VegPriceIndia.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('Veg_price_in_india.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datesk</th>\n",
       "      <th>Item Name</th>\n",
       "      <th>Date</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20110101</td>\n",
       "      <td>Kashini greens</td>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20110101</td>\n",
       "      <td>Pineapple juice</td>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20110101</td>\n",
       "      <td>Thiland Juice</td>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20110101</td>\n",
       "      <td>Thiland Jelly</td>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20110101</td>\n",
       "      <td>Orange juice</td>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "     datesk        Item Name                 Date  price\n",
       "0  20110101   Kashini greens  2011-01-01 00:00:00    NaN\n",
       "1  20110101  Pineapple juice  2011-01-01 00:00:00    NaN\n",
       "2  20110101    Thiland Juice  2011-01-01 00:00:00    NaN\n",
       "3  20110101    Thiland Jelly  2011-01-01 00:00:00    NaN\n",
       "4  20110101     Orange juice  2011-01-01 00:00:00    NaN"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
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   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <th>Item Name</th>\n",
       "      <th>Date</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>868960</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Komark fruit</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868961</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Dry dates</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868962</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Tamarind seedless</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>230.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868963</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Dry fruit mixed</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868964</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Tender Coconut(S)</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "          datesk          Item Name        Date  price\n",
       "868960  20180227       Komark fruit  27/02/2018   28.0\n",
       "868961  20180227          Dry dates  27/02/2018   20.0\n",
       "868962  20180227  Tamarind seedless  27/02/2018  230.0\n",
       "868963  20180227    Dry fruit mixed  27/02/2018    NaN\n",
       "868964  20180227  Tender Coconut(S)  27/02/2018   25.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(868965, 4)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['datesk', 'Item Name', 'Date', 'price']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datesk</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.689650e+05</td>\n",
       "      <td>551948.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.014152e+07</td>\n",
       "      <td>51.664567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.062121e+04</td>\n",
       "      <td>68.496674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.011010e+07</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.012101e+07</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.014073e+07</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.016051e+07</td>\n",
       "      <td>67.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.018023e+07</td>\n",
       "      <td>863.000000</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "             datesk          price\n",
       "count  8.689650e+05  551948.000000\n",
       "mean   2.014152e+07      51.664567\n",
       "std    2.062121e+04      68.496674\n",
       "min    2.011010e+07       0.000000\n",
       "25%    2.012101e+07       0.000000\n",
       "50%    2.014073e+07      30.000000\n",
       "75%    2.016051e+07      67.000000\n",
       "max    2.018023e+07     863.000000"
      ]
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     "execution_count": 13,
     "metadata": {},
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   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datesk    20180227.0\n",
       "price          863.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "863.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['price'].max()"
   ]
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   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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      ]
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     "execution_count": 19,
     "metadata": {},
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   "source": [
    "df['price'].mean()"
   ]
  },
  {
   "cell_type": "code",
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       "           ... \n",
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       "20170620    331\n",
       "Name: datesk, Length: 2613, dtype: int64"
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     "metadata": {},
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   "source": [
    "df['datesk'].value_counts()"
   ]
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   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sachin/.virtualenvs/keras_tf_p3/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: 'argmax' is deprecated, use 'idxmax' instead. The behavior of 'argmax'\n",
      "will be corrected to return the positional maximum in the future.\n",
      "Use 'series.values.argmax' to get the position of the maximum now.\n",
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       "      <th>813795</th>\n",
       "      <td>20170915</td>\n",
       "      <td>Peas Ooty</td>\n",
       "      <td>15/09/2017</td>\n",
       "      <td>863.0</td>\n",
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      "text/plain": [
       "          datesk  Item Name        Date  price\n",
       "813795  20170915  Peas Ooty  15/09/2017  863.0"
      ]
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     "execution_count": 21,
     "metadata": {},
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   ],
   "source": [
    "df.iloc[[df['price'].argmax()]]"
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   "cell_type": "code",
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     "text": [
      "/home/sachin/.virtualenvs/keras_tf_p3/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: 'argmax' is deprecated, use 'idxmax' instead. The behavior of 'argmax'\n",
      "will be corrected to return the positional maximum in the future.\n",
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   "source": [
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   ]
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    {
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     "text": [
      "/home/sachin/.virtualenvs/keras_tf_p3/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: 'argmax' is deprecated, use 'idxmax' instead. The behavior of 'argmax'\n",
      "will be corrected to return the positional maximum in the future.\n",
      "Use 'series.values.argmax' to get the position of the maximum now.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
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     "data": {
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    {
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     "text": [
      "/home/sachin/.virtualenvs/keras_tf_p3/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: 'argmax' is deprecated, use 'idxmax' instead. The behavior of 'argmax'\n",
      "will be corrected to return the positional maximum in the future.\n",
      "Use 'series.values.argmax' to get the position of the maximum now.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
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   "execution_count": 26,
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    {
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     "output_type": "stream",
     "text": [
      "/home/sachin/.virtualenvs/keras_tf_p3/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: 'argmax' is deprecated, use 'idxmax' instead. The behavior of 'argmax'\n",
      "will be corrected to return the positional maximum in the future.\n",
      "Use 'series.values.argmax' to get the position of the maximum now.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
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       "863.0"
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    "df.iloc[[df['price'].argmax()]]['price'][813795]"
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   "execution_count": 27,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datesk</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20110101</td>\n",
       "      <td>Kashini greens</td>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20110101</td>\n",
       "      <td>Pineapple juice</td>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20110101</td>\n",
       "      <td>Thiland Juice</td>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "     datesk        Item Name                 Date  price\n",
       "0  20110101   Kashini greens  2011-01-01 00:00:00    NaN\n",
       "1  20110101  Pineapple juice  2011-01-01 00:00:00    NaN\n",
       "2  20110101    Thiland Juice  2011-01-01 00:00:00    NaN"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
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   "outputs": [
    {
     "data": {
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       "     datesk        Item Name                 Date  price\n",
       "0  20110101   Kashini greens  2011-01-01 00:00:00    NaN\n",
       "1  20110101  Pineapple juice  2011-01-01 00:00:00    NaN\n",
       "2  20110101    Thiland Juice  2011-01-01 00:00:00    NaN\n",
       "3  20110101    Thiland Jelly  2011-01-01 00:00:00    NaN"
      ]
     },
     "execution_count": 28,
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    "df.loc[:3]"
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  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sachin/.virtualenvs/keras_tf_p3/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: 'argmax' is deprecated, use 'idxmax' instead. The behavior of 'argmax'\n",
      "will be corrected to return the positional maximum in the future.\n",
      "Use 'series.values.argmax' to get the position of the maximum now.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
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       "863.0"
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     "execution_count": 30,
     "metadata": {},
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   "source": [
    "df.loc[df['price'].argmax(),'price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sachin/.virtualenvs/keras_tf_p3/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: 'argmax' is deprecated, use 'idxmax' instead. The behavior of 'argmax'\n",
      "will be corrected to return the positional maximum in the future.\n",
      "Use 'series.values.argmax' to get the position of the maximum now.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "863.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.at[df['price'].argmax(),'price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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       "      <th>281169</th>\n",
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       "      <th>281172</th>\n",
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       "      <th>281173</th>\n",
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       "      <th>636808</th>\n",
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       "          datesk              Item Name        Date  price\n",
       "666729  20160628                  Dates  28/06/2016    0.0\n",
       "281169  20130424   Ber fruit/Bore fruit  24/04/2013    0.0\n",
       "281172  20130424            Apple Simla  24/04/2013    0.0\n",
       "281173  20130424        Apple Australia  24/04/2013    0.0\n",
       "636808  20160330  Chillies Bajji yagani  30/03/2016    0.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
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   "source": [
    "df.sort_values('price').head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.groupby.DataFrameGroupBy object at 0x7f77d4a23128>"
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     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
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  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
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       "      <th>22318</th>\n",
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       "      <th>22323</th>\n",
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       "      <th>22326</th>\n",
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       "      <th>22328</th>\n",
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       "      <th>22329</th>\n",
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       "      <th>22330</th>\n",
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       "      <th>22331</th>\n",
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       "      <td>Drumstik</td>\n",
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       "      <th>22333</th>\n",
       "      <td>20110309</td>\n",
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       "      <td>26.0</td>\n",
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       "      <th>22337</th>\n",
       "      <td>20110309</td>\n",
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       "      <th>22340</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Grapes Dry 250gm</td>\n",
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       "      <th>22341</th>\n",
       "      <td>20110309</td>\n",
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       "      <th>22342</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Lime Local</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>52.0</td>\n",
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       "      <th>22344</th>\n",
       "      <td>20110309</td>\n",
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       "      <th>22345</th>\n",
       "      <td>20110309</td>\n",
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       "      <th>22347</th>\n",
       "      <td>20110309</td>\n",
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       "      <th>22348</th>\n",
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       "      <th>22349</th>\n",
       "      <td>20110309</td>\n",
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       "      <th>22352</th>\n",
       "      <td>20110309</td>\n",
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       "      <th>22353</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Sponge Gourd</td>\n",
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       "      <td>32.0</td>\n",
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       "    <tr>\n",
       "      <th>22354</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Raddish Red</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>24.0</td>\n",
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       "    <tr>\n",
       "      <th>22367</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Orange malt</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
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       "    <tr>\n",
       "      <th>22368</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Grapes Blore blue</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>24.0</td>\n",
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       "    <tr>\n",
       "      <th>22369</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Guava Allahabad(Red)</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>45.0</td>\n",
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       "      <th>...</th>\n",
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       "      <td>Brucoli</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>87.0</td>\n",
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       "      <th>868908</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Curry leave</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>70.0</td>\n",
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       "    <tr>\n",
       "      <th>868910</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Hesaru kalu</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>90.0</td>\n",
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       "    <tr>\n",
       "      <th>868911</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Chikadi kai</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868912</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Thogari kai</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>32.0</td>\n",
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       "    <tr>\n",
       "      <th>868913</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Arive greens</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>25.0</td>\n",
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       "    <tr>\n",
       "      <th>868915</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Avare seed (FB seeds)</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>84.0</td>\n",
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       "    <tr>\n",
       "      <th>868916</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Garlic</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>59.0</td>\n",
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       "    <tr>\n",
       "      <th>868917</th>\n",
       "      <td>20180227</td>\n",
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       "    <tr>\n",
       "      <th>868918</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Parvala</td>\n",
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       "      <td>86.0</td>\n",
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       "    <tr>\n",
       "      <th>868921</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Straw Berry</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>45.0</td>\n",
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       "    <tr>\n",
       "      <th>868925</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Tender Coconut(M)</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868928</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Litchi Local</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>200.0</td>\n",
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       "    <tr>\n",
       "      <th>868933</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Chakota greens</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868935</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Thinda</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868936</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Litchi Taiwan/chaina</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>228.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868937</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Berry ball</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868940</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Tamrind Chatisghar</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868941</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Jambo fruit(Nerale)</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868942</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Cherry kashmir</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>372.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868944</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Baby corn cleaned</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868950</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Kiwi fruit</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>280.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868952</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Tender Coconut</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868953</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Berry Green</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>148.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868956</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Berry Soft</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>282.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868957</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Baby corn</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868958</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Cherry fruit</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868960</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Komark fruit</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868962</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Tamarind seedless</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>230.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868964</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Tender Coconut(S)</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>332780 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          datesk                Item Name                 Date  price\n",
       "296     20110101             Sponge Gourd  2011-01-01 00:00:00   32.0\n",
       "22312   20110309        Baby corn cleaned  2011-09-03 00:00:00   56.0\n",
       "22315   20110309        Chilakarive green  2011-09-03 00:00:00   22.0\n",
       "22318   20110309           Chakota greens  2011-09-03 00:00:00   21.0\n",
       "22320   20110309             Arive greens  2011-09-03 00:00:00   24.0\n",
       "22322   20110309              Chikadi kai  2011-09-03 00:00:00   30.0\n",
       "22323   20110309           Garlic cleaned  2011-09-03 00:00:00  140.0\n",
       "22324   20110309                   Garlic  2011-09-03 00:00:00   92.0\n",
       "22326   20110309                   Ginger  2011-09-03 00:00:00   22.0\n",
       "22328   20110309         Ground nut Local  2011-09-03 00:00:00   56.0\n",
       "22329   20110309             Double Beans  2011-09-03 00:00:00   32.0\n",
       "22330   20110309       Double Beans seeds  2011-09-03 00:00:00   96.0\n",
       "22331   20110309                 Drumstik  2011-09-03 00:00:00  100.0\n",
       "22333   20110309       Cauliflower per Kg  2011-09-03 00:00:00   26.0\n",
       "22337   20110309          Mushroom Button  2011-09-03 00:00:00  160.0\n",
       "22340   20110309         Grapes Dry 250gm  2011-09-03 00:00:00  220.0\n",
       "22341   20110309         Grapes Red globe  2011-09-03 00:00:00  290.0\n",
       "22342   20110309               Lime Local  2011-09-03 00:00:00   52.0\n",
       "22344   20110309             Ladys finger  2011-09-03 00:00:00   43.0\n",
       "22345   20110309               Herali kai  2011-09-03 00:00:00   36.0\n",
       "22346   20110309              Grapes T.S.  2011-09-03 00:00:00   80.0\n",
       "22347   20110309  Avarekai (Field Beans )  2011-09-03 00:00:00   35.0\n",
       "22348   20110309  Banana cooking R.Banana  2011-09-03 00:00:00   28.0\n",
       "22349   20110309                   Jukani  2011-09-03 00:00:00   80.0\n",
       "22352   20110309              Snake Gourd  2011-09-03 00:00:00   23.0\n",
       "22353   20110309             Sponge Gourd  2011-09-03 00:00:00   32.0\n",
       "22354   20110309              Raddish Red  2011-09-03 00:00:00   24.0\n",
       "22367   20110309              Orange malt  2011-09-03 00:00:00   50.0\n",
       "22368   20110309        Grapes Blore blue  2011-09-03 00:00:00   24.0\n",
       "22369   20110309     Guava Allahabad(Red)  2011-09-03 00:00:00   45.0\n",
       "...          ...                      ...                  ...    ...\n",
       "868907  20180227                  Brucoli           27/02/2018   87.0\n",
       "868908  20180227              Curry leave           27/02/2018   70.0\n",
       "868910  20180227              Hesaru kalu           27/02/2018   90.0\n",
       "868911  20180227              Chikadi kai           27/02/2018   23.0\n",
       "868912  20180227              Thogari kai           27/02/2018   32.0\n",
       "868913  20180227             Arive greens           27/02/2018   25.0\n",
       "868915  20180227    Avare seed (FB seeds)           27/02/2018   84.0\n",
       "868916  20180227                   Garlic           27/02/2018   59.0\n",
       "868917  20180227               Sham gadde           27/02/2018   82.0\n",
       "868918  20180227                  Parvala           27/02/2018   86.0\n",
       "868921  20180227              Straw Berry           27/02/2018   45.0\n",
       "868925  20180227        Tender Coconut(M)           27/02/2018   22.0\n",
       "868928  20180227             Litchi Local           27/02/2018  200.0\n",
       "868933  20180227           Chakota greens           27/02/2018   33.0\n",
       "868935  20180227                   Thinda           27/02/2018   54.0\n",
       "868936  20180227     Litchi Taiwan/chaina           27/02/2018  228.0\n",
       "868937  20180227               Berry ball           27/02/2018   67.0\n",
       "868940  20180227       Tamrind Chatisghar           27/02/2018  150.0\n",
       "868941  20180227      Jambo fruit(Nerale)           27/02/2018  160.0\n",
       "868942  20180227           Cherry kashmir           27/02/2018  372.0\n",
       "868944  20180227        Baby corn cleaned           27/02/2018  145.0\n",
       "868950  20180227               Kiwi fruit           27/02/2018  280.0\n",
       "868952  20180227           Tender Coconut           27/02/2018   25.0\n",
       "868953  20180227              Berry Green           27/02/2018  148.0\n",
       "868956  20180227               Berry Soft           27/02/2018  282.0\n",
       "868957  20180227                Baby corn           27/02/2018   29.0\n",
       "868958  20180227             Cherry fruit           27/02/2018  150.0\n",
       "868960  20180227             Komark fruit           27/02/2018   28.0\n",
       "868962  20180227        Tamarind seedless           27/02/2018  230.0\n",
       "868964  20180227        Tender Coconut(S)           27/02/2018   25.0\n",
       "\n",
       "[332780 rows x 4 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['price']>20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datesk</th>\n",
       "      <th>Item Name</th>\n",
       "      <th>Date</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22312</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Baby corn cleaned</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22324</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Garlic</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22328</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Ground nut Local</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22330</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Double Beans seeds</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22342</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Lime Local</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22344</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Ladys finger</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22346</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Grapes T.S.</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22349</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Jukani</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22367</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Orange malt</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22369</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Guava Allahabad(Red)</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22387</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Apple Economy</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22393</th>\n",
       "      <td>20110309</td>\n",
       "      <td>G.nut oil Rice Bron 1 lt</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22394</th>\n",
       "      <td>20110309</td>\n",
       "      <td>G.nut oil Premium1 lt</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22395</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Ground nut oil 1 lt</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22414</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Grapes Flame</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22415</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Grapes Sonika</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22417</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Grapes Sharad</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22420</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Hesaru kalu</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22421</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Molake kalu</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22423</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Parvala</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22430</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Jam 1/2 kg</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22431</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Jam 1 kg</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22432</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Coconut oil 500 ml</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22433</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Stevia powder</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22434</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Pepper</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22447</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Peas seeds</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22448</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Anjura/Fig</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22449</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Butter fruit</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22458</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Chilles small cleaned (C.B.P)</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22460</th>\n",
       "      <td>20110309</td>\n",
       "      <td>Times Rose berry</td>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868850</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Grapes T.S.</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868851</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Chicco(Sapota)</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868853</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Mango Alphans</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868861</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Guava Allahabad(Red)</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868864</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Banana chandra</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868865</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Chicco(Sapota) rapined</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868867</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Custerd Apple</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868869</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Banana Yellaki</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868870</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Grapes Sonika</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868873</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Banana Nendra</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868874</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Grapes Dilkush</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868876</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Kakadi</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868879</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Spring Onion(Cleaned)</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868880</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Chillies Bajji yagani</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868882</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Banana cooking R.Banana</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868889</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Sweet corn</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868890</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Jukani</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868895</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Sweet corn seeds spok</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868899</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Molake kalu</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868906</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Sweet corn cleaned</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868907</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Brucoli</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868908</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Curry leave</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868910</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Hesaru kalu</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868915</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Avare seed (FB seeds)</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868916</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Garlic</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868917</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Sham gadde</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868918</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Parvala</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868921</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Straw Berry</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868935</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Thinda</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868937</th>\n",
       "      <td>20180227</td>\n",
       "      <td>Berry ball</td>\n",
       "      <td>27/02/2018</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>123198 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          datesk                      Item Name                 Date  price\n",
       "22312   20110309              Baby corn cleaned  2011-09-03 00:00:00   56.0\n",
       "22324   20110309                         Garlic  2011-09-03 00:00:00   92.0\n",
       "22328   20110309               Ground nut Local  2011-09-03 00:00:00   56.0\n",
       "22330   20110309             Double Beans seeds  2011-09-03 00:00:00   96.0\n",
       "22342   20110309                     Lime Local  2011-09-03 00:00:00   52.0\n",
       "22344   20110309                   Ladys finger  2011-09-03 00:00:00   43.0\n",
       "22346   20110309                    Grapes T.S.  2011-09-03 00:00:00   80.0\n",
       "22349   20110309                         Jukani  2011-09-03 00:00:00   80.0\n",
       "22367   20110309                    Orange malt  2011-09-03 00:00:00   50.0\n",
       "22369   20110309           Guava Allahabad(Red)  2011-09-03 00:00:00   45.0\n",
       "22387   20110309                  Apple Economy  2011-09-03 00:00:00   95.0\n",
       "22393   20110309       G.nut oil Rice Bron 1 lt  2011-09-03 00:00:00   69.0\n",
       "22394   20110309          G.nut oil Premium1 lt  2011-09-03 00:00:00   80.0\n",
       "22395   20110309            Ground nut oil 1 lt  2011-09-03 00:00:00   84.0\n",
       "22414   20110309                   Grapes Flame  2011-09-03 00:00:00   75.0\n",
       "22415   20110309                  Grapes Sonika  2011-09-03 00:00:00   87.0\n",
       "22417   20110309                  Grapes Sharad  2011-09-03 00:00:00   88.0\n",
       "22420   20110309                    Hesaru kalu  2011-09-03 00:00:00   75.0\n",
       "22421   20110309                    Molake kalu  2011-09-03 00:00:00   75.0\n",
       "22423   20110309                        Parvala  2011-09-03 00:00:00   55.0\n",
       "22430   20110309                     Jam 1/2 kg  2011-09-03 00:00:00   55.0\n",
       "22431   20110309                       Jam 1 kg  2011-09-03 00:00:00   75.0\n",
       "22432   20110309             Coconut oil 500 ml  2011-09-03 00:00:00   61.0\n",
       "22433   20110309                  Stevia powder  2011-09-03 00:00:00   60.0\n",
       "22434   20110309                         Pepper  2011-09-03 00:00:00   70.0\n",
       "22447   20110309                     Peas seeds  2011-09-03 00:00:00   78.0\n",
       "22448   20110309                     Anjura/Fig  2011-09-03 00:00:00   60.0\n",
       "22449   20110309                   Butter fruit  2011-09-03 00:00:00   52.0\n",
       "22458   20110309  Chilles small cleaned (C.B.P)  2011-09-03 00:00:00   41.0\n",
       "22460   20110309               Times Rose berry  2011-09-03 00:00:00   65.0\n",
       "...          ...                            ...                  ...    ...\n",
       "868850  20180227                    Grapes T.S.           27/02/2018   60.0\n",
       "868851  20180227                 Chicco(Sapota)           27/02/2018   46.0\n",
       "868853  20180227                  Mango Alphans           27/02/2018   76.0\n",
       "868861  20180227           Guava Allahabad(Red)           27/02/2018   89.0\n",
       "868864  20180227                 Banana chandra           27/02/2018   82.0\n",
       "868865  20180227         Chicco(Sapota) rapined           27/02/2018   52.0\n",
       "868867  20180227                  Custerd Apple           27/02/2018   49.0\n",
       "868869  20180227                 Banana Yellaki           27/02/2018   60.0\n",
       "868870  20180227                  Grapes Sonika           27/02/2018   75.0\n",
       "868873  20180227                  Banana Nendra           27/02/2018   55.0\n",
       "868874  20180227                 Grapes Dilkush           27/02/2018   78.0\n",
       "868876  20180227                         Kakadi           27/02/2018   42.0\n",
       "868879  20180227          Spring Onion(Cleaned)           27/02/2018   59.0\n",
       "868880  20180227          Chillies Bajji yagani           27/02/2018   42.0\n",
       "868882  20180227        Banana cooking R.Banana           27/02/2018   54.0\n",
       "868889  20180227                     Sweet corn           27/02/2018   45.0\n",
       "868890  20180227                         Jukani           27/02/2018   70.0\n",
       "868895  20180227          Sweet corn seeds spok           27/02/2018   46.0\n",
       "868899  20180227                    Molake kalu           27/02/2018   70.0\n",
       "868906  20180227             Sweet corn cleaned           27/02/2018   98.0\n",
       "868907  20180227                        Brucoli           27/02/2018   87.0\n",
       "868908  20180227                    Curry leave           27/02/2018   70.0\n",
       "868910  20180227                    Hesaru kalu           27/02/2018   90.0\n",
       "868915  20180227          Avare seed (FB seeds)           27/02/2018   84.0\n",
       "868916  20180227                         Garlic           27/02/2018   59.0\n",
       "868917  20180227                     Sham gadde           27/02/2018   82.0\n",
       "868918  20180227                        Parvala           27/02/2018   86.0\n",
       "868921  20180227                    Straw Berry           27/02/2018   45.0\n",
       "868935  20180227                         Thinda           27/02/2018   54.0\n",
       "868937  20180227                     Berry ball           27/02/2018   67.0\n",
       "\n",
       "[123198 rows x 4 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['price'] > 40) & (df['price'] < 100)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item Name\n",
       "Amla                       56.359863\n",
       "Anjura/Fig                 57.066090\n",
       "Apple Australia            71.581088\n",
       "Apple Chaina Delicious    132.000000\n",
       "Apple Chilli               43.308083\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Item Name')['price'].mean().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item Name\n",
       "Amla          56.359863\n",
       "Anjura/Fig    57.066090\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Item Name')['price'].mean().head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[20110101, 'Kashini greens', datetime.datetime(2011, 1, 1, 0, 0),\n",
       "        nan],\n",
       "       [20110101, 'Pineapple juice', datetime.datetime(2011, 1, 1, 0, 0),\n",
       "        nan],\n",
       "       [20110101, 'Thiland Juice', datetime.datetime(2011, 1, 1, 0, 0),\n",
       "        nan],\n",
       "       ...,\n",
       "       [20180227, 'Tamarind seedless', '27/02/2018', 230.0],\n",
       "       [20180227, 'Dry fruit mixed', '27/02/2018', nan],\n",
       "       [20180227, 'Tender Coconut(S)', '27/02/2018', 25.0]], dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, row in df.iterrows():\n",
    "    print(row)\n",
    "    if index == 1:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Item Name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kashini greens</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pineapple juice</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Thiland Juice</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thiland Jelly</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Orange juice</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Item Name  price\n",
       "0   Kashini greens    NaN\n",
       "1  Pineapple juice    NaN\n",
       "2    Thiland Juice    NaN\n",
       "3    Thiland Jelly    NaN\n",
       "4     Orange juice    NaN"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Item Name','price']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Item Name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kashini greens</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pineapple juice</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Thiland Juice</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thiland Jelly</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Orange juice</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Item Name  price\n",
       "0   Kashini greens    NaN\n",
       "1  Pineapple juice    NaN\n",
       "2    Thiland Juice    NaN\n",
       "3    Thiland Jelly    NaN\n",
       "4     Orange juice    NaN"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,['Item Name','price']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['Item Name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df[['Item Name']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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></th>\n",
       "      <th>datesk</th>\n",
       "      <th>Item Name</th>\n",
       "      <th>Date</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20110101</td>\n",
       "      <td>Kashini greens</td>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20110101</td>\n",
       "      <td>Pineapple juice</td>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20110101</td>\n",
       "      <td>Thiland Juice</td>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     datesk        Item Name                 Date  price\n",
       "0  20110101   Kashini greens  2011-01-01 00:00:00    NaN\n",
       "1  20110101  Pineapple juice  2011-01-01 00:00:00    NaN\n",
       "2  20110101    Thiland Juice  2011-01-01 00:00:00    NaN"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[0:3,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'price range')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f781e2f7a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df['price'].plot.hist(bins=20)\n",
    "ax.set_xlabel('price range')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Item Name\n",
      "Amla                       56.359863\n",
      "Anjura/Fig                 57.066090\n",
      "Apple Australia            71.581088\n",
      "Apple Chaina Delicious    132.000000\n",
      "Apple Chilli               43.308083\n",
      "Name: price, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'price range - Mean')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f77dce06588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(df.groupby('Item Name')['price'].mean().head())\n",
    "ax = df.groupby('Item Name')['price'].mean().head().plot.hist(bins=10)\n",
    "ax.set_xlabel('price range - Mean')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "keras_tf_p3",
   "language": "python",
   "name": "keras_tf_p3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}