Chocolate Companies.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 223
    },
    "executionInfo": {
     "elapsed": 371,
     "status": "ok",
     "timestamp": 1627552541479,
     "user": {
      "displayName": "Parto Shahroodi",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgVRnuIKtxzeAYtLGbbb7NnFc-HXqxuTOJ6OMw7uw=s64",
      "userId": "08853559073941204272"
     },
     "user_tz": -270
    },
    "id": "JUEwzKpr2RUk",
    "outputId": "684b5cff-21a6-46d3-8084-e090addb1731"
   },
   "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>Company</th>\n",
       "      <th>Specific Bean\\nOrigin</th>\n",
       "      <th>Review\\nDate</th>\n",
       "      <th>Cocoa\\nPercent</th>\n",
       "      <th>Company\\nLocation</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Bean\\nType</th>\n",
       "      <th>Broad Bean\\nOrigin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A. Morin</td>\n",
       "      <td>Agua Grande</td>\n",
       "      <td>2016</td>\n",
       "      <td>63%</td>\n",
       "      <td>France</td>\n",
       "      <td>3.75</td>\n",
       "      <td>Blend</td>\n",
       "      <td>Sao Tome</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A. Morin</td>\n",
       "      <td>Kpime</td>\n",
       "      <td>2015</td>\n",
       "      <td>70%</td>\n",
       "      <td>France</td>\n",
       "      <td>2.75</td>\n",
       "      <td>Blend</td>\n",
       "      <td>Togo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A. Morin</td>\n",
       "      <td>Atsane</td>\n",
       "      <td>2015</td>\n",
       "      <td>70%</td>\n",
       "      <td>France</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Blend</td>\n",
       "      <td>Togo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A. Morin</td>\n",
       "      <td>Akata</td>\n",
       "      <td>2015</td>\n",
       "      <td>70%</td>\n",
       "      <td>France</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Blend</td>\n",
       "      <td>Togo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A. Morin</td>\n",
       "      <td>Quilla</td>\n",
       "      <td>2015</td>\n",
       "      <td>70%</td>\n",
       "      <td>France</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Blend</td>\n",
       "      <td>Peru</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Company Specific Bean\\nOrigin  Review\\nDate Cocoa\\nPercent  \\\n",
       "0  A. Morin           Agua Grande          2016            63%   \n",
       "1  A. Morin                 Kpime          2015            70%   \n",
       "2  A. Morin                Atsane          2015            70%   \n",
       "3  A. Morin                 Akata          2015            70%   \n",
       "4  A. Morin                Quilla          2015            70%   \n",
       "\n",
       "  Company\\nLocation  Rating Bean\\nType Broad Bean\\nOrigin  \n",
       "0            France    3.75      Blend           Sao Tome  \n",
       "1            France    2.75      Blend               Togo  \n",
       "2            France    3.00      Blend               Togo  \n",
       "3            France    3.50      Blend               Togo  \n",
       "4            France    3.50      Blend               Peru  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('chocolate.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 442,
     "status": "ok",
     "timestamp": 1627552552538,
     "user": {
      "displayName": "Parto Shahroodi",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgVRnuIKtxzeAYtLGbbb7NnFc-HXqxuTOJ6OMw7uw=s64",
      "userId": "08853559073941204272"
     },
     "user_tz": -270
    },
    "id": "IPLy4mOvN4LB",
    "outputId": "6f1dcea8-4503-4063-a268-1a5891412bf1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1795, 8)\n",
      "Index(['Company', 'Specific Bean\\nOrigin', 'Review\\nDate', 'Cocoa\\nPercent',\n",
      "       'Company\\nLocation', 'Rating', 'Bean\\nType', 'Broad Bean\\nOrigin'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "s = df.shape\n",
    "cols = df.columns\n",
    "print(s)\n",
    "print(cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "executionInfo": {
     "elapsed": 405,
     "status": "ok",
     "timestamp": 1627552932160,
     "user": {
      "displayName": "Parto Shahroodi",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgVRnuIKtxzeAYtLGbbb7NnFc-HXqxuTOJ6OMw7uw=s64",
      "userId": "08853559073941204272"
     },
     "user_tz": -270
    },
    "id": "J6w12_CDsUaK"
   },
   "outputs": [],
   "source": [
    "l = []\n",
    "for i in cols:\n",
    "    l.append(i.replace('\\n',' '))\n",
    "    \n",
    "        \n",
    "df.columns = l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 452,
     "status": "ok",
     "timestamp": 1626769058517,
     "user": {
      "displayName": "Parto Shahroodi",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgVRnuIKtxzeAYtLGbbb7NnFc-HXqxuTOJ6OMw7uw=s64",
      "userId": "08853559073941204272"
     },
     "user_tz": -270
    },
    "id": "cGLlHp8C4mkT",
    "outputId": "71575d1a-8c77-4437-c8b2-86b149100891"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1795 entries, 0 to 1794\n",
      "Data columns (total 8 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   Company               1795 non-null   object \n",
      " 1   Specific Bean Origin  1795 non-null   object \n",
      " 2   Review Date           1795 non-null   int64  \n",
      " 3   Cocoa Percent         1795 non-null   object \n",
      " 4   Company Location      1795 non-null   object \n",
      " 5   Rating                1795 non-null   float64\n",
      " 6   Bean Type             1795 non-null   object \n",
      " 7   Broad Bean Origin     1794 non-null   object \n",
      "dtypes: float64(1), int64(1), object(6)\n",
      "memory usage: 112.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 456,
     "status": "ok",
     "timestamp": 1626769062163,
     "user": {
      "displayName": "Parto Shahroodi",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgVRnuIKtxzeAYtLGbbb7NnFc-HXqxuTOJ6OMw7uw=s64",
      "userId": "08853559073941204272"
     },
     "user_tz": -270
    },
    "id": "OozOj4u-5KEd",
    "outputId": "a314621d-0e65-4bd2-ce71-ae99d26b0f92"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1795 entries, 0 to 1794\n",
      "Data columns (total 8 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   Company               1795 non-null   string \n",
      " 1   Specific Bean Origin  1795 non-null   string \n",
      " 2   Review Date           1795 non-null   int64  \n",
      " 3   Cocoa Percent         1795 non-null   string \n",
      " 4   Company Location      1795 non-null   string \n",
      " 5   Rating                1795 non-null   float64\n",
      " 6   Bean Type             1795 non-null   string \n",
      " 7   Broad Bean Origin     1794 non-null   string \n",
      "dtypes: float64(1), int64(1), string(6)\n",
      "memory usage: 112.3 KB\n"
     ]
    }
   ],
   "source": [
    "df['Company'] = df['Company'].astype('string')\n",
    "df['Specific Bean Origin'] = df['Specific Bean Origin'].astype('string')\n",
    "df['Cocoa Percent'] = df['Cocoa Percent'].astype('string')\n",
    "df['Company Location'] = df['Company Location'].astype('string')\n",
    "df['Bean Type'] = df['Bean Type'].astype('string')\n",
    "df['Broad Bean Origin'] = df['Broad Bean Origin'].astype('string')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 223
    },
    "executionInfo": {
     "elapsed": 449,
     "status": "ok",
     "timestamp": 1626769123179,
     "user": {
      "displayName": "Parto Shahroodi",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgVRnuIKtxzeAYtLGbbb7NnFc-HXqxuTOJ6OMw7uw=s64",
      "userId": "08853559073941204272"
     },
     "user_tz": -270
    },
    "id": "HXZtM_L--I9o",
    "outputId": "4246a6bd-a533-460b-e5b4-ad85ae5fbc2e"
   },
   "outputs": [],
   "source": [
    "df['Cocoa Percent'] = df['Cocoa Percent'].apply(lambda x : x.replace('%',''))\n",
    "df['Cocoa Percent'] = df['Cocoa Percent'].astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x169ab3904f0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.displot(df, x = 'Cocoa Percent', bins = 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Company</th>\n",
       "      <th>Specific Bean Origin</th>\n",
       "      <th>Review Date</th>\n",
       "      <th>Cocoa Percent</th>\n",
       "      <th>Company Location</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Bean Type</th>\n",
       "      <th>Broad Bean Origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A. Morin</td>\n",
       "      <td>Agua Grande</td>\n",
       "      <td>2016</td>\n",
       "      <td>63.0</td>\n",
       "      <td>France</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Blend</td>\n",
       "      <td>Sao Tome</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A. Morin</td>\n",
       "      <td>Kpime</td>\n",
       "      <td>2015</td>\n",
       "      <td>70.0</td>\n",
       "      <td>France</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Blend</td>\n",
       "      <td>Togo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A. Morin</td>\n",
       "      <td>Atsane</td>\n",
       "      <td>2015</td>\n",
       "      <td>70.0</td>\n",
       "      <td>France</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Blend</td>\n",
       "      <td>Togo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A. Morin</td>\n",
       "      <td>Akata</td>\n",
       "      <td>2015</td>\n",
       "      <td>70.0</td>\n",
       "      <td>France</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Blend</td>\n",
       "      <td>Togo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A. Morin</td>\n",
       "      <td>Quilla</td>\n",
       "      <td>2015</td>\n",
       "      <td>70.0</td>\n",
       "      <td>France</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Blend</td>\n",
       "      <td>Peru</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Company Specific Bean Origin  Review Date  Cocoa Percent Company Location  \\\n",
       "0  A. Morin          Agua Grande         2016           63.0           France   \n",
       "1  A. Morin                Kpime         2015           70.0           France   \n",
       "2  A. Morin               Atsane         2015           70.0           France   \n",
       "3  A. Morin                Akata         2015           70.0           France   \n",
       "4  A. Morin               Quilla         2015           70.0           France   \n",
       "\n",
       "   Rating Bean Type Broad Bean Origin  \n",
       "0    75.0     Blend          Sao Tome  \n",
       "1    55.0     Blend              Togo  \n",
       "2    60.0     Blend              Togo  \n",
       "3    70.0     Blend              Togo  \n",
       "4    70.0     Blend              Peru  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "darsad = 100/max(df.Rating)\n",
    "df['Rating'] = df.Rating.apply(lambda x : x*darsad)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#قیمت صد گرم = درصد کاکائو × امتیاز × ۲۵\n",
    "df['price(100g)'] = 25 * df.Rating * df['Cocoa Percent']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "dark_chocolates = df[df['Cocoa Percent'] > 70]\n",
    "dark_chocolates = dark_chocolates.reset_index()\n",
    "dark_chocolates = dark_chocolates.drop('index', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Company</th>\n",
       "      <th>Specific Bean Origin</th>\n",
       "      <th>Review Date</th>\n",
       "      <th>Cocoa Percent</th>\n",
       "      <th>Company Location</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Bean Type</th>\n",
       "      <th>Broad Bean Origin</th>\n",
       "      <th>price(100g)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adi</td>\n",
       "      <td>Vanua Levu, Toto-A</td>\n",
       "      <td>2011</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Fiji</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Trinitario</td>\n",
       "      <td>Fiji</td>\n",
       "      <td>143000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adi</td>\n",
       "      <td>Vanua Levu</td>\n",
       "      <td>2011</td>\n",
       "      <td>88.0</td>\n",
       "      <td>Fiji</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Trinitario</td>\n",
       "      <td>Fiji</td>\n",
       "      <td>169400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adi</td>\n",
       "      <td>Vanua Levu, Ami-Ami-CA</td>\n",
       "      <td>2011</td>\n",
       "      <td>72.0</td>\n",
       "      <td>Fiji</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Trinitario</td>\n",
       "      <td>Fiji</td>\n",
       "      <td>138600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Akesson's (Pralus)</td>\n",
       "      <td>Bali (west), Sukrama Family, Melaya area</td>\n",
       "      <td>2011</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Trinitario</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>154687.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Akesson's (Pralus)</td>\n",
       "      <td>Madagascar, Ambolikapiky P.</td>\n",
       "      <td>2010</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Criollo</td>\n",
       "      <td>Madagascar</td>\n",
       "      <td>103125.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Company                      Specific Bean Origin  Review Date  \\\n",
       "0                 Adi                        Vanua Levu, Toto-A         2011   \n",
       "1                 Adi                                Vanua Levu         2011   \n",
       "2                 Adi                    Vanua Levu, Ami-Ami-CA         2011   \n",
       "3  Akesson's (Pralus)  Bali (west), Sukrama Family, Melaya area         2011   \n",
       "4  Akesson's (Pralus)               Madagascar, Ambolikapiky P.         2010   \n",
       "\n",
       "   Cocoa Percent Company Location  Rating   Bean Type Broad Bean Origin  \\\n",
       "0           80.0             Fiji    65.0  Trinitario              Fiji   \n",
       "1           88.0             Fiji    70.0  Trinitario              Fiji   \n",
       "2           72.0             Fiji    70.0  Trinitario              Fiji   \n",
       "3           75.0      Switzerland    75.0  Trinitario         Indonesia   \n",
       "4           75.0      Switzerland    55.0     Criollo        Madagascar   \n",
       "\n",
       "   price(100g)  \n",
       "0     143000.0  \n",
       "1     169400.0  \n",
       "2     138600.0  \n",
       "3     154687.5  \n",
       "4     103125.0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dark_chocolates.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "has_Trinitario = dark_chocolates['Bean Type'].str.contains('Trinitario') == True\n",
    "dark_chocolates.loc[has_Trinitario, 'price(100g)'] *= 1.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29113975.0\n"
     ]
    }
   ],
   "source": [
    "priceSum = dark_chocolates.loc[has_Trinitario, 'price(100g)'].sum()\n",
    "print(priceSum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df[df['Cocoa Percent'] <= 70]\n",
    "data = data.reset_index()\n",
    "data.drop('index', axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Review Date</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A. Morin</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.5</td>\n",
       "      <td>66.363636</td>\n",
       "      <td>70.0</td>\n",
       "      <td>63.75</td>\n",
       "      <td>75.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMMA</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acalli</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adi</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aequare (Gianduja)</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hello cocoa</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hexx</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iQ Chocolate</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>organicfair</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>twenty-four blackbirds</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.500000</td>\n",
       "      <td>70.0</td>\n",
       "      <td>62.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>416 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Review Date             2006  2007  2008  2009   2010  2011  2012       2013  \\\n",
       "Company                                                                        \n",
       "A. Morin                 NaN   NaN   NaN   NaN    NaN   NaN  72.5  66.363636   \n",
       "AMMA                     NaN   NaN   NaN   NaN  71.25   NaN   NaN  65.000000   \n",
       "Acalli                   NaN   NaN   NaN   NaN    NaN   NaN   NaN        NaN   \n",
       "Adi                      NaN   NaN   NaN   NaN    NaN  65.0   NaN        NaN   \n",
       "Aequare (Gianduja)       NaN   NaN   NaN  57.5    NaN   NaN   NaN        NaN   \n",
       "...                      ...   ...   ...   ...    ...   ...   ...        ...   \n",
       "hello cocoa              NaN   NaN   NaN   NaN    NaN   NaN   NaN        NaN   \n",
       "hexx                     NaN   NaN   NaN   NaN    NaN   NaN   NaN        NaN   \n",
       "iQ Chocolate             NaN   NaN   NaN   NaN    NaN   NaN  57.5        NaN   \n",
       "organicfair              NaN   NaN   NaN   NaN    NaN   NaN   NaN  57.000000   \n",
       "twenty-four blackbirds   NaN   NaN   NaN   NaN    NaN  55.0   NaN  57.500000   \n",
       "\n",
       "Review Date             2014   2015  2016  2017  \n",
       "Company                                          \n",
       "A. Morin                70.0  63.75  75.0   NaN  \n",
       "AMMA                     NaN    NaN   NaN   NaN  \n",
       "Acalli                   NaN  75.00   NaN   NaN  \n",
       "Adi                      NaN    NaN   NaN   NaN  \n",
       "Aequare (Gianduja)       NaN    NaN   NaN   NaN  \n",
       "...                      ...    ...   ...   ...  \n",
       "hello cocoa              NaN  62.50   NaN   NaN  \n",
       "hexx                     NaN  61.00   NaN   NaN  \n",
       "iQ Chocolate             NaN    NaN   NaN   NaN  \n",
       "organicfair              NaN    NaN   NaN   NaN  \n",
       "twenty-four blackbirds  70.0  62.50   NaN   NaN  \n",
       "\n",
       "[416 rows x 12 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "companies = df.groupby(['Company', 'Review Date'])['Rating'].agg('mean').unstack()\n",
    "companies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\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>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A. Morin</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.5</td>\n",
       "      <td>66.363636</td>\n",
       "      <td>70.0</td>\n",
       "      <td>63.75</td>\n",
       "      <td>75.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMMA</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acalli</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adi</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aequare (Gianduja)</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hello cocoa</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hexx</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iQ Chocolate</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>organicfair</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>twenty-four blackbirds</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.500000</td>\n",
       "      <td>70.0</td>\n",
       "      <td>62.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>416 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        2006  2007  2008  2009   2010  2011  2012       2013  \\\n",
       "A. Morin                 NaN   NaN   NaN   NaN    NaN   NaN  72.5  66.363636   \n",
       "AMMA                     NaN   NaN   NaN   NaN  71.25   NaN   NaN  65.000000   \n",
       "Acalli                   NaN   NaN   NaN   NaN    NaN   NaN   NaN        NaN   \n",
       "Adi                      NaN   NaN   NaN   NaN    NaN  65.0   NaN        NaN   \n",
       "Aequare (Gianduja)       NaN   NaN   NaN  57.5    NaN   NaN   NaN        NaN   \n",
       "...                      ...   ...   ...   ...    ...   ...   ...        ...   \n",
       "hello cocoa              NaN   NaN   NaN   NaN    NaN   NaN   NaN        NaN   \n",
       "hexx                     NaN   NaN   NaN   NaN    NaN   NaN   NaN        NaN   \n",
       "iQ Chocolate             NaN   NaN   NaN   NaN    NaN   NaN  57.5        NaN   \n",
       "organicfair              NaN   NaN   NaN   NaN    NaN   NaN   NaN  57.000000   \n",
       "twenty-four blackbirds   NaN   NaN   NaN   NaN    NaN  55.0   NaN  57.500000   \n",
       "\n",
       "                        2014   2015  2016  2017  \n",
       "A. Morin                70.0  63.75  75.0   NaN  \n",
       "AMMA                     NaN    NaN   NaN   NaN  \n",
       "Acalli                   NaN  75.00   NaN   NaN  \n",
       "Adi                      NaN    NaN   NaN   NaN  \n",
       "Aequare (Gianduja)       NaN    NaN   NaN   NaN  \n",
       "...                      ...    ...   ...   ...  \n",
       "hello cocoa              NaN  62.50   NaN   NaN  \n",
       "hexx                     NaN  61.00   NaN   NaN  \n",
       "iQ Chocolate             NaN    NaN   NaN   NaN  \n",
       "organicfair              NaN    NaN   NaN   NaN  \n",
       "twenty-four blackbirds  70.0  62.50   NaN   NaN  \n",
       "\n",
       "[416 rows x 12 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "companies.columns.name = None\n",
    "companies.index.name = None\n",
    "companies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_ratings = companies[companies.columns[6:]].mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A. Morin                  69.522727\n",
       "AMMA                      65.000000\n",
       "Acalli                    75.000000\n",
       "Adi                             NaN\n",
       "Aequare (Gianduja)              NaN\n",
       "                            ...    \n",
       "hello cocoa               62.500000\n",
       "hexx                      61.000000\n",
       "iQ Chocolate              57.500000\n",
       "organicfair               57.000000\n",
       "twenty-four blackbirds    63.333333\n",
       "Length: 416, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "lk = mean_ratings.copy()\n",
    "\n",
    "lk = lk.reset_index()\n",
    "\n",
    "lk = lk.sort_values([0,'index'],ascending=[False,True]).head(10)\n",
    "lk = lk.set_index('index')\n",
    "p = lk[0]\n",
    "p.index.names = [None]\n",
    "\n",
    "best_ratings = p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Patric</th>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scharffen Berger</th>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tobago Estate (Pralus)</th>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heirloom Cacao Preservation (Zokoko)</th>\n",
       "      <td>77.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ocelot</th>\n",
       "      <td>77.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Valrhona</th>\n",
       "      <td>76.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matale</th>\n",
       "      <td>76.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duffy's</th>\n",
       "      <td>75.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acalli</th>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amedei</th>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              0\n",
       "Patric                                80.000000\n",
       "Scharffen Berger                      80.000000\n",
       "Tobago Estate (Pralus)                80.000000\n",
       "Heirloom Cacao Preservation (Zokoko)  77.500000\n",
       "Ocelot                                77.500000\n",
       "Valrhona                              76.666667\n",
       "Matale                                76.250000\n",
       "Duffy's                               75.833333\n",
       "Acalli                                75.000000\n",
       "Amedei                                75.000000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Patric                                  80.000000\n",
       "Scharffen Berger                        80.000000\n",
       "Tobago Estate (Pralus)                  80.000000\n",
       "Heirloom Cacao Preservation (Zokoko)    77.500000\n",
       "Ocelot                                  77.500000\n",
       "Valrhona                                76.666667\n",
       "Matale                                  76.250000\n",
       "Duffy's                                 75.833333\n",
       "Acalli                                  75.000000\n",
       "Amedei                                  75.000000\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Company</th>\n",
       "      <th>Specific Bean Origin</th>\n",
       "      <th>Review Date</th>\n",
       "      <th>Cocoa Percent</th>\n",
       "      <th>Company Location</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Bean Type</th>\n",
       "      <th>Broad Bean Origin</th>\n",
       "      <th>price(100g)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Acalli</td>\n",
       "      <td>Chulucanas, El Platanal</td>\n",
       "      <td>2015</td>\n",
       "      <td>70.0</td>\n",
       "      <td>U.S.A.</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Blend</td>\n",
       "      <td>Peru</td>\n",
       "      <td>131250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Acalli</td>\n",
       "      <td>Tumbes, Norandino</td>\n",
       "      <td>2015</td>\n",
       "      <td>70.0</td>\n",
       "      <td>U.S.A.</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Criollo</td>\n",
       "      <td>Peru</td>\n",
       "      <td>131250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amedei</td>\n",
       "      <td>Piura, Blanco de Criollo</td>\n",
       "      <td>2012</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Italy</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Blend</td>\n",
       "      <td>Peru</td>\n",
       "      <td>131250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Amedei</td>\n",
       "      <td>Porcelana</td>\n",
       "      <td>2007</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Italy</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Criollo (Porcelana)</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>140000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amedei</td>\n",
       "      <td>Nine</td>\n",
       "      <td>2007</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Italy</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Blend</td>\n",
       "      <td>Other</td>\n",
       "      <td>150000.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Valrhona</td>\n",
       "      <td>Caraibe</td>\n",
       "      <td>2006</td>\n",
       "      <td>66.0</td>\n",
       "      <td>France</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Trinitario</td>\n",
       "      <td>Carribean</td>\n",
       "      <td>99000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Valrhona</td>\n",
       "      <td>Guanaja</td>\n",
       "      <td>2006</td>\n",
       "      <td>70.0</td>\n",
       "      <td>France</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Criollo, Trinitario</td>\n",
       "      <td>South America</td>\n",
       "      <td>140000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Valrhona</td>\n",
       "      <td>Gran Couva 2005 P.</td>\n",
       "      <td>2006</td>\n",
       "      <td>64.0</td>\n",
       "      <td>France</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Trinitario</td>\n",
       "      <td>Trinidad</td>\n",
       "      <td>80000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Valrhona</td>\n",
       "      <td>Porcelana, Maracaibo, Palmira P. 2005</td>\n",
       "      <td>2006</td>\n",
       "      <td>64.0</td>\n",
       "      <td>France</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Criollo (Porcelana)</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>96000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Valrhona</td>\n",
       "      <td>Sambirano, Ampamakia 2005, Millot P.</td>\n",
       "      <td>2006</td>\n",
       "      <td>64.0</td>\n",
       "      <td>France</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Trinitario</td>\n",
       "      <td>Madagascar</td>\n",
       "      <td>112000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company                   Specific Bean Origin  Review Date  \\\n",
       "0     Acalli                Chulucanas, El Platanal         2015   \n",
       "1     Acalli                      Tumbes, Norandino         2015   \n",
       "2     Amedei               Piura, Blanco de Criollo         2012   \n",
       "3     Amedei                              Porcelana         2007   \n",
       "4     Amedei                                   Nine         2007   \n",
       "..       ...                                    ...          ...   \n",
       "76  Valrhona                                Caraibe         2006   \n",
       "77  Valrhona                                Guanaja         2006   \n",
       "78  Valrhona                     Gran Couva 2005 P.         2006   \n",
       "79  Valrhona  Porcelana, Maracaibo, Palmira P. 2005         2006   \n",
       "80  Valrhona   Sambirano, Ampamakia 2005, Millot P.         2006   \n",
       "\n",
       "    Cocoa Percent Company Location  Rating            Bean Type  \\\n",
       "0            70.0           U.S.A.    75.0                Blend   \n",
       "1            70.0           U.S.A.    75.0              Criollo   \n",
       "2            70.0            Italy    75.0                Blend   \n",
       "3            70.0            Italy    80.0  Criollo (Porcelana)   \n",
       "4            75.0            Italy    80.0                Blend   \n",
       "..            ...              ...     ...                  ...   \n",
       "76           66.0           France    60.0           Trinitario   \n",
       "77           70.0           France    80.0  Criollo, Trinitario   \n",
       "78           64.0           France    50.0           Trinitario   \n",
       "79           64.0           France    60.0  Criollo (Porcelana)   \n",
       "80           64.0           France    70.0           Trinitario   \n",
       "\n",
       "   Broad Bean Origin  price(100g)  \n",
       "0               Peru     131250.0  \n",
       "1               Peru     131250.0  \n",
       "2               Peru     131250.0  \n",
       "3          Venezuela     140000.0  \n",
       "4              Other     150000.0  \n",
       "..               ...          ...  \n",
       "76         Carribean      99000.0  \n",
       "77     South America     140000.0  \n",
       "78          Trinidad      80000.0  \n",
       "79         Venezuela      96000.0  \n",
       "80        Madagascar     112000.0  \n",
       "\n",
       "[81 rows x 9 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst = best_ratings.index\n",
    "ds = df[(df.Company == lst[0]) | (df.Company == lst[1]) | (df.Company == lst[2]) | (df.Company == lst[3]) | (df.Company == lst[4]) | (df.Company == lst[5]) |\n",
    "   (df.Company == lst[6]) | (df.Company == lst[7]) | (df.Company == lst[8]) | (df.Company == lst[9])]\n",
    "ds = ds.reset_index().drop('index', axis=1)\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9895125.0\n"
     ]
    }
   ],
   "source": [
    "priceSum = ds['price(100g)'].sum()\n",
    "print(priceSum)"
   ]
  }
 ],
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