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualisation\n",
    "Now we move into interesting part. First, we make count plots to see value counts for each country\n",
    "#### Count PLot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style = 'darkgrid')\n",
    "f, ax = plt.subplots(1,1,figsize = (15,5))\n",
    "sns.countplot(x = 'Country', data = data)\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pie Plot\n",
    "If you want to plot something about percentage, then Pie Plot is a right choice. let's find out the percentage of total ramen based on style first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = 'Pack', 'Bowl', 'Cup' , 'Tray', 'Box' #We can't include 'Bar' and 'Can' because they only appear once in our data.\n",
    "size = [1531, 481, 450, 108, 6]\n",
    "\n",
    "f, ax = plt.subplots(1,1, figsize= (10,10))\n",
    "\n",
    "ax.pie(size, labels = labels, autopct = '%1.2f%%', startangle = 180)\n",
    "ax.axis('equal')\n",
    "ax.set_title(\"Style\", size = 20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___________________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, that's all my analysis. If you think I missed something, feel free to comment. Thank you for reading this notebook!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}