 Mohit _DS_11_Project_ Submission.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "00450487",
   "metadata": {},
   "source": [
    "# Project IMUS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66f3e0b4",
   "metadata": {},
   "source": [
    "## Dataset description:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "927e4b0f",
   "metadata": {},
   "source": [
    "### What is an IMU?\n",
    "\n",
    "An inertial measurement unit (IMU) measures and reports raw or filtered angular rate and specific force/acceleration experienced by the object it is attached to. Data outputs for an IMU are typically body-frame accelerations, angular rates, and (optionally) magnetic field measurements."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8724bf91",
   "metadata": {},
   "source": [
    "## The columns of the dataset are:\n",
    "\n",
    "1. Category: The category to which this IMU belongs.\n",
    "\n",
    "2. Manufacturer: Name of the manufacturer.\n",
    "\n",
    "3. part number: Discrete part number\n",
    "\n",
    "4. sku: stock keeping unit\n",
    "\n",
    "5. alt_sku: alternate stock keeping unit\n",
    "\n",
    "6. Description: description of the product\n",
    "\n",
    "7. Status: Whether it is in stock or not\n",
    "\n",
    "8. Quantity: Quantity available\n",
    "\n",
    "9. Price: Price of the product\n",
    "\n",
    "10. sku_create_date: when was the product procured"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7859e12",
   "metadata": {},
   "source": [
    "## Importing Related Resources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2763b856",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "pd.plotting.register_matplotlib_converters()\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "68ed0848",
   "metadata": {},
   "outputs": [],
   "source": [
    "file ='imus.xlsx'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "58dcc5f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat(pd.read_excel(\"imus.xlsx\",sheet_name= None),ignore_index = True) # dedining df variable to file , merging of all sheet with concat method."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81a71904",
   "metadata": {},
   "source": [
    "## Pre-Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27981fe4",
   "metadata": {},
   "source": [
    "### Intial Insepection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6f8252a0",
   "metadata": {},
   "outputs": [
    {
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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>category</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>part_number</th>\n",
       "      <th>sku</th>\n",
       "      <th>alt_sku</th>\n",
       "      <th>description</th>\n",
       "      <th>status</th>\n",
       "      <th>qty</th>\n",
       "      <th>price</th>\n",
       "      <th>sku_create_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1539</td>\n",
       "      <td>MS-OPEN VALUE SUBSCRIPTION</td>\n",
       "      <td>021-09108</td>\n",
       "      <td>BA0011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1YR OFFICESTD ALNG LICSAPK OLV, D AP</td>\n",
       "      <td>No Stock</td>\n",
       "      <td>0</td>\n",
       "      <td>143.58</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1539</td>\n",
       "      <td>MS-OPEN VALUE SUBSCRIPTION</td>\n",
       "      <td>059-07014</td>\n",
       "      <td>BA0025</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1YR WORD ALNG LICSAPK OLV D AP,</td>\n",
       "      <td>No Stock</td>\n",
       "      <td>0</td>\n",
       "      <td>56.09</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1539</td>\n",
       "      <td>MS-OPEN VALUE SUBSCRIPTION</td>\n",
       "      <td>065-06444</td>\n",
       "      <td>BA0039</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1YR EXCEL ALNG LICSAPK OLV D AP,</td>\n",
       "      <td>No Stock</td>\n",
       "      <td>0</td>\n",
       "      <td>56.09</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1539</td>\n",
       "      <td>MS-OPEN VALUE SUBSCRIPTION</td>\n",
       "      <td>076-04355</td>\n",
       "      <td>BA0067</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1YR PRJCT ALNG LICSAPK OLV D AP,</td>\n",
       "      <td>No Stock</td>\n",
       "      <td>0</td>\n",
       "      <td>217.60</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1582</td>\n",
       "      <td>MS-OPEN VALUE SUBSCRIPTION</td>\n",
       "      <td>077-05318</td>\n",
       "      <td>BA0080</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1YR ACCESS ALNG LICSAPK OLV D, AP</td>\n",
       "      <td>No Stock</td>\n",
       "      <td>0</td>\n",
       "      <td>56.09</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   category                manufacturer part_number     sku alt_sku  \\\n",
       "0      1539  MS-OPEN VALUE SUBSCRIPTION   021-09108  BA0011     NaN   \n",
       "1      1539  MS-OPEN VALUE SUBSCRIPTION   059-07014  BA0025     NaN   \n",
       "2      1539  MS-OPEN VALUE SUBSCRIPTION   065-06444  BA0039     NaN   \n",
       "3      1539  MS-OPEN VALUE SUBSCRIPTION   076-04355  BA0067     NaN   \n",
       "4      1582  MS-OPEN VALUE SUBSCRIPTION   077-05318  BA0080     NaN   \n",
       "\n",
       "                            description    status  qty   price sku_create_date  \n",
       "0  1YR OFFICESTD ALNG LICSAPK OLV, D AP  No Stock    0  143.58      2020-08-17  \n",
       "1       1YR WORD ALNG LICSAPK OLV D AP,  No Stock    0   56.09      2020-08-17  \n",
       "2      1YR EXCEL ALNG LICSAPK OLV D AP,  No Stock    0   56.09      2020-08-17  \n",
       "3      1YR PRJCT ALNG LICSAPK OLV D AP,  No Stock    0  217.60      2020-08-17  \n",
       "4     1YR ACCESS ALNG LICSAPK OLV D, AP  No Stock    0   56.09      2020-08-17  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f220f42e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Rows and Columns in dataset (2627926, 10) .\n"
     ]
    }
   ],
   "source": [
    "print('Number of Rows and Columns in dataset',df.shape,'.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4e381276",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data Type of each column:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "category                    int64\n",
       "manufacturer               object\n",
       "part_number                object\n",
       "sku                        object\n",
       "alt_sku                    object\n",
       "description                object\n",
       "status                     object\n",
       "qty                         int64\n",
       "price                     float64\n",
       "sku_create_date    datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Data Type of each column:')\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "44e48f51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Four type of columns in dataset.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "object            6\n",
       "int64             2\n",
       "float64           1\n",
       "datetime64[ns]    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Four type of columns in dataset.')\n",
    "df.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6016dc6",
   "metadata": {},
   "source": [
    "###  Concise Summary of a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a45a55d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2627926 entries, 0 to 2627925\n",
      "Data columns (total 10 columns):\n",
      " #   Column           Dtype         \n",
      "---  ------           -----         \n",
      " 0   category         int64         \n",
      " 1   manufacturer     object        \n",
      " 2   part_number      object        \n",
      " 3   sku              object        \n",
      " 4   alt_sku          object        \n",
      " 5   description      object        \n",
      " 6   status           object        \n",
      " 7   qty              int64         \n",
      " 8   price            float64       \n",
      " 9   sku_create_date  datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(1), int64(2), object(6)\n",
      "memory usage: 200.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c97ddaf4",
   "metadata": {},
   "source": [
    "## Cleaning of dataset for further analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c84ab94d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.astype({\"category\":'category'}) # changing type of category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48649bfe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d035e43c",
   "metadata": {},
   "source": [
    "### Checking null type of data in dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "105d87c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "category                 0\n",
       "manufacturer             0\n",
       "part_number              0\n",
       "sku                      0\n",
       "alt_sku            2613442\n",
       "description              0\n",
       "status                   0\n",
       "qty                      0\n",
       "price                    0\n",
       "sku_create_date          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8298a1b",
   "metadata": {},
   "source": [
    "lot of null value in alt_sku so better to drop it for further analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f22b9216",
   "metadata": {},
   "source": [
    "### Droping extra columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6ab6e437",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['sku','alt_sku','description'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a2dc9306",
   "metadata": {},
   "outputs": [
    {
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       "      <th>manufacturer</th>\n",
       "      <th>part_number</th>\n",
       "      <th>status</th>\n",
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       "      <td>2020-08-17</td>\n",
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       "      <td>No Stock</td>\n",
       "      <td>0</td>\n",
       "      <td>56.09</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1539</td>\n",
       "      <td>MS-OPEN VALUE SUBSCRIPTION</td>\n",
       "      <td>076-04355</td>\n",
       "      <td>No Stock</td>\n",
       "      <td>0</td>\n",
       "      <td>217.60</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1582</td>\n",
       "      <td>MS-OPEN VALUE SUBSCRIPTION</td>\n",
       "      <td>077-05318</td>\n",
       "      <td>No Stock</td>\n",
       "      <td>0</td>\n",
       "      <td>56.09</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category                manufacturer part_number    status  qty   price  \\\n",
       "0     1539  MS-OPEN VALUE SUBSCRIPTION   021-09108  No Stock    0  143.58   \n",
       "1     1539  MS-OPEN VALUE SUBSCRIPTION   059-07014  No Stock    0   56.09   \n",
       "2     1539  MS-OPEN VALUE SUBSCRIPTION   065-06444  No Stock    0   56.09   \n",
       "3     1539  MS-OPEN VALUE SUBSCRIPTION   076-04355  No Stock    0  217.60   \n",
       "4     1582  MS-OPEN VALUE SUBSCRIPTION   077-05318  No Stock    0   56.09   \n",
       "\n",
       "  sku_create_date  \n",
       "0      2020-08-17  \n",
       "1      2020-08-17  \n",
       "2      2020-08-17  \n",
       "3      2020-08-17  \n",
       "4      2020-08-17  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18e9ca2a",
   "metadata": {},
   "source": [
    "### Renaming columns for convience "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "22c0206e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns={'sku_create_date': 'date', 'part_number': 'product'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d1581526",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
       "      <td>1539</td>\n",
       "      <td>MS-OPEN VALUE SUBSCRIPTION</td>\n",
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       "      <td>No Stock</td>\n",
       "      <td>0</td>\n",
       "      <td>56.09</td>\n",
       "      <td>2020-08-17</td>\n",
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       "      <th>3</th>\n",
       "      <td>1539</td>\n",
       "      <td>MS-OPEN VALUE SUBSCRIPTION</td>\n",
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       "      <td>2020-08-17</td>\n",
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       "      <td>MS-OPEN VALUE SUBSCRIPTION</td>\n",
       "      <td>077-05318</td>\n",
       "      <td>No Stock</td>\n",
       "      <td>0</td>\n",
       "      <td>56.09</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category                manufacturer    product    status  qty   price  \\\n",
       "0     1539  MS-OPEN VALUE SUBSCRIPTION  021-09108  No Stock    0  143.58   \n",
       "1     1539  MS-OPEN VALUE SUBSCRIPTION  059-07014  No Stock    0   56.09   \n",
       "2     1539  MS-OPEN VALUE SUBSCRIPTION  065-06444  No Stock    0   56.09   \n",
       "3     1539  MS-OPEN VALUE SUBSCRIPTION  076-04355  No Stock    0  217.60   \n",
       "4     1582  MS-OPEN VALUE SUBSCRIPTION  077-05318  No Stock    0   56.09   \n",
       "\n",
       "        date  \n",
       "0 2020-08-17  \n",
       "1 2020-08-17  \n",
       "2 2020-08-17  \n",
       "3 2020-08-17  \n",
       "4 2020-08-17  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dd87e9b",
   "metadata": {},
   "source": [
    "### Dividing Dataset on basis of availablity of stock."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2751f9a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stock availablity:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "No Stock    1914138\n",
       "In Stock     713788\n",
       "Name: status, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Stock availablity:')\n",
    "df['status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d2948e24",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_st= df[df.status == 'In Stock']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0518e95b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_nos= df[df.status == 'No Stock']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37269895",
   "metadata": {},
   "source": [
    "## For data In Stock:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "12dcaf0f",
   "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>category</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>product</th>\n",
       "      <th>status</th>\n",
       "      <th>qty</th>\n",
       "      <th>price</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>9920</td>\n",
       "      <td>CASE LOGIC-PERSONAL &amp; PORTABLE</td>\n",
       "      <td>3200943</td>\n",
       "      <td>In Stock</td>\n",
       "      <td>7</td>\n",
       "      <td>99.00</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>531</td>\n",
       "      <td>OMNITRON SYSTEMS</td>\n",
       "      <td>7206-0</td>\n",
       "      <td>In Stock</td>\n",
       "      <td>2</td>\n",
       "      <td>59.09</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>9070</td>\n",
       "      <td>ZEBRA ENTERPRISE MCD-A1</td>\n",
       "      <td>SAC5070-800CR</td>\n",
       "      <td>In Stock</td>\n",
       "      <td>3</td>\n",
       "      <td>500.06</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>9080</td>\n",
       "      <td>ZEBRA ENTERPRISE MCD-A1</td>\n",
       "      <td>KT-CLMPT-RS507-01R</td>\n",
       "      <td>In Stock</td>\n",
       "      <td>115</td>\n",
       "      <td>86.34</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>9080</td>\n",
       "      <td>ZEBRA ENTERPRISE MCD-A1</td>\n",
       "      <td>KT-PAD-RS507-10R</td>\n",
       "      <td>In Stock</td>\n",
       "      <td>42</td>\n",
       "      <td>51.80</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    category                    manufacturer             product    status  \\\n",
       "253     9920  CASE LOGIC-PERSONAL & PORTABLE             3200943  In Stock   \n",
       "271      531                OMNITRON SYSTEMS              7206-0  In Stock   \n",
       "320     9070         ZEBRA ENTERPRISE MCD-A1       SAC5070-800CR  In Stock   \n",
       "324     9080         ZEBRA ENTERPRISE MCD-A1  KT-CLMPT-RS507-01R  In Stock   \n",
       "326     9080         ZEBRA ENTERPRISE MCD-A1    KT-PAD-RS507-10R  In Stock   \n",
       "\n",
       "     qty   price       date  \n",
       "253    7   99.00 2020-08-17  \n",
       "271    2   59.09 2020-08-17  \n",
       "320    3  500.06 2020-08-17  \n",
       "324  115   86.34 2020-08-17  \n",
       "326   42   51.80 2020-08-17  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_st.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e93abc76",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_st.drop([\"status\"], axis=1) # no need of stock column now so dropping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "09320d62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>category</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>product</th>\n",
       "      <th>qty</th>\n",
       "      <th>price</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>9920</td>\n",
       "      <td>CASE LOGIC-PERSONAL &amp; PORTABLE</td>\n",
       "      <td>3200943</td>\n",
       "      <td>7</td>\n",
       "      <td>99.00</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>531</td>\n",
       "      <td>OMNITRON SYSTEMS</td>\n",
       "      <td>7206-0</td>\n",
       "      <td>2</td>\n",
       "      <td>59.09</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>9070</td>\n",
       "      <td>ZEBRA ENTERPRISE MCD-A1</td>\n",
       "      <td>SAC5070-800CR</td>\n",
       "      <td>3</td>\n",
       "      <td>500.06</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>9080</td>\n",
       "      <td>ZEBRA ENTERPRISE MCD-A1</td>\n",
       "      <td>KT-CLMPT-RS507-01R</td>\n",
       "      <td>115</td>\n",
       "      <td>86.34</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>9080</td>\n",
       "      <td>ZEBRA ENTERPRISE MCD-A1</td>\n",
       "      <td>KT-PAD-RS507-10R</td>\n",
       "      <td>42</td>\n",
       "      <td>51.80</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627921</th>\n",
       "      <td>9945</td>\n",
       "      <td>VERBATIM CORPORATION</td>\n",
       "      <td>93975</td>\n",
       "      <td>12</td>\n",
       "      <td>50.53</td>\n",
       "      <td>2020-08-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627922</th>\n",
       "      <td>9945</td>\n",
       "      <td>VERBATIM CORPORATION</td>\n",
       "      <td>93975</td>\n",
       "      <td>12</td>\n",
       "      <td>50.53</td>\n",
       "      <td>2020-08-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627923</th>\n",
       "      <td>9945</td>\n",
       "      <td>VERBATIM CORPORATION</td>\n",
       "      <td>93975</td>\n",
       "      <td>12</td>\n",
       "      <td>50.53</td>\n",
       "      <td>2020-08-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627924</th>\n",
       "      <td>9945</td>\n",
       "      <td>VERBATIM CORPORATION</td>\n",
       "      <td>93975</td>\n",
       "      <td>12</td>\n",
       "      <td>50.53</td>\n",
       "      <td>2020-08-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627925</th>\n",
       "      <td>9945</td>\n",
       "      <td>VERBATIM CORPORATION</td>\n",
       "      <td>93975</td>\n",
       "      <td>12</td>\n",
       "      <td>50.53</td>\n",
       "      <td>2020-08-28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>713788 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        category                    manufacturer             product  qty  \\\n",
       "253         9920  CASE LOGIC-PERSONAL & PORTABLE             3200943    7   \n",
       "271          531                OMNITRON SYSTEMS              7206-0    2   \n",
       "320         9070         ZEBRA ENTERPRISE MCD-A1       SAC5070-800CR    3   \n",
       "324         9080         ZEBRA ENTERPRISE MCD-A1  KT-CLMPT-RS507-01R  115   \n",
       "326         9080         ZEBRA ENTERPRISE MCD-A1    KT-PAD-RS507-10R   42   \n",
       "...          ...                             ...                 ...  ...   \n",
       "2627921     9945            VERBATIM CORPORATION               93975   12   \n",
       "2627922     9945            VERBATIM CORPORATION               93975   12   \n",
       "2627923     9945            VERBATIM CORPORATION               93975   12   \n",
       "2627924     9945            VERBATIM CORPORATION               93975   12   \n",
       "2627925     9945            VERBATIM CORPORATION               93975   12   \n",
       "\n",
       "          price       date  \n",
       "253       99.00 2020-08-17  \n",
       "271       59.09 2020-08-17  \n",
       "320      500.06 2020-08-17  \n",
       "324       86.34 2020-08-17  \n",
       "326       51.80 2020-08-17  \n",
       "...         ...        ...  \n",
       "2627921   50.53 2020-08-28  \n",
       "2627922   50.53 2020-08-28  \n",
       "2627923   50.53 2020-08-28  \n",
       "2627924   50.53 2020-08-28  \n",
       "2627925   50.53 2020-08-28  \n",
       "\n",
       "[713788 rows x 6 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_st"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a315cd7",
   "metadata": {},
   "source": [
    "### 1. Which are the top Manufacturers with price, quantity?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "53d81202",
   "metadata": {},
   "outputs": [],
   "source": [
    "mfg_grp = df_st.groupby('manufacturer').sum()#groupby with mfg column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "73d99de7",
   "metadata": {},
   "outputs": [],
   "source": [
    "mfg_sort = mfg_grp.sort_values(by=['qty', 'price'],ascending= False)[:10]#sorting by qantity and price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1e2f8975",
   "metadata": {},
   "outputs": [],
   "source": [
    "mfg_sort_in = mfg_sort.reset_index()#resetting of index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "eb6ae3d3",
   "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>manufacturer</th>\n",
       "      <th>qty</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>STARTECH.COM</td>\n",
       "      <td>16069681</td>\n",
       "      <td>7266737.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DELL CSG SERVICE WARRANTIES</td>\n",
       "      <td>13578833</td>\n",
       "      <td>150917.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TRIPP LITE CONNECTIVITY</td>\n",
       "      <td>8283765</td>\n",
       "      <td>768146.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LOGITECH - COMPUTER ACCESSORIES</td>\n",
       "      <td>8251159</td>\n",
       "      <td>1005042.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JABRA BUSINESS</td>\n",
       "      <td>4829490</td>\n",
       "      <td>392963.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PLANTRONICS INC</td>\n",
       "      <td>3825984</td>\n",
       "      <td>703634.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CONFIG 6</td>\n",
       "      <td>3825896</td>\n",
       "      <td>2140.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BELKIN - CABLES</td>\n",
       "      <td>3508335</td>\n",
       "      <td>262931.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DELL ESG WARRANTIES</td>\n",
       "      <td>3149689</td>\n",
       "      <td>173018.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>EPOS</td>\n",
       "      <td>3127008</td>\n",
       "      <td>1127406.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      manufacturer       qty       price\n",
       "0                     STARTECH.COM  16069681  7266737.29\n",
       "1      DELL CSG SERVICE WARRANTIES  13578833   150917.91\n",
       "2          TRIPP LITE CONNECTIVITY   8283765   768146.62\n",
       "3  LOGITECH - COMPUTER ACCESSORIES   8251159  1005042.67\n",
       "4                   JABRA BUSINESS   4829490   392963.51\n",
       "5                  PLANTRONICS INC   3825984   703634.71\n",
       "6                         CONFIG 6   3825896     2140.78\n",
       "7                  BELKIN - CABLES   3508335   262931.76\n",
       "8              DELL ESG WARRANTIES   3149689   173018.32\n",
       "9                             EPOS   3127008  1127406.70"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mfg_sort_in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3ab97a19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(11.7,8.27)}) #bar plot\n",
    "top_mfg_qty_price = sns.barplot(x = 'qty',y = 'price',hue=\"manufacturer\",  data= mfg_sort_in).set(title='Top Manufacturers with Price, Quantity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28b26301",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f83d853b",
   "metadata": {},
   "source": [
    "### 2. Which are the most profitable products?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "40d84df6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mohit\\AppData\\Local\\Temp/ipykernel_7272/1120783067.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_st['Total_Price'] =df_st['qty']*df_st['price']\n"
     ]
    }
   ],
   "source": [
    "df_st['Total_Price'] =df_st['qty']*df_st['price'] # multiplying quantity with price for total price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "9dafabfc",
   "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>category</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>product</th>\n",
       "      <th>qty</th>\n",
       "      <th>price</th>\n",
       "      <th>date</th>\n",
       "      <th>Total_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>9920</td>\n",
       "      <td>CASE LOGIC-PERSONAL &amp; PORTABLE</td>\n",
       "      <td>3200943</td>\n",
       "      <td>7</td>\n",
       "      <td>99.00</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>693.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>531</td>\n",
       "      <td>OMNITRON SYSTEMS</td>\n",
       "      <td>7206-0</td>\n",
       "      <td>2</td>\n",
       "      <td>59.09</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>118.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>9070</td>\n",
       "      <td>ZEBRA ENTERPRISE MCD-A1</td>\n",
       "      <td>SAC5070-800CR</td>\n",
       "      <td>3</td>\n",
       "      <td>500.06</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>1500.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>9080</td>\n",
       "      <td>ZEBRA ENTERPRISE MCD-A1</td>\n",
       "      <td>KT-CLMPT-RS507-01R</td>\n",
       "      <td>115</td>\n",
       "      <td>86.34</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>9929.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>9080</td>\n",
       "      <td>ZEBRA ENTERPRISE MCD-A1</td>\n",
       "      <td>KT-PAD-RS507-10R</td>\n",
       "      <td>42</td>\n",
       "      <td>51.80</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>2175.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627921</th>\n",
       "      <td>9945</td>\n",
       "      <td>VERBATIM CORPORATION</td>\n",
       "      <td>93975</td>\n",
       "      <td>12</td>\n",
       "      <td>50.53</td>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>606.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627922</th>\n",
       "      <td>9945</td>\n",
       "      <td>VERBATIM CORPORATION</td>\n",
       "      <td>93975</td>\n",
       "      <td>12</td>\n",
       "      <td>50.53</td>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>606.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627923</th>\n",
       "      <td>9945</td>\n",
       "      <td>VERBATIM CORPORATION</td>\n",
       "      <td>93975</td>\n",
       "      <td>12</td>\n",
       "      <td>50.53</td>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>606.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627924</th>\n",
       "      <td>9945</td>\n",
       "      <td>VERBATIM CORPORATION</td>\n",
       "      <td>93975</td>\n",
       "      <td>12</td>\n",
       "      <td>50.53</td>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>606.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627925</th>\n",
       "      <td>9945</td>\n",
       "      <td>VERBATIM CORPORATION</td>\n",
       "      <td>93975</td>\n",
       "      <td>12</td>\n",
       "      <td>50.53</td>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>606.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>713788 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        category                    manufacturer             product  qty  \\\n",
       "253         9920  CASE LOGIC-PERSONAL & PORTABLE             3200943    7   \n",
       "271          531                OMNITRON SYSTEMS              7206-0    2   \n",
       "320         9070         ZEBRA ENTERPRISE MCD-A1       SAC5070-800CR    3   \n",
       "324         9080         ZEBRA ENTERPRISE MCD-A1  KT-CLMPT-RS507-01R  115   \n",
       "326         9080         ZEBRA ENTERPRISE MCD-A1    KT-PAD-RS507-10R   42   \n",
       "...          ...                             ...                 ...  ...   \n",
       "2627921     9945            VERBATIM CORPORATION               93975   12   \n",
       "2627922     9945            VERBATIM CORPORATION               93975   12   \n",
       "2627923     9945            VERBATIM CORPORATION               93975   12   \n",
       "2627924     9945            VERBATIM CORPORATION               93975   12   \n",
       "2627925     9945            VERBATIM CORPORATION               93975   12   \n",
       "\n",
       "          price       date  Total_Price  \n",
       "253       99.00 2020-08-17       693.00  \n",
       "271       59.09 2020-08-17       118.18  \n",
       "320      500.06 2020-08-17      1500.18  \n",
       "324       86.34 2020-08-17      9929.10  \n",
       "326       51.80 2020-08-17      2175.60  \n",
       "...         ...        ...          ...  \n",
       "2627921   50.53 2020-08-28       606.36  \n",
       "2627922   50.53 2020-08-28       606.36  \n",
       "2627923   50.53 2020-08-28       606.36  \n",
       "2627924   50.53 2020-08-28       606.36  \n",
       "2627925   50.53 2020-08-28       606.36  \n",
       "\n",
       "[713788 rows x 7 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f132cc45",
   "metadata": {},
   "outputs": [],
   "source": [
    "pr_tot_grp = df_st.groupby(['product','Total_Price'])# groupby product with total price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "144ce5d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "top_sort = pr_tot_grp.sum().sort_values(by='Total_Price',ascending=[False])[:10] # sorting with respet to tota price column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3827546e",
   "metadata": {},
   "outputs": [],
   "source": [
    "most_profitable_products = top_sort.reset_index() #resetting of index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a3f07e2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "profit_product = sns.barplot(x = 'product',y = 'Total_Price',data = most_profitable_products).set(title='most_profitables_products')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fc7c492",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9593a55e",
   "metadata": {},
   "source": [
    "### 3. Which is the product where you have seen a decrease in the price?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "183bfab2",
   "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>category</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>product</th>\n",
       "      <th>qty</th>\n",
       "      <th>price</th>\n",
       "      <th>date</th>\n",
       "      <th>Total_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>9920</td>\n",
       "      <td>CASE LOGIC-PERSONAL &amp; PORTABLE</td>\n",
       "      <td>3200943</td>\n",
       "      <td>7</td>\n",
       "      <td>99.00</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>693.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>531</td>\n",
       "      <td>OMNITRON SYSTEMS</td>\n",
       "      <td>7206-0</td>\n",
       "      <td>2</td>\n",
       "      <td>59.09</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>118.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>9070</td>\n",
       "      <td>ZEBRA ENTERPRISE MCD-A1</td>\n",
       "      <td>SAC5070-800CR</td>\n",
       "      <td>3</td>\n",
       "      <td>500.06</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>1500.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>9080</td>\n",
       "      <td>ZEBRA ENTERPRISE MCD-A1</td>\n",
       "      <td>KT-CLMPT-RS507-01R</td>\n",
       "      <td>115</td>\n",
       "      <td>86.34</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>9929.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>9080</td>\n",
       "      <td>ZEBRA ENTERPRISE MCD-A1</td>\n",
       "      <td>KT-PAD-RS507-10R</td>\n",
       "      <td>42</td>\n",
       "      <td>51.80</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>2175.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627921</th>\n",
       "      <td>9945</td>\n",
       "      <td>VERBATIM CORPORATION</td>\n",
       "      <td>93975</td>\n",
       "      <td>12</td>\n",
       "      <td>50.53</td>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>606.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627922</th>\n",
       "      <td>9945</td>\n",
       "      <td>VERBATIM CORPORATION</td>\n",
       "      <td>93975</td>\n",
       "      <td>12</td>\n",
       "      <td>50.53</td>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>606.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627923</th>\n",
       "      <td>9945</td>\n",
       "      <td>VERBATIM CORPORATION</td>\n",
       "      <td>93975</td>\n",
       "      <td>12</td>\n",
       "      <td>50.53</td>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>606.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627924</th>\n",
       "      <td>9945</td>\n",
       "      <td>VERBATIM CORPORATION</td>\n",
       "      <td>93975</td>\n",
       "      <td>12</td>\n",
       "      <td>50.53</td>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>606.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627925</th>\n",
       "      <td>9945</td>\n",
       "      <td>VERBATIM CORPORATION</td>\n",
       "      <td>93975</td>\n",
       "      <td>12</td>\n",
       "      <td>50.53</td>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>606.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>713788 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        category                    manufacturer             product  qty  \\\n",
       "253         9920  CASE LOGIC-PERSONAL & PORTABLE             3200943    7   \n",
       "271          531                OMNITRON SYSTEMS              7206-0    2   \n",
       "320         9070         ZEBRA ENTERPRISE MCD-A1       SAC5070-800CR    3   \n",
       "324         9080         ZEBRA ENTERPRISE MCD-A1  KT-CLMPT-RS507-01R  115   \n",
       "326         9080         ZEBRA ENTERPRISE MCD-A1    KT-PAD-RS507-10R   42   \n",
       "...          ...                             ...                 ...  ...   \n",
       "2627921     9945            VERBATIM CORPORATION               93975   12   \n",
       "2627922     9945            VERBATIM CORPORATION               93975   12   \n",
       "2627923     9945            VERBATIM CORPORATION               93975   12   \n",
       "2627924     9945            VERBATIM CORPORATION               93975   12   \n",
       "2627925     9945            VERBATIM CORPORATION               93975   12   \n",
       "\n",
       "          price       date  Total_Price  \n",
       "253       99.00 2020-08-17       693.00  \n",
       "271       59.09 2020-08-17       118.18  \n",
       "320      500.06 2020-08-17      1500.18  \n",
       "324       86.34 2020-08-17      9929.10  \n",
       "326       51.80 2020-08-17      2175.60  \n",
       "...         ...        ...          ...  \n",
       "2627921   50.53 2020-08-28       606.36  \n",
       "2627922   50.53 2020-08-28       606.36  \n",
       "2627923   50.53 2020-08-28       606.36  \n",
       "2627924   50.53 2020-08-28       606.36  \n",
       "2627925   50.53 2020-08-28       606.36  \n",
       "\n",
       "[713788 rows x 7 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0c24ce3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "pric_01 = df_st.drop(['manufacturer', 'category','qty',\"Total_Price\"], axis=1) #droping avoidable columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "37d68428",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 713788 entries, 253 to 2627925\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count   Dtype         \n",
      "---  ------   --------------   -----         \n",
      " 0   product  713788 non-null  object        \n",
      " 1   price    713788 non-null  float64       \n",
      " 2   date     713788 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(1), object(1)\n",
      "memory usage: 21.8+ MB\n"
     ]
    }
   ],
   "source": [
    "pric_01.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "7b5b9678",
   "metadata": {},
   "outputs": [],
   "source": [
    "p_max = pric_01.loc[pric_01.groupby('product').date.idxmax(),:] #price according to recent date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "228e8b97",
   "metadata": {},
   "outputs": [],
   "source": [
    "p_min = pric_01.loc[pric_01.groupby('product').date.idxmin(),:] # price on intial date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "8b4fb127",
   "metadata": {},
   "outputs": [],
   "source": [
    "p_max['price_max'] =p_max['price'] # new column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "9034ef93",
   "metadata": {},
   "outputs": [],
   "source": [
    "p_min['price_min'] =p_min['price']  # new column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "64e5f559",
   "metadata": {},
   "outputs": [
    {
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       "      <td>4001.17</td>\n",
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       "    <tr>\n",
       "      <th>2356040</th>\n",
       "      <td>ZT62063-T010100Z</td>\n",
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       "    <tr>\n",
       "      <th>1731241</th>\n",
       "      <td>ZT62063-T01A100Z</td>\n",
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       "      <td>ZUCUBJCK</td>\n",
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       "      <td>16.74</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                  product    price       date  price_max\n",
       "1728303           0-00496    59.62 2020-08-24      59.62\n",
       "463475            0-00498    96.65 2020-08-17      96.65\n",
       "2098347  003-000884-01-OE   398.68 2020-08-24     398.68\n",
       "1438732             00385     1.80 2020-08-24       1.80\n",
       "1438760             00388     2.48 2020-08-24       2.48\n",
       "...                   ...      ...        ...        ...\n",
       "1731234  ZT62062-T01A100Z  4001.17 2020-08-24    4001.17\n",
       "2356040  ZT62063-T010100Z  3797.41 2020-08-25    3797.41\n",
       "1731241  ZT62063-T01A100Z  4140.69 2020-08-24    4140.69\n",
       "1731246  ZT62063-T110100Z  4344.44 2020-08-24    4344.44\n",
       "1741603          ZUCUBJCK    16.74 2020-08-24      16.74\n",
       "\n",
       "[30672 rows x 4 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "p_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "cd6bc6dd",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "                 product    price       date  price_min\n",
       "463476           0-00496    59.62 2020-08-17      59.62\n",
       "463475           0-00498    96.65 2020-08-17      96.65\n",
       "834073  003-000884-01-OE   398.68 2020-08-17     398.68\n",
       "173447             00385     1.80 2020-08-17       1.80\n",
       "173475             00388     2.48 2020-08-17       2.48\n",
       "...                  ...      ...        ...        ...\n",
       "466408  ZT62062-T01A100Z  4001.17 2020-08-17    4001.17\n",
       "466414  ZT62063-T010100Z  3797.41 2020-08-17    3797.41\n",
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       "466420  ZT62063-T110100Z  4344.44 2020-08-17    4344.44\n",
       "476823          ZUCUBJCK    16.74 2020-08-17      16.74\n",
       "\n",
       "[30672 rows x 4 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "702b8f35",
   "metadata": {},
   "outputs": [],
   "source": [
    "p_max = p_max.drop(['price','date'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "cb21e6b0",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1741603</th>\n",
       "      <td>ZUCUBJCK</td>\n",
       "      <td>16.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30672 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  product  price_max\n",
       "1728303           0-00496      59.62\n",
       "463475            0-00498      96.65\n",
       "2098347  003-000884-01-OE     398.68\n",
       "1438732             00385       1.80\n",
       "1438760             00388       2.48\n",
       "...                   ...        ...\n",
       "1731234  ZT62062-T01A100Z    4001.17\n",
       "2356040  ZT62063-T010100Z    3797.41\n",
       "1731241  ZT62063-T01A100Z    4140.69\n",
       "1731246  ZT62063-T110100Z    4344.44\n",
       "1741603          ZUCUBJCK      16.74\n",
       "\n",
       "[30672 rows x 2 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "51638aa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "p_min = p_min.drop(['price','date'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "8928ca29",
   "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>product</th>\n",
       "      <th>price_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>463476</th>\n",
       "      <td>0-00496</td>\n",
       "      <td>59.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463475</th>\n",
       "      <td>0-00498</td>\n",
       "      <td>96.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834073</th>\n",
       "      <td>003-000884-01-OE</td>\n",
       "      <td>398.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173447</th>\n",
       "      <td>00385</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173475</th>\n",
       "      <td>00388</td>\n",
       "      <td>2.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466408</th>\n",
       "      <td>ZT62062-T01A100Z</td>\n",
       "      <td>4001.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466414</th>\n",
       "      <td>ZT62063-T010100Z</td>\n",
       "      <td>3797.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466415</th>\n",
       "      <td>ZT62063-T01A100Z</td>\n",
       "      <td>4140.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466420</th>\n",
       "      <td>ZT62063-T110100Z</td>\n",
       "      <td>4344.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476823</th>\n",
       "      <td>ZUCUBJCK</td>\n",
       "      <td>16.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30672 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 product  price_min\n",
       "463476           0-00496      59.62\n",
       "463475           0-00498      96.65\n",
       "834073  003-000884-01-OE     398.68\n",
       "173447             00385       1.80\n",
       "173475             00388       2.48\n",
       "...                  ...        ...\n",
       "466408  ZT62062-T01A100Z    4001.17\n",
       "466414  ZT62063-T010100Z    3797.41\n",
       "466415  ZT62063-T01A100Z    4140.69\n",
       "466420  ZT62063-T110100Z    4344.44\n",
       "476823          ZUCUBJCK      16.74\n",
       "\n",
       "[30672 rows x 2 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "469784f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "final = pd.merge(p_max, p_min , on='product', how='outer') # mergin two new column on producat column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "686dfd94",
   "metadata": {},
   "outputs": [],
   "source": [
    "final['final_price']= final['price_max']-final['price_min']# final price by diffrence on two dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "4cfd7b31",
   "metadata": {},
   "outputs": [],
   "source": [
    "pric_Dec = final.sort_values(by=['final_price'], ascending=True ,ignore_index=True)[0:10] # sorting and slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "143b8a78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Product whose Price Decreased')]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(y = 'product',\n",
    "            x = 'final_price',\n",
    "            data = pric_Dec).set(title='Product whose Price Decreased') # bar plot between producat and final price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adcbb2ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e1bf13a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e2ebd4b8",
   "metadata": {},
   "source": [
    "### 4. Which is the product where you have seen an increase in the price?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "ca559be8",
   "metadata": {},
   "outputs": [],
   "source": [
    "pric_Inc =final.sort_values(by=['final_price'], ascending=False ,ignore_index=True)[0:10] # sorting and slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "1327f717",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aMmSIOnbsqJYtW1Z+8wAAAIBrqNotQ+rSpYvS0tKUm5urgoICJScny8fHp9Lzt27dqkWLFkm68KH+u+++k5eX1xW3bxiGZs2apTNnzqikpEQrVqxQnz59VFZWprFjx6pfv36aOnWq7amAl5eXkpKSlJSUpLi4OOXn52vUqFGaNGmSLShIF5Yn1apVS7t27ZJ04enIpcbzWzVr1pQktWjRQuPGjVNERIQKCgou61oAAADgz6p2TxYaNmyoiIgIDRs2TCUlJQoLC1ObNm0qPX/cuHGaOnWqgoKCVKNGDUVFRcnDw+Oq2n/88cc1ePBglZaWytfXV4GBgdq4caN++OEHlZWV6bPPPpP0/58k/NaqVauUk5OjJUuWaMmSJZKk3r17a9KkSYqPj1d0dLTy8/PVqlWrq1pONGLECG3ZskVxcXGKjY294usBAACAK2UxDMOwdxGoOhNnf6ycX8/ZuwwAAABcwvK5Q5WdnXfd+3VwsMjV1aXy49exFgAAAAA3EMICAAAAAFOEBQAAAACmCAsAAAAATBEWAAAAAJgiLAAAAAAwRVgAAAAAYIqwAAAAAMAUYQEAAACAKcICAAAAAFOEBQAAAACmCAsAAAAATFkMwzDsXQQAAADwv6ywqER5Zwuve78ODha5urpUetzxOtYCOzh1Kl9WK3kQl8/NrY6ys/PsXQZuIMwZXCnmDK4G88Y+WIYEAAAAwBRhAQAAAIApwgIAAAAAU4QFAAAAAKYICwAAAABMERYAAAAAmCIsAAAAADDFl7IBAAAAdlZaXKRfzxRf9375Urb/cfv++byKz56ydxkAAAC4hAeee1PS9Q8Lf4RlSAAAAABMERYAAAAAmCIsAAAAADBFWAAAAABgirAAAAAAwBRhAQAAAIApwgIAAAAAU4QFAAAAAKYICwAAAABMERYAAAAAmCIsAAAAADBFWAAAAABgirAAAAAAwFS1DAtr166Vv7+/fH19tWzZMkmSp6enQkJCyv359ttvde7cOU2YMEFBQUHq37+/UlNTy7WVmZmpbt26/WH7khQZGSlfX19b+xs3bix33XPPPafVq1dXWveKFSsUGBiooKAgRUZGqri4WJK0f/9+hYaGys/PT1OnTlVpaakkKTw8XH369FFISIiCg4MVFBSkdevWSZISExOVmJhoa/vgwYPq3r27kpOTr/R2AgAAAFfF0d4F/F5mZqYSEhK0evVqOTk5adCgQfL29pYkJSUlVTj/tddeU9OmTZWYmKj09HQNHz5cKSkpkqRt27Zp1qxZys7O/sP27733Xn333Xd699135e7uXqGmmJgYpaWlqVOnTqZ1Hz58WIsXL9bq1atVu3ZtTZkyRcuXL9djjz2myZMnKzY2Vu3atVNUVJRWrlypIUOGSJJiY2Nt4ztw4IDCwsLUvXv3cm2np6drzJgxiomJ0cMPP3yVdxYAAAC4MtXuyUJqaqo6deqkevXqydnZWX5+ftqwYUOl548fP15PPfWUJOno0aOqW7eu7diqVavK/XT+Uu0XFBTo+PHjioqKUlBQkF599VVZrVZJF55EPPTQQ+rXr1+ldTg5OSkmJkYuLi6yWCxq0aKFjh8/rmPHjqmwsFDt2rWTJIWGhlY6Hk9PTzk7OysjI8O27/DhwxozZoymT59OUAAAAMB1Ve3CQlZWltzc3Gzb7u7uyszMlKRyS5BmzZplO8fR0VGjRo3SE088oREjRtj2JyYmqkWLFpfVfk5Ojjp16qRZs2Zp5cqV2rlzp1atWiVJGj16tP72t79dsm4PDw917dpVkpSbm6tly5bpoYceqtCfm5ubbTy/t2PHDklSs2bNJEkZGRkaPny4PDw81LNnz0v2DwAAAFxr1W4ZktVqlcVisW0bhmHbNluGdNHixYt17NgxDRo0SO3bt9c999xzRe03adJECxcutO0PDw/Xxx9/rEcfffSK6s/MzNTo0aM1YMAAeXt7a9euXZWOR5Kio6Pl7OyssrIy1a1bV/Pnz1ft2rUlSRs3btSCBQuUkJCgd955R8OGDbuiWgAAAIA/o9qFhUaNGmnnzp227ezs7ArvEPzW119/rbvuukvu7u7y8PBQ+/btdfDgwUrDQmXtHzhwQEeOHJGfn5+kCx/qHR0rvz379u1TdHS0JKl169aKi4tTenq6Ro8erfDwcI0cOdLW32/fmcjJySk3nt++s/B7w4cPV8+ePdW4cWMNGTJEHTt2VMuWLSutCQAAALiWqt0ypC5duigtLU25ubkqKChQcnKyfHx8Kj1/69atWrRokaQLS4y+++47eXl5XXH7hmFo1qxZOnPmjEpKSrRixQr16dOn0na8vLyUlJSkpKQkxcXFKT8/X6NGjdKkSZNsQUG6sDypVq1a2rVrl6QLT0cuNZ7fqlmzpiSpRYsWGjdunCIiIlRQUHBZ1wIAAAB/VrV7stCwYUNFRERo2LBhKikpUVhYmNq0aVPp+ePGjdPUqVMVFBSkGjVqKCoqSh4eHlfV/uOPP67BgwertLRUvr6+CgwMvOy6V61apZycHC1ZskRLliyRJPXu3VuTJk1SfHy8oqOjlZ+fr1atWl3VcqIRI0Zoy5YtiouLU2xs7BVfDwAAAFwpi2EYhr2LQNXZ98/nVXz2lL3LAAAAwCU88Nybys7Ou+79OjhY5OrqUvnx61gLAAAAgBsIYQEAAACAKcICAAAAAFOEBQAAAACmCAsAAAAATBEWAAAAAJgiLAAAAAAwRVgAAAAAYIqwAAAAAMAUYQEAAACAKcICAAAAAFOEBQAAAACmLIZhGPYuAgAAAPhfVlpcpF/PFF/3fh0cLHJ1dan0uON1rAV2cOpUvqxW8iAun5tbHWVn59m7DNxAmDO4UswZXA3mjX2wDAkAAACAKcICAAAAAFOEBQAAAACmCAsAAAAATBEWAAAAAJgiLAAAAAAwRVgAAAAAYIrvWbjJXepLNoDKuLnVsXcJuMEwZ66touJinT1TZO8yAICwcLN79oMZysnPtXcZAIAr8PaIBZIICwDsj2VIAAAAAEwRFgAAAACYIiwAAAAAMEVYAAAAAGCKsAAAAADAFGEBAAAAgCnCAgAAAABThAUAAAAApggLAAAAAEwRFgAAAACYIiwAAAAAMEVYAAAAAGCKsAAAAADAlKO9CzCzdu1avf766yotLdXw4cM1dOhQeXp6qmXLluXOmzlzpu69915FRUXp559/liSNHTtWAQEBslqtmj17tnbs2KFatWrp73//u/72t79JksLDw5WbmytHR0dbO23bttWePXs0e/ZsnTt3Tp6enpozZ46cnJy0f/9+TZ06VefOndODDz6oGTNm2K79rRUrVmjp0qWyWCxq3bq1ZsyYccnrw8PDdfLkSTk7O8swDBmGoSeeeEL+/v5KTEyUJE2YMEGSdPDgQY0cOVIvvPCCfH19q+zeAwAAABdVu7CQmZmphIQErV69Wk5OTho0aJC8vb0lSUlJSRXOT0hIUOPGjbVgwQKdOnVKISEh8vb21pYtW5Senq41a9bIarVq6NChuu+++9SqVSsdOXJEW7ZsKfeBPz8/XxMmTNCbb76pli1b6umnn9aqVas0ZMgQTZ48WbGxsWrXrp2ioqK0cuVKDRkypFwdhw8f1uLFi7V69WrVrl1bU6ZM0fLly/XYY49d8vrY2Fjb+A4cOKCwsDB17969XNvp6ekaM2aMYmJi9PDDD1/T+w0AAABUptotQ0pNTVWnTp1Ur149OTs7y8/PTxs2bKj0/I4dOyo8PFyS5Orqqnr16iknJ0c//PCDHnroITk5OemWW26Rt7e3Nm3aZHsCMXLkSAUHB+vdd9+VJH3xxRdq166d7elFdHS0+vTpo2PHjqmwsFDt2rWTJIWGhprW4+TkpJiYGLm4uMhisahFixY6fvz4ZV8vSZ6ennJ2dlZGRoZt3+HDhzVmzBhNnz6doAAAAIDrqtqFhaysLLm5udm23d3dlZmZKUkKCQmx/Zk1a5YkqWvXrmrcuLEkad26dSouLta9996r+++/X5s2bVJBQYHOnj2rL774Qjk5OTp79qw6d+6shQsX6u2339b777+vL774QhkZGXJ2dlZERIRCQkKUmJio2267rUI9bm5utnp+y8PDQ127dpUk5ebmatmyZXrooYcu+3pJ2rFjhySpWbNmkqSMjAwNHz5cHh4e6tmz59XeUgAAAOCqVLtlSFarVRaLxbZtGIZt22wZ0kXr16/XrFmz9Oabb8rR0VEDBgxQRkaGHn30UTVs2FBdunRRUVGR2rdvr/bt29uuCwsL07Zt23T77bcrJSVFK1asUOPGjTV16lQtWrRIXbp0qbQeM5mZmRo9erQGDBggb29v7dq165LXR0dHy9nZWWVlZapbt67mz5+v2rVrS5I2btyoBQsWKCEhQe+8846GDRt2BXcSAAAA+HOqXVho1KiRdu7cadvOzs6Wu7v7Ja9ZunSpFi9erMWLF8vT01OSdObMGYWHh+vZZ5+VJM2YMUNNmzbVzp07VVJSos6dO0u68OHd0dFRDRo0UNu2bdWkSRNJUr9+/fTuu+8qNDRU2dnZtr5ycnLk7u6uffv2KTo6WpLUunVrxcXFKT09XaNHj1Z4eLhGjhxpG4/Z9Rf99p2F3xs+fLh69uypxo0ba8iQIerYsWOFl7wBAACAqlLtliF16dJFaWlpys3NVUFBgZKTk+Xj41Pp+Z9//rnefvttvffee7agIEl79uzRtGnTZBiGTp48qc8//1wPP/yw8vLyNHfuXBUVFSk/P18fffSR+vTpo27duun777/XiRMnJElbtmxRq1at5OHhoVq1amnXrl2SLjzd8PHxkZeXl5KSkpSUlKS4uDjl5+dr1KhRmjRpki0oSKr0+stRs2ZNSVKLFi00btw4RUREqKCg4MpuKAAAAHCVqt2ThYYNGyoiIkLDhg1TSUmJwsLC1KZNm0rPf/XVV1VUVKSxY8fa9sXGxqpXr17atm2bAgMDJUnTpk3TnXfeqTvvvFPffvut+vfvL6vVqiFDhtiWJc2cOVNjx45VUVGR7rvvPj3//POSpPj4eEVHRys/P1+tWrUyXQ60atUq5eTkaMmSJVqyZIkkqXfv3po0adJlXf9HRowYoS1btiguLk6xsbFXfD0AAABwpSyGYRj2LgJV59kPZignP9feZQAArsDbIxYoOzvP3mVUGTe3Ojf1+FA1mDdVw8HBIldXl8qPX8daAAAAANxACAsAAAAATBEWAAAAAJgiLAAAAAAwRVgAAAAAYIqwAAAAAMAUYQEAAACAKcICAAAAAFOEBQAAAACmCAsAAAAATBEWAAAAAJgiLAAAAAAwZTEMw7B3EQAA4P8rKi7W2TNF9i6jyri51VF2dp69y8ANhnlTNRwcLHJ1dan0uON1rAV2cOpUvqxW8iAuH/8zxpVizgDAzYtlSAAAAABMERYAAAAAmCIsAAAAADBFWAAAAABgirAAAAAAwBRhAQAAAIApwgIAAAAAU3zPwk3uUl+yAVTGza2OvUvADYY5c22VFBbpdF6xvcsAAMLCzW7L08+qIOeUvcsAAFwB/3eWSIQFANUAy5AAAAAAmCIsAAAAADBFWAAAAABgirAAAAAAwBRhAQAAAIApwgIAAAAAU4QFAAAAAKYICwAAAABMERYAAAAAmCIsAAAAADBFWAAAAABgirAAAAAAwBRhAQAAAICpahkW1q5dK39/f/n6+mrZsmWSJE9PT4WEhJT78+233+rcuXOaMGGCgoKC1L9/f6Wmptraeeutt9S3b1/5+fkpOTm5XB/5+fkKDAzU0aNHK/T/7rvvKjw83Lb9888/Kzw8XMHBwRo1apTOnDljWveKFSsUGBiooKAgRUZGqri4WJK0f/9+hYaGys/PT1OnTlVpaakkKTw8XH369FFISIiCg4MVFBSkdevWSZISExOVmJhoa/vgwYPq3r17hXEAAAAAVaXahYXMzEwlJCRo+fLl+vjjj7VixQodOnRIkpSUlFTuT9u2bbVkyRI1bdpUa9eu1SuvvKLnnntOkrR3716tWbNGSUlJWr58uebOnavTp09Lkr799lsNHjxYR44cqdD/oUOHtGjRItu2YRh64oknNGbMGK1Zs0b33XdfueMXHT58WIsXL9b777+vNWvWyGq1avny5ZKkyZMna9q0afrss89kGIZWrlxpuy42NlZJSUlas2aN4uPj9fzzzysvL69c2+np6RozZoxiYmLk6+v7p+4vAAAAcLmqXVhITU1Vp06dVK9ePTk7O8vPz08bNmyo9Pzx48frqaeekiQdPXpUdevWlSRt375dffr0Ua1ateTq6qqOHTtq69atkqSVK1cqJiZG7u7u5doqLi7WtGnTNHHiRNu+77//Xs7OzvLx8ZEkjR07VkOHDq1Qh5OTk2JiYuTi4iKLxaIWLVro+PHjOnbsmAoLC9WuXTtJUmhoaKXj8fT0lLOzszIyMmz7Dh8+rDFjxmj69Ol6+OGHL33zAAAAgGvI0d4F/F5WVpbc3Nxs2+7u7tq7d68kKSQkxLbf29tbUVFRkiRHR0eNGjVKaWlpmjlzpq0dLy8v2/lubm46efKkJCkuLs6071deeUUDBgzQnXfeadv33//+Vw0aNFBUVJT279+vu+++Wy+88EKFaz08POTh4SFJys3N1bJlyzR79uwK43Fzc1NmZqZp/zt27JAkNWvWTFu2bFFGRoaGDx+upk2bqmfPnpXcMQAAAKBqVLuwYLVaZbFYbNuGYdi2k5KSKr1u8eLFOnbsmAYNGqT27dvLarVWOMfBofIHKV988YVOnDihyMhIffXVV7b9paWl+vrrr/Xuu+/Ky8tL8+fP15w5czRnzhzTdjIzMzV69GgNGDBA3t7e2rVrV6XjkaTo6Gg5OzurrKxMdevW1fz581W7dm1J0saNG7VgwQIlJCTonXfe0bBhwyqtHwAAALjWql1YaNSokXbu3Gnbzs7OrrBc6Le+/vpr3XXXXXJ3d5eHh4fat2+vgwcPqlGjRsrOzi7XTrNmzSpt55NPPtHBgwcVEhKi8+fPKycnR0899ZQGDhyopk2b2p5SBAYGauLEidq3b5+io6MlSa1bt1ZcXJzS09M1evRohYeHa+TIkbbx/LaOnJyccuOJjY2Vt7e3aU3Dhw9Xz5491bhxYw0ZMkQdO3ZUy5YtL3X7AAAAgGum2r2z0KVLF6WlpSk3N1cFBQVKTk62vS9gZuvWrbYXjrOysvTdd9/Jy8tLPj4+Sk5OVkFBgXJzc/Xll1+qc+fOlbYze/ZsrV+/XklJSYqNjVXr1q01f/58tW/fXrm5ufrxxx8lSZs3b1arVq3k5eVle9E6Li5O+fn5GjVqlCZNmmQLCtKF5Um1atXSrl27JF14OnKp8fxWzZo1JUktWrTQuHHjFBERoYKCgsu6FgAAAPizqt2ThYYNGyoiIkLDhg1TSUmJwsLC1KZNm0rPHzdunKZOnaqgoCDVqFFDUVFRtvcHgoODFRYWptLSUk2cOFENGza84npuueUWLVy4UNHR0SooKFCjRo00d+7cCuetWrVKOTk5WrJkiZYsWSJJ6t27tyZNmqT4+HhFR0crPz9frVq1uqrlRCNGjNCWLVsUFxen2NjYK74eAAAAuFIWwzAMexeBqrPl6WdVkHPK3mUAAK6A/ztLlJ2d98cn3qDc3Orc1OND1WDeVA0HB4tcXV0qP34dawEAAABwAyEsAAAAADBFWAAAAABgirAAAAAAwBRhAQAAAIApwgIAAAAAU4QFAAAAAKYICwAAAABMERYAAAAAmCIsAAAAADBFWAAAAABgirAAAAAAwJSjvQtA1eo1L97eJQAArlBJYZG9SwAASYSFm96pU/myWg17l4EbiJtbHWVn59m7DNxAmDMAcPNiGRIAAAAAU4QFAAAAAKYICwAAAABMERYAAAAAmCIsAAAAADBFWAAAAABgirAAAAAAwBTfs3CTc3V1sXcJuAG5udWxdwm4wTBnrq3iolKdOVtg7zIAgLBws1v48jqdOX3e3mUAAK5AVFyYvUsAAEksQwIAAABQCcICAAAAAFOEBQAAAACmCAsAAAAATBEWAAAAAJgiLAAAAAAwRVgAAAAAYIqwAAAAAMAUYQEAAACAKcICAAAAAFOEBQAAAACmCAsAAAAATBEWAAAAAJiqlmFh7dq18vf3l6+vr5YtWyZJ8vT0VEhISLk/3377rc6dO6cJEyYoKChI/fv3V2pqqq2dt956S3379pWfn5+Sk5PL9ZGfn6/AwEAdPXq0Qv/vvvuuwsPDK+zfunWrevfuXWndK1asUGBgoIKCghQZGani4mJJ0v79+xUaGio/Pz9NnTpVpaWlkqTw8HD16dNHISEhCg4OVlBQkNatWydJSkxMVGJioq3tgwcPqnv37hXGAQAAAFQVR3sX8HuZmZlKSEjQ6tWr5eTkpEGDBsnb21uSlJSUVOH81157TU2bNlViYqLS09M1fPhwpaSkaO/evVqzZo2SkpKUn5+vgQMHqmPHjqpXr56+/fZbRUdH68iRIxXaO3TokBYtWqSmTZuW25+Tk6OXXnqp0roPHz6sxYsXa/Xq1apdu7amTJmi5cuX67HHHtPkyZMVGxurdu3aKSoqSitXrtSQIUMkSbGxsbbxHThwQGFhYerevXu5ttPT0zVmzBjFxMTo4YcfvqL7CQAAAFytavdkITU1VZ06dVK9evXk7OwsPz8/bdiwodLzx48fr6eeekqSdPToUdWtW1eStH37dvXp00e1atWSq6urOnbsqK1bt0qSVq5cqZiYGLm7u5drq7i4WNOmTdPEiRMr9BMdHa3x48dXWoeTk5NiYmLk4uIii8WiFi1a6Pjx4zp27JgKCwvVrl07SVJoaGil4/H09JSzs7MyMjJs+w4fPqwxY8Zo+vTpBAUAAABcV9XuyUJWVpbc3Nxs2+7u7tq7d68kKSQkxLbf29tbUVFRkiRHR0eNGjVKaWlpmjlzpq0dLy8v2/lubm46efKkJCkuLs6071deeUUDBgzQnXfeWW7/O++8o/vvv19t27attG4PDw95eHhIknJzc7Vs2TLNnj27wnjc3NyUmZlp2saOHTskSc2aNdOWLVuUkZGh4cOHq2nTpurZs2elfQMAAABVodqFBavVKovFYts2DMO2bbYM6aLFixfr2LFjGjRokNq3by+r1VrhHAeHyh+kfPHFFzpx4oQiIyP11Vdf2fb/9NNPSk5O1ttvv20LG5eSmZmp0aNHa8CAAfL29tauXbsqHY904YmFs7OzysrKVLduXc2fP1+1a9eWJG3cuFELFixQQkKC3nnnHQ0bNuwP+wcAAACulWoXFho1aqSdO3fatrOzsyssF/qtr7/+WnfddZfc3d3l4eGh9u3b6+DBg2rUqJGys7PLtdOsWbNK2/nkk0908OBBhYSE6Pz588rJydFTTz2lu+++W9nZ2RowYIBKSkqUlZWlIUOGKDIyUtHR0ZKk1q1bKy4uTunp6Ro9erTCw8M1cuRI23h+W0dOTk658fz2nYXfGz58uHr27KnGjRtryJAh6tixo1q2bPkHdxAAAAC4NqrdOwtdunRRWlqacnNzVVBQoOTkZPn4+FR6/tatW7Vo0SJJF5Yefffdd/Ly8pKPj4+Sk5NVUFCg3Nxcffnll+rcuXOl7cyePVvr169XUlKSYmNj1bp1a82fP18TJ07UZ599pqSkJC1atEju7u5avny5vLy8lJSUpKSkJMXFxSk/P1+jRo3SpEmTbEFBurA8qVatWtq1a5ekC09HLjWe36pZs6YkqUWLFho3bpwiIiJUUFBwWdcCAAAAf1a1e7LQsGFDRUREaNiwYSopKVFYWJjatGlT6fnjxo3T1KlTFRQUpBo1aigqKsr2/kBwcLDCwsJUWlqqiRMnqmHDhlVW96pVq5STk6MlS5ZoyZIlkqTevXtr0qRJio+PV3R0tPLz89WqVaurWk40YsQIbdmyRXFxcYqNjb3W5QMAAAAVWAzDMP7opKioKM2aNavcvokTJ+rVV1+tssJwbSx8eZ3OnD5v7zIAAFcgKi5M2dl59i6jyri51bmpx4eqwbypGg4OFrm6ulR6/JJPFmJiYpSZmaldu3YpNzfXtr+0tFS//PLLtasSAAAAQLVzybAQFhamgwcP6sCBA/Lz87Ptr1Gjhu17AwAAAADcnC4ZFry8vOTl5aUuXbrol19+UYcOHXT69Gnt3LlTf/nLX65XjQAAAADs4LJ+G9J7771nez+hsLBQixYt0v/93/9VaWEAAAAA7OuywsKmTZv01ltvSbrwvQHvvvuu1q1bV6WFAQAAALCvywoLJSUltt/5L134/f+//RZiAAAAADefy/qehb/+9a965plnFBYWJovFoo8//lht27at6toAAAAA2NFlPVl44YUX1KBBA82ePVtz586Vq6urpk6dWtW1AQAAALCjy3qy4OzsrMjIyKquBQAAAEA1cllhYezYsab7//nPf17TYgAAAABUH5cVFn77hWwlJSX67LPP1Lp16yorCgAAAID9XVZYeOSRRypsh4eHV0lBAAAAAKqHywoLv2cYhrKysq51LagCT072t3cJAIArVFxUau8SAEDSVb6z8NNPP6ljx45VUhCurVOn8mW1GvYuAzcQN7c6ys7Os3cZuIEwZwDg5nXF7yxYLBYNHjxY3bp1q7KiAAAAANjfVb2zAAAAAODmd8mw0LJlS1kslkqP79+//5oXBAAAAKB6uGRYSEtLk2EYWrBggTw8PDRw4EDVqFFDq1ev1vHjx69XjQAAAADswOFSB2+//XbVr19f3333nR5//HHVrVtXLi4uGjZsmL7++uvrVSMAAAAAO7hkWLiooKBAP//8s237wIEDKikpqbKiAAAAANjfZb3g/NRTT2ngwIHy9PSU1WpVenq64uPjq7o2AAAAAHZkMQzjsn4J/6lTp7Rr1y5ZLBY98MADql+/flXXBgC4CsVFRTpztvi69cf3LOBKMWdwNZg3VcPBwSJXV5dKj1/WkwWr1arVq1dr+/btKi0t1YEDBzR27Fg5Ol7VF0DjOnrzpSidPX3K3mUAuI6env0vSdcvLAAAbl6X9c7CK6+8oi+//FLDhw/XiBEjtGfPHs2dO7eqawMAAABgR5f1aGDHjh368MMPVbNmTUlSz549FRwcrKioqCotDgAAAID9XNaTBcMwbEFBkpycnMptAwAAALj5XFZYaNmypWbNmqX//ve/+uWXXzR79my1aNGiqmsDAAAAYEeXFRZiYmJ09uxZDRo0SI8++qhyc3P1wgsvVHVtAAAAAOzost5Z+Ne//qU5c+ZUdS0AAAAAqpHLerKwdevWKi4DAAAAQHVzWU8W7rzzTo0cOVJ//etfVbt2bdv+ESNGVFlhAAAAAOzrssJCvXr1JEnff/+9atSooTp16lRlTQAAAACqgctahjR69Gj99NNP2rZtmzZv3qxffvlFEyZMqOraAAAAANjRZYWFqKgoPfroo/r222/1n//8R35+fpo6dWpV1wYAAADAji4rLBQUFGjgwIGqWbOmnJycFB4erpycnKquDQAAAIAdXVZYuPvuu7V7927b9k8//aQ777yzyopau3at/P395evrq2XLluno0aNq3bq1QkJCFBwcrH79+umVV16R1WqVJB06dEiDBg1ScHCwwsPDdezYMUlScXGxYmNj1b9/fwUEBCglJaVcP6WlpRo4cKBWr15t2/fWW2+pb9++8vPzU3Jysm3/5s2bFRoaqn79+ik2Nta07rKyMsXExCgwMFABAQF6++23Kx3TRZ6engoJCVH//v0VFBSkQYMG6cCBA5Kk8PBwffXVV7ZzP/nkE/Xs2VM///zzVd5ZAAAA4PJd1gvOx48fV3h4uDw9PeXo6KgffvhBbm5uCgoKknThg/C1kpmZqYSEBK1evVpOTk4aNGiQvL295e7urqSkJElSSUmJBg8erAcffFA9evTQjBkzNG7cOPn4+Oi9997TvHnz9Morr+jNN9/Ur7/+qo8++kiHDh3SyJEjtX37dlksFknSwoULdeTIEVvfe/fu1Zo1a5SUlKT8/HwNHDhQHTt2VF5enmJiYvTBBx/I1dVVw4cP17Zt29SjR49yta9evVqnT5/WmjVrVFhYqLCwMHXo0EENGjQwHdO9994rSbZxSdLSpUs1bdo0rVixolzb69ev17x58/T222/rrrvuumb3GwAAAKjMZYWFZ599tqrrsElNTVWnTp1sv4HJz89PGzZsKHdOUVGRSkpKdPvtt0uSlixZIkdHR1mtVh0/fly33XabpAsfsF9++WVZLBY1b95cS5YskWEYslgs2r17t3788Uf16tXL1u727dvVp08f1apVS7Vq1VLHjh21detW5ebmyt/fX40aNZIkJSQkqFatWhVqb968udq2bSsHBwc5OzurSZMmOnHihH766SfTMY0fP75CG97e3po3b165fcnJyZo3b57+/e9/q0mTJld3YwEAAIArdFlhoWPHjlVdh01WVpbc3Nxs2+7u7lq/fr2ysrIUEhIiwzB07NgxtW3bVi1btpQkOTo66uzZs/L391dhYaGWLl0qScrIyNA333yjmTNnqqysTBEREbr33nuVn5+v2bNn6/XXX1d8fHy5vr28vGzbbm5uOnnypE6cOKGaNWtq7NixOnHihHr27KmnnnqqQu3t2rWz/ffu3bu1d+9ezZ07V++//36FMe3du7fC9YZh6NNPP1X79u1t+zZt2qTly5fr8ccfJygAAADgurqsdxauJ6vValsmJMn2JODiMqQ1a9YoLS1Nzs7O5d4duO2225SSkqJ58+bpiSeeUFlZmcrKynTy5EktW7ZMM2bM0LPPPqu8vDzNmDFD//jHP9SgQYMKff+eg4ODysrKlJaWplmzZmnFihXau3evPvroo0rH8M0332jixImKj49X3bp1Kx3TRSEhIQoJCZG/v7/S09M1c+ZM27HNmzfrzTff1Lvvvqt9+/Zd2c0EAAAA/oTLerJwPTVq1Eg7d+60bWdnZ6t9+/Y6fPiwbZ+Tk5P69++v+fPnS5LWrVunfv36yWKxyMfHR4WFhTpz5owaNGiggIAAWSwWtWzZUo0aNdL+/fuVlpamn376SYmJiTpx4oS+/PJLOTo6qlGjRsrOzi7Xd7NmzXT+/Hl17txZ9evXlyQ9/PDD2rt3r4qKivT+++9LkgYNGqTBgwcrOTlZ06dPV0JCgry9vSsdk7u7u237t+8s/N706dPVqVMnPfvss3rmmWf00UcflfsWbQAAAKCqVLsnC126dFFaWppyc3NVUFCg5ORk+fj4VDjvyy+/1P333y/pwm8w2rhxo23/7bffrvr166tXr15at26dJOmXX37RiRMndN999yklJUVJSUlKSkpS7969NXHiRAUHB8vHx0fJyckqKChQbm6uvvzyS3Xu3Fm9evVSSkqKzp49q7KyMu3YsUOtWrXS4MGDbe0MHjxYe/fu1fTp0/XWW2/ZgsKVjMlMzZo1JUmPPvqo7r777nJPHQAAAICqVO2eLDRs2FAREREaNmyYSkpKFBYWpvr169veWbBYLCorK5OHh4defPFFSdKcOXP0wgsvaOHChapTp45effVVSRdezJ45c6YCAgIkSbGxsapTp06lfbdp00bBwcEKCwtTaWmpJk6cqIYNG6phw4YaPXq0hgwZopKSEnXt2lUDBgyocP3rr7+usrIyPf/887Z9EydO1EMPPVRhTG3atLniexMXF6fAwECtXbvW9puoAAAAgKpiMQzDsHcRqDpvvhSls6dP2bsMANfR07P/pezsvOvWn5tbnevaH258zBlcDeZN1XBwsMjV1aXy49exFgAAAAA3EMICAAAAAFOEBQAAAACmCAsAAAAATBEWAAAAAJgiLAAAAAAwRVgAAAAAYIqwAAAAAMAUYQEAAACAKcICAAAAAFOEBQAAAACmCAsAAAAATBEWAAAAAJhytHcBqFqjn59l7xIAXGfFRUX2LgEAcJMgLNzkTp3Kl9Vq2LsM3EDc3OooOzvP3mUAAIBqgGVIAAAAAEwRFgAAAACYIiwAAAAAMEVYAAAAAGCKsAAAAADAFGEBAAAAgCnCAgAAAABTfM/CTc7V1cXeJeAG5OZWx94l4E8oLS7Vr2cK7F0GAOAmQFi4yf2w9EuV5BXauwwA11HbcT3tXQIA4CbBMiQAAAAApggLAAAAAEwRFgAAAACYIiwAAAAAMEVYAAAAAGCKsAAAAADAFGEBAAAAgCnCAgAAAABThAUAAAAApggLAAAAAEwRFgAAAACYIiwAAAAAMFUtw8LatWvl7+8vX19fLVu2TEePHlXr1q0VEhKi4OBg9evXT6+88oqsVqsk6dChQxo0aJCCg4MVHh6uY8eOSZKKi4sVGxur/v37KyAgQCkpKZKkc+fOacKECQoKClL//v2VmpoqSbJarYqLi1Pfvn0VEhKiDz74wFbTokWL5Ofnp6CgIL3++uumdZeVlSkmJkaBgYEKCAjQ22+/XemYLvL09FRISIj69++voKAgDRo0SAcOHJAkhYeH66uvvrKd+8knn6hnz576+eefr8FdBgAAAC7N0d4F/F5mZqYSEhK0evVqOTk5adCgQfL29pa7u7uSkpIkSSUlJRo8eLAefPBB9ejRQzNmzNC4cePk4+Oj9957T/PmzdMrr7yiN998U7/++qs++ugjHTp0SCNHjtT27du1ZMkSNW3aVImJiUpPT9fw4cOVkpKiDz/8UOnp6VqzZo2sVquGDh2q++67T2fPntXatWv14Ycf6tZbb9WTTz6p5ORk+fr6lqt99erVOn36tNasWaPCwkKFhYWpQ4cOatCggemY7r33XkmyjUuSli5dqmnTpmnFihXl2l6/fr3mzZunt99+W3fddVfV/iUAAAAAqoZhITU1VZ06dVK9evUkSX5+ftqwYUO5c4qKilRSUqLbb79dkrRkyRI5OjrKarXq+PHjuu222yRd+ID98ssvy2KxqHnz5lqyZIkMw9D48eNVWloqSTp69Kjq1q0rSfrhhx/00EMPycnJSZLk7e2tTZs2qXbt2urWrZtcXFwkSd27d9fnn39eISw0b95cbdu2lYODg5ydndWkSROdOHFCP/30k+mYxo8fX2H83t7emjdvXrl9ycnJmjdvnv7973+rSZMmV3trAQAAgCtS7ZYhZWVlyc3Nzbbt7u6u3bt3Kysry7YMqUePHnJ1dVXLli0lSY6Ojjp79qztycKjjz4qScrIyNA333yjIUOGaODAgcrJyZGDg4PtmlGjRumJJ57QiBEjJEn333+/Nm3apIKCAp09e1ZffPGFcnJy1KpVK6WkpOj06dMqKirS5s2blZOTU6H2du3aqUWLFpKk3bt3a+/everQoYPpmDIzMytcbxiGPv30U7Vv3962b9OmTXr66acVFBREUAAAAMB1Ve2eLFitVlksFtu2YRiyWCzlliEVFxfr6aefVmxsrGbOnClJuu2225SSkqLt27friSee0KZNm1RWVqaTJ09q2bJlOnDggEaPHq3169erTp06kqTFixfr2LFjGjRokNq3b68BAwYoIyNDjz76qBo2bKguXbqoqKhInTt3VmhoqMLDw1WvXj117txZ3377baVj+OabbxQREaH4+HjVrVu30jFdFBISYhvXPffcYxuTJG3evFlvvvmmJk6cqF69esnLy+sa3GUAAADgj1W7JwuNGjVSdna2bTs7O7vcT9olycnJSf3799fu3bslSevWrZNhGJIkHx8fFRYW6syZM2rQoIECAgJksVjUsmVLNWrUSIcPH9bXX3+trKwsSZKHh4fat2+vgwcP6syZMwoPD9fatWv15ptvqrCwUH/5y1+Un58vX19frV27VkuXLpWTk5OaNGmi9957TyEhIQoJCdF7770n6cKSoUmTJumVV15R165dKx2Tu7u7bTspKUlJSUlav369XnvtNd155522Y9OnT1enTp307LPP6plnntG5c+eu5e0GAAAAKlXtwkKXLl2Ulpam3NxcFRQUKDk5WT4+PhXO+/LLL3X//fdLkt566y1t3LjRtv/2229X/fr11atXL61bt06S9Msvv+jEiRNq1qyZtm7dqkWLFkm6sOzpu+++k5eXl/bs2aNp06bJMAydPHlSn3/+uR5++GEdPXpU48aNU2lpqfLy8rRq1Sr169dPgwcPtn3QHzx4sPbu3avp06frrbfekre39xWPyUzNmjUlSY8++qjuvvvuck8dAAAAgKpU7ZYhNWzYUBERERo2bJhKSkoUFham+vXr295ZsFgsKisrk4eHh1588UVJ0pw5c/TCCy9o4cKFqlOnjl599VVJ0rPPPquZM2cqICBAkhQbG6s6depo3Lhxmjp1qoKCglSjRg1FRUXJw8NDjRs31rZt2xQYGChJmjZtmu2n/L6+vgoODlZZWZkee+wxPfDAAxVqf/3111VWVqbnn3/etm/ixIl66KGHKoypTZs2V3xv4uLiFBgYqLVr1yooKOiKrwcAAACuhMW4uH4HN6Ufln6pkrxCe5cB4DpqO66nsrPzrlt/bm51rmt/uPExZ3A1mDdVw8HBIldXl8qPX8daAAAAANxACAsAAAAATBEWAAAAAJgiLAAAAAAwRVgAAAAAYIqwAAAAAMAUYQEAAACAKcICAAAAAFOEBQAAAACmCAsAAAAATBEWAAAAAJgiLAAAAAAwRVgAAAAAYMrR3gWgat0f3sneJQC4zkqLS+1dAgDgJkFYuMmdOpUvq9Wwdxm4gbi51VF2dp69ywAAANUAy5AAAAAAmCIsAAAAADBFWAAAAABgirAAAAAAwBRhAQAAAIApwgIAAAAAU4QFAAAAAKb4noWbnKuri71LwA3Iza2OvUu4IRUXl+jMmUJ7lwEAwDVDWLjJvfHGGzp79qy9ywD+JzzzzDOSCAsAgJsHy5AAAAAAmCIsAAAAADBFWAAAAABgirAAAAAAwBRhAQAAAIApwgIAAAAAU4QFAAAAAKYICwAAAABMERYAAAAAmCIsAAAAADBFWAAAAABgirAAAAAAwFSVhIWjR4+qdevWCgkJKfdn2bJlkqQff/xRw4YNU3BwsAICAjR16lSdP3/edv1//vMfDR8+XMHBwQoMDNT06dNVWFgoSSouLtYzzzyjoKAghYSEKDU1VZJkGIZeeukl9e3bV/7+/tq1a5etvbfeekt9+/aVn5+fkpOTbfvXrl0rf39/+fr62mozs3z5cgUHB8vf3199+vTRnDlzVFxcXO6c0tJSDRw4UKtXr7btKy4uVnh4uG07MzNT3bp1K3fdpWq4nH4BAACAquJYVQ27u7srKSnJ9FhERIRmzZql9u3by2q1asaMGVqwYIEiIyP1448/avz48Vq4cKHatm2r0tJSvfjii3rhhRf08ssvKykpSVarVWvXrtWBAwc0ZswYbd++XZ999pnS09O1bt06ZWRk6B//+IfWrVunH374QWvWrFFSUpLy8/M1cOBAdezYUUVFRUpISNDq1avl5OSkQYMGydvbW/fee2+5Wv/5z39qy5YteuONN9SwYUMVFxcrMjJSCQkJev75523nLVy4UEeOHCl37ddff60OHTpIkrZt26ZZs2YpOzvbdjwzM7PSGi63XwAAAKCq2GUZUk5Oju1JgYODg8aPH69+/fpJkhYvXqwBAwaobdu2kiRHR0dNnjxZDz/8sCTJarWqoKBAZWVlKigo0C233CLpwodxf39/OTg4qFmzZrrjjju0Z88ebd++XX369FGtWrXk6uqqjh07auvWrUpNTVWnTp1Ur149OTs7y8/PTxs2bChXZ1FRkd544w3FxcWpYcOGkiQnJydNnTpVd911l+283bt368cff1SvXr3KXb99+3b5+PhIklatWqXExMRyxyur4XL7BQAAAKpSlT1ZyMrKUkhISLl9c+fOlaenpyIjI/XEE0/I3d1d3t7eeuihh9SzZ09J0v79+9WnT59y17m4uMjPz0+S9Mgjj+ijjz5S9+7ddfbsWc2bN8/Wn7u7u+0aNzc3nTx5UllZWfLy8qqw32KxyM3Nzbbf3d1de/fuLdfvoUOH5OjoWOFpQ/369TVw4EBJUn5+vmbPnq3XX39d8fHx5c7bu3evpkyZIkkVgsLFms1quJx+AQAAgKpml2VIoaGh8vX1VVpamlJTUzVlyhQFBQVp6tSpslgsqlWrVqXtvvbaa2rXrp3ee+89HTlyRI899phatWolq9Uqi8ViO88wDDk4OMhqtVZow8HBQWVlZRXO/+32Rb/dt3v3bs2YMUPShacjX3zxhWbMmKF//OMfatCgQbnrjh49Kg8PDzk4VP7wxqzmi9t/1C8AAABQ1a77MqQjR45o4cKFcnFxUZ8+fRQTE6P33ntPH3zwgSSpdevW2rdvX7lr8vPzNXbsWBUXF2vTpk0KDQ2VxWJRs2bN1LZtW+3du1eNGjVSVlaW7ZqcnBy5u7urUaNG5d4TyM7OvuT+37r77rtVXFysw4cPS5L++te/KikpSUlJScrJyVF+fr7S0tKUmJiokJAQbd68Wa+++qrWrFmjbdu2qXv37pe8F5XV8Ef9AgAAANfDdQ8L9evX1zvvvKO0tDTbvv379+u+++6TJD322GN67733bEuCSkpKNGfOHLm4uMjJyUktW7bU559/LknKzc3Vd999p/vuu08+Pj5au3atysrKlJGRoSNHjsjLy0s+Pj5KTk5WQUGBcnNz9eWXX6pz587q0qWL0tLSlJubq4KCAiUnJ9veL7jo1ltv1dixYxUZGanMzExJF54GbNq0SQ4ODnJxcVFKSortg3zv3r01ceJEBQcHKyUl5Q/DQmU1/FG/AAAAwPVwXd9Z6NChg6Kjo7Vo0SK9/PLLio6OVs2aNdWsWTPbuweenp56+eWXFRcXp4KCApWUlKhLly6Kjo6WJEVGRuqFF15QQECAHBwc9PTTT+uuu+5S06ZNtXfvXgUHB0uS4uLidMstt6hNmzYKDg5WWFiYSktLNXHiRNtLwxERERo2bJhKSkoUFhamNm3aVBjH448/LldXV40bN06lpaXKy8tT69attXLlykrHXlxcrPz8fLm6ul7yHjVs2LDSGq6mXwAAAOBashiGYdi7CFSdN954Q2fPnrV3GcD/hGeeeUbZ2Xn2LuO6c3Or8z85blw95gyuBvOmajg4WOTq6lL58etYCwAAAIAbCGEBAAAAgCnCAgAAAABThAUAAAAApggLAAAAAEwRFgAAAACYIiwAAAAAMEVYAAAAAGCKsAAAAADAFGEBAAAAgCnCAgAAAABThAUAAAAApggLAAAAAEw52rsAVK0xY8bYuwTgf0ZxcYm9SwAA4JoiLNzkTp3Kl9Vq2LsM3EDc3OooOzvP3mUAAIBqgGVIAAAAAEwRFgAAAACYIiwAAAAAMEVYAAAAAGCKsAAAAADAFGEBAAAAgCnCAgAAAABTfM/CTc7V1cXeJeAG5OZWx94lXHelJcX69XSRvcsAAKBaISzc5L7aNF9FBaftXQZQ7fkETpdEWAAA4LdYhgQAAADAFGEBAAAAgCnCAgAAAABThAUAAAAApggLAAAAAEwRFgAAAACYIiwAAAAAMEVYAAAAAGCKsAAAAADAFGEBAAAAgCnCAgAAAABThAUAAAAApqpdWHjttdcUEBCggIAAzZ07V5K0YsUKBQYGKigoSJGRkSouLpYkbdy4UUFBQQoICNCUKVNs+38vJSVFwcHBCgwM1HPPPWc7b//+/QoNDZWfn5+mTp2q0tLSctfNnz9fiYmJldZa2fXHjx/X0KFD1bdvXz3xxBM6d+6cJOns2bN6/PHH1a9fPw0dOlTZ2dmSpOLiYk2ePFn9+vXTI488ovT0dEmSYRh66aWX1LdvX/n7+2vXrl1Xe1sBAACAK1atwkJqaqpSUlL00Ucf6eOPP9b333+vRYsWafHixXr//fe1Zs0aWa1WLV++XOfPn9fMmTO1ZMkSffrppyoqKtJHH31k2u7UqVOVkJCgTz75RIWFhUpKSpIkTZ48WdOmTdNnn30mwzC0cuVKSVJeXp6ioqK0ZMmSS9Zb2fUzZszQkCFDtGHDBrVu3Vr/93//J+lC+HjwwQe1fv16/e1vf1NcXJwkaenSpbr11lu1fv16RUVFKTIyUpL02WefKT09XevWrdPChQsVGRlZIdAAAAAAVaVahQU3NzdNmTJFTk5Oqlmzpu655x4VFxcrJiZGLi4uslgsatGihY4fPy5nZ2dt3rxZDRo0UEFBgU6dOqXbbrvNtN2ysjLl5+errKxMRUVFqlWrlo4dO6bCwkK1a9dOkhQaGqoNGzZIkjZt2qS77rpLI0aMqLTWyq4vKSnRN998Iz8/vwrtbt26VUFBQZKkwMBAbd++XSUlJdq6dauCg4MlSR06dFBubq6OHz+ubdu2yd/fXw4ODmrWrJnuuOMO7dmz50/fZwAAAOByVKuw0Lx5c9uH7yNHjmj9+vUKDAxU165dJUm5ublatmyZHnroIUlSzZo1tW3bNvXs2VO//vqrunXrZtru9OnTFR4eru7du+vXX39V3759lZWVJTc3N9s5bm5uyszMlCT1799fjz/+uGrUqFFprZVd/+uvv8rFxUWOjo4V2v3tNY6OjnJxcVFubq5pWydPnlRWVpbc3d0r7AcAAACuh2oVFi46ePCgRo4cqeeee0533XWXJCkzM1PDhw/XgAED5O3tbTu3R48e+uqrr9SrVy9Nnz69QlvZ2dmKj4/XJ598opSUFLVt21azZ8+W1WqVxWKxnWcYRrntP1LZ9WbtVNauYRhycHCocM3F/WZ9ODhUy78yAAAA3ISq3SfPXbt26bHHHtMzzzyjRx55RJKUnp6uQYMG6ZFHHtGTTz4pSTp9+rRSUlJs1wUFBenAgQPat2+fQkJCFBISoqlTp2rnzp1q0aKF/vKXv8jBwUGPPvqovv76azVq1Mj2grEk5eTklPsp/u9lZmba2h0zZkyl19evX195eXkqKyuTdCGsXGzX3d1dOTk5kqTS0lKdO3dO9erVU8OGDZWVlVWhrUaNGpnuBwAAAK6HahUWTpw4oSeffFLx8fEKCAiQJOXn52vUqFGaNGmSRo4caTvXMAxNnjxZx48flyRt2LBBf/3rX+Xl5aWkpCQlJSUpLi5OLVq00N69e20f0jdt2iQvLy95eHioVq1att8wlJSUJB8fn0pra9iwoa3dN954o9Lra9asqQcffFDr1q2TJH388ce2dnv06KGPP/5YkrRu3To9+OCDqlmzpnr06GF76Xrnzp2qVauWGjduLB8fH61du1ZlZWXKyMjQkSNH5OXlda1uNwAAAHBJjvYu4LcWL16soqIizZkzx7bP399fOTk5WrJkie23E/Xu3VuTJk3Siy++qH/84x+yWCy69957NWPGjApt3nPPPZo0aZKGDRumGjVqqGnTppo5c6YkKT4+XtHR0crPz1erVq00bNiwK6q3sutjYmI0ZcoUvf7667rjjjs0b948SdKkSZM0ZcoUBQQEqE6dOoqPj5ckhYeHa9q0aQoICJCTk5PtV8b27dtXe/futb38HBcXp1tuueWKagQAAACulsUwDMPeRaDqfLVpvooKTtu7DKDa8wmcruzsPHuXcUNyc6vDvcMVYc7gajBvqoaDg0Wuri6VH7+OtQAAAAC4gRAWAAAAAJgiLAAAAAAwRVgAAAAAYIqwAAAAAMAUYQEAAACAKcICAAAAAFOEBQAAAACmCAsAAAAATBEWAAAAAJgiLAAAAAAwRVgAAAAAYIqwAAAAAMCUo70LQNXyfugpe5cA3BBKS4rtXQIAANUOYeEmd+pUvqxWw95l4Abi5lZH2dl59i4DAABUAyxDAgAAAGCKsAAAAADAFGEBAAAAgCnCAgAAAABThAUAAAAApggLAAAAAEwRFgAAAACY4nsWbnKuri72LgE3IDe3OvYu4bIUlZTq7OkCe5cBAMBNi7Bwk3vxsy/16/kie5cBVIl5j/SwdwkAANzUWIYEAAAAwBRhAQAAAIApwgIAAAAAU4QFAAAAAKYICwAAAABMERYAAAAAmCIsAAAAADBFWAAAAABgirAAAAAAwBRhAQAAAIApwgIAAAAAU4QFAAAAAKYIC5fpq6++Urdu3XTq1CnbvjfffFMTJkxQeHi4+vTpo5CQEAUHBysoKEjr1q2TJBmGoZdeekl9+/aVv7+/du3aZWuvffv2CgkJUf/+/dW3b19NnDhR+fn52rBhg3r37q3z58/b+srJyVGXLl30ww8/XN+BAwAA4H8WYeEyeXt7KygoSNHR0ZKkPXv2aOXKlYqLi5MkxcbGKikpSWvWrFF8fLyef/555eXl6bPPPlN6errWrVunhQsXKjIyUqWlpZKk1q1bKykpSR9//LE2bNggSfrXv/6lvn37qnXr1lqwYIGt/5kzZ2rIkCG6//77r/PIAQAA8L+KsHAFIiIidPz4cb3zzjt6/vnn9dJLL+m2226rcJ6np6ecnZ2VkZGhbdu2yd/fXw4ODmrWrJnuuOMO7dmzx7T9jh076uDBg5KkmJgYrV27Vj/88IM2b96sY8eOaezYsVU6PgAAAOC3HO1dwI3EyclJ8fHxCgkJ0eOPP6727dubnrdjxw5JUrNmzZSVlSV3d3fbMTc3N508ebLcPkk6f/68Nm/erI4dO0qSXF1dNWXKFM2YMUN5eXlKTEyUoyN/XQAAALh++PR5hXbv3q3bb79daWlpGj9+vO0DfHR0tJydnVVWVqa6detq/vz5ql27tqxWqywWi+16wzDk4HDhgc53332nkJAQSVJpaak6deqkESNG2M4NDg7WBx98oH79+umee+65jqMEAAAACAtX5NChQ0pMTNT777+vqKgovf7665owYYKkC+8seHt7V7imUaNGysrKsm3n5OTI3d1dVqtVrVu31tKlSy/Zp4eHhzw8PK7tQAAAAIDLwDsLl6moqEgRERGaPHmymjRpojlz5ujdd9/Vf/7zn0te5+Pjo7Vr16qsrEwZGRk6cuSIvLy8rk/RAAAAwJ/Ak4XLNGvWLDVv3ty2bMjDw0ORkZGaPHmy6tSpU+l1ffv21d69exUcHCxJiouL0y233HJdagYAAAD+DIthGIa9i0DVefGzL/Xr+SJ7lwFUiXmP9FB2dp69y/if5+ZWh78HXBHmDK4G86ZqODhY5OrqUvnx61gLAAAAgBsIYQEAAACAKcICAAAAAFOEBQAAAACmCAsAAAAATBEWAAAAAJgiLAAAAAAwRVgAAAAAYIqwAAAAAMAUYQEAAACAKcICAAAAAFOEBQAAAACmCAsAAAAATDnauwBUrRf8Otm7BKDKFJWU2rsEAABuaoSFm9ypU/myWg17l4EbiJtbHWVn59m7DAAAUA2wDAkAAACAKZ4s3OQcHCz2LgE3IOYNrhRzBleKOYOrwby59v7onloMw2CNCgAAAIAKWIYEAAAAwBRhAQAAAIApwgIAAAAAU4QFAAAAAKYICwAAAABMERYAAAAAmCIsAAAAADBFWAAAAABgirAAAAAAwBRh4Sa1du1a+fv7y9fXV8uWLbN3OahGXnvtNQUEBCggIEBz586VJKWmpiooKEi+vr5KSEiwnbt//36FhobKz89PU6dOVWlpqb3KRjXw0ksvacqUKZKYM/hjmzdvVmhoqPr166fY2FhJzBtcWlJSku3fp5deekkSc6ZaMHDTOXnypNGrVy/j119/Nc6dO2cEBQUZBw8etHdZqAa++OILY+DAgUZRUZFRXFxsDBs2zFi7dq3Ro0cP47///a9RUlJijBw50ti6dathGIYREBBg7NmzxzAMw4iMjDSWLVtmx+phT6mpqYa3t7fx/PPPGwUFBcwZXNJ///tfo1u3bsaJEyeM4uJiY/DgwcbWrVuZN6jU+fPnjQ4dOhinTp0ySkpKjLCwMGPTpk3MmWqAJws3odTUVHXq1En16tWTs7Oz/Pz8tGHDBnuXhWrAzc1NU6ZMkZOTk2rWrKl77rlHR44cUdOmTdWkSRM5OjoqKChIGzZs0LFjx1RYWKh27dpJkkJDQ5lH/6NOnz6thIQEjR07VpK0d+9e5gwuaePGjfL391ejRo1Us2ZNJSQk6NZbb2XeoFJlZWWyWq0qKChQaWmpSktL5eLiwpypBhztXQCuvaysLLm5udm23d3dtXfvXjtWhOqiefPmtv8+cuSI1q9fr7///e8V5ktmZmaFeeTm5qbMzMzrWi+qh2nTpikiIkInTpyQZP7/GOYMfisjI0M1a9bU2LFjdeLECfXs2VPNmzdn3qBSLi4umjRpkvr166dbb71VHTp04P811QRPFm5CVqtVFovFtm0YRrlt4ODBgxo5cqSee+45NWnSxHS+MI8gSR988IHuuOMOde7c2bavsrnBnMFFZWVlSktL06xZs7RixQrt3btXv/zyC/MGlfrxxx/14YcfasuWLdqxY4ccHBx05MgR5kw1wJOFm1CjRo20c+dO23Z2drbc3d3tWBGqk127dmnixImKiopSQECAvv76a2VnZ9uOX5wvjRo1Krc/JyeHefQ/aN26dcrOzlZISIjOnDmj8+fP69ixY6pRo4btHOYMfq9Bgwbq3Lmz6tevL0l6+OGHtWHDBuYNKpWSkqLOnTvL1dVV0oWlRYsXL2bOVAM8WbgJdenSRWlpacrNzVVBQYGSk5Pl4+Nj77JQDZw4cUJPPvmk4uPjFRAQIElq27atDh8+rIyMDJWVlemTTz6Rj4+PPDw8VKtWLe3atUvShd9SwTz637NkyRJ98sknSkpK0sSJE9W7d2+9+eabzBlcUq9evZSSkqKzZ8+qrKxMO3bsUN++fZk3qFTLli2Vmpqq8+fPyzAMbd68mX+fqgmeLNyEGjZsqIiICA0bNkwlJSUKCwtTmzZt7F0WqoHFixerqKhIc+bMse0bNGiQ5syZowkTJqioqEg9evRQ3759JUnx8fGKjo5Wfn6+WrVqpWHDhtmrdFQjtWrVYs7gktq2bavRo0dryJAhKikpUdeuXTV48GDdfffdzBuY6tatm3744QeFhoaqZs2a8vLy0oQJE9S1a1fmjJ1ZDMMw7F0EAAAAgOqHZUgAAAAATBEWAAAAAJgiLAAAAAAwRVgAAAAAYIqwAAAAAMAUYQEAUC3ExMSod+/eGj16tA4dOnTV7Xh6eio3N/ea1JSZmalBgwZdk7YA4EbEr04FAFQLLVu21NatW9WoUaM/1Y6np6fS0tJs3x4MALh6fCkbAMDuhgwZIsMwNGbMGB06dEgrV67U+fPnlZCQoCZNmujgwYMqLS3VjBkz9MADD+jw4cOaOXOmzp07p+zsbLVs2VLz589XrVq1Lqu/8PBw3X///dq1a5d+/fVXhYSEaOLEiTp69KiGDh2qe+65R8eOHdOcOXM0cuRI7dmzR6WlpXr55Ze1detW1ahRQ+3bt1dMTIycnJz0+uuvKzk5WVarVR4eHoqJiVHDhg2r+K4BQNVjGRIAwO6WL18uSfr3v/+tO+64w7Z/7969GjlypD7++GOFhoYqISFBkrRy5Ur1799fK1euVHJyso4ePaqtW7deUZ+HDx/We++9p48++kjr1q3Tli1bJEknT57UuHHj9Nlnn8nNza1cjd9//72SkpL0ySef6Ny5c1q3bp0+/vhj/fTTT/rggw+UlJSkHj16KDo6+k/eEQCoHniyAACotho3bqz77rtPknT//ffro48+kiRNnjxZX3zxhd544w0dOXJEWVlZOn/+/BW1PXDgQNWsWVM1a9ZU3759lZKSoubNm8vR0VHt2rWrcH5qaqpCQkJ0yy23SJLmz58vSZo0aZL27dunAQMGSJKsVqsKCgqucsQAUL0QFgAA1dbFD+aSZLFYdPE1u6efflplZWXq16+fevbsqRMnTuhKX8FzdPz//wQahiEHhwsP252cnModMztfknJycmS1WmW1WjV69GgNGTJEklRcXKwzZ85cUS0AUF2xDAkAcMNJSUnRk08+KX9/f0nSt99+q7KysitqY82aNbJarTpz5ozWr1+v3r17X/L8zp0765NPPlFxcbGsVqumT5+uTz/9VN26ddOqVauUn58vSVqwYIGee+65qxsYAFQzPFkAANxwIiIi9OSTT8rZ2VkuLi7q0KGD/vvf/15RG4WFhQoLC9O5c+c0ZMgQde7cWUePHq30/EGDBunYsWMKDQ2VYRjq2LGjwsPD5eDgoMzMTD366KOyWCy64447NGfOnD87RACoFvjVqQCA/znh4eEaOnSo+vbta+9SAKBa48kCAOCm8+WXX2r27Nmmx7y9va9zNQBw4+LJAgAAAABTvOAMAAAAwBRhAQAAAIApwgIAAAAAU4QFAAAAAKYICwAAAABMERYAAAAAmPp/1LhSt52QJowAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar = sns.barplot(y = 'product',\n",
    "            x = 'final_price',\n",
    "            data = pric_Inc).set(title='Product whose Price Increased') # bar plot between producat and final price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5058ea83",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fab95e1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f88fbb75",
   "metadata": {},
   "source": [
    "### 5. Which is the top category by price and quantity?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "d58daf80",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_grp = df_st.groupby('category').sum() # groupby with respect to category column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "d5ea7c4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_grp.drop(['Total_Price'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "d88ef9e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_grp_srt_q_p = cat_grp.sort_values(by=['qty', 'price'],ascending= False)[:10] #sorting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "84e22f3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_grp_srt_q_p_ind = cat_grp_srt_q_p.reset_index() #resetiing index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "4e47b493",
   "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",
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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>category</th>\n",
       "      <th>qty</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1221</td>\n",
       "      <td>17378795</td>\n",
       "      <td>346527.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2220</td>\n",
       "      <td>12851216</td>\n",
       "      <td>1741955.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9970</td>\n",
       "      <td>8618185</td>\n",
       "      <td>1389155.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2240</td>\n",
       "      <td>8264370</td>\n",
       "      <td>1520556.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3350</td>\n",
       "      <td>7068656</td>\n",
       "      <td>580035.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9995</td>\n",
       "      <td>5015339</td>\n",
       "      <td>970241.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>303</td>\n",
       "      <td>4902118</td>\n",
       "      <td>4544824.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2201</td>\n",
       "      <td>4715114</td>\n",
       "      <td>558368.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1251</td>\n",
       "      <td>4347126</td>\n",
       "      <td>13541.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>101</td>\n",
       "      <td>3877634</td>\n",
       "      <td>11079362.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category       qty        price\n",
       "0     1221  17378795    346527.07\n",
       "1     2220  12851216   1741955.11\n",
       "2     9970   8618185   1389155.66\n",
       "3     2240   8264370   1520556.20\n",
       "4     3350   7068656    580035.13\n",
       "5     9995   5015339    970241.61\n",
       "6      303   4902118   4544824.27\n",
       "7     2201   4715114    558368.49\n",
       "8     1251   4347126     13541.88\n",
       "9      101   3877634  11079362.36"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_grp_srt_q_p_ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e40f27eb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5dcd1c7e",
   "metadata": {},
   "source": [
    "### 6. Which products are aging?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "292a87c7",
   "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",
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       "    }\n",
       "\n",
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       "        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>category</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>product</th>\n",
       "      <th>qty</th>\n",
       "      <th>price</th>\n",
       "      <th>date</th>\n",
       "      <th>Total_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>9920</td>\n",
       "      <td>CASE LOGIC-PERSONAL &amp; PORTABLE</td>\n",
       "      <td>3200943</td>\n",
       "      <td>7</td>\n",
       "      <td>99.00</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>693.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>531</td>\n",
       "      <td>OMNITRON SYSTEMS</td>\n",
       "      <td>7206-0</td>\n",
       "      <td>2</td>\n",
       "      <td>59.09</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>118.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>9070</td>\n",
       "      <td>ZEBRA ENTERPRISE MCD-A1</td>\n",
       "      <td>SAC5070-800CR</td>\n",
       "      <td>3</td>\n",
       "      <td>500.06</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>1500.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>9080</td>\n",
       "      <td>ZEBRA ENTERPRISE MCD-A1</td>\n",
       "      <td>KT-CLMPT-RS507-01R</td>\n",
       "      <td>115</td>\n",
       "      <td>86.34</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>9929.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>9080</td>\n",
       "      <td>ZEBRA ENTERPRISE MCD-A1</td>\n",
       "      <td>KT-PAD-RS507-10R</td>\n",
       "      <td>42</td>\n",
       "      <td>51.80</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>2175.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627921</th>\n",
       "      <td>9945</td>\n",
       "      <td>VERBATIM CORPORATION</td>\n",
       "      <td>93975</td>\n",
       "      <td>12</td>\n",
       "      <td>50.53</td>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>606.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627922</th>\n",
       "      <td>9945</td>\n",
       "      <td>VERBATIM CORPORATION</td>\n",
       "      <td>93975</td>\n",
       "      <td>12</td>\n",
       "      <td>50.53</td>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>606.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627923</th>\n",
       "      <td>9945</td>\n",
       "      <td>VERBATIM CORPORATION</td>\n",
       "      <td>93975</td>\n",
       "      <td>12</td>\n",
       "      <td>50.53</td>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>606.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627924</th>\n",
       "      <td>9945</td>\n",
       "      <td>VERBATIM CORPORATION</td>\n",
       "      <td>93975</td>\n",
       "      <td>12</td>\n",
       "      <td>50.53</td>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>606.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627925</th>\n",
       "      <td>9945</td>\n",
       "      <td>VERBATIM CORPORATION</td>\n",
       "      <td>93975</td>\n",
       "      <td>12</td>\n",
       "      <td>50.53</td>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>606.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>713788 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        category                    manufacturer             product  qty  \\\n",
       "253         9920  CASE LOGIC-PERSONAL & PORTABLE             3200943    7   \n",
       "271          531                OMNITRON SYSTEMS              7206-0    2   \n",
       "320         9070         ZEBRA ENTERPRISE MCD-A1       SAC5070-800CR    3   \n",
       "324         9080         ZEBRA ENTERPRISE MCD-A1  KT-CLMPT-RS507-01R  115   \n",
       "326         9080         ZEBRA ENTERPRISE MCD-A1    KT-PAD-RS507-10R   42   \n",
       "...          ...                             ...                 ...  ...   \n",
       "2627921     9945            VERBATIM CORPORATION               93975   12   \n",
       "2627922     9945            VERBATIM CORPORATION               93975   12   \n",
       "2627923     9945            VERBATIM CORPORATION               93975   12   \n",
       "2627924     9945            VERBATIM CORPORATION               93975   12   \n",
       "2627925     9945            VERBATIM CORPORATION               93975   12   \n",
       "\n",
       "          price       date  Total_Price  \n",
       "253       99.00 2020-08-17       693.00  \n",
       "271       59.09 2020-08-17       118.18  \n",
       "320      500.06 2020-08-17      1500.18  \n",
       "324       86.34 2020-08-17      9929.10  \n",
       "326       51.80 2020-08-17      2175.60  \n",
       "...         ...        ...          ...  \n",
       "2627921   50.53 2020-08-28       606.36  \n",
       "2627922   50.53 2020-08-28       606.36  \n",
       "2627923   50.53 2020-08-28       606.36  \n",
       "2627924   50.53 2020-08-28       606.36  \n",
       "2627925   50.53 2020-08-28       606.36  \n",
       "\n",
       "[713788 rows x 7 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "e15f3289",
   "metadata": {},
   "outputs": [],
   "source": [
    "qty_01 = df_st.drop(['manufacturer', 'category','price','Total_Price'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "df6f6c74",
   "metadata": {},
   "outputs": [],
   "source": [
    "q_max = qty_01.loc[qty_01.groupby('qty').date.idxmax(),:] # quantity on recent date , higher  quantity on recent date left more aged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "ec37ee29",
   "metadata": {},
   "outputs": [],
   "source": [
    "qty_Inc =q_max.sort_values(by=['qty'], ascending=False ,ignore_index=True)[0:10] # sorting and silicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "4413e64e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Aged Product')]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iY8eONdkeHR2tkpISVVZWKjw8PPBykNPbJTV5jt1uV3h4uCoqKoJ1OgAAAEBA0F4TPWjQIA0aNCjweNSoUVq+fLmuvvrqwDbLsmSz2eT3+2Wz2ZptP/3nl3318ZefExLS8s8JevQIb/G++HbR0RHtvYQLEnNve8y87THztsfM2wdzb3tnM/OgRfQ777wjr9erwYMHSzoVubGxsSotLQ3sU1paKqfTqZiYmCbby8rK5HQ6FRUVperqavl8PoWGhgb2l07dxS4rK1NMTIwaGxtVW1uryMjIFq+vvLxGfr91xn34YG650tLqVjkOMzfTGnNn5maYedvj+tL2mHn74PrS9s4085AQ2xlvugbt5RzV1dVasmSJ6uvrVVNTo1deeUX33Xef9u/fr4MHD8rn8ykvL09ut1uxsbHq1KmTdu7cKUnKzc2V2+2Ww+GQy+VSfn6+JCknJ0dut1uSFB8fr5ycHElSfn6+XC6XHA5HsE4HAAAACAjaneihQ4fqvffe08iRI+X3+zVu3DgNGjRIixYt0rRp01RfX6/4+HglJSVJkjIzM5WRkaGamhr17dtXEydOlCTNnTtXaWlpWrFihXr16qWlS5dKkmbMmKG0tDQlJycrIiJCmZmZwToVAAAAoImg/pzoe++9V/fee2+TbYMHD9b69eub7RsXF6e1a9c22x4bG6tVq1Y12x4ZGamVK1e22loBAACAluI3FgIAAACGiGgAAADAEBENAAAAGCKiAQAAAENENAAAAGCIiAYAAAAMEdEAAACAISIaAAAAMEREAwAAAIaIaAAAAMAQEQ0AAAAYIqIBAAAAQ0Q0AAAAYIiIBgAAAAwR0QAAAIAhIhoAAAAwREQDAAAAhohoAAAAwBARDQAAABgiogEAAABDRDQAAABgiIgGAAAADBHRAAAAgCEiGgAAADBERAMAAACGiGgAAADAEBENAAAAGCKiAQAAAENENAAAAGCIiAYAAAAMEdEAAACAISIaAAAAMEREAwAAAIaIaAAAAMAQEQ0AAAAYIqIBAAAAQ0Q0AAAAYIiIBgAAAAwR0QAAAIAhIhoAAAAwREQDAAAAhohoAAAAwBARDQAAABgiogEAAABDRDQAAABgiIgGAAAADBHRAAAAgCEiGgAAADBERAMAAACGiGgAAADAEBENAAAAGCKiAQAAAENENAAAAGCIiAYAAAAMEdEAAACAISIaAAAAMEREAwAAAIaIaAAAAMAQEQ0AAAAYIqIBAAAAQ0Q0AAAAYIiIBgAAAAwR0QAAAIChoEf04sWLlZaWJkkqLi6Wx+NRQkKCsrKyAvvs2bNHKSkpSkxM1OzZs9XY2ChJOnLkiMaPH6+kpCRNnTpVtbW1kqSqqipNmjRJw4YN0/jx41VaWhrs0wAAAAACghrRb731ll555RVJUl1dndLT05Wdna38/Hzt3r1bRUVFkqTU1FTNmTNHhYWFsixLa9askSTNmzdP48aNU0FBgfr166fs7GxJ0rJly+RyubRx40aNHj1aCxYsCOZpAAAAAE0ELaK/+OILZWVlacqUKZKkXbt2qU+fPurdu7fsdrs8Ho8KCgp0+PBh1dXVaeDAgZKklJQUFRQUyOv1aseOHUpMTGyyXZK2bNkij8cjSRo+fLi2bt0qr9cbrFMBAAAAmrAH68Bz5szRzJkzdfToUUnSsWPHFB0dHXi70+lUSUlJs+3R0dEqKSlRZWWlwsPDZbfbm2z/6rHsdrvCw8NVUVGhnj17tnh9PXqEn/U54l+ioyPaewkXJObe9ph522PmbY+Ztw/m3vbOZuZBieiXXnpJvXr10uDBg7Vu3TpJkt/vl81mC+xjWZZsNts3bj/955d99fGXnxMSYnZTvby8Rn6/dcZ9+GBuudLS6lY5DjM30xpzZ+ZmmHnb4/rS9ph5++D60vbONPOQENsZb7oGJaLz8/NVWlqqm2++WcePH9eJEyd0+PBhhYaGBvYpLS2V0+lUTExMk28MLCsrk9PpVFRUlKqrq+Xz+RQaGhrYXzp1F7usrEwxMTFqbGxUbW2tIiMjg3EqAAAAQDNBeU30M888o7y8POXm5mr69Om6/vrr9cc//lH79+/XwYMH5fP5lJeXJ7fbrdjYWHXq1Ek7d+6UJOXm5srtdsvhcMjlcik/P1+SlJOTI7fbLUmKj49XTk6OpFPB7nK55HA4gnEqAAAAQDNBe030V3Xq1EmLFi3StGnTVF9fr/j4eCUlJUmSMjMzlZGRoZqaGvXt21cTJ06UJM2dO1dpaWlasWKFevXqpaVLl0qSZsyYobS0NCUnJysiIkKZmZltdRoAAABA8CM6JSVFKSkpkqTBgwdr/fr1zfaJi4vT2rVrm22PjY3VqlWrmm2PjIzUypUrW3+xAAAAQAvwGwsBAAAAQ0Q0AAAAYIiIBgAAAAwR0QAAAIAhIhoAAAAwREQDAAAAhohoAAAAwBARDQAAABgiogEAAABDRDQAAABgiIgGAAAADBHRAAAAgCEiGgAAADBERAMAAACGiGgAAADAEBENAAAAGCKiAQAAAENENAAAAGCIiAYAAAAMEdEAAACAISIaAAAAMEREAwAAAIaIaAAAAMAQEQ0AAAAYIqIBAAAAQ0Q0AAAAYIiIBgAAAAwR0QAAAIAhIhoAAAAwREQDAAAAhohoAAAAwBARDQAAABgiogEAAABDRDQAAABgiIgGAAAADBHRAAAAgCEiGgAAADBERAMAAACGiGgAAADAEBENAAAAGCKiAQAAAENENAAAAGCIiAYAAAAMEdEAAACAISIaAAAAMEREAwAAAIaIaAAAAMAQEQ0AAAAYIqIBAAAAQy2K6GnTpqm4uDjYawEAAADOCS2K6BtvvFHZ2dlKTEzUU089pS+++CLIywIAAAA6rhZF9IgRI/T8888rOztb5eXlGjVqlFJTU7Vr165grw8AAADocFr8mmi/36+DBw/qwIED8vl86tGjhx588EEtX748mOsDAAAAOhx7S3bKysrSunXr1Lt3b40bN06PPvqoHA6HTpw4oaFDh2r69OnBXicAAADQYbQooisqKvTkk08qLi6uyfauXbvqf/7nf4KyMAAAAKCjatHLOXw+X7OAPn33eciQIa2/KgAAAKADO+Od6Llz56qkpEQ7d+5URUVFYLvX69Xnn38e9MUBAAAAHdEZI3rUqFHat2+fPvzwQyUlJcmyrFNPsts1cODAtlgfAAAA0OGcMaL79++v/v37a8WKFVq3bl2Tt7300kuBvz/33HPBWR0AAADQAbXoGwtvuOEGffLJJ7rtttvkcDiUm5urxsZGJScnB3t9AAAAQIfTooh+9913tXr1aoWGhkqSfvazn+m2225TYmJiUBcHAAAAdEQt+ukcFRUVqq+vDzyura1VXV1d0BYFAAAAdGQtuhM9fPhw3X777brxxhtlWZY2btyoiRMnfuvzHn30URUWFspms2nUqFG68847VVxcrIULF6q+vl7Dhg3TzJkzJUl79uzR7NmzVVtbK5fLpXnz5slut+vIkSNKTU1VeXm5Lr30UmVmZuqiiy5SVVWV7r//fh06dEhRUVFatmyZoqOjz24aAAAAQAu06E70jBkzNG3aNB0/flz19fV66KGHNG7cuDM+5+2339Zf//pXrV+/Xi+//LJWrVqlvXv3Kj09XdnZ2crPz9fu3btVVFQkSUpNTdWcOXNUWFgoy7K0Zs0aSdK8efM0btw4FRQUqF+/fsrOzpYkLVu2TC6XSxs3btTo0aO1YMGCs5kDAAAA0GItimhJSkhI0O9+9zvNmjVLP/7xj791/x//+Md67rnnZLfbVV5eLp/Pp6qqKvXp00e9e/eW3W6Xx+NRQUGBDh8+rLq6usCPzUtJSVFBQYG8Xq927NgReO316e2StGXLFnk8Hkmn7pRv3bpVXq/X9PwBAAAAYy16Oce/y+FwaPny5Xr66aeVlJSkY8eONXnJhdPpVElJSbPt0dHRKikpUWVlpcLDw2W325tsl9TkOXa7XeHh4aqoqFDPnj1btLYePcJb6zQhKTo6or2XcEFi7m2Pmbc9Zt72mHn7YO5t72xmHtSIlk79evBf/epXmjJlig4cOCCbzRZ4m2VZstls8vv9X7v99J9f9tXHX35OSEiLb6yrvLxGfr91xn34YG650tLqVjkOMzfTGnNn5maYedvj+tL2mHn74PrS9s4085AQ2xlvura8Og198skn2rNnjySpS5cuSkhI0Pbt21VaWhrYp7S0VE6nUzExMU22l5WVyel0KioqStXV1fL5fE32l07dxS4rK5MkNTY2qra2VpGRkcE6HQAAACAgaBH9+eefKyMjQw0NDWpoaNCmTZs0ZswY7d+/XwcPHpTP51NeXp7cbrdiY2PVqVMn7dy5U5KUm5srt9sth8Mhl8ul/Px8SVJOTo7cbrckKT4+Xjk5OZKk/Px8uVwuORyOYJ0OAAAAEBC0l3PEx8dr165dGjlypEJDQ5WQkKDk5GRFRUVp2rRpqq+vV3x8vJKSkiRJmZmZysjIUE1Njfr27Rv4EXpz585VWlqaVqxYoV69emnp0qWSTv3EkLS0NCUnJysiIkKZmZnBOhUAAACgiaC+JnratGmaNm1ak22DBw/W+vXrm+0bFxentWvXNtseGxurVatWNdseGRmplStXtt5iAQAAgBYK2ss5AAAAgPMVEQ0AAAAYIqIBAAAAQ0Q0AAAAYIiIBgAAAAwR0QAAAIAhIhoAAAAwREQDAAAAhohoAAAAwBARDQAAABgiogEAAABDRDQAAABgiIgGAAAADBHRAAAAgCEiGgAAADBERAMAAACGiGgAAADAEBENAAAAGCKiAQAAAENENAAAAGCIiAYAAAAMEdEAAACAISIaAAAAMEREAwAAAIaIaAAAAMAQEQ0AAAAYIqIBAAAAQ0Q0AAAAYIiIBgAAAAwR0QAAAIAhIhoAAAAwREQDAAAAhohoAAAAwBARDQAAABgiogEAAABDRDQAAABgiIgGAAAADBHRAAAAgCEiGgAAADBERAMAAACGiGgAAADAEBENAAAAGCKiAQAAAENENAAAAGCIiAYAAAAMEdEAAACAISIaAAAAMEREAwAAAIaIaAAAAMAQEQ0AAAAYIqIBAAAAQ0Q0AAAAYIiIBgAAAAwR0QAAAIAhIhoAAAAwREQDAAAAhohoAAAAwBARDQAAABgiogEAAABDRDQAAABgiIgGAAAADBHRAAAAgKGgRvRjjz2m5ORkJScna8mSJZKk4uJieTweJSQkKCsrK7Dvnj17lJKSosTERM2ePVuNjY2SpCNHjmj8+PFKSkrS1KlTVVtbK0mqqqrSpEmTNGzYMI0fP16lpaXBPBUAAAAgIGgRXVxcrG3btumVV15RTk6OPvjgA+Xl5Sk9PV3Z2dnKz8/X7t27VVRUJElKTU3VnDlzVFhYKMuytGbNGknSvHnzNG7cOBUUFKhfv37Kzs6WJC1btkwul0sbN27U6NGjtWDBgmCdCgAAANBE0CI6OjpaaWlpCgsLk8Ph0H/913/pwIED6tOnj3r37i273S6Px6OCggIdPnxYdXV1GjhwoCQpJSVFBQUF8nq92rFjhxITE5tsl6QtW7bI4/FIkoYPH66tW7fK6/UG63QAAACAgKBF9BVXXBGI4gMHDmjjxo2y2WyKjo4O7ON0OlVSUqJjx4412R4dHa2SkhJVVlYqPDxcdru9yXZJTZ5jt9sVHh6uioqKYJ0OAAAAEGAP9jvYt2+fJk+erFmzZik0NFQHDhwIvM2yLNlsNvn9ftlstmbbT//5ZV99/OXnhIS0/HOCHj3CzU4EZxQdHdHeS7ggMfe2x8zbHjNve8y8fTD3tnc2Mw9qRO/cuVPTp09Xenq6kpOT9fbbbzf5BsDS0lI5nU7FxMQ02V5WVian06moqChVV1fL5/MpNDQ0sL906i52WVmZYmJi1NjYqNraWkVGRrZ4beXlNfL7rTPuwwdzy5WWVrfKcZi5mdaYOzM3w8zbHteXtsfM2wfXl7Z3ppmHhNjOeNM1aC/nOHr0qO6++25lZmYqOTlZkjRgwADt379fBw8elM/nU15entxut2JjY9WpUyft3LlTkpSbmyu32y2HwyGXy6X8/HxJUk5OjtxutyQpPj5eOTk5kqT8/Hy5XC45HI5gnQ4AAAAQELQ70U899ZTq6+u1aNGiwLYxY8Zo0aJFmjZtmurr6xUfH6+kpCRJUmZmpjIyMlRTU6O+fftq4sSJkqS5c+cqLS1NK1asUK9evbR06VJJ0owZM5SWlqbk5GRFREQoMzMzWKcCAAAANBG0iM7IyFBGRsbXvm39+vXNtsXFxWnt2rXNtsfGxmrVqlXNtkdGRmrlypVnv1AAAADAEL+xEAAAADBERAMAAACGiGgAAADAEBENAAAAGCKiAQAAAENENAAAAGCIiAYAAAAMEdEAAACAISIaAAAAMEREAwAAAIaIaAAAAMAQEQ0AAAAYIqIBAAAAQ0Q0AAAAYIiIBgAAAAwR0QAAAIAhIhoAAAAwREQDAAAAhohoAAAAwBARDQAAABgiogEAAABDRDQAAABgiIgGAAAADBHRAAAAgCEiGgAAADBERAMAAACGiGgAAADAEBENAAAAGCKiAQAAAENENAAAAGCIiAYAAAAMEdEAAACAISIaAAAAMEREAwAAAIaIaAAAAMAQEQ0AAAAYIqIBAAAAQ0Q0AAAAYIiIBgAAAAwR0QAAAIAhIhoAAAAwREQDAAAAhohoAAAAwBARDQAAABgiogEAAABDRDQAAABgiIgGAAAADBHRAAAAgCEiGgAAADBERAMAAACGiGgAAADAEBENAAAAGCKiAQAAAENENAAAAGCIiAYAAAAMEdEAAACAISIaAAAAMEREAwAAAIaIaAAAAMAQEQ0AAAAYIqIBAAAAQ0Q0AAAAYIiIBgAAAAwFNaJramo0fPhwff7555Kk4uJieTweJSQkKCsrK7Dfnj17lJKSosTERM2ePVuNjY2SpCNHjmj8+PFKSkrS1KlTVVtbK0mqqqrSpEmTNGzYMI0fP16lpaXBPA0AAACgiaBF9HvvvaexY8fqwIEDkqS6ujqlp6crOztb+fn52r17t4qKiiRJqampmjNnjgoLC2VZltasWSNJmjdvnsaNG6eCggL169dP2dnZkqRly5bJ5XJp48aNGj16tBYsWBCs0wAAAACaCVpEr1mzRnPnzpXT6ZQk7dq1S3369FHv3r1lt9vl8XhUUFCgw4cPq66uTgMHDpQkpaSkqKCgQF6vVzt27FBiYmKT7ZK0ZcsWeTweSdLw4cO1detWeb3eYJ0KAAAA0IQ9WAf+6t3hY8eOKTo6OvDY6XSqpKSk2fbo6GiVlJSosrJS4eHhstvtTbZ/9Vh2u13h4eGqqKhQz549W7y+Hj3C/+1zQ3PR0RHtvYQLEnNve8y87THztsfM2wdzb3tnM/OgRfRX+f1+2Wy2wGPLsmSz2b5x++k/v+yrj7/8nJAQs5vq5eU18vutM+7DB3PLlZZWt8pxmLmZ1pg7MzfDzNse15e2x8zbB9eXtnemmYeE2M5407XNfjpHTExMk28ALC0tldPpbLa9rKxMTqdTUVFRqq6uls/na7K/dOoudllZmSSpsbFRtbW1ioyMbKtTAQAAwAWuzSJ6wIAB2r9/vw4ePCifz6e8vDy53W7FxsaqU6dO2rlzpyQpNzdXbrdbDodDLpdL+fn5kqScnBy53W5JUnx8vHJyciRJ+fn5crlccjgcbXUqAAAAuMC12cs5OnXqpEWLFmnatGmqr69XfHy8kpKSJEmZmZnKyMhQTU2N+vbtq4kTJ0qS5s6dq7S0NK1YsUK9evXS0qVLJUkzZsxQWlqakpOTFRERoczMzLY6DQAAACD4Eb158+bA3wcPHqz169c32ycuLk5r165ttj02NlarVq1qtj0yMlIrV65s3YUCAAAALcRvLAQAAAAMEdEAAACAISIaAAAAMEREAwAAAIaIaAAAAMAQEQ0AAAAYIqIBAAAAQ0Q0AAAAYIiIBgAAAAwR0QAAAIAhIhoAAAAwREQDAAAAhohoAAAAwBARDQAAABgiogEAAABDRDQAAABgiIgGAAAADBHRAAAAgCEiGgAAADBERAMAAACGiGgAAADAEBENAAAAGCKiAQAAAENENAAAAGCIiAYAAAAMEdEAAACAISIaAAAAMEREAwAAAIaIaAAAAMAQEQ0AAAAYIqIBAAAAQ0Q0AAAAYIiIBgAAAAwR0QAAAIAhIhoAAAAwREQDAAAAhohoAAAAwBARDQAAABgiogEAAABDRDQAAABgiIgGAAAADBHRAAAAgCEiGgAAADBERAMAAACGiGgAAADAEBENAAAAGCKiAQAAAENENAAAAGCIiAYAAAAMEdEAAACAISIaAAAAMEREAwAAAIaIaAAAAMAQEQ0AAAAYIqIBAAAAQ0Q0AAAAYIiIBgAAAAwR0QAAAIAhIhoAAAAwREQDAAAAhohoAAAAwBARDQAAABg6pyN6w4YNuummm5SQkKA//elP7b0cAAAAXCDs7b2Af1dJSYmysrK0bt06hYWFacyYMbrmmmt0+eWXt/fSAAAAcJ47ZyO6uLhYP/nJTxQZGSlJSkxMVEFBge65554WPT8kxNai/S7uftG/u8QLSkvn2RJh3Xq02rHOd60194vDo1rlOBeC1pp5l4v5OG+p1ry+fCeya6sd63zWmjPvFsnHeku11twdEZ1b5TgXgjPN/Nv+/7BZlmW19oLawhNPPKETJ05o5syZkqSXXnpJu3bt0vz589t5ZQAAADjfnbOvifb7/bLZ/vUZgmVZTR4DAAAAwXLORnRMTIxKS0sDj0tLS+V0OttxRQAAALhQnLMR/dOf/lRvvfWWKioqdPLkSb322mtyu93tvSwAAABcAM7Zbyzs2bOnZs6cqYkTJ8rr9WrUqFH6wQ9+0N7LAgAAwAXgnP3GQgAAAKC9nLMv5wAAAADaCxENAAAAGCKiAQAAAENENAAAAGCIiA6S3NxcJScnKzk5WYsXL5Z06leVezweJSQkKCsrK7DvBx98oFtvvVUjRozQ5MmTVVVV9bXHXL16tZKTk3XTTTdp8eLF+ur3hM6aNUvr1q2TJJWXl+vmm28O/O/666/XoEGDJEkpKSmB7YmJibrqqqtUVlYWjDG0qfaeuSTt3LlTo0aN0s0336xf/OIXOnz4sCTp448/1pgxYzRixAhNmDAhsP180BHmfuTIEY0fP15JSUmaOnWqamtrJUnHjx/Xr371K40YMUKjRo3Snj17Wvv020VHnvnbb7+ta665JnCNeeCBB1r79NtFR5j5N11fDhw4oDvuuEMej0cTJkzQ/v37W/v0201HnntNTY1+85vfaOTIkRo5cqQ++OCD1j79dmEy89dff10ej0fJyclKS0tTQ0PD1x7zgQceUEJCQuC68Prrrzd5+5YtW3T99dcHHu/atUu33nqrPB6PJk+eHPi9IB3u+mKh1Z04ccL60Y9+ZJWXl1ter9caNWqUtWnTJis+Pt767LPPLK/Xa/3yl7+0tmzZYlmWZY0dOzbw94ULF1pLly5tdszPPvvMuvHGG63a2lqrsbHRuv32260333zTsizL+uc//2lNnjzZ+sEPfmC9/PLLzZ7r8/msO+64w1q/fn2zt6WmplorVqxozdNvFx1l5kOHDrX27NljWZZlvfTSS9aUKVMsy7KsO+64wyoqKrIsy7JWr15t3XfffcEbRhvqKHOfNGmSlZeXZ1mWZT322GPWkiVLLMuyrKysrMDfN23aZI0ZMyZ4w2gjHX3mTz31lLVy5cqgzqCtdZSZf9P1ZcyYMYH9/va3v1kjRowI3jDaUEefe3p6uvXf//3flmVZVlFRkTVq1KjgDaONmMy8trbWGjJkiFVaWmpZlmXde++91p///OevPe7w4cOtkpKSr31baWmplZSUZA0dOtSyLMvy+/1WfHy89dZbb1mWZVmvvvqqNXnyZMuyOt71hTvRQeDz+eT3+3Xy5Ek1NjaqsbFR4eHh6tOnj3r37i273S6Px6OCggJJp36F+em7OCdPnlTnzp2bHbN379569dVX1bVrV1VVVammpkbdunWTJG3YsEE///nPNWzYsK9dz8svv6wuXbrI4/E02f7WW29p7969+tWvftWap98uOsLMGxoaNGPGDMXFxUmSrrzySh09elSS9Mwzz8jtdsvv9+vIkSOB45zrOsLcvV6vduzYocTEREmnvtJi8v7ONR195u+//762bdsmj8ejKVOmBP4bOJd1hJmf6fqyZ88eJSUlSZIGDhyoY8eO6dChQ8EbSBvpyHO3LEuvvfaaJk2aJElyu9165JFHgjqPtmAy865du2rz5s26+OKLdfLkSZWXl3/tv20nT57UkSNHlJ6eLo/Ho+XLl8vv9wfenpGRoXvuuSfwuLKyUnV1dfrJT34iSRo6dKi2bdumhoaGDnd9IaKDIDw8XDNmzNCwYcMUHx+v2NhYHTt2TNHR0YF9nE6nSkpKJElpaWnKyMjQkCFDVFxcrDFjxnztcR0Oh9asWaMbbrhB0dHRgf+o77rrLo0ePfprn+Pz+bRy5Ur95je/afa25cuXa+bMmQoNDT3bU253HWHmYWFhuvnmmyWdupg/9thjuuGGGyRJdrtdVVVVcrvdeuGFF3Tbbbe1+gzaQ0eYe2VlpcLDw2W3n/rdUdHR0YH398tf/lJvvfWWhgwZooyMDE2fPr3VZ9DWOvrMIyIiNGHCBG3YsEHx8fGaOXNmq8+grXWEmZ/p+nLVVVfp1VdflXTq5sgXX3wR+PL3uawjz728vFxhYWFavXq1br/9dk2cOFE+ny8YY2hTpjN3OBwqKirSddddp8rKSg0ZMqTZMcvKyvSTn/xEjzzyiNasWaN33nlHa9eulSQ999xzuuqqqzRgwIDA/t27d1fXrl21bds2SdKrr74qr9erysrKDnd9IaKDYO/evXr55Zf1f//3f3rzzTcVEhKiAwcOyGazBfaxLEs2m011dXWaPXu2nn32WW3btk3jxo3Tb3/722889m233abt27fr4osv1mOPPfata3nzzTf1n//5n7ryyiubbN+3b58qKys1dOjQf/9EO5CONPOGhgbdf//9amxs1OTJkwPbu3Xrpm3btmnp0qWaOnXqeXHB7QhzP338Lzv9eP78+Ro/fry2bdump59+WjNnzgzcqTpXdfSZP/TQQ0pISJAkjR07Vh9//LGqq6vP5pTbXUeY+Wlfd31ZtGiRXnvtNY0YMUJ/+ctfFBcXJ4fDcfYn3s468tx9Pp/KysoUERGhF198UZMnT9bdd9/dKufdnkxmflp8fLy2b9+uoUOH6sEHH2x2zN69e+vxxx+X0+lUly5dNGHCBBUVFemjjz7Sa6+9pl//+tdN9rfZbFq+fLmeeOIJjRw5UtXV1YqMjJTD4ehw1xciOgi2bdumwYMHq0ePHgoLC1NKSoq2b9/e5M5AaWmpnE6nPvroI3Xq1CnwK8tvv/12vf3223r//fcDL5yfPXu2jh49qp07d0o6dVczOTlZH3744beu5Y033tBNN93U4u3nqo4y89raWt11111qbGzUihUrAv+Q5efnB755xe12q66uTsePHw/GKNpUR5h7VFSUqqurA5+UnH5/krRp0ybdeuutkqRBgwapR48e+uSTT4Iyi7bSkWfu9/u1YsWKZp8gnutf7eoIM5e++frS2Nioxx9/XOvXr9eMGTP0+eef65JLLgnSNNpOR5579+7dZbfbNXz4cEnStddeqxMnTqi8vDxI02gbJjP/4osvAneLJcnj8ejDDz9sNvMPP/xQhYWFgf0sy5LdbldBQYFKS0t16623atKkSTp27JjGjRsn6dT/N6tWrVJOTo5GjBghv9+vyMjIDnd9IaKDIC4uTsXFxTpx4oQsy9LmzZs1YMAA7d+/XwcPHpTP51NeXp7cbrf69Omjf/7zn/r0008lnfpHv3///urfv79yc3OVm5urBQsWqLq6WqmpqaqqqpJlWSosLNTVV1/9rWv5+9//LpfL1eLt56qOMvPU1FT16dNHy5YtU1hYWGD7008/Hfhu5L/+9a/q3r27oqKigjeQNtIR5u5wOORyuZSfny9JysnJkdvtDqzvjTfekHTqJxgcO3ZMl156aZCnElwdeeYhISF6/fXXA/9g5uTkaMCAAeratWvwBxNEHWHm0jdfX7KysrRp0yZJ0tq1a9W/f3917949eANpIx157mFhYfrpT38aeBnN3//+d3Xp0uWcn7vJzC3LUmpqqo4cOSJJKigo0A9/+MNmM7csS4888oiOHz8ur9erF198UTfeeKOmT5+uwsJC5ebm6g9/+IOcTqdWr14tSUpPT9euXbsknfqeoqSkpA55fbG323s+jw0ZMkT/+Mc/lJKSIofDof79+2vatGm69tprNW3aNNXX1ys+Pl5JSUmy2WxauHCh7r33XlmWpR49enztNyd873vf06RJkzRmzBiFhobK5XLpzjvv/Na1HDp0SDExMV+7vWfPnq1yvh1BR5j5P/7xD23atEmXX365brnlFkmnXjv25JNPatGiRfrd736nxx9/XBEREVq+fHnQZtGWOsLcJWnu3LlKS0vTihUr1KtXLy1dulTSqS9zz5kzR08++aTCwsK0ePFiRUREBGUWbaWjz3zx4sWBj/WoqCgtWbIkKHNoSx1h5me6vtx///367W9/q8cee0w9e/bUwoULgzaLttTR575gwQLNmTNHq1evlt1uV1ZWlkJCzu17k6Yznz9/viZPniybzabLL79c8+bNa3bMuLg4TZo0SWPHjlVjY6MSEhICd/C/yYMPPqi5c+fq5MmTuvLKK7VgwQJJHe/6YrNOf40ZAAAAQIuc258yAQAAAO2AiAYAAAAMEdEAAACAISIaAAAAMEREAwAAAIaIaABAQEVFRbPfcGpi165dmjNnTiuuCAA6JiIaANBqPv74Y5WUlLT3MgAg6PhlKwBwDtu+fbsyMzP1H//xH/r000/VuXNnLVq0SE8++aS++OILHTp0SNddd52mTJmiefPmae/evbLZbPrZz36m++67T3a7Xa+99pqysrLUpUsX9evXL3DsdevWqbCwUE888USzx7W1tXr44Yf17rvvKjQ0VDfccIPGjh2r5cuXq7q6Wg888MB580s/AODrcCcaAM5xu3fv1oQJE7RhwwalpKQoNTVVklRXV6dXX31VqampevjhhxUZGakNGzbo5Zdf1ocffqinn35aZWVlSk9P1+9//3utW7dOsbGxLXqfy5cvV319vfLz85WTk6N3331Xn332maZPny6Xy0VAAzjvEdEAcI6Li4uTy+WSJN16663as2ePvvjiC1199dWBfbZu3ao77rhDNptNYWFhGjNmjLZu3aqdO3fqe9/7ni6//HJJ0u23396i91lcXKxRo0YpNDRUYWFhev7553XNNde0/skBQAfFyzkA4BwXGhrabFtISIi6du0aeOz3+2Wz2Zo8bmxslCRZlhXYbrf/658Fm83W5G1er7fJfl8+3tGjR9W5c+ezPBMAOHdwJxoAznF79+7V3r17JUkvvviiBg0apG7dujXZZ8iQIXr++edlWZYaGhq0Zs0a/fSnP9WPfvQjffzxx4Hnr1u3LvCcqKgo7du3T/X19fJ6vSosLAy8bfDgwXrllVfk9/vV0NCg6dOna8eOHQoNDQ3EOQCcz4hoADjHXXzxxVq2bJk8Ho/eeOMNLVmypNk+GRkZqqiokMfjkcfj0aWXXqopU6YoKipKmZmZuv/++3XLLbfo888/Dzzn2muv1Y9+9CMNGzZMd9xxR5NvOrznnnvkcDh08803a+TIkYqPj1dCQoIGDhyoQ4cO6Z577mmTcweA9mKzvvy1OgDAOWX79u2aP3++8vLy2nspAHBB4U40AAAAYIg70QAAAIAh7kQDAAAAhohoAAAAwBARDQAAABgiogEAAABDRDQAAABgiIgGAAAADP1/7CxFZynKH5IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = 'product',\n",
    "            y = 'qty',\n",
    "            data =qty_Inc ).set(title='Aged Product') # bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63a0d935",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1b7ab6a7",
   "metadata": {},
   "source": [
    "### 7. How to Decrease Product aging?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b02d306",
   "metadata": {},
   "source": [
    "Keeping aging SKUs can have a significant negative impact on your cash flow and daily operations.\n",
    "\n",
    "Here are five strategies for dealing with unsold, aging inventory.\n",
    "\n",
    "    1. Shift Inventory To Non-Prime Locations\n",
    "    2. Liquidation\n",
    "    3. Clearance/Sales\n",
    "    4. Donation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12214518",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "094c832e",
   "metadata": {},
   "source": [
    "## For Data Not In Stock:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72b6317b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_nos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "2b4b6a87",
   "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>category</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>product</th>\n",
       "      <th>qty</th>\n",
       "      <th>price</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1539</td>\n",
       "      <td>MS-OPEN VALUE SUBSCRIPTION</td>\n",
       "      <td>021-09108</td>\n",
       "      <td>0</td>\n",
       "      <td>143.58</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1539</td>\n",
       "      <td>MS-OPEN VALUE SUBSCRIPTION</td>\n",
       "      <td>059-07014</td>\n",
       "      <td>0</td>\n",
       "      <td>56.09</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1539</td>\n",
       "      <td>MS-OPEN VALUE SUBSCRIPTION</td>\n",
       "      <td>065-06444</td>\n",
       "      <td>0</td>\n",
       "      <td>56.09</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1539</td>\n",
       "      <td>MS-OPEN VALUE SUBSCRIPTION</td>\n",
       "      <td>076-04355</td>\n",
       "      <td>0</td>\n",
       "      <td>217.60</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1582</td>\n",
       "      <td>MS-OPEN VALUE SUBSCRIPTION</td>\n",
       "      <td>077-05318</td>\n",
       "      <td>0</td>\n",
       "      <td>56.09</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627418</th>\n",
       "      <td>3360</td>\n",
       "      <td>TARGUS</td>\n",
       "      <td>AMB580TT</td>\n",
       "      <td>0</td>\n",
       "      <td>19.53</td>\n",
       "      <td>2020-08-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627775</th>\n",
       "      <td>505</td>\n",
       "      <td>ENGENIUS</td>\n",
       "      <td>EAP1300</td>\n",
       "      <td>0</td>\n",
       "      <td>85.24</td>\n",
       "      <td>2020-08-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627822</th>\n",
       "      <td>9910</td>\n",
       "      <td>KINGSTON</td>\n",
       "      <td>HX-MPFS-SM</td>\n",
       "      <td>0</td>\n",
       "      <td>13.75</td>\n",
       "      <td>2020-08-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627853</th>\n",
       "      <td>291</td>\n",
       "      <td>KINGSTON TECHNOLOGY FLASH</td>\n",
       "      <td>SD10VG2/16GBCP</td>\n",
       "      <td>0</td>\n",
       "      <td>16.51</td>\n",
       "      <td>2020-08-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627857</th>\n",
       "      <td>291</td>\n",
       "      <td>KINGSTON</td>\n",
       "      <td>IKD300S/4GB</td>\n",
       "      <td>0</td>\n",
       "      <td>52.83</td>\n",
       "      <td>2020-08-28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1914138 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        category                manufacturer         product  qty   price  \\\n",
       "0           1539  MS-OPEN VALUE SUBSCRIPTION       021-09108    0  143.58   \n",
       "1           1539  MS-OPEN VALUE SUBSCRIPTION       059-07014    0   56.09   \n",
       "2           1539  MS-OPEN VALUE SUBSCRIPTION       065-06444    0   56.09   \n",
       "3           1539  MS-OPEN VALUE SUBSCRIPTION       076-04355    0  217.60   \n",
       "4           1582  MS-OPEN VALUE SUBSCRIPTION       077-05318    0   56.09   \n",
       "...          ...                         ...             ...  ...     ...   \n",
       "2627418     3360                      TARGUS        AMB580TT    0   19.53   \n",
       "2627775      505                    ENGENIUS         EAP1300    0   85.24   \n",
       "2627822     9910                    KINGSTON      HX-MPFS-SM    0   13.75   \n",
       "2627853      291   KINGSTON TECHNOLOGY FLASH  SD10VG2/16GBCP    0   16.51   \n",
       "2627857      291                    KINGSTON     IKD300S/4GB    0   52.83   \n",
       "\n",
       "              date  \n",
       "0       2020-08-17  \n",
       "1       2020-08-17  \n",
       "2       2020-08-17  \n",
       "3       2020-08-17  \n",
       "4       2020-08-17  \n",
       "...            ...  \n",
       "2627418 2020-08-28  \n",
       "2627775 2020-08-28  \n",
       "2627822 2020-08-28  \n",
       "2627853 2020-08-28  \n",
       "2627857 2020-08-28  \n",
       "\n",
       "[1914138 rows x 6 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nos.drop([\"status\"], axis=1) # no need of stock column now so dropping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2316133e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af43b42c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4d817c27",
   "metadata": {},
   "source": [
    "### 1. Which are the top Manufacturers with price, quantity?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f00f754",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "02e0cb8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mfg_grp_n = df_nos.groupby('manufacturer').sum() # group by mfg column\n",
    "\n",
    "mfg_sort = mfg_grp_n.sort_values(by=['qty', 'price'],ascending= False)[:10] # sorting and silicing\n",
    "\n",
    "mfg_sort_in_n = mfg_sort.reset_index() # resetting index\n",
    "\n",
    "mfg_sort_in_n\n",
    "\n",
    "sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
    "top_mfg_qty_price_n = sns.barplot(x = 'qty',y = 'price',hue=\"manufacturer\",  data= mfg_sort_in_n).set(title='Top Manufacturers with Price, Quantity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33251369",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31866a31",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8d9eb1d9",
   "metadata": {},
   "source": [
    "### 2. Which are the most profitable products?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "2f6515b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pr_tot_grp_n = df_nos.groupby(['product','price'])  # group by\n",
    "\n",
    "top_sort_n = pr_tot_grp_n.sum().sort_values(by='price',ascending=[False])[:10] # sorting and silicing\n",
    "\n",
    "most_profitable_products_n = top_sort_n.reset_index() # resetting index\n",
    "\n",
    "profit_product_n = sns.barplot(x = 'product',y = 'price',data = most_profitable_products_n).set(title='most_profitables_products')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "bd5edc16",
   "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>category</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>product</th>\n",
       "      <th>status</th>\n",
       "      <th>qty</th>\n",
       "      <th>price</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1539</td>\n",
       "      <td>MS-OPEN VALUE SUBSCRIPTION</td>\n",
       "      <td>021-09108</td>\n",
       "      <td>No Stock</td>\n",
       "      <td>0</td>\n",
       "      <td>143.58</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1539</td>\n",
       "      <td>MS-OPEN VALUE SUBSCRIPTION</td>\n",
       "      <td>059-07014</td>\n",
       "      <td>No Stock</td>\n",
       "      <td>0</td>\n",
       "      <td>56.09</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1539</td>\n",
       "      <td>MS-OPEN VALUE SUBSCRIPTION</td>\n",
       "      <td>065-06444</td>\n",
       "      <td>No Stock</td>\n",
       "      <td>0</td>\n",
       "      <td>56.09</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1539</td>\n",
       "      <td>MS-OPEN VALUE SUBSCRIPTION</td>\n",
       "      <td>076-04355</td>\n",
       "      <td>No Stock</td>\n",
       "      <td>0</td>\n",
       "      <td>217.60</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1582</td>\n",
       "      <td>MS-OPEN VALUE SUBSCRIPTION</td>\n",
       "      <td>077-05318</td>\n",
       "      <td>No Stock</td>\n",
       "      <td>0</td>\n",
       "      <td>56.09</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627418</th>\n",
       "      <td>3360</td>\n",
       "      <td>TARGUS</td>\n",
       "      <td>AMB580TT</td>\n",
       "      <td>No Stock</td>\n",
       "      <td>0</td>\n",
       "      <td>19.53</td>\n",
       "      <td>2020-08-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627775</th>\n",
       "      <td>505</td>\n",
       "      <td>ENGENIUS</td>\n",
       "      <td>EAP1300</td>\n",
       "      <td>No Stock</td>\n",
       "      <td>0</td>\n",
       "      <td>85.24</td>\n",
       "      <td>2020-08-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627822</th>\n",
       "      <td>9910</td>\n",
       "      <td>KINGSTON</td>\n",
       "      <td>HX-MPFS-SM</td>\n",
       "      <td>No Stock</td>\n",
       "      <td>0</td>\n",
       "      <td>13.75</td>\n",
       "      <td>2020-08-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627853</th>\n",
       "      <td>291</td>\n",
       "      <td>KINGSTON TECHNOLOGY FLASH</td>\n",
       "      <td>SD10VG2/16GBCP</td>\n",
       "      <td>No Stock</td>\n",
       "      <td>0</td>\n",
       "      <td>16.51</td>\n",
       "      <td>2020-08-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627857</th>\n",
       "      <td>291</td>\n",
       "      <td>KINGSTON</td>\n",
       "      <td>IKD300S/4GB</td>\n",
       "      <td>No Stock</td>\n",
       "      <td>0</td>\n",
       "      <td>52.83</td>\n",
       "      <td>2020-08-28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1914138 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        category                manufacturer         product    status  qty  \\\n",
       "0           1539  MS-OPEN VALUE SUBSCRIPTION       021-09108  No Stock    0   \n",
       "1           1539  MS-OPEN VALUE SUBSCRIPTION       059-07014  No Stock    0   \n",
       "2           1539  MS-OPEN VALUE SUBSCRIPTION       065-06444  No Stock    0   \n",
       "3           1539  MS-OPEN VALUE SUBSCRIPTION       076-04355  No Stock    0   \n",
       "4           1582  MS-OPEN VALUE SUBSCRIPTION       077-05318  No Stock    0   \n",
       "...          ...                         ...             ...       ...  ...   \n",
       "2627418     3360                      TARGUS        AMB580TT  No Stock    0   \n",
       "2627775      505                    ENGENIUS         EAP1300  No Stock    0   \n",
       "2627822     9910                    KINGSTON      HX-MPFS-SM  No Stock    0   \n",
       "2627853      291   KINGSTON TECHNOLOGY FLASH  SD10VG2/16GBCP  No Stock    0   \n",
       "2627857      291                    KINGSTON     IKD300S/4GB  No Stock    0   \n",
       "\n",
       "          price       date  \n",
       "0        143.58 2020-08-17  \n",
       "1         56.09 2020-08-17  \n",
       "2         56.09 2020-08-17  \n",
       "3        217.60 2020-08-17  \n",
       "4         56.09 2020-08-17  \n",
       "...         ...        ...  \n",
       "2627418   19.53 2020-08-28  \n",
       "2627775   85.24 2020-08-28  \n",
       "2627822   13.75 2020-08-28  \n",
       "2627853   16.51 2020-08-28  \n",
       "2627857   52.83 2020-08-28  \n",
       "\n",
       "[1914138 rows x 7 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2d78422",
   "metadata": {},
   "source": [
    "### 3. Which is the product where you have seen a decrease in the price?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "60620b0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_nos\n",
    "\n",
    "pric_02 = df_nos.drop(['manufacturer', 'category','qty'], axis=1)\n",
    "\n",
    "p_max_n = pric_02.loc[pric_02.groupby('product').date.idxmax(),:] #price according to recent date\n",
    "\n",
    "p_min_n = pric_02.loc[pric_02.groupby('product').date.idxmin(),:]  #price according to intial date\n",
    "\n",
    "p_max_n['price_max'] =p_max_n['price']# new column\n",
    "\n",
    "p_min_n['price_min'] =p_min_n['price'] # new column\n",
    "\n",
    "\n",
    "p_max_n = p_max_n.drop(['price','date'], axis=1)\n",
    "\n",
    "\n",
    "p_min_n = p_min_n.drop(['price','date'], axis=1)\n",
    "\n",
    "\n",
    "\n",
    "final_n = pd.merge(p_max_n, p_min_n , on='product', how='outer')# mergin two new column on producat column\n",
    "\n",
    "final_n['final_price']= final_n['price_max']-final_n['price_min']# final price by diffrence on two dates\n",
    "\n",
    "pric_Dec_n = final_n.sort_values(by=['final_price'], ascending=True ,ignore_index=True)[0:10] # sorting and silicing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "abf3e8af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Product whose Price Decreased')]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(y = 'product',\n",
    "            x = 'final_price',\n",
    "            data = pric_Dec_n).set(title='Product whose Price Decreased') # bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a67cbcff",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6efdf92c",
   "metadata": {},
   "source": [
    "### 4. Which is the product where you have seen an increase in the price?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10d97284",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "88aaeaf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pric_Inc_n =final_n.sort_values(by=['final_price'], ascending=False ,ignore_index=True)[0:10] # sorting and silicing\n",
    "\n",
    "bar = sns.barplot(y = 'product',\n",
    "            x = 'final_price',\n",
    "            data = pric_Inc_n).set(title='Product whose Price Increased') # bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0082641",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "74a0b040",
   "metadata": {},
   "source": [
    "### 5. Which is the top category by price and quantity?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "95ba5585",
   "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>category</th>\n",
       "      <th>qty</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1221</td>\n",
       "      <td>0</td>\n",
       "      <td>1.539977e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1241</td>\n",
       "      <td>0</td>\n",
       "      <td>6.036206e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1231</td>\n",
       "      <td>0</td>\n",
       "      <td>4.445039e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1569</td>\n",
       "      <td>0</td>\n",
       "      <td>3.213863e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>525</td>\n",
       "      <td>0</td>\n",
       "      <td>2.871297e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1577</td>\n",
       "      <td>0</td>\n",
       "      <td>2.064920e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>531</td>\n",
       "      <td>0</td>\n",
       "      <td>1.861489e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>661</td>\n",
       "      <td>0</td>\n",
       "      <td>1.643297e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>1.282264e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1582</td>\n",
       "      <td>0</td>\n",
       "      <td>1.120179e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category  qty         price\n",
       "0     1221    0  1.539977e+09\n",
       "1     1241    0  6.036206e+08\n",
       "2     1231    0  4.445039e+08\n",
       "3     1569    0  3.213863e+08\n",
       "4      525    0  2.871297e+08\n",
       "5     1577    0  2.064920e+08\n",
       "6      531    0  1.861489e+08\n",
       "7      661    0  1.643297e+08\n",
       "8      101    0  1.282264e+08\n",
       "9     1582    0  1.120179e+08"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_grp_n = df_nos.groupby('category').sum()  # groupby with respect to category column.\n",
    "\n",
    "\n",
    "cat_grp_srt_q_p_n = cat_grp_n.sort_values(by=['qty', 'price'],ascending= False)[:10] # sorting and silicing\n",
    "\n",
    "cat_grp_srt_q_p_ind_n = cat_grp_srt_q_p_n.reset_index() # resetting index\n",
    "\n",
    "cat_grp_srt_q_p_ind_n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "092d1610",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed6eba68",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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