Clustering .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from category_encoders import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Channel</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub Category</th>\n",
       "      <th>Manufacturer</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Date</th>\n",
       "      <th>Units</th>\n",
       "      <th>Price</th>\n",
       "      <th>Price Tier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MASS</td>\n",
       "      <td>FRAG MASS</td>\n",
       "      <td>WOMEN</td>\n",
       "      <td>CHANEL INC</td>\n",
       "      <td>CHANEL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8/1/2017</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>a.less than $25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PRESTIGE</td>\n",
       "      <td>FRAGRANCES</td>\n",
       "      <td>MEN</td>\n",
       "      <td>CHANEL INC</td>\n",
       "      <td>CHANEL</td>\n",
       "      <td>6253422.0</td>\n",
       "      <td>8/1/2017</td>\n",
       "      <td>23</td>\n",
       "      <td>271888</td>\n",
       "      <td>d.$100+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PRESTIGE</td>\n",
       "      <td>FRAGRANCES</td>\n",
       "      <td>WOMEN</td>\n",
       "      <td>CHANEL INC</td>\n",
       "      <td>CHANEL</td>\n",
       "      <td>17512844.0</td>\n",
       "      <td>8/1/2017</td>\n",
       "      <td>87</td>\n",
       "      <td>201297</td>\n",
       "      <td>d.$100+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PRESTIGE</td>\n",
       "      <td>MAKEUP</td>\n",
       "      <td>MAKEUP TOTAL ALL OTHER</td>\n",
       "      <td>CHANEL INC</td>\n",
       "      <td>CHANEL</td>\n",
       "      <td>-957.0</td>\n",
       "      <td>8/1/2017</td>\n",
       "      <td>58</td>\n",
       "      <td>-17</td>\n",
       "      <td>a.less than $25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PRESTIGE</td>\n",
       "      <td>MAKEUP</td>\n",
       "      <td>MAKEUP TOTAL EYE</td>\n",
       "      <td>CHANEL INC</td>\n",
       "      <td>CHANEL</td>\n",
       "      <td>3763488.0</td>\n",
       "      <td>8/1/2017</td>\n",
       "      <td>56</td>\n",
       "      <td>67205</td>\n",
       "      <td>c.$50 - $100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Channel    Category            Sub Category Manufacturer   Brand  \\\n",
       "0      MASS   FRAG MASS                   WOMEN   CHANEL INC  CHANEL   \n",
       "1  PRESTIGE  FRAGRANCES                     MEN   CHANEL INC  CHANEL   \n",
       "2  PRESTIGE  FRAGRANCES                   WOMEN   CHANEL INC  CHANEL   \n",
       "3  PRESTIGE      MAKEUP  MAKEUP TOTAL ALL OTHER   CHANEL INC  CHANEL   \n",
       "4  PRESTIGE      MAKEUP        MAKEUP TOTAL EYE   CHANEL INC  CHANEL   \n",
       "\n",
       "      Revenue      Date  Units   Price       Price Tier  \n",
       "0         0.0  8/1/2017     82       0  a.less than $25  \n",
       "1   6253422.0  8/1/2017     23  271888          d.$100+  \n",
       "2  17512844.0  8/1/2017     87  201297          d.$100+  \n",
       "3      -957.0  8/1/2017     58     -17  a.less than $25  \n",
       "4   3763488.0  8/1/2017     56   67205     c.$50 - $100  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"/Users/shyam.muralidharan/Downloads/Revlon Dataset Updated Aug 2019.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['Date'], axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Channel</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub Category</th>\n",
       "      <th>Manufacturer</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Units</th>\n",
       "      <th>Price</th>\n",
       "      <th>Price Tier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MASS</td>\n",
       "      <td>FRAG MASS</td>\n",
       "      <td>WOMEN</td>\n",
       "      <td>CHANEL INC</td>\n",
       "      <td>CHANEL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>a.less than $25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PRESTIGE</td>\n",
       "      <td>FRAGRANCES</td>\n",
       "      <td>MEN</td>\n",
       "      <td>CHANEL INC</td>\n",
       "      <td>CHANEL</td>\n",
       "      <td>6253422.0</td>\n",
       "      <td>23</td>\n",
       "      <td>271888</td>\n",
       "      <td>d.$100+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PRESTIGE</td>\n",
       "      <td>FRAGRANCES</td>\n",
       "      <td>WOMEN</td>\n",
       "      <td>CHANEL INC</td>\n",
       "      <td>CHANEL</td>\n",
       "      <td>17512844.0</td>\n",
       "      <td>87</td>\n",
       "      <td>201297</td>\n",
       "      <td>d.$100+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PRESTIGE</td>\n",
       "      <td>MAKEUP</td>\n",
       "      <td>MAKEUP TOTAL ALL OTHER</td>\n",
       "      <td>CHANEL INC</td>\n",
       "      <td>CHANEL</td>\n",
       "      <td>-957.0</td>\n",
       "      <td>58</td>\n",
       "      <td>-17</td>\n",
       "      <td>a.less than $25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PRESTIGE</td>\n",
       "      <td>MAKEUP</td>\n",
       "      <td>MAKEUP TOTAL EYE</td>\n",
       "      <td>CHANEL INC</td>\n",
       "      <td>CHANEL</td>\n",
       "      <td>3763488.0</td>\n",
       "      <td>56</td>\n",
       "      <td>67205</td>\n",
       "      <td>c.$50 - $100</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>6870</th>\n",
       "      <td>PRESTIGE</td>\n",
       "      <td>MAKEUP</td>\n",
       "      <td>MAKEUP TOTAL FACE</td>\n",
       "      <td>LOREAL INC</td>\n",
       "      <td>URBAN DECAY</td>\n",
       "      <td>15440296.0</td>\n",
       "      <td>52</td>\n",
       "      <td>296929</td>\n",
       "      <td>d.$100+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6871</th>\n",
       "      <td>PRESTIGE</td>\n",
       "      <td>MAKEUP</td>\n",
       "      <td>MAKEUP TOTAL LIP</td>\n",
       "      <td>LOREAL INC</td>\n",
       "      <td>URBAN DECAY</td>\n",
       "      <td>2679173.0</td>\n",
       "      <td>94</td>\n",
       "      <td>28502</td>\n",
       "      <td>c.$50 - $100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6872</th>\n",
       "      <td>PRESTIGE</td>\n",
       "      <td>MAKEUP</td>\n",
       "      <td>MAKEUP TOTAL NAIL</td>\n",
       "      <td>LOREAL INC</td>\n",
       "      <td>URBAN DECAY</td>\n",
       "      <td>458.0</td>\n",
       "      <td>89</td>\n",
       "      <td>5</td>\n",
       "      <td>a.less than $25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6873</th>\n",
       "      <td>PRESTIGE</td>\n",
       "      <td>MAKEUP</td>\n",
       "      <td>MAKEUP TOTAL SETS</td>\n",
       "      <td>LOREAL INC</td>\n",
       "      <td>URBAN DECAY</td>\n",
       "      <td>609980.0</td>\n",
       "      <td>10</td>\n",
       "      <td>60998</td>\n",
       "      <td>c.$50 - $100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6874</th>\n",
       "      <td>PRESTIGE</td>\n",
       "      <td>SKINCARE</td>\n",
       "      <td>SKINCARE TOTAL FACE</td>\n",
       "      <td>LOREAL INC</td>\n",
       "      <td>URBAN DECAY</td>\n",
       "      <td>73159.0</td>\n",
       "      <td>71</td>\n",
       "      <td>1030</td>\n",
       "      <td>a.less than $25</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>6875 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "       Channel    Category            Sub Category Manufacturer        Brand  \\\n",
       "0         MASS   FRAG MASS                   WOMEN   CHANEL INC       CHANEL   \n",
       "1     PRESTIGE  FRAGRANCES                     MEN   CHANEL INC       CHANEL   \n",
       "2     PRESTIGE  FRAGRANCES                   WOMEN   CHANEL INC       CHANEL   \n",
       "3     PRESTIGE      MAKEUP  MAKEUP TOTAL ALL OTHER   CHANEL INC       CHANEL   \n",
       "4     PRESTIGE      MAKEUP        MAKEUP TOTAL EYE   CHANEL INC       CHANEL   \n",
       "...        ...         ...                     ...          ...          ...   \n",
       "6870  PRESTIGE      MAKEUP       MAKEUP TOTAL FACE   LOREAL INC  URBAN DECAY   \n",
       "6871  PRESTIGE      MAKEUP        MAKEUP TOTAL LIP   LOREAL INC  URBAN DECAY   \n",
       "6872  PRESTIGE      MAKEUP       MAKEUP TOTAL NAIL   LOREAL INC  URBAN DECAY   \n",
       "6873  PRESTIGE      MAKEUP       MAKEUP TOTAL SETS   LOREAL INC  URBAN DECAY   \n",
       "6874  PRESTIGE    SKINCARE     SKINCARE TOTAL FACE   LOREAL INC  URBAN DECAY   \n",
       "\n",
       "         Revenue  Units   Price       Price Tier  \n",
       "0            0.0     82       0  a.less than $25  \n",
       "1      6253422.0     23  271888          d.$100+  \n",
       "2     17512844.0     87  201297          d.$100+  \n",
       "3         -957.0     58     -17  a.less than $25  \n",
       "4      3763488.0     56   67205     c.$50 - $100  \n",
       "...          ...    ...     ...              ...  \n",
       "6870  15440296.0     52  296929          d.$100+  \n",
       "6871   2679173.0     94   28502     c.$50 - $100  \n",
       "6872       458.0     89       5  a.less than $25  \n",
       "6873    609980.0     10   60998     c.$50 - $100  \n",
       "6874     73159.0     71    1030  a.less than $25  \n",
       "\n",
       "[6875 rows x 9 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Channel         0\n",
       "Category        0\n",
       "Sub Category    0\n",
       "Manufacturer    0\n",
       "Brand           0\n",
       "Revenue         0\n",
       "Units           0\n",
       "Price           0\n",
       "Price Tier      0\n",
       "ratio           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dims = [\"Revenue\", \"Units\"]\n",
    "dim_1 = dims[0]\n",
    "dim_2 = dims[1]\n",
    "df['ratio'] = df[dim_1]/df[dim_2]\n",
    "df = df[~df.isin([np.nan, np.inf, -np.inf]).any(1)]\n",
    "df.isnull().values.any()\n",
    "df.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100701579.40798967"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nwe = df['ratio'][df['Brand'] == 'CHANEL'].sum()\n",
    "nwe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "brand_list = list(df['Manufacturer'].unique())\n",
    "# revenue = list(df.iloc[:,5:8].columns)\n",
    "revenue = list(df.loc[:, ['ratio']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ratio']"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1728x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(24,12))\n",
    "for ar in brand_list:\n",
    "    revenue_produce = []\n",
    "    for yr in revenue:\n",
    "        revenue_produce.append(df[yr][df['Manufacturer'] == ar].sum())\n",
    "    plt.plot(revenue_produce, label=ar)\n",
    "plt.xticks(np.arange(53), tuple(brand_list), rotation=60)\n",
    "plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=8, mode=\"expand\", borderaxespad=0.)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>CHANEL INC</th>\n",
       "      <th>ESTEE LAUDER INC</th>\n",
       "      <th>COTY CORP</th>\n",
       "      <th>UNILEVER CORP</th>\n",
       "      <th>LOREAL SA</th>\n",
       "      <th>LOREAL INC</th>\n",
       "      <th>LOREAL USA</th>\n",
       "      <th>LOREAL SA CORP</th>\n",
       "      <th>JOHNSON &amp; JOHNSON</th>\n",
       "      <th>PROCTER &amp; GAMBLE COMPANY</th>\n",
       "      <th>PRIVATE LABEL</th>\n",
       "      <th>REVLON CORP</th>\n",
       "      <th>MACANDREWS FORBES HOLDINGS INC</th>\n",
       "      <th>HELEN OF TROY LTD</th>\n",
       "      <th>P&amp;G CORP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.007016e+08</td>\n",
       "      <td>3.323089e+08</td>\n",
       "      <td>7.183584e+07</td>\n",
       "      <td>9.295927e+07</td>\n",
       "      <td>9.953314e+07</td>\n",
       "      <td>2.534066e+08</td>\n",
       "      <td>135435.295517</td>\n",
       "      <td>2.777640e+08</td>\n",
       "      <td>7.235039e+07</td>\n",
       "      <td>1.029952e+08</td>\n",
       "      <td>6.281262e+07</td>\n",
       "      <td>8.664487e+07</td>\n",
       "      <td>519525.794543</td>\n",
       "      <td>2666.863857</td>\n",
       "      <td>6.867448e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CHANEL INC  ESTEE LAUDER INC     COTY CORP  UNILEVER CORP     LOREAL SA  \\\n",
       "0  1.007016e+08      3.323089e+08  7.183584e+07   9.295927e+07  9.953314e+07   \n",
       "\n",
       "     LOREAL INC     LOREAL USA  LOREAL SA CORP  JOHNSON & JOHNSON  \\\n",
       "0  2.534066e+08  135435.295517    2.777640e+08       7.235039e+07   \n",
       "\n",
       "   PROCTER & GAMBLE COMPANY  PRIVATE LABEL   REVLON CORP  \\\n",
       "0              1.029952e+08   6.281262e+07  8.664487e+07   \n",
       "\n",
       "   MACANDREWS FORBES HOLDINGS INC  HELEN OF TROY LTD      P&G CORP  \n",
       "0                   519525.794543        2666.863857  6.867448e+07  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df_dict = {}\n",
    "for ar in brand_list:\n",
    "    yearly_produce = []\n",
    "    for yr in revenue:\n",
    "        yearly_produce.append(df[yr][df['Manufacturer']==ar].sum())\n",
    "    new_df_dict[ar] = yearly_produce\n",
    "new_df = pd.DataFrame(new_df_dict)\n",
    "\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn import preprocessing\n",
    "# # Get column names first\n",
    "# names = new_df.columns\n",
    "# # Create the Scaler object\n",
    "# scaler = preprocessing.StandardScaler()\n",
    "# # Fit your data on the scaler object\n",
    "# scaled_df = scaler.fit_transform(new_df)\n",
    "# new_df = pd.DataFrame(scaled_df, columns=names)\n",
    "# new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 1)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = pd.DataFrame.transpose(new_df)\n",
    "new_df.columns = revenue\n",
    "\n",
    "new_df.head()\n",
    "new_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new = new_df.shape[0]\n",
    "new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>ratio</th>\n",
       "      <th>Mean_Produce</th>\n",
       "      <th>Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CHANEL INC</th>\n",
       "      <td>1.007016e+08</td>\n",
       "      <td>1.007016e+08</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ESTEE LAUDER INC</th>\n",
       "      <td>3.323089e+08</td>\n",
       "      <td>3.323089e+08</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COTY CORP</th>\n",
       "      <td>7.183584e+07</td>\n",
       "      <td>7.183584e+07</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNILEVER CORP</th>\n",
       "      <td>9.295927e+07</td>\n",
       "      <td>9.295927e+07</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOREAL SA</th>\n",
       "      <td>9.953314e+07</td>\n",
       "      <td>9.953314e+07</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOREAL INC</th>\n",
       "      <td>2.534066e+08</td>\n",
       "      <td>2.534066e+08</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOREAL USA</th>\n",
       "      <td>1.354353e+05</td>\n",
       "      <td>1.354353e+05</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOREAL SA CORP</th>\n",
       "      <td>2.777640e+08</td>\n",
       "      <td>2.777640e+08</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOHNSON &amp; JOHNSON</th>\n",
       "      <td>7.235039e+07</td>\n",
       "      <td>7.235039e+07</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PROCTER &amp; GAMBLE COMPANY</th>\n",
       "      <td>1.029952e+08</td>\n",
       "      <td>1.029952e+08</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRIVATE LABEL</th>\n",
       "      <td>6.281262e+07</td>\n",
       "      <td>6.281262e+07</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REVLON CORP</th>\n",
       "      <td>8.664487e+07</td>\n",
       "      <td>8.664487e+07</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MACANDREWS FORBES HOLDINGS INC</th>\n",
       "      <td>5.195258e+05</td>\n",
       "      <td>5.195258e+05</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HELEN OF TROY LTD</th>\n",
       "      <td>2.666864e+03</td>\n",
       "      <td>2.666864e+03</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P&amp;G CORP</th>\n",
       "      <td>6.867448e+07</td>\n",
       "      <td>6.867448e+07</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       ratio  Mean_Produce  Rank\n",
       "CHANEL INC                      1.007016e+08  1.007016e+08   5.0\n",
       "ESTEE LAUDER INC                3.323089e+08  3.323089e+08   1.0\n",
       "COTY CORP                       7.183584e+07  7.183584e+07  10.0\n",
       "UNILEVER CORP                   9.295927e+07  9.295927e+07   7.0\n",
       "LOREAL SA                       9.953314e+07  9.953314e+07   6.0\n",
       "LOREAL INC                      2.534066e+08  2.534066e+08   3.0\n",
       "LOREAL USA                      1.354353e+05  1.354353e+05  14.0\n",
       "LOREAL SA CORP                  2.777640e+08  2.777640e+08   2.0\n",
       "JOHNSON & JOHNSON               7.235039e+07  7.235039e+07   9.0\n",
       "PROCTER & GAMBLE COMPANY        1.029952e+08  1.029952e+08   4.0\n",
       "PRIVATE LABEL                   6.281262e+07  6.281262e+07  12.0\n",
       "REVLON CORP                     8.664487e+07  8.664487e+07   8.0\n",
       "MACANDREWS FORBES HOLDINGS INC  5.195258e+05  5.195258e+05  13.0\n",
       "HELEN OF TROY LTD               2.666864e+03  2.666864e+03  15.0\n",
       "P&G CORP                        6.867448e+07  6.867448e+07  11.0"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_produce = []\n",
    "for i in range(15):\n",
    "    mean_produce.append(new_df.iloc[i,:].values.mean())\n",
    "new_df['Mean_Produce'] = mean_produce\n",
    "\n",
    "new_df['Rank'] = new_df['Mean_Produce'].rank(ascending=False)\n",
    "\n",
    "new_df.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ratio</th>\n",
       "      <th>Mean_Produce</th>\n",
       "      <th>Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CHANEL INC</th>\n",
       "      <td>1.007016e+08</td>\n",
       "      <td>1.007016e+08</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ESTEE LAUDER INC</th>\n",
       "      <td>3.323089e+08</td>\n",
       "      <td>3.323089e+08</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COTY CORP</th>\n",
       "      <td>7.183584e+07</td>\n",
       "      <td>7.183584e+07</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNILEVER CORP</th>\n",
       "      <td>9.295927e+07</td>\n",
       "      <td>9.295927e+07</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOREAL SA</th>\n",
       "      <td>9.953314e+07</td>\n",
       "      <td>9.953314e+07</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOREAL INC</th>\n",
       "      <td>2.534066e+08</td>\n",
       "      <td>2.534066e+08</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOREAL USA</th>\n",
       "      <td>1.354353e+05</td>\n",
       "      <td>1.354353e+05</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOREAL SA CORP</th>\n",
       "      <td>2.777640e+08</td>\n",
       "      <td>2.777640e+08</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOHNSON &amp; JOHNSON</th>\n",
       "      <td>7.235039e+07</td>\n",
       "      <td>7.235039e+07</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PROCTER &amp; GAMBLE COMPANY</th>\n",
       "      <td>1.029952e+08</td>\n",
       "      <td>1.029952e+08</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRIVATE LABEL</th>\n",
       "      <td>6.281262e+07</td>\n",
       "      <td>6.281262e+07</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REVLON CORP</th>\n",
       "      <td>8.664487e+07</td>\n",
       "      <td>8.664487e+07</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MACANDREWS FORBES HOLDINGS INC</th>\n",
       "      <td>5.195258e+05</td>\n",
       "      <td>5.195258e+05</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HELEN OF TROY LTD</th>\n",
       "      <td>2.666864e+03</td>\n",
       "      <td>2.666864e+03</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P&amp;G CORP</th>\n",
       "      <td>6.867448e+07</td>\n",
       "      <td>6.867448e+07</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       ratio  Mean_Produce  Rank\n",
       "CHANEL INC                      1.007016e+08  1.007016e+08   5.0\n",
       "ESTEE LAUDER INC                3.323089e+08  3.323089e+08   1.0\n",
       "COTY CORP                       7.183584e+07  7.183584e+07  10.0\n",
       "UNILEVER CORP                   9.295927e+07  9.295927e+07   7.0\n",
       "LOREAL SA                       9.953314e+07  9.953314e+07   6.0\n",
       "LOREAL INC                      2.534066e+08  2.534066e+08   3.0\n",
       "LOREAL USA                      1.354353e+05  1.354353e+05  14.0\n",
       "LOREAL SA CORP                  2.777640e+08  2.777640e+08   2.0\n",
       "JOHNSON & JOHNSON               7.235039e+07  7.235039e+07   9.0\n",
       "PROCTER & GAMBLE COMPANY        1.029952e+08  1.029952e+08   4.0\n",
       "PRIVATE LABEL                   6.281262e+07  6.281262e+07  12.0\n",
       "REVLON CORP                     8.664487e+07  8.664487e+07   8.0\n",
       "MACANDREWS FORBES HOLDINGS INC  5.195258e+05  5.195258e+05  13.0\n",
       "HELEN OF TROY LTD               2.666864e+03  2.666864e+03  15.0\n",
       "P&G CORP                        6.867448e+07  6.867448e+07  11.0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<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>0</th>\n",
       "      <td>1.007016e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.323089e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.183584e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.295927e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.953314e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.534066e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.354353e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.777640e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.235039e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.029952e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6.281262e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8.664487e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5.195258e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.666864e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>6.867448e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0\n",
       "0   1.007016e+08\n",
       "1   3.323089e+08\n",
       "2   7.183584e+07\n",
       "3   9.295927e+07\n",
       "4   9.953314e+07\n",
       "5   2.534066e+08\n",
       "6   1.354353e+05\n",
       "7   2.777640e+08\n",
       "8   7.235039e+07\n",
       "9   1.029952e+08\n",
       "10  6.281262e+07\n",
       "11  8.664487e+07\n",
       "12  5.195258e+05\n",
       "13  2.666864e+03\n",
       "14  6.867448e+07"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = new_df.iloc[:,:-2].values\n",
    "\n",
    "X = pd.DataFrame(X)\n",
    "X "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.cluster import KMeans\n",
    "# wcss = []\n",
    "# for i in range(1,11):\n",
    "#     kmeans = KMeans(n_clusters=i,init='k-means++',max_iter=300,n_init=10,random_state=0)\n",
    "#     kmeans.fit(X)\n",
    "#     wcss.append(kmeans.inertia_)\n",
    "# print(wcss)\n",
    "# plt.plot(range(1,11),wcss)\n",
    "# plt.title('The Elbow Method')\n",
    "# plt.xlabel('Number of clusters')\n",
    "# plt.ylabel('WCSS')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.4212654930544533e+17, 2.1098325560287604e+16, 5199870284021811.0, 2231843458304809.2, 533092909521382.75, 236452375659748.28, 83913653820383.31, 34207399258420.2, 14271592608139.287, 6480238301915.665]\n"
     ]
    }
   ],
   "source": [
    "wcss = []\n",
    "for n in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters=n)\n",
    "    kmeans.fit(X)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "    \n",
    "            \n",
    "print(wcss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    }
   ],
   "source": [
    "from math import sqrt\n",
    "x1, y1 = 1, wcss[0]\n",
    "x2, y2 = 11, wcss[len(wcss)-1]\n",
    "\n",
    "distances = []\n",
    "for i in range(len(wcss)):\n",
    "    x0 = i+2\n",
    "    y0 = wcss[i]\n",
    "    numerator = abs((y2-y1)*x0 - (x2-x1)*y0 + x2*y1 - y2*x1)\n",
    "    denominator = sqrt((y2 - y1)**2 + (x2 - x1)**2)\n",
    "    distances.append(numerator/denominator)\n",
    "    \n",
    "print(distances.index(max(distances)) + 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shyam.muralidharan/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=4,init='k-means++',max_iter=300,n_init=10,random_state=0) \n",
    "y_kmeans = kmeans.fit_predict(X)\n",
    "\n",
    "X = X.as_matrix(columns=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.00701579e+08],\n",
       "       [3.32308900e+08],\n",
       "       [7.18358409e+07],\n",
       "       [9.29592669e+07],\n",
       "       [9.95331385e+07],\n",
       "       [2.53406609e+08],\n",
       "       [1.35435296e+05],\n",
       "       [2.77763970e+08],\n",
       "       [7.23503872e+07],\n",
       "       [1.02995153e+08],\n",
       "       [6.28126245e+07],\n",
       "       [8.66448696e+07],\n",
       "       [5.19525795e+05],\n",
       "       [2.66686386e+03],\n",
       "       [6.86744840e+07]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\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>ratio</th>\n",
       "      <th>Mean_Produce</th>\n",
       "      <th>Rank</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CHANEL INC</th>\n",
       "      <td>1.007016e+08</td>\n",
       "      <td>1.007016e+08</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ESTEE LAUDER INC</th>\n",
       "      <td>3.323089e+08</td>\n",
       "      <td>3.323089e+08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COTY CORP</th>\n",
       "      <td>7.183584e+07</td>\n",
       "      <td>7.183584e+07</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNILEVER CORP</th>\n",
       "      <td>9.295927e+07</td>\n",
       "      <td>9.295927e+07</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOREAL SA</th>\n",
       "      <td>9.953314e+07</td>\n",
       "      <td>9.953314e+07</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOREAL INC</th>\n",
       "      <td>2.534066e+08</td>\n",
       "      <td>2.534066e+08</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOREAL USA</th>\n",
       "      <td>1.354353e+05</td>\n",
       "      <td>1.354353e+05</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOREAL SA CORP</th>\n",
       "      <td>2.777640e+08</td>\n",
       "      <td>2.777640e+08</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOHNSON &amp; JOHNSON</th>\n",
       "      <td>7.235039e+07</td>\n",
       "      <td>7.235039e+07</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PROCTER &amp; GAMBLE COMPANY</th>\n",
       "      <td>1.029952e+08</td>\n",
       "      <td>1.029952e+08</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRIVATE LABEL</th>\n",
       "      <td>6.281262e+07</td>\n",
       "      <td>6.281262e+07</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REVLON CORP</th>\n",
       "      <td>8.664487e+07</td>\n",
       "      <td>8.664487e+07</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MACANDREWS FORBES HOLDINGS INC</th>\n",
       "      <td>5.195258e+05</td>\n",
       "      <td>5.195258e+05</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HELEN OF TROY LTD</th>\n",
       "      <td>2.666864e+03</td>\n",
       "      <td>2.666864e+03</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P&amp;G CORP</th>\n",
       "      <td>6.867448e+07</td>\n",
       "      <td>6.867448e+07</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       ratio  Mean_Produce  Rank  cluster\n",
       "CHANEL INC                      1.007016e+08  1.007016e+08   5.0        2\n",
       "ESTEE LAUDER INC                3.323089e+08  3.323089e+08   1.0        3\n",
       "COTY CORP                       7.183584e+07  7.183584e+07  10.0        2\n",
       "UNILEVER CORP                   9.295927e+07  9.295927e+07   7.0        2\n",
       "LOREAL SA                       9.953314e+07  9.953314e+07   6.0        2\n",
       "LOREAL INC                      2.534066e+08  2.534066e+08   3.0        1\n",
       "LOREAL USA                      1.354353e+05  1.354353e+05  14.0        0\n",
       "LOREAL SA CORP                  2.777640e+08  2.777640e+08   2.0        1\n",
       "JOHNSON & JOHNSON               7.235039e+07  7.235039e+07   9.0        2\n",
       "PROCTER & GAMBLE COMPANY        1.029952e+08  1.029952e+08   4.0        2\n",
       "PRIVATE LABEL                   6.281262e+07  6.281262e+07  12.0        2\n",
       "REVLON CORP                     8.664487e+07  8.664487e+07   8.0        2\n",
       "MACANDREWS FORBES HOLDINGS INC  5.195258e+05  5.195258e+05  13.0        0\n",
       "HELEN OF TROY LTD               2.666864e+03  2.666864e+03  15.0        0\n",
       "P&G CORP                        6.867448e+07  6.867448e+07  11.0        2"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['cluster'] = kmeans.labels_\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Revenue'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2896\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2897\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2898\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Revenue'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-48-3c512bae3ebc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbarplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnew_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cluster'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Revenue'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Cluster Number\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Number of movies\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2993\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2994\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2995\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2996\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2997\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2897\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2898\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2899\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2900\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2901\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Revenue'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x504 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "plt.figure(figsize=(12,7))\n",
    "axis = sns.barplot(x=np.arange(0,4,1),y=new_df.groupby(['cluster']).count()['Revenue'].values)\n",
    "x=axis.set_xlabel(\"Cluster Number\")\n",
    "x=axis.set_ylabel(\"Number of movies\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a26efe5c0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.swarmplot(new_df.cluster,new_df.Revenue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 2, 9, 1]\n"
     ]
    }
   ],
   "source": [
    "size_array = list(new_df.groupby(['cluster']).count()['ratio'].values)\n",
    "print(size_array)\n",
    "# size_array.sort()\n",
    "# size_array\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "3\n",
      "@@@@ 0\n",
      "$$$ 3\n",
      "                                        ratio   Mean_Produce  Rank  cluster\n",
      "MACANDREWS FORBES HOLDINGS INC  519525.794543  519525.794543  13.0        0\n",
      "HELEN OF TROY LTD                 2666.863857    2666.863857  15.0        0\n",
      "LOREAL USA                      135435.295517  135435.295517  14.0        0\n",
      "{'ratio': {'MACANDREWS FORBES HOLDINGS INC': 519525.79454304936, 'HELEN OF TROY LTD': 2666.863856769048, 'LOREAL USA': 135435.29551712738}, 'Mean_Produce': {'MACANDREWS FORBES HOLDINGS INC': 519525.79454304936, 'HELEN OF TROY LTD': 2666.863856769048, 'LOREAL USA': 135435.29551712738}, 'Rank': {'MACANDREWS FORBES HOLDINGS INC': 13.0, 'HELEN OF TROY LTD': 15.0, 'LOREAL USA': 14.0}, 'cluster': {'MACANDREWS FORBES HOLDINGS INC': 0, 'HELEN OF TROY LTD': 0, 'LOREAL USA': 0}}\n",
      "1\n",
      "2\n",
      "@@@@ 1\n",
      "$$$ 2\n",
      "                       ratio  Mean_Produce  Rank  cluster\n",
      "LOREAL SA CORP  2.777640e+08  2.777640e+08   2.0        1\n",
      "LOREAL INC      2.534066e+08  2.534066e+08   3.0        1\n",
      "{'ratio': {'LOREAL SA CORP': 277763970.1942007, 'LOREAL INC': 253406608.5200934}, 'Mean_Produce': {'LOREAL SA CORP': 277763970.1942007, 'LOREAL INC': 253406608.5200934}, 'Rank': {'LOREAL SA CORP': 2.0, 'LOREAL INC': 3.0}, 'cluster': {'LOREAL SA CORP': 1, 'LOREAL INC': 1}}\n",
      "2\n",
      "9\n",
      "@@@@ 2\n",
      "$$$ 9\n",
      "                                 ratio  Mean_Produce  Rank  cluster\n",
      "JOHNSON & JOHNSON         7.235039e+07  7.235039e+07   9.0        2\n",
      "CHANEL INC                1.007016e+08  1.007016e+08   5.0        2\n",
      "REVLON CORP               8.664487e+07  8.664487e+07   8.0        2\n",
      "UNILEVER CORP             9.295927e+07  9.295927e+07   7.0        2\n",
      "COTY CORP                 7.183584e+07  7.183584e+07  10.0        2\n",
      "PRIVATE LABEL             6.281262e+07  6.281262e+07  12.0        2\n",
      "P&G CORP                  6.867448e+07  6.867448e+07  11.0        2\n",
      "LOREAL SA                 9.953314e+07  9.953314e+07   6.0        2\n",
      "PROCTER & GAMBLE COMPANY  1.029952e+08  1.029952e+08   4.0        2\n",
      "{'ratio': {'JOHNSON & JOHNSON': 72350387.24934801, 'CHANEL INC': 100701579.40798967, 'REVLON CORP': 86644869.58539186, 'UNILEVER CORP': 92959266.88842577, 'COTY CORP': 71835840.94523938, 'PRIVATE LABEL': 62812624.48074561, 'P&G CORP': 68674484.04135968, 'LOREAL SA': 99533138.48242053, 'PROCTER & GAMBLE COMPANY': 102995153.29506788}, 'Mean_Produce': {'JOHNSON & JOHNSON': 72350387.24934801, 'CHANEL INC': 100701579.40798967, 'REVLON CORP': 86644869.58539186, 'UNILEVER CORP': 92959266.88842577, 'COTY CORP': 71835840.94523938, 'PRIVATE LABEL': 62812624.48074561, 'P&G CORP': 68674484.04135968, 'LOREAL SA': 99533138.48242053, 'PROCTER & GAMBLE COMPANY': 102995153.29506788}, 'Rank': {'JOHNSON & JOHNSON': 9.0, 'CHANEL INC': 5.0, 'REVLON CORP': 8.0, 'UNILEVER CORP': 7.0, 'COTY CORP': 10.0, 'PRIVATE LABEL': 12.0, 'P&G CORP': 11.0, 'LOREAL SA': 6.0, 'PROCTER & GAMBLE COMPANY': 4.0}, 'cluster': {'JOHNSON & JOHNSON': 2, 'CHANEL INC': 2, 'REVLON CORP': 2, 'UNILEVER CORP': 2, 'COTY CORP': 2, 'PRIVATE LABEL': 2, 'P&G CORP': 2, 'LOREAL SA': 2, 'PROCTER & GAMBLE COMPANY': 2}}\n",
      "3\n",
      "1\n",
      "@@@@ 3\n",
      "$$$ 1\n",
      "                         ratio  Mean_Produce  Rank  cluster\n",
      "ESTEE LAUDER INC  3.323089e+08  3.323089e+08   1.0        3\n",
      "{'ratio': {'ESTEE LAUDER INC': 332308900.1600368}, 'Mean_Produce': {'ESTEE LAUDER INC': 332308900.1600368}, 'Rank': {'ESTEE LAUDER INC': 1.0}, 'cluster': {'ESTEE LAUDER INC': 3}}\n"
     ]
    }
   ],
   "source": [
    "dis = {}\n",
    "for i in range(len(size_array)):\n",
    "    print(i)\n",
    "    x= size_array[i]\n",
    "    print(x)\n",
    "    print(\"@@@@\",size_array.index((size_array)[i]))\n",
    "    print(\"$$$\",(size_array)[i])\n",
    "    new = new_df[new_df['cluster']== i].sample(x, replace=False)\n",
    "    print(new)\n",
    "    dis = new.to_dict()\n",
    "    print(dis)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Revenue': {'CHANEL': 1531116934.75,\n",
       "  'REVLON': 1746853677.788,\n",
       "  'COVER GIRL': 1752298319.0,\n",
       "  'ESTEE LAUDER': 1753451362.0,\n",
       "  'LANCOME': 1993707849.1090002,\n",
       "  'MAC': 1416014402.0},\n",
       " 'Price': {'CHANEL': 100701587.0,\n",
       "  'REVLON': 87167060.0,\n",
       "  'COVER GIRL': 71835845.0,\n",
       "  'ESTEE LAUDER': 85466558.0,\n",
       "  'LANCOME': 112893041.0,\n",
       "  'MAC': 85233472.0},\n",
       " 'Mean_Produce': {'CHANEL': 815909260.875,\n",
       "  'REVLON': 917010368.894,\n",
       "  'COVER GIRL': 912067082.0,\n",
       "  'ESTEE LAUDER': 919458960.0,\n",
       "  'LANCOME': 1053300445.0545001,\n",
       "  'MAC': 750623937.0},\n",
       " 'Rank': {'CHANEL': 8.0,\n",
       "  'REVLON': 6.0,\n",
       "  'COVER GIRL': 7.0,\n",
       "  'ESTEE LAUDER': 5.0,\n",
       "  'LANCOME': 4.0,\n",
       "  'MAC': 9.0},\n",
       " 'cluster': {'CHANEL': 3,\n",
       "  'REVLON': 3,\n",
       "  'COVER GIRL': 3,\n",
       "  'ESTEE LAUDER': 3,\n",
       "  'LANCOME': 3,\n",
       "  'MAC': 3}}"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "# as requested in comment\n",
    "exDict = dis\n",
    "\n",
    "with open('file.txt', 'w') as file:\n",
    "     file.write(json.dumps(exDict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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>Revenue</th>\n",
       "      <th>Units</th>\n",
       "      <th>Mean_Produce</th>\n",
       "      <th>Rank</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>URBAN DECAY</th>\n",
       "      <td>1.235282e+09</td>\n",
       "      <td>9536.0</td>\n",
       "      <td>6.176456e+08</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OLAY</th>\n",
       "      <td>9.624620e+08</td>\n",
       "      <td>15296.0</td>\n",
       "      <td>4.812387e+08</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEGREE</th>\n",
       "      <td>1.065570e+09</td>\n",
       "      <td>6226.0</td>\n",
       "      <td>5.327881e+08</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SECRET</th>\n",
       "      <td>1.032550e+09</td>\n",
       "      <td>7585.0</td>\n",
       "      <td>5.162787e+08</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRIVATE LABEL</th>\n",
       "      <td>1.077815e+09</td>\n",
       "      <td>41969.0</td>\n",
       "      <td>5.389287e+08</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT COSMETICS</th>\n",
       "      <td>1.097865e+09</td>\n",
       "      <td>10003.0</td>\n",
       "      <td>5.489375e+08</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PANTENE</th>\n",
       "      <td>9.549141e+08</td>\n",
       "      <td>5221.0</td>\n",
       "      <td>4.774596e+08</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DOVE</th>\n",
       "      <td>1.037733e+09</td>\n",
       "      <td>4930.0</td>\n",
       "      <td>5.188689e+08</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEUTROGENA</th>\n",
       "      <td>1.146375e+09</td>\n",
       "      <td>24108.0</td>\n",
       "      <td>5.731995e+08</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GARNIER</th>\n",
       "      <td>1.136905e+09</td>\n",
       "      <td>17000.0</td>\n",
       "      <td>5.684612e+08</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Revenue    Units  Mean_Produce  Rank  cluster\n",
       "URBAN DECAY    1.235282e+09   9536.0  6.176456e+08  10.0        0\n",
       "OLAY           9.624620e+08  15296.0  4.812387e+08  18.0        0\n",
       "DEGREE         1.065570e+09   6226.0  5.327881e+08  15.0        0\n",
       "SECRET         1.032550e+09   7585.0  5.162787e+08  17.0        0\n",
       "PRIVATE LABEL  1.077815e+09  41969.0  5.389287e+08  14.0        0\n",
       "IT COSMETICS   1.097865e+09  10003.0  5.489375e+08  13.0        0\n",
       "PANTENE        9.549141e+08   5221.0  4.774596e+08  19.0        0\n",
       "DOVE           1.037733e+09   4930.0  5.188689e+08  16.0        0\n",
       "NEUTROGENA     1.146375e+09  24108.0  5.731995e+08  11.0        0\n",
       "GARNIER        1.136905e+09  17000.0  5.684612e+08  12.0        0"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df[new_df['cluster']==size_array.index(sorted(size_array)[0])].sample(10)\n"
   ]
  },
  {
   "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>Revenue</th>\n",
       "      <th>Units</th>\n",
       "      <th>Mean_Produce</th>\n",
       "      <th>Rank</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MAC</th>\n",
       "      <td>1.416014e+09</td>\n",
       "      <td>12269.0</td>\n",
       "      <td>7.080133e+08</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COVER GIRL</th>\n",
       "      <td>1.752298e+09</td>\n",
       "      <td>16156.0</td>\n",
       "      <td>8.761572e+08</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REVLON</th>\n",
       "      <td>1.746854e+09</td>\n",
       "      <td>44258.0</td>\n",
       "      <td>8.734490e+08</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LANCOME</th>\n",
       "      <td>1.993708e+09</td>\n",
       "      <td>22908.0</td>\n",
       "      <td>9.968654e+08</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHANEL</th>\n",
       "      <td>1.531117e+09</td>\n",
       "      <td>16310.0</td>\n",
       "      <td>7.655666e+08</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ESTEE LAUDER</th>\n",
       "      <td>1.753451e+09</td>\n",
       "      <td>16948.0</td>\n",
       "      <td>8.767342e+08</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Revenue    Units  Mean_Produce  Rank  cluster\n",
       "MAC           1.416014e+09  12269.0  7.080133e+08   9.0        3\n",
       "COVER GIRL    1.752298e+09  16156.0  8.761572e+08   6.0        3\n",
       "REVLON        1.746854e+09  44258.0  8.734490e+08   7.0        3\n",
       "LANCOME       1.993708e+09  22908.0  9.968654e+08   4.0        3\n",
       "CHANEL        1.531117e+09  16310.0  7.655666e+08   8.0        3\n",
       "ESTEE LAUDER  1.753451e+09  16948.0  8.767342e+08   5.0        3"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df[new_df['cluster']==size_array.index(sorted(size_array)[-1])].sample(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Cannot take a larger sample than population when 'replace=False'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-35-03d3dc498a26>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnew_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cluster'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0msize_array\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize_array\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(self, n, frac, replace, weights, random_state, axis)\u001b[0m\n\u001b[1;32m   4968\u001b[0m             )\n\u001b[1;32m   4969\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4970\u001b[0;31m         \u001b[0mlocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis_length\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4971\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_copy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mmtrand.pyx\u001b[0m in \u001b[0;36mnumpy.random.mtrand.RandomState.choice\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Cannot take a larger sample than population when 'replace=False'"
     ]
    }
   ],
   "source": [
    "new_df[new_df['cluster']==size_array.index(sorted(size_array)[1])].sample(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Revenue</th>\n",
       "      <th>Units</th>\n",
       "      <th>Mean_Produce</th>\n",
       "      <th>Rank</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LOREAL</th>\n",
       "      <td>3.899134e+09</td>\n",
       "      <td>40094.0</td>\n",
       "      <td>1.949587e+09</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Revenue    Units  Mean_Produce  Rank  cluster\n",
       "LOREAL  3.899134e+09  40094.0  1.949587e+09   1.0        2"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df[new_df['cluster']==size_array.index(sorted(size_array)[2])].sample(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0,1],s=200,c='red',label='Brand 1')\n",
    "plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1,1],s=200,c='blue',label='Brand 2')\n",
    "plt.scatter(X[y_kmeans == 2, 0], X[y_kmeans == 2,1],s=200,c='green',label='Brand 3')\n",
    "plt.scatter(X[y_kmeans == 3, 0], X[y_kmeans == 3,1],s=200,c='black',label='Brand 4')\n",
    "\n",
    "\n",
    "plt.title('Clusters of brand names using metrics')\n",
    "plt.xlabel('Revenue')\n",
    "plt.ylabel('Units')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11ffe5208>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=50, cmap='viridis')\n",
    "\n",
    "centers = kmeans.cluster_centers_\n",
    "plt.scatter(centers[:, 0], centers[:, 1], c='black', s=200, alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int32)"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "#######K means using pCs\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2).fit(iris.data)\n",
    "pca_2d = pca.transform(iris.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pylab as pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0, pca_2d.shape[0]):\n",
    "    if iris.target[i] == 0:\n",
    "        c1 = pl.scatter(pca_2d[i,0],pca_2d[i,1],c='r',\n",
    "         marker='+')\n",
    "    elif iris.target[i] == 1:\n",
    "        c2 = pl.scatter(pca_2d[i,0],pca_2d[i,1],c='g',\n",
    "            marker='o')\n",
    "    elif iris.target[i] == 2:\n",
    "        c3 = pl.scatter(pca_2d[i,0],pca_2d[i,1],c='b',\n",
    "            marker='*')\n",
    "pl.legend([c1, c2, c3], ['Setosa', 'Versicolor',\n",
    "    'Virginica'])\n",
    "pl.title('Iris dataset with 3 clusters and knownoutcomes')\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "we = [{'name': 'FillRate', 'function': 'mean', 'business_friendly_name': 'Fill Rate', 'display_format': '@'}, {'name': 'FillRate', 'function': 'mean', 'business_friendly_name': 'Fill Rate', 'display_format': '&'}]\n",
    "fish_list = [fish for fish in we if fish['display_format'] == '%']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fish_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import string as st\n",
    "passward = ''.join(random.choices(st.ascii_letters + st.digits, k =14))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hAThTWlZK1dbSL'"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "passward\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas_profiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"/Users/shyam.muralidharan/Downloads/Revlon Dataset Updated Aug 2019.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shyam.muralidharan/anaconda3/lib/python3.7/site-packages/pandas_profiling/describe.py:392: FutureWarning: The join_axes-keyword is deprecated. Use .reindex or .reindex_like on the result to achieve the same functionality.\n",
      "  variable_stats = pd.concat(ldesc, join_axes=pd.Index([names]), axis=1)\n"
     ]
    }
   ],
   "source": [
    "profile = pandas_profiling.ProfileReport(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile.to_file(outputfile=\"./report.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "No tables found",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-188-f15f843916e6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_html\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/Users/shyam.muralidharan/Downloads/report.html'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/io/html.py\u001b[0m in \u001b[0;36mread_html\u001b[0;34m(io, match, flavor, header, index_col, skiprows, attrs, parse_dates, thousands, encoding, decimal, converters, na_values, keep_default_na, displayed_only)\u001b[0m\n\u001b[1;32m   1103\u001b[0m         \u001b[0mna_values\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_values\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m         \u001b[0mkeep_default_na\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeep_default_na\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1105\u001b[0;31m         \u001b[0mdisplayed_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdisplayed_only\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1106\u001b[0m     )\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/io/html.py\u001b[0m in \u001b[0;36m_parse\u001b[0;34m(flavor, io, match, attrs, encoding, displayed_only, **kwargs)\u001b[0m\n\u001b[1;32m    910\u001b[0m             \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    911\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 912\u001b[0;31m         \u001b[0mraise_with_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mretained\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m     \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/compat/__init__.py\u001b[0m in \u001b[0;36mraise_with_traceback\u001b[0;34m(exc, traceback)\u001b[0m\n\u001b[1;32m     45\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtraceback\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mEllipsis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m         \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraceback\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraceback\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: No tables found"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"/Users/shyam.muralidharan/Downloads/Revlon Dataset Updated Aug 2019.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['year'] = pd.DatetimeIndex(df['Date']).year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "is_2018 =  df['year']==2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3300, 11)\n"
     ]
    }
   ],
   "source": [
    "gapminder_2002 = df[is_2018]\n",
    "print(gapminder_2002.shape)"
   ]
  },
  {
   "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>Channel</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub Category</th>\n",
       "      <th>Manufacturer</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Date</th>\n",
       "      <th>Units</th>\n",
       "      <th>Price</th>\n",
       "      <th>Price Tier</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>MASS</td>\n",
       "      <td>FRAG MASS</td>\n",
       "      <td>WOMEN</td>\n",
       "      <td>CHANEL INC</td>\n",
       "      <td>CHANEL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1/1/2018</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>a.less than $25</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>PRESTIGE</td>\n",
       "      <td>FRAGRANCES</td>\n",
       "      <td>MEN</td>\n",
       "      <td>CHANEL INC</td>\n",
       "      <td>CHANEL</td>\n",
       "      <td>5127128.0</td>\n",
       "      <td>1/1/2018</td>\n",
       "      <td>65</td>\n",
       "      <td>78879</td>\n",
       "      <td>c.$50 - $100</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>PRESTIGE</td>\n",
       "      <td>FRAGRANCES</td>\n",
       "      <td>WOMEN</td>\n",
       "      <td>CHANEL INC</td>\n",
       "      <td>CHANEL</td>\n",
       "      <td>13733659.0</td>\n",
       "      <td>1/1/2018</td>\n",
       "      <td>49</td>\n",
       "      <td>280279</td>\n",
       "      <td>d.$100+</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>PRESTIGE</td>\n",
       "      <td>MAKEUP</td>\n",
       "      <td>MAKEUP TOTAL ALL OTHER</td>\n",
       "      <td>CHANEL INC</td>\n",
       "      <td>CHANEL</td>\n",
       "      <td>-424.0</td>\n",
       "      <td>1/1/2018</td>\n",
       "      <td>84</td>\n",
       "      <td>-5</td>\n",
       "      <td>a.less than $25</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>PRESTIGE</td>\n",
       "      <td>MAKEUP</td>\n",
       "      <td>MAKEUP TOTAL EYE</td>\n",
       "      <td>CHANEL INC</td>\n",
       "      <td>CHANEL</td>\n",
       "      <td>3132009.0</td>\n",
       "      <td>1/1/2018</td>\n",
       "      <td>81</td>\n",
       "      <td>38667</td>\n",
       "      <td>c.$50 - $100</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Channel    Category            Sub Category Manufacturer   Brand  \\\n",
       "65      MASS   FRAG MASS                   WOMEN   CHANEL INC  CHANEL   \n",
       "66  PRESTIGE  FRAGRANCES                     MEN   CHANEL INC  CHANEL   \n",
       "67  PRESTIGE  FRAGRANCES                   WOMEN   CHANEL INC  CHANEL   \n",
       "68  PRESTIGE      MAKEUP  MAKEUP TOTAL ALL OTHER   CHANEL INC  CHANEL   \n",
       "69  PRESTIGE      MAKEUP        MAKEUP TOTAL EYE   CHANEL INC  CHANEL   \n",
       "\n",
       "       Revenue      Date  Units   Price       Price Tier  year  \n",
       "65         0.0  1/1/2018     82       0  a.less than $25  2018  \n",
       "66   5127128.0  1/1/2018     65   78879     c.$50 - $100  2018  \n",
       "67  13733659.0  1/1/2018     49  280279          d.$100+  2018  \n",
       "68      -424.0  1/1/2018     84      -5  a.less than $25  2018  \n",
       "69   3132009.0  1/1/2018     81   38667     c.$50 - $100  2018  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gapminder_2002.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "l= ['cluster', 'clusters','clustering']\n",
    "dims = [\"cluster\",\"clusters\",\"clustering\"]\n",
    "for i in l:\n",
    "    if i in dims:\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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