Data_Cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "21e34ee4",
   "metadata": {},
   "source": [
    "# Brazilian Ecommerce Olist data collection and pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84de6e2a",
   "metadata": {},
   "source": [
    "The data is collected from [Kaggle](https://www.kaggle.com/datasets/olistbr/brazilian-ecommerce). This dataset consists of real transaction data of 100k orders from 2016 to 2018 made at multiple marketplaces in Brazil. \n",
    "\n",
    "We have a total of nine CSV files and below is the data diagram:\n",
    "<figure>\n",
    "<img src=\"kaggel olist data.png\" style=\"width:100%\">\n",
    "<figcaption align = \"center\"> Source of the image:https://www.kaggle.com/datasets/olistbr/brazilian-ecommerce </figcaption>\n",
    "</figure>\n",
    "\n",
    "**Our aim in this notbook is:**\n",
    "- To get a high level picture of dataset we have.\n",
    "- Check for null and duplicates.\n",
    "- Preserve as much data and information as we can using imputation so that we can do time series forecasting, do broad level analysis and later apply other machine learning modelling as this data has lots of vital information for the business.\n",
    "- Create a final data wireframe after merging all the individual dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1bd1652",
   "metadata": {},
   "source": [
    "# Index\n",
    "\n",
    "[1. Importing Libraries](#Importing_libraries) </br>\n",
    "[2. Importing Data](#Importing_Data)</br>\n",
    "[3. Reading each dataframe separately](#reading) </br>\n",
    "$\\;\\;\\;\\;$    [3.1 Customer Dataset](#customer_dataset)</br>\n",
    "$\\;\\;\\;\\;$    [3.2 Geoloc Dataset](#geoloc_dataset) </br>\n",
    "$\\;\\;\\;\\;$    [3.3 Seller Dataset](#seller_dataset) </br>\n",
    "$\\;\\;\\;\\;$    [3.4 Payments Dataset](#payment_dataset) </br>\n",
    "$\\;\\;\\;\\;$    [3.5 Order item Dataset](#order_item_dataset) </br>\n",
    "$\\;\\;\\;\\;$    [3.6 Order Dataset](#order_dataset) </br>\n",
    "$\\;\\;\\;\\;$    [3.7 Product Dataset](#product_dataset) </br>\n",
    "$\\;\\;\\;\\;$    [3.8 Review Dataset](#review_dataset) </br>\n",
    "$\\;\\;\\;\\;$    [3.9 Product_eng Dataset](#eng_dataset) </br>\n",
    "[4. Joining all the tables](#joining_tables)</br>\n",
    "[5. Scraping holiday data](#scraping) </br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6ab239c",
   "metadata": {},
   "source": [
    "## 1. Importing Libraries\n",
    "\n",
    "<a id = 'Importing_libraries' ></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3e7b6d96",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_theme(style= 'darkgrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb8e3206",
   "metadata": {},
   "source": [
    "We will start with loading all the individual dataset and try to do following tasks:\n",
    "\n",
    "   - Understand data dictionary to get an overview of numerical and categorical columns.\n",
    "   - Correct the data format.\n",
    "   - Clean the data by deleting redundant columns, imputing the null values and deleting the duplicate rows and columns."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8b068e5",
   "metadata": {},
   "source": [
    "## 2. Importing Data\n",
    "\n",
    "<a id = 'Importing_Data' ></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c776ccbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading all the datasets\n",
    "customers=pd.read_csv('data/olist_customers_dataset.csv')\n",
    "geoloc=pd.read_csv('data/olist_geolocation_dataset.csv')\n",
    "order_items=pd.read_csv('data/olist_order_items_dataset.csv')\n",
    "payments=pd.read_csv('data/olist_order_payments_dataset.csv')\n",
    "reviews=pd.read_csv('data/olist_order_reviews_dataset.csv')\n",
    "orders=pd.read_csv('data/olist_orders_dataset.csv')\n",
    "products=pd.read_csv('data/olist_products_dataset.csv')\n",
    "sellers=pd.read_csv('data/olist_sellers_dataset.csv')\n",
    "product_eng=pd.read_csv('data/product_category_name_translation.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f96937b3",
   "metadata": {},
   "source": [
    "Let us quickly check the shape of each table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e9d64e46",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The customers data set has 99441 rows and 5 columns.\n",
      "The geoloc data set has 1000163 rows and 5 columns.\n",
      "The sellers data set has 3095 rows and 4 columns.\n",
      "The payments data set has 103886 rows and 5 columns.\n",
      "The order_items data set has 112650 rows and 7 columns.\n",
      "The orders data set has 99441 rows and 8 columns.\n",
      "The products data set has 32951 rows and 9 columns.\n",
      "The reviews data set has 99224 rows and 7 columns.\n",
      "The product_eng data set has 71 rows and 2 columns.\n"
     ]
    }
   ],
   "source": [
    "#shape of the each dataframe\n",
    "print(f\"The customers data set has {customers.shape[0]} rows and {customers.shape[1]} columns.\")\n",
    "print(f\"The geoloc data set has {geoloc.shape[0]} rows and {geoloc.shape[1]} columns.\")\n",
    "print(f\"The sellers data set has {sellers.shape[0]} rows and {sellers.shape[1]} columns.\")\n",
    "print(f\"The payments data set has {payments.shape[0]} rows and {payments.shape[1]} columns.\")\n",
    "print(f\"The order_items data set has {order_items.shape[0]} rows and {order_items.shape[1]} columns.\")\n",
    "print(f\"The orders data set has {orders.shape[0]} rows and {orders.shape[1]} columns.\")\n",
    "print(f\"The products data set has {products.shape[0]} rows and {products.shape[1]} columns.\")\n",
    "print(f\"The reviews data set has {reviews.shape[0]} rows and {reviews.shape[1]} columns.\")\n",
    "print(f\"The product_eng data set has {product_eng.shape[0]} rows and {product_eng.shape[1]} columns.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec51fe4c",
   "metadata": {},
   "source": [
    "## 3. Reading each dataframe separately\n",
    "\n",
    "<a id = 'reading' ></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72bf561d",
   "metadata": {},
   "source": [
    "Let us start with reading the customer dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f55f47b0",
   "metadata": {},
   "source": [
    "## 3.1 Customers dataset\n",
    "\n",
    "<a id = 'customer_dataset' ></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "07be6aee",
   "metadata": {
    "scrolled": true
   },
   "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>customer_id</th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>customer_zip_code_prefix</th>\n",
       "      <th>customer_city</th>\n",
       "      <th>customer_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06b8999e2fba1a1fbc88172c00ba8bc7</td>\n",
       "      <td>861eff4711a542e4b93843c6dd7febb0</td>\n",
       "      <td>14409</td>\n",
       "      <td>franca</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18955e83d337fd6b2def6b18a428ac77</td>\n",
       "      <td>290c77bc529b7ac935b93aa66c333dc3</td>\n",
       "      <td>9790</td>\n",
       "      <td>sao bernardo do campo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4e7b3e00288586ebd08712fdd0374a03</td>\n",
       "      <td>060e732b5b29e8181a18229c7b0b2b5e</td>\n",
       "      <td>1151</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b2b6027bc5c5109e529d4dc6358b12c3</td>\n",
       "      <td>259dac757896d24d7702b9acbbff3f3c</td>\n",
       "      <td>8775</td>\n",
       "      <td>mogi das cruzes</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4f2d8ab171c80ec8364f7c12e35b23ad</td>\n",
       "      <td>345ecd01c38d18a9036ed96c73b8d066</td>\n",
       "      <td>13056</td>\n",
       "      <td>campinas</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        customer_id                customer_unique_id  \\\n",
       "0  06b8999e2fba1a1fbc88172c00ba8bc7  861eff4711a542e4b93843c6dd7febb0   \n",
       "1  18955e83d337fd6b2def6b18a428ac77  290c77bc529b7ac935b93aa66c333dc3   \n",
       "2  4e7b3e00288586ebd08712fdd0374a03  060e732b5b29e8181a18229c7b0b2b5e   \n",
       "3  b2b6027bc5c5109e529d4dc6358b12c3  259dac757896d24d7702b9acbbff3f3c   \n",
       "4  4f2d8ab171c80ec8364f7c12e35b23ad  345ecd01c38d18a9036ed96c73b8d066   \n",
       "\n",
       "   customer_zip_code_prefix          customer_city customer_state  \n",
       "0                     14409                 franca             SP  \n",
       "1                      9790  sao bernardo do campo             SP  \n",
       "2                      1151              sao paulo             SP  \n",
       "3                      8775        mogi das cruzes             SP  \n",
       "4                     13056               campinas             SP  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading head and tail of the customer dataframe\n",
    "customers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "69fc0c67",
   "metadata": {
    "scrolled": false
   },
   "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>customer_id</th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>customer_zip_code_prefix</th>\n",
       "      <th>customer_city</th>\n",
       "      <th>customer_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99436</th>\n",
       "      <td>17ddf5dd5d51696bb3d7c6291687be6f</td>\n",
       "      <td>1a29b476fee25c95fbafc67c5ac95cf8</td>\n",
       "      <td>3937</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99437</th>\n",
       "      <td>e7b71a9017aa05c9a7fd292d714858e8</td>\n",
       "      <td>d52a67c98be1cf6a5c84435bd38d095d</td>\n",
       "      <td>6764</td>\n",
       "      <td>taboao da serra</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99438</th>\n",
       "      <td>5e28dfe12db7fb50a4b2f691faecea5e</td>\n",
       "      <td>e9f50caf99f032f0bf3c55141f019d99</td>\n",
       "      <td>60115</td>\n",
       "      <td>fortaleza</td>\n",
       "      <td>CE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99439</th>\n",
       "      <td>56b18e2166679b8a959d72dd06da27f9</td>\n",
       "      <td>73c2643a0a458b49f58cea58833b192e</td>\n",
       "      <td>92120</td>\n",
       "      <td>canoas</td>\n",
       "      <td>RS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99440</th>\n",
       "      <td>274fa6071e5e17fe303b9748641082c8</td>\n",
       "      <td>84732c5050c01db9b23e19ba39899398</td>\n",
       "      <td>6703</td>\n",
       "      <td>cotia</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            customer_id                customer_unique_id  \\\n",
       "99436  17ddf5dd5d51696bb3d7c6291687be6f  1a29b476fee25c95fbafc67c5ac95cf8   \n",
       "99437  e7b71a9017aa05c9a7fd292d714858e8  d52a67c98be1cf6a5c84435bd38d095d   \n",
       "99438  5e28dfe12db7fb50a4b2f691faecea5e  e9f50caf99f032f0bf3c55141f019d99   \n",
       "99439  56b18e2166679b8a959d72dd06da27f9  73c2643a0a458b49f58cea58833b192e   \n",
       "99440  274fa6071e5e17fe303b9748641082c8  84732c5050c01db9b23e19ba39899398   \n",
       "\n",
       "       customer_zip_code_prefix    customer_city customer_state  \n",
       "99436                      3937        sao paulo             SP  \n",
       "99437                      6764  taboao da serra             SP  \n",
       "99438                     60115        fortaleza             CE  \n",
       "99439                     92120           canoas             RS  \n",
       "99440                      6703            cotia             SP  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading the tail\n",
    "customers.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5f8179a3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99441 entries, 0 to 99440\n",
      "Data columns (total 5 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   customer_id               99441 non-null  object\n",
      " 1   customer_unique_id        99441 non-null  object\n",
      " 2   customer_zip_code_prefix  99441 non-null  int64 \n",
      " 3   customer_city             99441 non-null  object\n",
      " 4   customer_state            99441 non-null  object\n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "#getting overview of datasets that we have.\n",
    "customers.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "57d4025e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                 0\n",
       "customer_unique_id          0\n",
       "customer_zip_code_prefix    0\n",
       "customer_city               0\n",
       "customer_state              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the null values\n",
    "customers.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aef2f9c9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking duplicates across rows\n",
    "customers.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "50fc6400",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking duplicates across columns\n",
    "customers.T.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6211a25a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99441"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the unique values in column customer unique id and customer id\n",
    "customers['customer_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4ac4c3b2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "96096"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the number of unique customers\n",
    "customers['customer_unique_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0d50f1ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3345"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Repeat customers\n",
    "99441-96096"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "67abf67f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9663619633752677"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#percentage of repeat customers\n",
    "96096/99441"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31b016db",
   "metadata": {},
   "source": [
    "#### Quick findings:\n",
    "\n",
    "- Customers dataset has information about geolocation of customers.\n",
    "- We have a total of 99441 customer ids which is the primary key for this table. These customers ids are created when a user makes a purchase. They are actually transaction ids.\n",
    "- We have a total of 96096 unique customer ids. It shows that we have around 96.6 % of new customers. Only 3.4% of the customers have made repeat purchase from the olist platform. It is because olist was founded in 2015 and they started selling online in 2016. The data we downloaded from Kaggel is from 2016 to 2018, when it was fairly new thus we only have new customers.\n",
    "- This dataset has **four** columns of object datatype and **one** column with numeric datatype.\n",
    "- There are no duplicates across rows or columns.\n",
    "- There is no null value."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34338b68",
   "metadata": {},
   "source": [
    "## 3.2 Geoloc dataset\n",
    "<a id = 'geoloc_dataset' ></a>\n",
    "We can look at the Geoloc dataset and see what important columns we can utilize later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9fed81dc",
   "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>geolocation_zip_code_prefix</th>\n",
       "      <th>geolocation_lat</th>\n",
       "      <th>geolocation_lng</th>\n",
       "      <th>geolocation_city</th>\n",
       "      <th>geolocation_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1037</td>\n",
       "      <td>-23.545621</td>\n",
       "      <td>-46.639292</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1046</td>\n",
       "      <td>-23.546081</td>\n",
       "      <td>-46.644820</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1046</td>\n",
       "      <td>-23.546129</td>\n",
       "      <td>-46.642951</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1041</td>\n",
       "      <td>-23.544392</td>\n",
       "      <td>-46.639499</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1035</td>\n",
       "      <td>-23.541578</td>\n",
       "      <td>-46.641607</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   geolocation_zip_code_prefix  geolocation_lat  geolocation_lng  \\\n",
       "0                         1037       -23.545621       -46.639292   \n",
       "1                         1046       -23.546081       -46.644820   \n",
       "2                         1046       -23.546129       -46.642951   \n",
       "3                         1041       -23.544392       -46.639499   \n",
       "4                         1035       -23.541578       -46.641607   \n",
       "\n",
       "  geolocation_city geolocation_state  \n",
       "0        sao paulo                SP  \n",
       "1        sao paulo                SP  \n",
       "2        sao paulo                SP  \n",
       "3        sao paulo                SP  \n",
       "4        sao paulo                SP  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading head and tail of the geoloc dataframe\n",
    "geoloc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1a970e0c",
   "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>geolocation_zip_code_prefix</th>\n",
       "      <th>geolocation_lat</th>\n",
       "      <th>geolocation_lng</th>\n",
       "      <th>geolocation_city</th>\n",
       "      <th>geolocation_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1000158</th>\n",
       "      <td>99950</td>\n",
       "      <td>-28.068639</td>\n",
       "      <td>-52.010705</td>\n",
       "      <td>tapejara</td>\n",
       "      <td>RS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000159</th>\n",
       "      <td>99900</td>\n",
       "      <td>-27.877125</td>\n",
       "      <td>-52.224882</td>\n",
       "      <td>getulio vargas</td>\n",
       "      <td>RS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000160</th>\n",
       "      <td>99950</td>\n",
       "      <td>-28.071855</td>\n",
       "      <td>-52.014716</td>\n",
       "      <td>tapejara</td>\n",
       "      <td>RS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000161</th>\n",
       "      <td>99980</td>\n",
       "      <td>-28.388932</td>\n",
       "      <td>-51.846871</td>\n",
       "      <td>david canabarro</td>\n",
       "      <td>RS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000162</th>\n",
       "      <td>99950</td>\n",
       "      <td>-28.070104</td>\n",
       "      <td>-52.018658</td>\n",
       "      <td>tapejara</td>\n",
       "      <td>RS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         geolocation_zip_code_prefix  geolocation_lat  geolocation_lng  \\\n",
       "1000158                        99950       -28.068639       -52.010705   \n",
       "1000159                        99900       -27.877125       -52.224882   \n",
       "1000160                        99950       -28.071855       -52.014716   \n",
       "1000161                        99980       -28.388932       -51.846871   \n",
       "1000162                        99950       -28.070104       -52.018658   \n",
       "\n",
       "        geolocation_city geolocation_state  \n",
       "1000158         tapejara                RS  \n",
       "1000159   getulio vargas                RS  \n",
       "1000160         tapejara                RS  \n",
       "1000161  david canabarro                RS  \n",
       "1000162         tapejara                RS  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading the tail\n",
    "geoloc.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "386053b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000163 entries, 0 to 1000162\n",
      "Data columns (total 5 columns):\n",
      " #   Column                       Non-Null Count    Dtype  \n",
      "---  ------                       --------------    -----  \n",
      " 0   geolocation_zip_code_prefix  1000163 non-null  int64  \n",
      " 1   geolocation_lat              1000163 non-null  float64\n",
      " 2   geolocation_lng              1000163 non-null  float64\n",
      " 3   geolocation_city             1000163 non-null  object \n",
      " 4   geolocation_state            1000163 non-null  object \n",
      "dtypes: float64(2), int64(1), object(2)\n",
      "memory usage: 38.2+ MB\n"
     ]
    }
   ],
   "source": [
    "#getting overview of datasets that we have.\n",
    "geoloc.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "db7f3572",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "geolocation_zip_code_prefix    0\n",
       "geolocation_lat                0\n",
       "geolocation_lng                0\n",
       "geolocation_city               0\n",
       "geolocation_state              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the null values\n",
    "geoloc.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1cd6c95e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "261831"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking duplicates across rows\n",
    "geoloc.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fbed0f6",
   "metadata": {},
   "source": [
    "We have a huge number of duplicates here. We can drop the duplicates only keeping the first of the matching row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1c7b8a22",
   "metadata": {},
   "outputs": [],
   "source": [
    "#checking duplicates across rows by keeping only the frist value and dropping the next matching value in place\n",
    "geoloc.drop_duplicates(keep='first', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "15302876",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking duplicates across rows\n",
    "geoloc.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "275d569e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#checking duplicates across columns \n",
    "#it has been commented as it took a lot of time to get the result\n",
    "#during first check we found no duplicates across columns\n",
    "# geoloc.T.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "059a31a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 738332 entries, 0 to 1000161\n",
      "Data columns (total 5 columns):\n",
      " #   Column                       Non-Null Count   Dtype  \n",
      "---  ------                       --------------   -----  \n",
      " 0   geolocation_zip_code_prefix  738332 non-null  int64  \n",
      " 1   geolocation_lat              738332 non-null  float64\n",
      " 2   geolocation_lng              738332 non-null  float64\n",
      " 3   geolocation_city             738332 non-null  object \n",
      " 4   geolocation_state            738332 non-null  object \n",
      "dtypes: float64(2), int64(1), object(2)\n",
      "memory usage: 33.8+ MB\n"
     ]
    }
   ],
   "source": [
    "geoloc.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f3f067c7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19015"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the unique values in geolocation_zip_code_prefix\n",
    "geoloc['geolocation_zip_code_prefix'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "447618df",
   "metadata": {},
   "source": [
    "Out of 738332 rows only 19015 are the unique zip code prefix. A zip code postfix can define a complete zip code but we don't have any. For sanity check we can check how many different latitude and longitude values do we have for a particular zip code prefix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d335dad9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38400    779\n",
       "35500    751\n",
       "11680    727\n",
       "11740    678\n",
       "36400    627\n",
       "        ... \n",
       "90038      1\n",
       "90002      1\n",
       "29174      1\n",
       "72230      1\n",
       "45824      1\n",
       "Name: geolocation_zip_code_prefix, Length: 19015, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking no of rows with same zip code.\n",
    "geoloc['geolocation_zip_code_prefix'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9c5c7290",
   "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>geolocation_zip_code_prefix</th>\n",
       "      <th>geolocation_lat</th>\n",
       "      <th>geolocation_lng</th>\n",
       "      <th>geolocation_city</th>\n",
       "      <th>geolocation_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>645450</th>\n",
       "      <td>38400</td>\n",
       "      <td>-18.915960</td>\n",
       "      <td>-48.278974</td>\n",
       "      <td>uberlandia</td>\n",
       "      <td>MG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645456</th>\n",
       "      <td>38400</td>\n",
       "      <td>-18.900442</td>\n",
       "      <td>-48.260759</td>\n",
       "      <td>uberlandia</td>\n",
       "      <td>MG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645460</th>\n",
       "      <td>38400</td>\n",
       "      <td>-18.913015</td>\n",
       "      <td>-48.262132</td>\n",
       "      <td>uberlandia</td>\n",
       "      <td>MG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645461</th>\n",
       "      <td>38400</td>\n",
       "      <td>-18.922381</td>\n",
       "      <td>-48.282111</td>\n",
       "      <td>uberlandia</td>\n",
       "      <td>MG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645471</th>\n",
       "      <td>38400</td>\n",
       "      <td>-18.922160</td>\n",
       "      <td>-48.271125</td>\n",
       "      <td>uberlandia</td>\n",
       "      <td>MG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650433</th>\n",
       "      <td>38400</td>\n",
       "      <td>-18.913148</td>\n",
       "      <td>-48.288200</td>\n",
       "      <td>uberlândia</td>\n",
       "      <td>MG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650434</th>\n",
       "      <td>38400</td>\n",
       "      <td>-18.915238</td>\n",
       "      <td>-48.297562</td>\n",
       "      <td>uberlandia</td>\n",
       "      <td>MG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650439</th>\n",
       "      <td>38400</td>\n",
       "      <td>-18.898682</td>\n",
       "      <td>-48.260058</td>\n",
       "      <td>uberlândia</td>\n",
       "      <td>MG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650440</th>\n",
       "      <td>38400</td>\n",
       "      <td>-18.887050</td>\n",
       "      <td>-48.265336</td>\n",
       "      <td>uberlandia</td>\n",
       "      <td>MG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650446</th>\n",
       "      <td>38400</td>\n",
       "      <td>-18.920965</td>\n",
       "      <td>-48.297735</td>\n",
       "      <td>uberlândia</td>\n",
       "      <td>MG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>779 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        geolocation_zip_code_prefix  geolocation_lat  geolocation_lng  \\\n",
       "645450                        38400       -18.915960       -48.278974   \n",
       "645456                        38400       -18.900442       -48.260759   \n",
       "645460                        38400       -18.913015       -48.262132   \n",
       "645461                        38400       -18.922381       -48.282111   \n",
       "645471                        38400       -18.922160       -48.271125   \n",
       "...                             ...              ...              ...   \n",
       "650433                        38400       -18.913148       -48.288200   \n",
       "650434                        38400       -18.915238       -48.297562   \n",
       "650439                        38400       -18.898682       -48.260058   \n",
       "650440                        38400       -18.887050       -48.265336   \n",
       "650446                        38400       -18.920965       -48.297735   \n",
       "\n",
       "       geolocation_city geolocation_state  \n",
       "645450       uberlandia                MG  \n",
       "645456       uberlandia                MG  \n",
       "645460       uberlandia                MG  \n",
       "645461       uberlandia                MG  \n",
       "645471       uberlandia                MG  \n",
       "...                 ...               ...  \n",
       "650433       uberlândia                MG  \n",
       "650434       uberlandia                MG  \n",
       "650439       uberlândia                MG  \n",
       "650440       uberlandia                MG  \n",
       "650446       uberlândia                MG  \n",
       "\n",
       "[779 rows x 5 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#quickly checking latitude and logitude values for a zipcode 38400\n",
    "geoloc[geoloc['geolocation_zip_code_prefix']==38400]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cd9159fe",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Latitude and Longitude of zip code prefix = 38400')\n",
    "sns.scatterplot(data= geoloc[geoloc['geolocation_zip_code_prefix']==38400], y='geolocation_lat', x='geolocation_lng' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c47bd960",
   "metadata": {},
   "source": [
    "#### Quick findings:\n",
    "\n",
    "- Geoloc dataset has information about latitudes and longitudes of various cities and states of Brazil.\n",
    "- We have a total of 19015 unique zip code prefix and there could be multiple latitude and longitude associated with that code that differentiate different locations within that zip code.\n",
    "    - For example for zip code prefix 38400 we have **779 rows with same city and state value but slightly different latitutde and longitude values.**\n",
    "- We found some duplicate values and deleted them.\n",
    "- This dataset has **two** columns of object datatype and **three** column with numeric datatype.\n",
    "- There is no null value."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9a7e313",
   "metadata": {},
   "source": [
    "**Since we don't have the complete zip code in both geoloc and customer dataset, we can take mean of the latitude and longitude coordinates for each zip code prefix and save it in separate dataframe.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e6fdba9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating another dataframe which has zip code prefix and mean values for latitude and longitude\n",
    "coordinates= geoloc.groupby('geolocation_zip_code_prefix')[['geolocation_lat', 'geolocation_lng']].mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e45b3ef1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19015 entries, 0 to 19014\n",
      "Data columns (total 3 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   geolocation_zip_code_prefix  19015 non-null  int64  \n",
      " 1   geolocation_lat              19015 non-null  float64\n",
      " 2   geolocation_lng              19015 non-null  float64\n",
      "dtypes: float64(2), int64(1)\n",
      "memory usage: 445.8 KB\n"
     ]
    }
   ],
   "source": [
    "coordinates.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53c377e3",
   "metadata": {},
   "source": [
    "### Merging the coordinates dataframe with the customer dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d9698fdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merging coordinates and customer\n",
    "customer_loc=pd.merge(left=customers, right=coordinates, left_on='customer_zip_code_prefix', \n",
    "                      right_on='geolocation_zip_code_prefix', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "419ce824",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 99441 entries, 0 to 99440\n",
      "Data columns (total 8 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   customer_id                  99441 non-null  object \n",
      " 1   customer_unique_id           99441 non-null  object \n",
      " 2   customer_zip_code_prefix     99441 non-null  int64  \n",
      " 3   customer_city                99441 non-null  object \n",
      " 4   customer_state               99441 non-null  object \n",
      " 5   geolocation_zip_code_prefix  99163 non-null  float64\n",
      " 6   geolocation_lat              99163 non-null  float64\n",
      " 7   geolocation_lng              99163 non-null  float64\n",
      "dtypes: float64(3), int64(1), object(4)\n",
      "memory usage: 6.8+ MB\n"
     ]
    }
   ],
   "source": [
    "#checking info\n",
    "customer_loc.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "becf3576",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop unnecesary column\n",
    "customer_loc.drop(columns=['geolocation_zip_code_prefix'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "773b5e04",
   "metadata": {
    "scrolled": false
   },
   "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>customer_id</th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>customer_zip_code_prefix</th>\n",
       "      <th>customer_city</th>\n",
       "      <th>customer_state</th>\n",
       "      <th>geolocation_lat</th>\n",
       "      <th>geolocation_lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06b8999e2fba1a1fbc88172c00ba8bc7</td>\n",
       "      <td>861eff4711a542e4b93843c6dd7febb0</td>\n",
       "      <td>14409</td>\n",
       "      <td>franca</td>\n",
       "      <td>SP</td>\n",
       "      <td>-20.499273</td>\n",
       "      <td>-47.396658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18955e83d337fd6b2def6b18a428ac77</td>\n",
       "      <td>290c77bc529b7ac935b93aa66c333dc3</td>\n",
       "      <td>9790</td>\n",
       "      <td>sao bernardo do campo</td>\n",
       "      <td>SP</td>\n",
       "      <td>-23.728396</td>\n",
       "      <td>-46.542250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4e7b3e00288586ebd08712fdd0374a03</td>\n",
       "      <td>060e732b5b29e8181a18229c7b0b2b5e</td>\n",
       "      <td>1151</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "      <td>-23.531309</td>\n",
       "      <td>-46.656690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b2b6027bc5c5109e529d4dc6358b12c3</td>\n",
       "      <td>259dac757896d24d7702b9acbbff3f3c</td>\n",
       "      <td>8775</td>\n",
       "      <td>mogi das cruzes</td>\n",
       "      <td>SP</td>\n",
       "      <td>-23.500670</td>\n",
       "      <td>-46.186348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4f2d8ab171c80ec8364f7c12e35b23ad</td>\n",
       "      <td>345ecd01c38d18a9036ed96c73b8d066</td>\n",
       "      <td>13056</td>\n",
       "      <td>campinas</td>\n",
       "      <td>SP</td>\n",
       "      <td>-22.975708</td>\n",
       "      <td>-47.143140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        customer_id                customer_unique_id  \\\n",
       "0  06b8999e2fba1a1fbc88172c00ba8bc7  861eff4711a542e4b93843c6dd7febb0   \n",
       "1  18955e83d337fd6b2def6b18a428ac77  290c77bc529b7ac935b93aa66c333dc3   \n",
       "2  4e7b3e00288586ebd08712fdd0374a03  060e732b5b29e8181a18229c7b0b2b5e   \n",
       "3  b2b6027bc5c5109e529d4dc6358b12c3  259dac757896d24d7702b9acbbff3f3c   \n",
       "4  4f2d8ab171c80ec8364f7c12e35b23ad  345ecd01c38d18a9036ed96c73b8d066   \n",
       "\n",
       "   customer_zip_code_prefix          customer_city customer_state  \\\n",
       "0                     14409                 franca             SP   \n",
       "1                      9790  sao bernardo do campo             SP   \n",
       "2                      1151              sao paulo             SP   \n",
       "3                      8775        mogi das cruzes             SP   \n",
       "4                     13056               campinas             SP   \n",
       "\n",
       "   geolocation_lat  geolocation_lng  \n",
       "0       -20.499273       -47.396658  \n",
       "1       -23.728396       -46.542250  \n",
       "2       -23.531309       -46.656690  \n",
       "3       -23.500670       -46.186348  \n",
       "4       -22.975708       -47.143140  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading the head\n",
    "customer_loc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7ca37695",
   "metadata": {},
   "outputs": [],
   "source": [
    "#renaming the coluumn name\n",
    "customer_loc.rename(columns = {'geolocation_lat':'customer_lat', 'geolocation_lng': 'customer_lng' }, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8c89ca3d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                   0\n",
       "customer_unique_id            0\n",
       "customer_zip_code_prefix      0\n",
       "customer_city                 0\n",
       "customer_state                0\n",
       "customer_lat                278\n",
       "customer_lng                278\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the null values\n",
    "customer_loc.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0d1f20c1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#plotting customer citu and state to see city and state with null values\n",
    "# customer_loc[customer_loc['customer_lat'].isnull()][['customer_city', 'customer_state']].value_counts().plot(kind='bar', figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "66911b19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=customer_loc, y='customer_lat', x='customer_lng')\n",
    "plt.title('Scatter plot:Latitude and Longitudes for customer location')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "877bd0b9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#separating the coordinates which have NAN\n",
    "nan_coord=customer_loc[customer_loc['customer_lat'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d5a56d4",
   "metadata": {},
   "source": [
    "The approach we are using to fill NANs:\n",
    "\n",
    "- We will aggreagate the columns on the basis of customer city and state and we will calculate the mean of the available latitude and longitude for that city state combination.\n",
    "- Using these calaculated latitude and longitudes we will fill the missing latitude and longitude where the city and state is matching. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f954420d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#a dataframe to save the mean of latitude and longitude for city-state combination\n",
    "d_tally= customer_loc.groupby(['customer_city', 'customer_state'])[['customer_lat', 'customer_lng'] ].mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "dd5c42d4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_city      0\n",
       "customer_state     0\n",
       "customer_lat      51\n",
       "customer_lng      51\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for null\n",
    "d_tally.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49139717",
   "metadata": {},
   "source": [
    "We thought that we will not have any nulls here, but we have 51 null values. We can fill these values using the mean of latitude and longitude for group i.e customer state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cec53902",
   "metadata": {
    "scrolled": true
   },
   "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>customer_city</th>\n",
       "      <th>customer_state</th>\n",
       "      <th>customer_lat</th>\n",
       "      <th>customer_lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>abadia dos dourados</td>\n",
       "      <td>MG</td>\n",
       "      <td>-18.477752</td>\n",
       "      <td>-47.406319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abadiania</td>\n",
       "      <td>GO</td>\n",
       "      <td>-16.193718</td>\n",
       "      <td>-48.709452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abaete</td>\n",
       "      <td>MG</td>\n",
       "      <td>-19.158364</td>\n",
       "      <td>-45.446897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abaetetuba</td>\n",
       "      <td>PA</td>\n",
       "      <td>-1.723644</td>\n",
       "      <td>-48.881349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abaiara</td>\n",
       "      <td>CE</td>\n",
       "      <td>-7.355970</td>\n",
       "      <td>-39.043267</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         customer_city customer_state  customer_lat  customer_lng\n",
       "0  abadia dos dourados             MG    -18.477752    -47.406319\n",
       "1            abadiania             GO    -16.193718    -48.709452\n",
       "2               abaete             MG    -19.158364    -45.446897\n",
       "3           abaetetuba             PA     -1.723644    -48.881349\n",
       "4              abaiara             CE     -7.355970    -39.043267"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#d_tally head\n",
    "d_tally.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "68f6be50",
   "metadata": {},
   "outputs": [],
   "source": [
    "# grouping by customer state and imputing the misisng latitude by mean of that group or customer state\n",
    "d_tally['customer_lat']=d_tally.groupby('customer_state', sort=False)['customer_lat'].transform(lambda x: x.fillna(x.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "51d81e00",
   "metadata": {},
   "outputs": [],
   "source": [
    "# grouping by customer state and imputing the misisng longitude by mean of that group or customer state\n",
    "d_tally['customer_lng']=d_tally.groupby('customer_state', sort=False)['customer_lng'].transform(lambda x: x.fillna(x.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "45644475",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_city     0\n",
       "customer_state    0\n",
       "customer_lat      0\n",
       "customer_lng      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for missing values\n",
    "d_tally.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3ce6520",
   "metadata": {},
   "source": [
    "Now, we can fill the missing latitude and longitudes using the latitude and longitude value available in our d_tally table where the city matches with the city of customer table. I am proceeding with this step on the assumption that none of the cities from different states have same name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6c953c6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in nan_coord.index:\n",
    "    customer_loc.loc[i, 'customer_lat']=d_tally[d_tally['customer_city']==customer_loc.loc[i, 'customer_city']]['customer_lat'].values[0]\n",
    "    customer_loc.loc[i, 'customer_lng']=d_tally[d_tally['customer_city']==customer_loc.loc[i, 'customer_city']]['customer_lng'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8126256b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                 0\n",
       "customer_unique_id          0\n",
       "customer_zip_code_prefix    0\n",
       "customer_city               0\n",
       "customer_state              0\n",
       "customer_lat                0\n",
       "customer_lng                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking if we still have any null value\n",
    "customer_loc.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ba7adfae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAApwAAAHKCAYAAACudJvSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAADVBklEQVR4nOzdd3iTVfvA8W9mk7RABwUElNmW2VJekL1xsbeLpaDIcgsoirhABV6RLcPXgQMERBR9VfRFVBBFkaFsgZ+KQKGLtkkzf3+EJyTNKqMN0PtzXV7SrOfkJHly5z7n3EflcrlcCCGEEEIIUULUkW6AEEIIIYS4uknAKYQQQgghSpQEnEIIIYQQokRJwCmEEEIIIUqUBJxCCCGEEKJEScAphBBCCCFKlAScQgghhBCiREnAKYQQQgghStQVEXAWrU1/JdeqL+22hzveldyXZZm8bpEl/X9hrsR+uxLbfLEu9DmXxb4SxXfeAef+/ft56KGHaNOmDY0aNaJt27Y8+OCD/P7775e8cVarlenTp/Pxxx97Ljt48CC33377JT/W+di6dSspKSls3bq12PcJ9FwC6dy5M5MmTbrYJnL8+HFGjRrF33//HfSxFy5cyLJlyy76WACTJk2ic+fOl+SxSlK4/r2Q17akrVmzhpSUFP766y/g0n4G/vrrL1JSUlizZs0lebxQtm7dyk033USjRo0YMWJEiR+vpFzKz01pGzJkCEOGDCn14wY6/12O54yZM2fSokULmjRpwtq1a/nqq6+YOHFiiR0vUq9HMLm5uUycOJFt27ad1/2K830TaXl5eYwePZq0tDSaN2/OkSNHSvR43s+/tM6zpXk+vxDnFXAeOHCAW2+9lczMTCZPnszrr7/OhAkTOHbsGLfeeiu//vrrJW3cyZMneeONN7Db7Z7LPvvsM7Zv335Jj1MaAj2XkrR582Y2btzoc9m8efMYM2aM5+/Zs2djNptLpT3iwnXs2JEVK1ZQqVIl4Mr9DLz00ks4nU4WL17MhAkTIt2cCyafm/MX6Pw3ZswY5s2bF8FW+dq/fz9LlizhxhtvZOnSpbRv35433niDf/75p8SO+fTTT/P000+X2OOfrz179rB27VqcTud53a843zeRtnbtWr7++msmTJjA/PnzqV69eqSbVOZoz+fG//nPf4iNjWXp0qXodDrP5V27duWWW25hwYIFLF68+JI3UlwaDRo0iHQTxAWIj48nPj4+0s24aNnZ2TRv3pzWrVtHuiniMnDddddFugk+srOzAejevTvNmjUrlWPWrVu3VI4TCZfb943y+t5xxx2oVKrINqaMOq8M56lTpwD/eRomk4nHH3+cW265xefy9evX069fP9LS0ujYsSMzZszAarV6rt+wYQN33HEH6enpNGrUiJtvvpnly5cD7tRwly5dAHj88cfp3Lkzc+fO9fwiTklJYe7cuQCerMkNN9xAo0aNuOmmm3j77bd92jJkyBAeffRR7r//fpo2bcq9994b8DnOnTuXzp0787///Y+bb76ZtLQ0Bg4cyJYtW0L2za5duxgxYgQtWrSgadOm3HfffRw4cCDoc4Fzw7fnm/52OBwsXryYHj16kJqaSpMmTbjttts8bVyzZg2PP/44AF26dPGk9b1T/CkpKYD7V6jy77lz53r+7c27rwFycnJ4/PHHadGiBc2bN2fGjBkBfxFv2LCBfv360bhxY9q0acPzzz9PQUFByOdmsViYNWsWN954I40aNaJp06bcdddd7Nmzx3ObSZMmMXz4cFavXu0Zou3VqxfffPONz2Pt3buXu+66i/T0dDp16sS6detCd+x5OHPmDNOnT6dr1640btyYHj16sGrVKp/bdO7cmTlz5vDSSy/RunVrUlNTGTFiBIcPH/a53Ycffki3bt1o3LgxvXr1YsuWLTRo0MDzvvAeUg/2GSj6GkHg1/OLL76gV69epKam0rdvX/bu3ev33LKzs5kyZQqtW7emcePGDBo0yO/9v3nzZm699VbS09Np3rw5Y8aM4Y8//gjYV8owz99//83atWt9piyE+tzAuc/I+++/T6dOnWjdujXfffddwOPYbDbmz59P165dSU1NpXv37qxevdpzfaDhy6JTKJxOJ6+++iqdO3emUaNGdO7cmX//+9/YbDZPP4Pv5+Z8nseWLVsYMmQIqampdOzYkQ8++ICTJ08ybtw40tPT6dChA2+88cZ5vx4pKSnMmzeP/v37869//YsFCxYE7KOiCgsLmT9/PjfffDONGzfmxhtvZPHixX6f50t9Lgf/IXWHw8E777xDz549Pf0zc+ZMCgsLPbcp7me/qHDnzLlz53reG8OGDaNz584MGTKEH3/8kR9//NHnPXIpX4+i78mUlBTee+89Jk2axL/+9S+uv/56nn/+eSwWCy+99BItW7akRYsWTJ482adfUlJSWL58ORMnTiQ9PZ3WrVt77hfsWOD7/t+6dStDhw4FYOjQoZ7bXorvG7i058yiwr2PhwwZ4jk/1qtXL+hQ/9y5c7nhhhvYuHEjPXv29MQTH374YcA+83Yx0yMWLVpEw4YNyczM9Ln83XffpUGDBmRkZADw008/MWLECJo3b+45P82dOzdoRrrodCxF0demODHUn3/+yejRo2nRogVpaWnceuutYT93RZ1XwNmxY0eOHTvGbbfdxjvvvMOhQ4c8wefNN99M3759Pbd9//33efjhh6lfvz7z5s1j1KhRvPvuu0ydOhWAjRs3MnbsWBo2bMiCBQuYO3cu1apV47nnnuOXX36hUqVKni/W0aNHM2/ePAYOHMiAAQMAWLFiBQMHDgRg6tSpzJkzh169erFo0SJuvvlmpk2bxvz5833a/9lnn6HT6Zg/f77ngxVIZmYmEydO5I477uDVV1/FaDRyzz33sHv37oC3/+GHH7j99ttxOp288MILPP/88/zzzz/cdtttHDp0KOBzAWjYsCErVqygY8eO5/MyMHPmTObPn8+tt97K0qVLefbZZ8nKyuKBBx6goKCAjh07Mnr0aCD4sMaKFSsAGDBggOffxeF0Ohk5ciQbN27k0Ucf5aWXXmL79u18+umnPrf7+OOPGTt2LLVr12b+/PmMGzeOdevWMWbMmJATyydMmMCqVau49957ef3115k0aZJn3rD3/Xbv3s2yZcu4//77mT9/Plqtlvvvv5+cnBwATpw4weDBg8nJyWHGjBk88MADzJw5kxMnThT7uQZjsVi44447WLduHXfffTcLFizgX//6F5MnT2bRokU+t33rrbf4448/mD59Os8//zy7d+/2+aCvXbuWSZMm0bRpUxYsWMBNN93EmDFjcDgcAY8d7DNQHF9//TX3338/SUlJzJs3j1tuuYXHHnvM5zaFhYUMGzaMr776ioceeoh58+ZRpUoVRo4c6fmCUU48DRs2ZOHChTz//PP88ccf3HvvvQFPfJUqVWLFihUkJibSoUMHVqxYQcOGDcN+bry98sorTJw4kYkTJ9KkSZOAz2/ixIksXryYAQMG8Nprr9GhQweeeOIJ1q5dW+w+WrJkCe+88w5jx47l9ddf5/bbb2fp0qWe1zXQ5+Z8nsfDDz9M586dWbRoETVr1uTpp59m6NChJCcnM2fOHBo2bMj06dPZuXNnsV8PxcKFC7npppv497//7QnwQnG5XNx3330sXbqUAQMGeM6ds2fP9hnmLYlzeSBTpkxh2rRpdO7cmYULF3LnnXeyfPlyv3NGuM9+IOHOmQMHDmTKlCmedsybN4+nn36aBg0a0KBBA897tiRfD++26vV65s2bR+/evXn77bfp06cP//zzDzNmzOC2225j1apVfgHBq6++yunTp5k9ezYjR45k5cqVfp/vUBo2bOjTB8p74FJ831zKc2ZRxXkfP/300z7nzVBD/RkZGTz77LMMHTqUxYsXU716dSZNmuT3Wb6UevXqhcPh4IsvvvC5/JNPPqFVq1YkJiayd+9ehg8fTmxsLK+88goLFy6kadOmzJs3j/Xr11/U8cPFUE6nk1GjRlFQUMDLL7/MggULiI2NZcyYMRw9erTYxzmvIfU77riDjIwMli1bxrPPPgtAXFwcbdu2ZciQIaSlpXkap/xSeOGFFzz3Lyws5MMPP8RqtXLw4EH69OnD5MmTPdenp6fTokULfvrpJ5o2bUr9+vUB99CLkp6vUqUKgOdL5/Dhw6xcuZKHH37Yk7Vs27YtKpWK1157jTvuuIO4uDgA1Go1zz33HCaTKeTzNJvNTJ06lT59+gDQsmVLunbtyuLFi5kzZ47f7WfNmsW1117L0qVL0Wg0njbccMMNzJ07l9mzZwd8LjExMUG/PEM5efIkDz30kM+vKYPBwPjx49m3bx/p6eme4ar69esHnKuiHLdKlSrn1YZNmzaxc+dOXnvtNU+g3LJlS59MhcvlYubMmbRr146ZM2d6Lq9ZsybDhw/nm2++CRhkW61W8vPzeeqpp+jWrRsA119/Pfn5+bz44otkZGR45jGeOXOGNWvWeJ6nyWRi8ODB/PDDD9x0002e+WJLliwhISEBgFq1ajFo0KBiP9dg1qxZw/79+3n33Xf517/+BUC7du2w2+0sWLCA2267jdjYWADKly/PggULPO+L//u//2Pu3LlkZWURFxfHq6++SqdOnXj++ec9j6PT6Zg1a1bAY1epUsXvM1Bc8+fPp2HDhp7Hbt++PYDPsT766CP27t3LypUrPZ/n9u3bM2TIEGbOnMnq1avZuXMnFouFUaNGUblyZQCuueYavvrqKwoKCoiJifE5rl6vp0mTJuj1euLj4z3tLs7nRnHbbbdx8803B31uBw4cYP369UyePNnzY7JVq1YcO3aMrVu3ej7L4fz44480bNiQ/v37A+73n9Fo9DynQJ+b83ke/fv356677gLc79lbb72V1NRU7r//fgAaNWrEV199xS+//EJqamqxXg9Fampq0JGbQDZt2sTmzZuZMWMGvXr1AqBNmzYYDAZeffVVhg0bRu3atUvsXO7t4MGDrFq1igcffNATvLRp04ZKlSoxYcIENm3aRIcOHYDwn/1AinPOVIa369at63OOhnOv+8qVK0vs9VDUqVPH8/3avHlzVq1ahc1mY+bMmWi1Wtq1a8fXX3/NL7/84nO/+Ph4Fi1ahFarpUOHDqjVaqZPn86BAwdISkoKe9yYmBifPlD+fSm+by7lObOo4ryP69atW+zzptls5oUXXqBVq1aA+3urU6dOfPPNN9SpUydsP16IqlWr0rx5c9avX89tt90GwLFjx/jll194+eWXAfeIXevWrZkxYwZqtdrzPDdu3MhPP/1Ez549L+jYxYmh7HY7hw4d4r777vN8DlNTU5k3b55Ppj2c816l/sADD/Dtt98ya9YsBgwYQExMDB9//DG33norb775pucJnDp1iq5du/rcd/jw4Xz00Ufo9XpGjhzJSy+9REFBAXv37uWzzz7zzP9Uhq+K44cffsDlctG5c2fsdrvnv86dO1NYWMjPP//suW316tXDBpsAGo2G7t27e/42GAy0b9/e57EUBQUF7Nq1i27dunk+IOD+0HTq1KlEVjvPmjWL4cOHk5mZyfbt21mzZo1nuPh8+u5CbNu2DZ1O5wlWwH3CV96EAH/88QfHjx/3e02aN29OTEwM33//fcDH1uv1LFu2jG7dunHy5El++uknVqxYwf/+9z+/5xYfH+8zB0w5mSiLOX7++WeaNGniCTYB0tLSqFq16kX3wY8//ki1atU8J05Fr169KCwsZMeOHZ7LGjdu7PO+8G7n0aNHOXbsmF8g5f3eu1QsFgu//fabX6al6DSYLVu2kJiYSMOGDT2vm8PhoFOnTuzevZucnBzS0tKIiopiwIABTJ8+nc2bN1OvXj0eeughv2AzmPP93ASa6uFNWVV7ww03+Fw+e/Zspk+fXqw2AbRo0YLNmzdzxx138J///IdDhw4xePDgoAHr+T6P9PR0z78rVqwI4AlcAM8X6pkzZ4DivR6K5OTkYj9PcL+PNRqN58edQvnS3rp1a6mdy3/88UcAvy/N7t27o9FofPox3Gc/kEt1zizJ10Ph/R7RarXExcXRqFEjtNpz+aHY2FjPe0TRvXt3n9sowff5rjgv6lL03aU6ZwZ77HDv4/PlHZQqxw83Hexi9e7dm23btnHy5EnAPY3FaDR6zml9+vRhyZIl2Gw2Dhw4wIYNG5g7dy4Oh+OivveLE0NVrFiRunXr8tRTTzFp0iQ+/fRTXC4Xjz/++Hm9z88rw6moUKECPXr0oEePHgD8/vvvTJgwgZkzZ9KrVy/P5FzvL/uiMjMzefrpp9mwYQMqlYoaNWp43oznU8vLe6J3IN5DqMoJPpz4+HifRVHgfi6BhmzOnDmDy+UK+NgVK1b0OylcCrt27eKZZ55h165dGAwG6tatS7Vq1YCSr4OWk5NDbGys5xeWIjEx0fNv5TV55plneOaZZ/weQ/lABfLtt98ybdo0/vjjD6Kjo0lJSSE6OhrwfW5Go9HnfsokcGVINycnJ+Avbe92XqicnJygrze4S4sEa6fSb06n0zNfp+jn5FK0saicnBxcLpff4iMlY6zIzs4mIyODhg0bBnycjIwM6taty/Lly1m8eDErV67kjTfeoHz58txxxx088MADfu+NQM73cxPqXKK0uzi3C2fkyJFER0ezevVqXnrpJV588UWSk5N54oknPBkPb+f7PAIF5EXfI96K83pUqFDBc7zzkZOTQ1xcnE+QAufef2fOnCm1c7lybi363lcCLu9+DPfZD+RSnTNL8vVQnO97RFH0s6y8Zt7nowtxKfruUp0zgz12uPfx+fJug3L8kv5uvfnmm3nuuef47LPPGDZsGJ988gk33nijpy0Wi4XnnnuOjz76CLvdTvXq1UlPT0er1V5U24oTQ6lUKl5//XUWLlzIl19+yYcffohOp6Nr165MnTrVk50Op9gB54kTJ+jfvz8PPPCA37yxBg0a8OCDDzJ27Fj+/PNPypcvD+A3ATY7O5vffvuNJk2a8Oijj3Lo0CH+85//0LRpU/R6PWazmQ8++KC4TQLwHOvNN9/0BCbeLiSjlZ2djcvl8lnJdurUqYAn3XLlyqFSqTwLqrxlZGQU+4Uorry8PEaOHElKSgqffPIJderUQa1W88033/D5559f1GMrz9fhcHh+Yebn5/vcJi4ujqysLJ/bwLk3LZx7TSZMmMD111/vdxzlhFzU//3f/zF27Fi6dOnCa6+95slivPPOO3z77bfn9Vzi4uICvibe7bxQFSpUCDhvRZnYHWjYJxDll/Pp06d9Li/6d3EVnffp/Ytc+ZFQtE+K9ke5cuWoWbOmz1QIb0oQrwynWK1Wfv75Z1asWMGiRYtISUnxyzQEcqk/N97nHKVfwZ1tz8zM9Kw6DtVH4P5yufPOO7nzzjs5ffo033zzDYsWLWL8+PFs3rwZvV5fos+jqOK+HheiQoUKZGVlYbfbfb6slR+EcXFxpXYuV84JGRkZPs/JZrMFHUotrkt5zizJ1+NiFf0sK+9J7x+Z4d7/RV2qvrtU58xgjx3ufXwpBfuBk5+fHzAGKa6YmBi6dOnCZ599Rtu2bdm7d69PDdgXXniBzz//nNmzZ9O6dWvPaG2gH8LFaauiuDFU5cqVmTp1Kk8//TR79+7lv//9L0uWLKFChQoBE0uBFHtIvWLFimi1Wt59992AY/Z//PEHUVFR1KhRg9q1axMXF8dXX33lc5uPP/6Ye+65x5Omvemmm2jZsqXnJL5p0ybgXOd4BzSeBhfJnjRv3hyArKwsGjdu7PkvOzub2bNnX1CAYbPZfAIci8XCpk2bAr6wJpOJRo0a8emnn/p8mM+cOcPGjRs9v/QDPZcL8ccff5Cdnc3QoUNJSkry9EfRvitOlqnobZRf1t5154rOE2rVqhV2u50NGzZ4LrNarT7D5LVr1yYhIYG//vrL5zWpUqUKs2bNCrpJwO7duyksLGTUqFE+Q2bKa3E+v+JatmzJ9u3bfTLcBw8e5M8//yz2YwTTvHlz/v77b78pFuvWrUOn05Gamlqsx6lSpQrXXXcdX375pc/l4U7kgV7bmJgYjh8/7nOZ92sXFRVFeno6X3zxhU8/fv311z73uf766/nnn39ISEjwee22bNnimaP4xhtv0LlzZ6xWK3q9nlatWvHcc88BFLtmYXE/N8Wl3N77fQnuxUZK28L1EbjniirzaRMSEujXrx933nknZ86cIS8vD/Dt/0v9PIoqzutxMY/tcDj8Fvwpw6X/+te/SvRcXrQtyuN6W79+PQ6H46L6sbjnzECKftZK8vW4WEU/y59//jkqlYqWLVsCxXv/F23/pfq+uVTnzECK8z6+lAJ9T+bk5FySRUW9e/dmx44dvPPOO1SqVMnz2oF7mliLFi3o2rWrJ9jcvXs3mZmZQd/DgdqqvKaK4sRQ27dvp3Xr1uzcuROVSkX9+vV56KGHSE5O9ntPhVLsDKdGo2Hq1KmMHTuW/v37c+edd1KnTh3MZjPff/8977zzDg888IDnl+r48eN59tlnmTp1KjfccANHjhxh9uzZ3H777cTHx5OamsrHH39Mw4YNqVKlCtu3b+e1115DpVJ55mqUK1cOcM+bqVOnDmlpaZ5o/JNPPiEtLY3k5GR69erFU089xd9//02jRo04fPgwr7zyCtWrV6dmzZrF7gxvTzzxBA8++CAJCQksW7aMgoICz2T2oh555BFGjBjByJEjGTx4MDabjcWLF2O1Whk3blzQ55KXl8fBgwe57rrrfH6FHjx40K80CrjnldSpU4eYmBjP5HCtVsvnn3/uKS+h9J3ST19++SXt27cPONm5fPnybN++nZ9++olmzZrRoUMHpk+fzlNPPcU999zD8ePHmTdvns+vnlatWtG2bVuefPJJTp8+TbVq1XjrrbfIzMz0ZIA1Gg0PPfQQU6ZMQaPR0KlTJ3Jzc1mwYAEnTpwIOhzVsGFDtFotM2bM4O6778ZqtbJmzRpPQeHzmUMzbNgwVq1axYgRIxg/fjwOh4PZs2f7TZUI5vPPP/cpxaQYMGAA/fr1491332XcuHHcf//9XHvttXz99desXr2acePGefo+HJVKxf3338+jjz7K008/zQ033MDevXs9KwODncSLfgauvfZaOnbsyPr160lNTaVWrVp8+OGHfhmFhx9+mGHDhjFu3DhuvfVWjhw5wsKFC31u069fP5YvX85dd93FfffdxzXXXMPmzZtZsmQJgwcPRqfT0bJlS2bOnMnYsWMZPHgwGo2G999/H71eT6dOnYr13KF4n5viqlevHjfffDMzZ87EYrHQsGFDvvvuO7788kvPop1OnTrx9ddf88ILL9C1a1d+/vlnvxXszZs35/XXX6dixYqkp6dz4sQJ/vOf/3D99dd7PqNFPzeX8nkUVZzX40K1b9+eFi1a8PTTT3Py5EkaNGjAjz/+yJIlS+jbt69n0UhJncu91a1bl759+zJv3jwsFgstWrRgz549zJs3jxYtWtCuXbsLfp61atUq1jkzEOW1VkqVleTrcbF27tzJo48+Su/evdm3bx9z5sxh0KBBXHvttUDx3v/K67Rx40YqVKhQ7L4L931zqc6ZgRT3fXyppKSkcM011zBv3jzKlSuHWq1m8eLFxZr2EE7btm2Jj4/n/fffZ/jw4T7fAampqXz22We899571KlTh71797Jw4UKfz1lRLVu2xGg08uKLL/Lggw+Sn5/PvHnzfEZeihND2e12DAYDEyZMYPz48VSsWJHNmzezZ8+ekBV/ijqvOZwdO3Zk5cqVLFu2jEWLFpGZmYler6dBgwa88sor3HjjjZ7b3nnnnZhMJpYtW8aqVauoXLkyd999t2cV1Isvvshzzz3nyT7UrFmTZ555hnXr1nkmOcfExHDXXXexYsUKNm7cyPfff8+NN97IRx99xKRJkxgwYABTp05l+vTpvPbaa7z//vscP36chIQEunXrxoMPPnjBvzinTp3KtGnTyMzMpGnTprz33nvUqFEj4G1btWrFf/7zH+bMmcPDDz+MXq+nWbNmvPTSS57VgYGey2+//cbQoUOZPn06/fr18zzerl272LVrl99xxo0bR5MmTViwYAEvv/wyDzzwANHR0dSvX5/ly5dzzz33sG3bNjp37kyLFi1o3bo1s2bNYsuWLQEL8t93330sWLCAe+65h08//ZRatWrx0ksvsXDhQu69917q1Knj8xop5s2bx8yZM5kzZw6FhYV069aNQYMG+WRBBg4cSHR0NEuXLmXFihWYTCaaNm3KzJkzPSfAomrUqMGsWbOYN28eo0ePpkKFCjRp0oS3336bIUOGsG3btrCLRxRxcXG89957vPDCC0yaNIno6GhGjhzp9ys4mHfeeSfg5V27dqV69eq8/fbbzJo1izlz5pCXl0ft2rV54YUXPKU3iqtnz54UFBSwbNkyVq9eTVJSEpMnT2by5MlBF7gF+gw8/vjj2O12ZsyYgVarpVu3bjzyyCM8+eSTnvs1a9aMJUuW8O9//5tx48ZRvXp1pk2bxn333ee5jclk4p133mHWrFnMmDGDM2fOUK1aNR555BHuvvtuwB3cLVq0iPnz5/Pwww/jcDho1KgRr7/+OrVr1y72cy/O5+Z8zJgxg3nz5vH222+TlZVFrVq1mD17tmdRVv/+/fm///s/PvzwQ1asWMH111/Pq6++6rNN6AMPPIBer2f16tXMnz+fcuXK0blzZx555BHPbYp+bi718/BWnNfjQimrUOfMmeP50Vi9enUeeughz0p6KLlzeVEvvPACNWrUYPXq1SxbtoxKlSoxZMgQxo4dW6wRm2DKlStXrHNmIHfeeSe7d+/mnnvuYfr06fTs2bPEXo+LNWzYME6cOMG4ceOIi4vjvvvuY9SoUZ7ri/P+T0pKokePHp5pTJ988skl+b4xGo2X7JxZVHHfx5eKRqNhzpw5TJs2jYcffpiKFSsybNgw/vjjj7D1Qovz2N27d+fNN9/0LHpSTJo0CZvNxuzZs7FarVSvXp3Ro0dz8OBBvv7664Cl9MqVK8ecOXOYNWsWY8eOpVq1aowbN87vh0a4GEqj0fD6668za9YsXnjhBXJzc6lZsybPPvusT+wSjspV0jNhrzBKYe19+/ZFuimiDPjkk09o0KCBT6C2ceNGRo0axUcffUS9evUi2DohxJUgJSWFcePGMX78+Eg3RYigLmiVuhDi0li3bh2vvPIKDz74INdccw1Hjhxhzpw5XH/99RJsCiGEuGpIwClEBL300kue4bnMzEwqVqzILbfc4ikELoQQQlwNZEhdCCGEEEKUqAufiS2EEEIIIUQxSMAphBBCCCFKlAScQgghhBCiREnAKYQQQgghSpSsUi8hLpcLp/PyX4+lVquuiHaWNumX4KRvApN+CU76JjDpl+Ai1TdqtcqzB7m4tCTgLCFOp4vMzPxINyMkrVZNXFw0ubkF2O3B9xMua6RfgpO+CUz6JTjpm8CkX4KLZN/Ex0ej0UjAWRJkSF0IIYQQQpQoCTiFEEIIIUSJkoBTCCGEEEKUKAk4hRBCCCFEiZKAUwghhBBClCgJOIUQQgghRImSgFMIIYQQQpQoCTiFEEIIIUSJkoBTCCGEEEKUKAk4hRBCCCFEiZKAUwghhBBClCgJOIUQQghxxXCpVBTYnZzKs1Jgd+JSyd7nVwJtpBsghBBCCFEcDpWKBat3sn1/huey9JRExvRLReNyRbBlIhzJcAohhBDisucKEGwCbN+XwYI1OyXTeZmTgFMIIYQQlz2zzeEXbCq278vAbHOUcovE+ZCAUwghhBCXvQKL/aKuF5ElAacQQgghLnsmQ+hlJ+GuF5ElAacQQgghLntGnYb0lMSA16WnJGLUaUq5ReJ8SMAphBBCiMueyuViTL9Uv6BTWaWuklXql7Uyl38+fPgw/fr146mnnqJfv34A7NmzhxdeeIHdu3cTGxvLkCFDGDFiRIRbKoQQQghvGpeLcf1SMdscFFjsmAxajDqNBJtXgDKV4bTZbDz66KMUFBR4LsvKyuKuu+6iZs2arF69mvHjx/Pqq6+yevXqCLZUCCGEEIGoXC5MWjUVY/SYtGoJNq8QZSrDOXfuXKKjo30uW7lyJXq9nqlTp6LVaqlTpw5Hjx5lyZIl9O/fP0ItFUIIIYS4epSZDOdPP/3EihUreOmll3wu37ZtG82bN0erPRd7t2zZksOHD3P69OnSbqYQQgghxFWnTAScubm5TJgwgSeffJJrrrnG57rjx49TpUoVn8sqVaoEwLFjx0qtjUIIIYQQV6syMaQ+depUmjRpQs+ePf2us1gs6PV6n8uioqIAKCwsvKjjarWXdzyv0ah9/i/cpF+Ck74JTPolOOmbwKRfgpO+uTpd9QHn2rVr2bZtGx9//HHA6w0GA1ar1ecyJdA0mUwXfFy1WkVcXHT4G14Gypc3RroJlyXpl+CkbwKTfglO+iYw6ZfgpG+uLld9wLl69WpOnz5Nx44dfS5/+umnWbZsGVWrVuXkyZM+1yl/V65c+YKP63S6yM0tCH/DCNJo1JQvbyQ314zD4Yx0cy4b0i/BSd8EJv0SnPRNYNIvwUWyb8qXN0pmtYRc9QHnzJkzsVgsPpfdeOON3H///XTr1o3169fz/vvv43A40GjcuxRs2bKFWrVqkZCQcFHHttuvjJOIw+G8YtpamqRfgpO+CUz6JTjpm8CkX4KTvrm6XPVhfOXKlalRo4bPfwAJCQlUq1aN/v37k5eXx+TJkzl48CBr1qzhzTffZNSoURFuuRBCCCHE1eGqDzjDSUhIYOnSpRw+fJi+ffsyb948JkyYQN++fSPdNCGEEEKIq8JVP6QeyL59+3z+Tk1NZcWKFRFqjRBCCCHE1a3MZziFEEIIIUTJkoBTCCGEEEKUKAk4hRBCCCFEiZKAUwghhBBClCgJOIUQQgghRImSgFMIIYQQQpQoCTiFEEIIIUSJkoBTCCGEEEKUKAk4hRBCCCFEiZKAUwghhBBClCgJOIUQQgghRImSgFMIIYQQQpQoCTiFEEIIIUSJkoBTCCGEEEKUKAk4hRBCCCFEiZKAUwghhBBClCgJOIUQQgghRImSgFMIIYQQQpQoCTiFEEIIIUSJkoBTCCGEEEKUKAk4hRBCCCFEiZKAUwghhBBClCgJOIUQQgghRImSgFMIIYQQQpQoCTiFEEIIIUSJkoBTCCGEEEKUKAk4hRBCCCFEiZKAUwghhBBClCgJOIUQQgghRImSgFMIIYQQQpQoCTiFEEIIIUSJkoBTCCGEEEKUKAk4hRBCCCFEiSozAefp06d57LHHaNmyJenp6dx7770cPHjQc/2ePXsYPHgwTZo0oWPHjixbtiyCrRVCCCGEuHqUmYBz9OjR/PnnnyxZsoRVq1ZhMBgYPnw4ZrOZrKws7rrrLmrWrMnq1asZP348r776KqtXr450s4UQQgghrnjaSDegNGRlZVG9enVGjx5NUlISAGPGjKF3794cOHCALVu2oNfrmTp1Klqtljp16nD06FGWLFlC//79I9x6IYQQQogrW5nIcMbFxfHvf//bE2yeOnWKZcuWUaVKFerWrcu2bdto3rw5Wu25+Ltly5YcPnyY06dPR6rZQgghhBBXhTKR4fT21FNPsXLlSvR6PQsXLsRkMnH8+HGSk5N9blepUiUAjh07RkJCwgUdS6u9vON5jUbt83/hJv0SnPRNYNIvwUnfBCb9Epz0zdWpzAWcw4YN49Zbb+W9995j7NixvPvuu1gsFvR6vc/toqKiACgsLLyg46jVKuLioi+6vaWhfHljpJtwWZJ+CU76JjDpl+CkbwKTfglO+ubqUuYCzrp16wLw3HPP8euvv7J8+XIMBgNWq9XndkqgaTKZLug4TqeL3NyCi2tsCdNo1JQvbyQ314zD4Yx0cy4b0i/BSd8EJv0SnPRNYNIvwUWyb8qXN0pmtYSUiYDz9OnTbNmyhVtuuQWNRgOAWq2mTp06nDx5kipVqnDy5Emf+yh/V65c+YKPa7dfGScRh8N5xbS1NEm/BCd9E5j0S3DSN4FJvwQnfXN1KRNh/MmTJ3nkkUf48ccfPZfZbDZ+//136tSpQ/Pmzfn5559xOBye67ds2UKtWrUueP6mEEIIIYRwKxMBZ7169Wjbti3PPPMM27ZtY//+/UycOJHc3FyGDx9O//79ycvLY/LkyRw8eJA1a9bw5ptvMmrUqEg3XQghhBDiilcmAk6VSsXs2bNp2bIlDz74IAMHDiQnJ4d33nmHqlWrkpCQwNKlSzl8+DB9+/Zl3rx5TJgwgb59+0a66UIIIYQQVzyVy+VyRboRVyOHw0lmZn6kmxGSVqsmLi6arKx8mSfjRfolOOmbwKRfgpO+CUz6JbhI9k18fLQsGioh0qtCCCGEEKJEScAphBBCCCFKlAScQgghhBCiREnAKYQQQgghSpQEnEIIIYQQokRJwCmEEEIIIUqUBJxCCCGEEKJEScAphBBCCCFKlAScQgghhBCiREnAKYQQQgghSpQEnEIIIYQQokRJwCmEEEIIIUqUBJxCCCGEEKJEScAphBBCCCFKlAScQgghhBCiREnAKYQQQgghSpQEnEIIIYQQokRJwCmEEEIIIUqUBJxCCCGEEKJEScAphBBCCCFKlAScQgghhBCiREnAKYQQQgghSpQEnEIIIYQQokRJwCmEEEIIIUqUBJxCCCGEEKJEScAphBBCCCFKlAScQgghhBCiREnAKYQQQgghSpQEnEIIIYQQokRJwCmEEEIIIUqUBJxCCCGEEKJEScAphBBCCCFKVJkIOLOzs5kyZQrt27enadOm3H777Wzbts1z/Z49exg8eDBNmjShY8eOLFu2LIKtFUIIIYS4upSJgPPhhx9mx44d/Pvf/2bVqlU0bNiQESNGcOjQIbKysrjrrruoWbMmq1evZvz48bz66qusXr060s0WQogrilOtJs/m5ERuIfk2J051mfiKEUIUgzbSDShpR48e5fvvv+e9996jadOmAEyePJlNmzbxySefYDAY0Ov1TJ06Fa1WS506dTh69ChLliyhf//+EW69EEJcGewqFfM/2MGOAxmey9KSEhk7IDWCrRJCXC6u+p+fcXFxLF68mEaNGnkuU6lUuFwucnJy2LZtG82bN0erPRd7t2zZksOHD3P69OlINFkIIa4oTrWa+at2+gSbADsOZDB/1U5O55oj1DIhxOXiqs9wli9fng4dOvhc9tlnn/F///d/tG3blldeeYXk5GSf6ytVqgTAsWPHSEhIuOBja7WXdzyv0ah9/i/cpF+Ck74JrKz3S7bZ7hdsKnYcyOBMvpX4aH0pt+ryVtbfM6FI31ydrvqAs6iff/6ZJ554gi5dutC5c2emT5+OXu97IoyKigKgsLDwgo+jVquIi4u+qLaWlvLljZFuwmVJ+iU46ZvAymq/HMsKPRqUb7ZT85oKpdSaK0tZfc8Uh/TN1aVMBZwbNmzg0UcfJS0tjX//+98AGAwGrFarz+2UQNNkMl3wsZxOF7m5BRfe2FKg0agpX95Ibq4Zh8MZ6eZcNqRfgpO+Cays94vJEPqrJNqoLbN9E0xZf8+EEsm+KV/eKJnVElJmAs7ly5fzwgsvcMMNNzBz5kxPVrNKlSqcPHnS57bK35UrV76oY9rtV8ZJxOFwXjFtLU3SL8FJ3wRWVvvFFKUlLSkx4LB6WlIi5aL1ZbZvwpF+CU765upSJsL4d999l+eee44777yT2bNn+wyhN2/enJ9//hmHw+G5bMuWLdSqVeui5m8KIURZoXY6GTsglbSkRJ/LlVXqCTI0KkSZd9VnOA8fPsy0adO44YYbGDVqlM/Kc4PBQP/+/Vm6dCmTJ09m5MiR7Ny5kzfffJNnnnkmgq0WQogri9blYvzANAoK7RSY7ZiMWkxRWvRlIq0hhAjnqg84P//8c2w2G19++SVffvmlz3V9+/blxRdfZOnSpbzwwgv07duXxMREJkyYQN++fSPUYiGEuDKpnU5idGpidGdHkZxOkOLvQghA5XK5XJFuxNXI4XCSmZkf6WaEpNWqiYuLJisrX+bJeJF+CU76JjDpl+CkbwKTfgkukn0THx8ti4ZKiPSqEEIIIYQoURJwCiGEEEKIEiUBpxBCCCGEKFEScAohhBBCiBIlAacQQgghhChREnAKIYQQQogSJQGnEEIIIYQoURJwCiGEEEKIEiUBpxBCCCGEKFEScAohhBBCiBIlAacQQgghhChREnAKIYQQQogSJQGnEEIIIYQoURJwCiGEEEKIEiUBpxBCCCGEKFEScAohhBBCiBIlAacQQgghhChREQk4f/rpJ/Lz8wNel5uby/r160u5RUIIIYQQoqREJOAcOnQohw4dCnjd77//zuOPP17KLRJCCCGEECVFW1oHmjhxIv/88w8ALpeLqVOnEhMT43e7I0eOULFixdJqlhBCCCGEKGGlluG86aabcLlcuFwuz2XK38p/arWaJk2aMH369NJqlhBCCOHhUqkosDs5lWelwO7EpVJFuklCXBVKLcPZuXNnOnfuDMCQIUOYOnUqderUKa3DCyGEECE5VCoWrN7J9v0ZnsvSUxIZ0y8VjVeyRAhx/iIyh/Ptt98OGWwGm98phBBClARXgGATYPu+DBas2SmZTiEuUqllOL3l5OQwa9YsfvrpJ2w2m2eY3eVyUVBQQE5ODnv27IlE04QQQpRBZpvDL9hUbN+XgdnmwKSVSoJCXKiIfHqmTZvG6tWrqVmzJhqNhnLlytG4cWNsNhu5ubk8++yzkWiWEEKIMqrAYr+o64UQoUUk4Pz2228ZN24cCxcu5LbbbqNKlSrMnj2b//73v6SkpHDw4MFINEsIIUQZZTKEHvALd70QIrSIBJy5ubn861//AiApKYndu3cDEB0dzd13383GjRsj0SwhhBBllFGnIT0lMeB16SmJGHWaUm6REFeXiASccXFxnDlzBoAaNWpw+vRpsrKyAKhcuTInTpyIRLOEEEKUUSqXizH9Uv2CTmWVukpWqQtxUSIyRtCqVSsWLVpESkoK1atXJzY2ljVr1jBixAj+97//ERcXF4lmCSGEKMM0Lhfj+qVitjkosNgxGbQYdRoJNoW4BCKS4XzggQc4ffo0kyZNQqVSce+99zJjxgyuv/563njjDfr37x+JZgkhhCjjVC4XJq2aijF6TFq1BJtCXCIRyXBWq1aNTz/9lCNHjgBw1113UbFiRX755RdSU1Pp27dvJJolhBBCRJRLpZIMq7gqRWzZncFgoF69ep6/e/bsSc+ePSPVHCGEECKiZKcjcTUrtYDz8ccfL/ZtVSoV06ZNK7G2LFiwgC1btvD22297LtuzZw8vvPACu3fvJjY2liFDhjBixIgSa4MQQgihCLfT0ThZuCSucKUWcG7durXYt1WV4BZib7zxBnPmzKF58+aey7Kysrjrrrvo2rUrzzzzDL/++ivPPPMMsbGxMp9UCCFEiZOdjsTVrtQCzq+//vqC7nfs2DEqVaqEVntxTT1x4gSTJ0/m559/platWj7XrVy5Er1ez9SpU9FqtdSpU4ejR4+yZMkSCTiFEEKUuOLsdGSK0ZdSa4S49C7rn0sOh4MuXbqwb9++i36s3377jQoVKrBu3TrS0tJ8rtu2bRvNmzf3CWpbtmzJ4cOHOX369EUfWwghhAhFdjoSV7vL/h3sukRzVjp37kznzp0DXnf8+HGSk5N9LqtUqRLgzrAmJCRckjYIIYQQgSg7HW3f5z+s7tnpSOZwiivYZR9wlgaLxYJe7ztUERUVBUBhYeEFP672Mp9vo9Goff4v3KRfgpO+CUz6JTjpm8AC9cuYfqksWLPTJ+hMT0lkTP9UdGoVUHLrGy4n8p65OknAibtEk9Vq9blMCTRNJtMFPaZarSIuLvqi21Yaypc3RroJlyXpl+CkbwKTfglO+iawov3y2OBm5OQVkm+2EW3UUSEminKmsjl3U94zVxcJOIEqVapw8uRJn8uUvytXrnxBj+l0usjNLbjotpUkjUZN+fJGcnPNOBzOSDfnsiH9Epz0TWDSL8FJ3wQWql+idWqide5RNnuhjaxCWySaGDGRfM+UL2+UzGoJkYATaN68Oe+//z4OhwONRgPAli1bqFWr1kXN37Tbr4yTq8PhvGLaWpqkX4KTvglM+iU46ZvApF+Ck765ukgYD/Tv35+8vDwmT57MwYMHWbNmDW+++SajRo2KdNOEEEIIIa54EnACCQkJLF26lMOHD9O3b1/mzZvHhAkTZE93IYQQQohLoEwOqb/44ot+l6WmprJixYoItEYIIYQQ4uoWkQzn1KlT2b59e9jbqdVq+vbtS1xcXCm0SgghhBBClISIBJwff/wxFosl7O1UKhXTp0+natWqpdAqIYQQQghREiIScDZu3JhNmzZF4tBCCHFJONVq8mxOTuQWkmO2czLr8i6DJoQQkRSROZwpKSm8/fbbfP7559StW9ev9JBKpWLatGmRaJoQQoRlV6mY/8EOdhw4tyNMWlIiYwekls2J8UIIEUZEzo1ffvmlZ6/ygwcPcvDgQZ/rVaqysX2XEOLK41Sr/YJNgB0HMpi/aifjB6ahdkrtQCGE8BaRgPPrr7+OxGGFEOKiFRTa/YJNxY4DGRQU2onRScU5IYTwFtGzotPpZO/evWzatIm8vDyys7Mj2RwhhAgr3xx6m8F8s72UWiKEEFeOiE03+uijj5g1axYnT55EpVKxatUq5s6di06nY9asWej1+kg1TQghgoo26sJcL7M4hRCiqIhkOD/99FMmTpxIy5YteeWVV3C5XADceOONbNq0iQULFkSiWUIIEZYpSktaUmLA69KSEjFFScAphBBFRSTgXLRoEbfddhsvv/wyN954o+fyfv36MW7cONavXx+JZgkhRFhqp5OxA1L9gk5llbosGBJCCH8R+Sl++PBhJk6cGPC6tLQ05s6dW8otEkKI4tO6XIwfmEZBoZ18s51oo5Zoow4dLux2V6SbJ4QQl52IZDgTEhI4dOhQwOsOHTrkV5dTCCEuN2qnkxidmsrl9cQatVSKM0W6SUIIcdmKSMDZrVs35syZw3//+1+sVivgrr25e/duFixYwM033xyJZgkhhBBCiBIQkSH1Bx98kP379/Pggw+iVrtj3iFDhlBQUECzZs144IEHItEsIYQQQghRAiIScOr1epYuXcr333/Pli1byMnJoVy5clx//fV06NBBdhoSQgghhLiKRLR+R5s2bWjTpk0kmyCEEEIIIUpYxALOzz//nF9++YXc3Fy/61QqFdOmTYtAq4QQQgghxKUWkYBz5syZLF26lJiYGMqXL+93vQypC1GyXCoVZpuDAosdk0GLUadB5ZJyPkIIIUpGRALODz/8kEGDBvHss89G4vBClGkOlYoFq3eyfX+G57L0lETG9EtFI0GnEEKIEhCRskiFhYVS+khcthwu+OvkGY5nWyiwO3FdRRl3V4BgE2D7vgwWrNl5VT1XIYQQl4+IZDhvvPFGvv76a1q3bh2JwwsRlEOlYsEHO67a7J/Z5vALNhXb92VgtjkwaSPyO1QIIcRVLCIB5xNPPMHAgQMZMmQIaWlpGAwGn+tVKhVjx46NRNOuakXn7UVrynZg4VSrz25NaCPGqMMYpWXjtv8Lmv0b1y/1ip/nWGCxh73eFKMvpdYIIYQoKyIScL799tscPnyYw4cP89NPP/ldLwHnpRds3t74QemRrY0VIXaVivkf7GDHgXP9kZaUyNgBqRw9nst3O4/73P5qyf6ZDKFf7XDXCyGEEBciIt+ey5cvp3v37nz33Xfs3bvX7789e/ZEollXrVDz9uau3I7jyk7anTenWs38VTt9gk2AHQcymL9qJ8N7NAp4v3DZwSuBUachPSUx4HXpKYkYdZpSbpEQQoiyICIBZ0FBAYMGDaJixYqROHyZE27eXn6ho5RbFFkFhXa/YFOx40AGBYWBA8urIfuncrkY0y/VL+hU5qle6VMGhBBCXJ4i8g3aunVrtm7dSosWLSJx+DIn/Lw9W5mat5dvtoW53r+/PNm/qyAg07hcjOuXKnU4hRBClJqIBJy9e/fmySef5OjRo6SnpxMTE+N3mz59+pR+w65S4eft6UqpJZeHaGPo5xtt9O2vqzH7p3K5MGnV535oXEXPTQghxOUnIgHn/fffD8D69etZv3693/UqlUoCzktImbe3fZ//MHJ6SiLRURpcDmcEWhYZxigtaUmJAYfV05ISMUZpWTixM2fyrZL9E0IIIS6BiAScX331VSQOW2Yp8/YWrNnpE3SmpyRy/6B0NC4nV/5ymOLTAGMHpPotHFJWqWtUUKVSObKy8rHbnZL9E0IIIS5SRALOatWqef5tNpvJy8sjNjYWna5sDe2WpkDz9qKjtFSMNZKVlR/p5pUupxOtSsXofo2x2p3km21EG3XotWq0gFY22xFCCCEuqYgtu922bRszZsxg165duM5mkFJTU3nooYdo2bJlpJp1VSs6b09TlgMrlwutRo3V7gTcHaHVqMF57m8hhBBCXBoRCTh/+eUXhg8fzrXXXsuYMWOoWLEiJ0+eZP369YwcOZK3336b9PT0SDRNlCFqp5MYnZoY3dmFM86yM4/VW9EdqGTOqhBCiEstIgHn7NmzadasGcuWLUOjOVdoety4cYwYMYK5c+fy+uuvR6JpQpQpwXagulr2jhdCCHF5iEjh9127djF06FCfYBNArVYzePBgdu7cWeptcjqdzJkzh3bt2pGWlsbdd9/N0aNHS70dQpSWUDtQLVizE5dKphYIIYS4NCKS4YyOjsZuD7wu2mazeeZ0lqYFCxbw/vvvM336dCpXrsyMGTO45557+OSTT9Dry05RdFF2KDtQJVWvwONDm+FAhbnQTr7ZRoxRh0ulkqF1IYQQl0REAs6mTZuyaNEi2rRpQ3R0tOfyvLw8Fi9eTLNmzUq1PVarlddff53HHnuMDh06APDKK6/Qrl07vvzyS7p3716q7RGiNBRY7O5gc0gz7C6Yv2pHwDJRWgk6hRBCXKSIBJyPPPII/fr1o2vXrnTs2JHExEQyMjLYuHEjhYWFTJs2rVTbs3fvXvLz831Wx5cvX54GDRrw008/ScAprkomg5ZHBzfDoVIx/4MdfoXwdxzIYP6qnYwfmIa6jC6oEkIIcWlEJOCsUaMGK1euZO7cuWzatImcnBwqVKhAixYtGDduHHXr1i3V9hw/fhyAa665xufySpUq8c8//1zw42q1EZkiW2wajdrn/8LtausXmwvyLWeHyk06TFFadCqI1qg5Y3FPbQm065JyeUGhndiz231eaN9YXe6Mar7ZRrloHQa9FrPFTl6RNl2prrb3zKUkfROY9Etw0jdXp4gEnMeOHeO6665j9uzZftcVFhbyyy+/0LRp01Jrj9lsBvCbqxkVFUVOTs4FPaZarSIuLjr8DS8D5csbI92Ey9LV0C/HT+cz74PAQ+XXVIzh+KFTYR8j32ynVtUKPpcVt28ysguw2pwsWrOTX/dnYNBreGxwM9Z9+4dfm8YNTKNKwpXxmQnmanjPlBTpm8CkX4KTvrm6RCTg7NKlCytWrCA1NdXvup07dzJy5Eh27NhRau0xGAyAey6n8m9wB79G44W94Z1OF7m5BZekfSVFo1FTvryR3FwzjjK0l3o4V0u/2Fz4BZtwbqh83MA0oo3hd/eKNmo9u1EVp28cLih0OFCr1Ow8eIpvfz3maUOv9nX8gk2lTQtW72BUn8Zo1Go0arDaXRTa7JgLHcQYdRijNJdtFvRqec+UBOmbwKRfgotk35Qvb5TMagkptYDzpZdeIjs7GwCXy8WCBQuIi4vzu92ePXsoV65caTULODeUfvLkSa677jrP5SdPnqRevXoX/Lh2+5VxEnE4nFdMW0vTld4v+TZnyKFyz2p03BnGQLdNS0rEFKX164egfaNS4VSpsNpc5JstpFwXR1pSIhoXnM4zY9DrSL4ujqP/5JCdZ/W56/Z9GRw7lc9nm48wsncjFq7x3es+PSWR0f0u70VMV/p7piRJ3wQm/RKc9M3VpdQCzjp16rBgwQIAVCoVu3fv9hvC1mg0lCtXjscff7y0mgVAvXr1iImJYevWrZ6AMzc3l99//53BgweXaluEuFTyzbaQ1xdY7BijVOhUGsYOSGX+qp0Bh97VTgsFdvcORAqbyx3QKkGrKUqLCrC7XH4LkJTHSYjW878d/5BSI57Hh12PWqNCo1ZxKtuCVqNi79Es7A4XtapVYMHqnX4B8PZ9GSxcvZOxA2QRkxBCXGlKLeAcMGAAAwYMAKBz587Mnz+f+vXrl9bhQ9Lr9QwePJiZM2cSHx9PtWrVmDFjBlWqVOGGG26IdPOEuCDhhsuNBi05eXYqxqjRqtWMG5h2tg6nnWijFlOUluOncnly8Y+AO8M4tl8qxzLyWHh2TiZAUvUKTBjSDFQqv6AVzg6Xr9rJ2IFp7Dp0mrc/2+u5Li0pkV7tavPy29tIqRFPh/RqJMYZqFcjjlta1USvU7P3aBbrNh3CYnWw50gmdocTq92J+exWnKhUqHBh0GslEBVCiMtUROZwfv31136XZWRkeIawi+5AVBruv/9+7HY7Tz75JBaLhebNm7Ns2TIp+i4uS8XZ/9wUpQ05VH46x0KV+LNzlJ1ONECMTk20Pop5QXYg2n7gFN/vPOYTbD42pBnzV+/k7p4Ngw7h/3oggxOZBZ77Kbzndq7csJ+l63bTOrUqyz7aTa/2dahXI46U6+KYPrYtv+w9Qe1qsSz6cJfP4yhB6xc/HOGuXo3Qq1UggacQQlxWIhJwFhQU8Nxzz9GgQQOGDBnCp59+yoQJE3A4HNSsWZPXX3/dr0RRSdNoNDz22GM89thjpXpcUbYVJ3Asqrj7n6udzqBD5ff0acT7X+zlrm4NoMjxlB2IAkmoYODIsRyevLsFCRUMWG0OMrLMJF8XF3YIP68g8PU7DmQwrHsD6tWIY+/RLBIrGD0r2Vdu2H/uOSYnUr9mAnuPZPrdHyClRhwLV++kXZOqpNateFnP9RRCiLImIgHnjBkz+Pzzz2nTpg0As2bNol69eowePZrZs2czc+ZMZs2aFYmmCVFqihs4OtVqCry2nDREaWmXVtnnfsr+5+P6pfoErFqXi3ED08g329xzNg1aTudYeP+LvQzr1iBgcFtgCbztLIDTBc+PbsOStbv9gti2aVVDPl+9LvjKz5OZBbz41k+kJSXSIb0ab3zyu/8czv0ZaDVqHh3cDLUKrDanZ8i9sNDKDddfR9u0quSbbRRaHaiitGigzGQ7L+THixBClJaIBJxfffUVkyZNokePHuzZs4e///6bCRMm0KVLF+x2O08//XQkmiVEqXEFCDbBP3C0B9gFSFmEc/9AmPPBbp/7mm0OTEU2HNA4nZQzaNFqVBRY7FSJNzH45vpk5xVijPIPTEyG4KeFqhVNvPbh7oDzNI0hhvDTUxLZezQr6OMqweiOAxksXbebpGvj+GnPCZ/bGPQabm5Vk4+/+8NnSP2u7vXo2rxO0HqjWpUKXA5O5dkpH6NFq9b6BfAWq528AtvZ8ktX3lzQ4v54EUKISIlIwJmdnU3t2rUB2LhxI1qt1pPtrFChAoWFhZFolhA41WqyzXb+zjzlCUZKIksWathaCRwNem3ILSfHDUwDdvtcV2CxY4rxn3escrkwadVElYsKG5gYdRrSUxLZvs+/fTa7K+g8zU++PRh0CH90v1SWrdsd8H5pSb7B6PZ9GfRsW9vvdsFqeLZOqx623qhKpWXeB9uYfHcL5gYITHu1q82M5dsAGNm7ESk14skrsHreA3anBa3agLnQjtlio0JMFA6nC5cLnC4XBWes5JjtGKK0OHGiVZVedrG4P16EECKSIhJwVqtWjX379tGsWTO++OILmjRpQkxMDADffPMN1atXj0SzRBnnVKk4mV2AMUqH0wUOp4tT2QUkVjCiVqn85joqQ5jmQjvlTFHYHA7yC2xEFyNLFmrYWrne6Qq95aS50P8xQmUnnWo15kI7g7omc3fPhhiitOw6cJw5H+z2CUxULhdj+qWyYM1On6AzLSmRAkvweZoffXuEjk2r+q12N0Zp0bhgeI+GWO1Ov8f0DvYUVpt/39WrEeczp1NhLrQXq5+eGN6CBUFW0QP07VSXutViWfftH8z74NzGE0qmdMWX+/huxzEeG9yMVV8f5OZWNf0C4PTkREb3T2XNpoP0bFeXnDMWTIaSzZoW58dL0ay3EEKUtogEnHfccQcvvvgiy5cv5/Dhw/z73/8GYPz48WzYsIEnn3wyEs0SZZlajdXuZMWXB/yyX6P6NsagVeNSqVCdDRqUIcw9RzJ5bHAzlv93X+Dh3CCZpVCBoXL9mSCLbBRFF+mkpyS6a2W6XD7z+aKNOrQaNQtX7fBb3e09NO8dmGhcLsb1S/WaE6jDWYwsWVaeg4de3UB6ciKj+qby/a6/qVM1lqlLf+COm1Jo3bgqPdvWxqB3D2PvPZrFjOXbsFgdPo8TY/Iv6RQoCA3UD8Gud0QFz87uOJDBiJ4Nef2T3wIGpAtX7+SePo2Ir2Bk3bd/kFIjjv9uOUJKjTh6t6/tM5902brdDLmlAaezzZitDp5e8gP1a8VzX99UdCWwgr44P14CZb2FEKI0RSTgHDJkCPHx8fz444+MHz+ebt26uRuj1TJ16lRuvfXWSDRLlGF2F7z24a6AwcZrH+5i7IA0Cm12os8GnsoQ5qCuyUG3apy/aifjBwYuUh5q2FoJHJ1hdlWNNup4bVIXCm2Oc7sGqVS4gPleQ6yDuiaz72hW2KH5ooGJMgyvXOZQqXG6gu9KlJ6SSGKskVce6kCUTs2k+d+SnWdl0tDmANStHsuzy7aGbJPyOJXiTD7HiY3Rc23lGKaMaOET3K3bdChsvdFoow5ccCbcKnqzza9sk2L7/gwcTmhWvxIrN+zn1s5JdGl2rTuTa7ETX17LqRwLf504w40tamIutPHc61sZ2bsRM8a3I99iw+F0olZrMHsVzL8Umc/i/HgRQohIi9iZqHv37nTv3t3nsldeeSVCrRFlncUaeljWYrW7s4Xl9D5DmMGGeZX7FRTaiQmwOjvYsLUyn1LlcoWto2mM0vrNXUxPTmRgl2T2eJUOCtdGZcjZOzApujLeGKVF43Si0bgLxJ/ILCCvwOYJ/I7+k8Ow7g3JyDLz2+HTVIk3UatqBbbvz/AsCPLOUK7bdIjHBjfztMH7+Y/ul8rBv7Jo16QqvdvXxumCayvF+BSbV/rgscHNitVPTmf47KzJqGXKiBY+heYVBr0Gm92BXqvhqREtqBhvDFpu6t3/7uW2G1M8pZ3mfbCD+/o2oHn9qjiKZIktVjums1npC1WcHy8X8/hCCHEpRCTgXLt2bdjb9OnTp8TbIa5cl7oETPhhWTsmg3tDAu8hzGDDvIoCs50YXeDhTP9ha9/nEaqO5tgBqfznY//h3+37M3C6zhVSL04b8802n8Ak2Mr48QNScTpdfu1JT0lkVJ/GPL1kCycyzaQlJdKl2bVUrGAEFew9mkVaUqJPWSSL1cGM5dvo1b6OZ0i6amI00Xr380+5No5Cu5PXPtxFz3a1A251qfw9oFPdkP2kcbl4bN63jBuUHjIw3bzzH1Zu2O8JZJWhfoNew2ODm/HWp3v4dX8GU0a0CLqj0pK1u+nZzr3gScl8392jHs3qXeN5PkXbOKZ/Kjq1+oKH2ovz40UIISItIgHnpEmTAl6uUqnQaDRoNBoJOEVQJVECJvywrBa9VgO4fDKBoWpLgjtrFopLpfIsUHK53H8HqqNZdBFOoc3O5l3/BHzMHQcy6N3+3CrvcG2MNuo8gYlTrQ66Mv7Xg6f4bsexwHucr9nFPX1Sef71re6tLFfvpGN6Ncb2d09F6NS0Or8dPu0T8FmsDk9QnJaUyPiBabiAfKuDAouVGJOWlBpxVIozhsw+33lTCj/+dixgP53OKsBic5KdZ2Xeyu1Ba4h6L1zacSADtQqev68NufmFOF3ngkeDXkPFCqHbM7xHA5+FTK1Tq3Myu8BvfrBy+wWr3dMaNBcRdIb78SKEEJEWkaWLX331ld9/H3/8MU8//TSVKlXi/fffj0SzxBUgXAkYl0p1QY+rDMsGkpaUiClKi/7sp0UZwoRz2btQ9wvGrlIx94Md3D9rI48v+J7xszYy94Md2Is8B43TiSlKS7RRy/2zNnLPtA2cyQ+dkfXOahanjUqgXhBixXd8eUPQOY47DmSQUMHg83fd6+JQO50YNWo0aidNktwruNOTfduiZCKz8yycyCzgux3HmLpkC1a7iwN/ZoVdPJWZW8jSdXvIyrUwYe63FFrtfLfjGH+eOEN2vo248gaevLsFAE8u/J6e7Woz55GOTBvdhikjWpBSI85v4dL2/Rnk5hfy7LKtGPQaT7D52OBm5IXJhpstdqJ07my4Qa+hoNCOMUoXdiX9yRwL+TYnTvWFnZaVObcVY/SYtGoJNoUQl5WIlUUKJCkpCZvNxnPPPce7775byq0SV4KSKgETbvha7XJ55sF5D2EGm4vouV+QjFWoTGKgxUZqp9Nn2D9cRtY7qxl0vuTZEj7exwk1tSDc0Ly5yGrpfLMNYwUDBYV2zIV27HYre/8vk17tajO0ewPMZ3c+Mhfa+G7H37z16V7g3NzM2e/+zOS7WgQs/xTouWbmFtK4TkWuqRjN2k2HWLlhPwa9hl7t65BatyKPD7+eKJ2GUzkWtu8/QZ1qcZ5FTKGer7Ilp1IHdHiPBiHbE2PS4Tjbp73a1yHfbCPcFNK8AhtPLPweOPe62B0O9Norrwi9EEIEctktX0xOTmbmzJmRboa4TJVkCRity8X4gWlnF8t415B0+S268B7CNBfaGd0v1V2H0+wezjSFq8MZpnZkoMVG3kFmlE4TcrV4Zq7F87fF6uC/W44wrHt9ss/UxgVUijNh1Gv82hgqkA03NG8ssho62qhjnlcpJoNew4hejYgtZ+BkZgE6rZpfD2SwbtMhJgxp5vP89Vo1Q7s35PjpfCrHRwddFONdNL5qYjRDu9Vn0dnFRQa9hglD/Pdkb5t2DcO6N8RcaGf6mDZEG3XYHE5efusnTmSa/Z6v8v96NeL44ocj6HVqnr+vtc+iKWWRUVpSIvv/L4uGtRN47t7WJMYZsNqdOMJEnN59t31/BgvX7GRItwa89uEOv/JasoWlEOJKdFkFnFarlZUrV5KQkBDppojLVEmXgFE7ncQatdSqWoGsrHzs9uBBo6dskFYPuNBr1USXOxvshslKFWeRUtHFRt6rsWcu38ZjQ5oF3dVHC8x7tOPZ/dN16DQqcvKsVIw1nguGA7Qx1IrvzFwL6cmJATPMaUmJnM6x+PztdLo8gV/fTnW5vn4VMnMtZJ2xcOjvHNZtOkRKjXgeG9zMJyAruoVlbIyeaWPasnjtLr9V6srcy/TkRPYdzaJu9VhP+/p2qsu6b323wYyN0XP7jfWYX6QmaXpyIs/e25opizd7Fj4pgawyJUHZR37xh/5zQB8b3Iz/bjnCza1qMmP5NurVjGdM/zQWrt7Bff1SOZ1rDrlgybvvwJ2tv6uHinED07A7bZzOtXt2PSq028jNt6PTqjmZZcag16BRqzEZtRj1khEVQlyeIhJwdu7cGVWReWpOp5OsrCwsFkvQRUVCXC0lYIqzSKmoosP+M97exqODm52tw+nOyHpnVr1raAIkFiMYDjW1oEndiqTWrRh4lXrfVLJyLcx+qAMFhTbsNhcnMgs88x4//u4PPvv+MOMGpRNf3kCTpETapVXFEKVl3aZD3NK6Fga9BovV4beFZXaelZOZBTSuk8DdPRqSb7ERpdcCLrbtOUHjOhUZ3qMBj7y6yTN1ACA9KZH3Pt/n8/weuDWdpR/t9puLun1/Bos+3MnEYdfz9vrf6dH23CIiZUpC1cRoFq0JXKtVrYLuZ+9jsTr4dX8GJ7MK2L7f/eNg4pBmjOrbOOAq9Xv7NGLy2eF0b8cy8vlsyxHGDkhl5Ybf2L7/lHtV+4BULLZCLFY7p3MsVIw18tJbWz0Z1lAbDgghRKREJOC8/vrr/QJOAJPJRJcuXWjdunUEWiWuBFdLCZhwtSNNUdqAgaH3sH+B2U6hzYEpSntu+P0SZLcCTS3Qa9U8+/pWmtev5HP8aJOOvUcyefDfGz2LbpRSPxmZZk/wePSfnKArxMcOSMXpcnhKEQWqG7r/zywa1a7IsiKloNKTExneoyFbf/sHi9WB92nF7vB/LyRUMIacA3x3TxV33lyfJxd973k+FquDL7Ye4c6b6wedBrF9fwY929X2WXikzP088FcOr7y/nYdvb8rYAalYrA4KzHZMRi2nss1MXvg92XlWn8cz6DVUijfSu31t/jyRx4hejdn069+s23SIBat20rtDHXfmtW5FtBo1sx5oj06rwVxoIzO30D0dRLKdQojLSERWqb/44otce+21nDx5kunTpzN9+nT69OnDp59+yv79gQtUC6FQ5k/Oe7QjL49ry7xHOzLuIkoiRYKSSSy6ejzcYiPlvjE6NZXK64nRqUskqFCOUVk5hlpFXHkjqzf+wf+dOMP9szaSEGtg6Ue7mb9qh0+gpZT6iYuNol6NOHYcyGDcoHS/YFO57fxVO9Gotaz79g96ta8TcHGSC1j51f6AdUff+OQ3bGeDy71Hszyr4A0BKgQUhFmAlG+2Y7HafZ5PWlIiI3o18gSQwRRtt/ec198OZzJ25v/Y8NOfnMo243S50GrUrPv2j4DB5tSRLTn0dw4ANruTk1kFJMYamTi0OUf/yaFqxWj2Hc1i8qLNPL1kC/l5hYALlUqFy+UCF+6tSC+waoMQQlxqEclwLl26lPnz5zN06FDPZTVq1KB3797MmjULo9Eo21uKkIpuu3glDKMXFSiTGG6xUaT4ttXG3Ec64nA42Xc0M+DtdxzIwOFweYKwhAqGsGWBlPqh+rMlhbwlVY/1Gx5XKNlFcA9/z3qgPYvX7gZcfllkQ5T/Y3szRmnIyLYxZUSLs4urjFjtTv7KyKNi+dB7jXoHmOnJ5+aABqJWq7DaHNzXtzGLPvSdmzqqb2OcLvju12N+2eBbuyYzfWwbXltzblrDPb0bEBsbeOejsQNS0V5EfU8hhLhUIhJwrly5koceeoiRI0d6LqtSpQqTJk0iPj6et956SwJOcUU63xXESibRs0DoMg0MAu0+lJ7iuyNPUflmOzEm91zVcNUF8s1nt9eM0uHE6RcohivJpFyfUiOe73ceI6VGHCoV3No1GThXDkqrVoecyhCl0zBz+Tbq14rn3j6NsTuc6DRqEmONZGSFXvijBJhpSYmM6N2I3LxCaletgF6n5uBf2dStHstHm3xXzDevX5m7ezZEBWRku1fIV4ozseSjwNlggNH9GvtMC0hNquy3xaly+/mrzhaVD9l7QghR8iIScJ44cYKGDRsGvK5x48bMnz+/lFskxMUriR2QSkKgfdJDZVWD1Qzdvi8Dp/NsfcpNh+jVvg71asRhtTnR69TEldNz7HQB6cmJYasHRBu1VI43EhWl4fH5W/zqhoYryaTXqX1WrVusDtZtOsTEoc1pm1bVs32mw+n0C0LhXPbQ6XLx5N0t2HnwFA+98o1nIc59/RrzzS9/cU+fRn5TA9KTExnVL5W/Tp5h7qMdcbngzU9+56c9Jzy3GTsgzWchlOKnPSew2p20TatKRraZlRv288qDHUJmgy023+DeHKbEljlAiS0hhChtEQk4r732WjZv3kyrVq38rtu6dStVqlSJQKuEuHDhdkAad5ksaAq1TzoqVcBANFzN0L4d6pByXZxfvcv0lEQGdUmmV/s6WAptIbODxigtT97VgkKbM+A+65XiTEFLMqWnJHJNxWi/HYMsVgcvvfUTvdrXoe61sZzJt6LRqCln0tG2ybkgVK9Tk5lrITHWwJvrf+e7nb5bhu44kMHitbsYckt93v3vXnq2q+3evvJsFluvU3Mq08zzr//Is/e25MONh/zamVAh9C5NvdvXJqGCAYNeQ6HNP1vszVLoe31xSmyZogyX5VQNIUTZEZGA8/bbb2fatGnY7Xa6du1KQkICmZmZbNiwgbfeeotHH300Es0S4oJdqh2Qzjf7eD6CZSqP/pOD1eHitQ8DzwEMF9CUj9Hz5vo9ATOgAHf3aIi50MqYAaksCDLPcPZ7vzCqXyp5Be4FNN77rIO7fubzo9uwdO1ufi0yrH9vn8Z8++vf7Dua5Te0b7E6OPB/WUTp1LRodA2fbzlC73Z1aFw7AYvNQWGhA5NRR6U4IwVWO22bVKdz8+s4+Fc2LtxzR5WgFKDutRWILx/lmTJstTvZ+vtxal1TAYBy0VEB3wfFnRLQq30ddGHeJ0WL/henxJYsHRJCRFpEAs4777yT48eP85///Ic33njDc7lGo2HYsGEMHz48Es0S4oJdih2QgmUfL1VdxWCZynGD0v3qQ8K5OYAjezcK+bhatdrvvsqWkvVqxHGmwIYL0IDPwqPoswH1glW/8tvhTPLNdn4/khkwE5qdZ2XVV/sZOzCNE5kFnl1+DvyVjc3upG71WBrVrsigLknsPnwaFVC3eiwuF1SKN2JzuDzD3B9/d9innVNGtGTlV/t9dkRSLvNeqJSe7J6bWXS4PC0pkbapVd11RIOsgi/OlABw72a0be+JkNncX/adoFe72qhV7gVTxjAltoxRWgrtTvQalXuLViGEiICI7TT0yCOPcO+99/Lrr7+SnZ1N+fLlSU1NJS4uLlJNEleYkswGnq+L3QHpfPdWvxDBMpXhVpDrNKEX2mTlFfpcphR7DzTEPrBzMs8u+8GnZmevdnXYcySLaKO7CPxTd7VgdL/GWO1Oz2ur06rRa9TMXbXDE4gpx3n94998dw0KcJz0lER6tKnNrkOnfLKgvdrXYcUG33JLgS4Dd3C3ZO1uUmrE+QScOw5ksOSj3QzsmkSMKfCPCmW3olA7DWVkm6ldtQIf/u8gE4Y0wwU+z6tJciL39G7Mw7O/AeD5+9rQs11tMjILghbrH9W3MZnZBaz63yH6d6pLxVijDK0LISIioltblitXjnbt2kWyCVeUyynAirSSzgaer4vdAelC9lY/X8GGXsNlZ/85nU+vs2WHivb30G71ySkScBbdKUjhvchICUSV24zo1QhjlJan7mpBYnzwEj/HTuVd0HECXQYELDIf6DKFMt/Sm0GvIfm6OK5vUAWb3REwO7lu0yGmjGiJSuUfRA7qkkyFcnre+Ph36tVw/+A++Fc2Q7vVZ2i3BlgK7Wg1KrYfyOBUltkTMOfmF/Lssq0APDCoCbfdkOSZW2o0aDmdY+GJBd9Rq2oF6tWKx2jQXpL3kRBCXIjLai91EdzlFmBFUmlkA8/Xxe6AdCF7q5+vYLsbhcu+ulwuv0U8ep2avUezOJNv5XSO7x7r5xuw7TiQwcjejSi02aiUYApZ4mfC0OY8MnvTBR+n6GWB5lYWd74l+Gdzlb+dLt/gPKVGPLHl9NSvFU+vduf6sEpCNN/t+JtPvz/MjS1rElc+iikjWrJiw37e9RrOV1bRm4wabr8phaTqsWg1al4Y3Zrdf5ymVrUKTJr/HQO7JtGiwTXYnU5ijDqeHdWa09lmykXrzv6wUF30+0gIIS6EBJxXgMsxwIqk0sgGXghlB6TzqcOpuJC91c9XsH3SzSFWkKcnJ3Lwr2y/kkd7j2axbtMhUq6Lo2Ks0V14XeXOJJ5PwOZpg8XGZ1sO07djUpjh/XPl1C7kOEUvM+g1DOqa7PPcYsK8Ft7zMYtmWb1X2A/skoTT6cLucLL3aBZ/ncz3K17/1IgWnsBy5Yb9VIk38e2vfwf8rBt0au68uT57Dmf6zi1NSaR9k2o8Mex6dDo1S9ft9ptiMLpfKlabA2fZ+m0qhLiMSMB5BbhcA6xIKY1s4IW60B2QLnRv9fMVaHejmCht0DmA9/VL5e+MPL+C5WlJ7qLvFWMNZOUWYnO4zhZKdwdYoQRaQGOM0jL4xnqczLGEvK/3a1/chTjelEL04A4248ob2Hc0y+e5jR2QFnTRjneBdwicZVVW2K/csJ8pI1p4hr2njGjh93iVYo1MGdHCJ9j1Pq734qvy0VH855Pf/Morbd+XwW9/nEajUvHNr38HvH7hmp2M65+GEy7bzQWEEFc3CTivAJdzgBUJpZENLG3Bso/F2Vv9Qo5VdHcjrVrNuIFpmItsswnw0Sb/eZLeu95MXfqD5/Lm9Stzb9/GxdqRx/syY5QWldNZjNdW53nscAtxih4nPSWRyvEm5jzSkXyzjbhyUQFX5y9bt5spI1p6MraKJkmJjBmQyrJ1uz2XhcuyajVqDHoNKTXiAz5vh9PlCUgBJg1t7vm3Mjz/3y1HAGjV+BpualmTnu1qezLMynzO+PIGYoy6oLU+t+/LoMDqQKsBjeyvLoSIgCvvm7kMKq0A63y3ZSwtgRZLPX/v9Ty5+Ee/217KbGBpi+je6k4nGvALRPNszpDZdavd3TbvTNzfJ/MY3T+V1z70n8+qrB5XFA2oi5PpTakRR+/2tXG5oHOza3ltjf/uToGO06NNbcbP/B8vj2/H4wu+Z84jHQNmMS1WB88u+4HZD3XwrJSPNupQAYV2O3f1aMhNLWtitTmpmhgdslvVahVTRrRErcInMFd2Jzp+Kt/n9kWH6/+75Qg3t6rpt+JfyTArhe6tNid2fejPaoHZBipIDFOeSwghSoIEnFeA0hhuvVy3ZQy1WKpo0FkS2cDSdrntrR4+u24LWAbJoNcwsncj7u7ZiPwCKyajzh08AzPub0e+2Y4xSkOUTutzEgqX6f1w437Wb/4/z+XXVY7hieHXY3M4vR5Tw/HTBUwY0sxnzqkSnBWYbcx6sH3I52axOsg6U4i50E756CimvLaZG1vWpPO/riXrjMWTlXzy7hYhP5s7D57iwJ9Z3NO7Ec/e24o8s43EOBN6rZqsXDP7/8xi7IA0EioYsNqcxJc3eKodKCvWA63EV/5WVt3rdWoMUaFP51FRGtSS3RRCRIgEnFeAkh5udbi4bLZl9M6yxpU3hFwsNW5g2tnhUXc20KDXkptvwaC/fLKzV7riDHEHKk9ksTqY98EO0pISSakRx76jWTSsHU+j2hX54Kv9IX/YaF0ur+F9d3YxSqdh7caD9OqQxK5DmbRsXJV6NeLci2BUKpat853bqOyrPvv9X/x2H4o26Vn60W6G92gQ8rkZorQ8vuB7XryvNTPGt8dstZN1xkKMScesB9ujUauwWOwBs7lF93W3WJ3kmW3ElosCXCz9aDe3tKlF4zoVWbHBXXTeoNcwcWhzBnZOxul0D9cXdyX+6RwLCRWMIYNfg06DVqOO+I8YIUTZJAHnFaIkh1vzCy9sW8ZLPQRfNMs655GOIYdzzYV2NGqoWCHK535Kdi2lRjx5BdaLrll6IfVPL9fpCefDrlKF3cXG5ggfFN15UwrrNh1iWPf6QbfA9P5h41SrA5ZGAsjItvD48OtZtGYXKzfs5/abUvjkuz/85i4WzQAq0lMScTpd3NKqJlG60AXtT+dY6NaqBvFxRuatcrfHoNfQt1NdmtWrjKXQQZRei6XQPczes605YEYV4GRmAS++9ROThjanYlwU9/ZtjMVq52SWmV7tapN8XRwajYq13xxi39FMerWvQ+UEEydOF4R8jaw2J2lJiTSqk4BOo+bWrsk+z195Lrd2TUanUaOSYFMIESFlLuCcPHkyDoeDF1980efyLVu2MGPGDA4ePEiVKlUYM2YMffr0iUwjgyip4dYCS+hh00DbMl7qIXhXgMcrznBuOZPeL9hUhnfnfbDDc9sLrVl6IfVPw/XNlVDAXynFpVa5GDsgLWB2fUz/VKYs3sxdPUJvfXmmwMZjg5uhQhX0B4T3D5tQVRlqVavA4rMLfQx6DdfXr+JXakhRtO6mMrdz4rxvsVgdxMbomTamLYs/3OW3P/vofqlk55rp2ynJE/wa9BomDHG/t94rUiPz3r6NefntbX7ZVIUyN9MUpaW8KcovoE5PTuSung09j6sEyal1KwZ8PEXlBBMpNeL4z8e/MaJXPRJjDbRrUtWnXurpHAuJsQYJNoUQEVVmAk6Hw8HMmTNZtWoVffv29bnu0KFDjBo1ihEjRjBz5kz+97//8cQTT1C5cmVatWoVoRaXHpMh9LBp0cLgTrUac6GdQV2TubtnQwxRWlTOAka+uPmCh+DNNv8sa3GGc50ul8/KXY1GFXTO2/nWLLW68Au0lMdauGYno/o0JutMITFn5ycCFFjtZGSZuf2megzpVp9T2Ra0GhV7j2bx7hd7uf3Gen4BbHpKIvf2bozD6USv02J32tFr3P/HpcFqd5RKcFo0EE6pEce6TYdYuHoH4wY28RniNkVpUblcVEmICVueSKVyr3S//caUkLdTftiE+qHhnU3t1b4OmbmhyyhFG3RMH9OGGJOOfUezfLa7zM6z8szSLUy9pxWncyye/dn3Hs1iydrd3NyqJvoom0+2dN23gbOpS9buYkSvRsxftcOvDcqK+bSkRCoF2EUJ3Ntm9sgy+1z2xQ9H6Nzs2pBZ2C27/vH0x82tavLt9j+54+YGPq/VtZXLoYFil+gSQoiSUCYCzkOHDvH444/z559/UrVqVb/r33zzTerVq8cDDzwAQO3atfn9999ZunRpmQg4o6OKvy1jqIzfskmtGfHi5qBD8KEE2l4x3HCuMUrLPdM2+Fw2vHuDkBmv86lZarXbGdq9PjpNQ8+Xt83h5OW3fmL7vgyOncrn2WVbMeg1TBnR0m9uojKP7+W3t5FSI55RfRsFDjb2ZbDow12euY6j+6fy6feHubl1TRYWWYGtlOa51LtLBXtdlZXQ3v0MMPeRTqz5336Gdq9PdJQ25PunasVoxg1Mo9AWegtN5YdNqB8a3mWIlEU1oRgN7qknhTa7T8Zb0aHptSxa418aCcBqd/oEyaGmDvy6P4Nh3Rr49YPyHvjvliPc2jUZqz34qv+i63lubFmTt9b/ztBu9Vn+X4LOEfW01+Yk84yNMwVW3ly/h1F9G6NRg8PpQKMqO3V6hRCXpzJxFvrxxx+pX78+n3zyCdWrV/e7ftu2bbRs2dLnspYtW/Lzzz/jKgNZAY0KxvRLJT0l0efyotsyOtXqoBm/+at24lSbMOg1YffmVjjVavJsTk7kFgZcYfvd9j8ZOyCVtCTfdikBbkHBuflt7lqHcVisoY+dby5e2wC0ag1vrd/D/bM28viC77l/1kbeWr+HZ+5tTVL1ClSON/HKQx2Yek8rXLhIui4Og17juf+OAxms+/YPerWv4y4hFKbEUL0acew4kMHb63/nlja1mL/KfyHXrwcyWLBqJ071pfvohnpdlfYX5cLF/375m+Wf/o5GrWJg5+SAr1OPNrV58JVvmPfBDvRaDW0aXxOwDZ4fNpyryhBIhWgdg7omM2VEC6L0WmLLRdEkOfBt05IS+W7HMeZ+sAO1Su3z2iiUPg9kx4EMn+x+uJqbhTYHt3VN4dWHO/LqQx149eEO3NO7EQmxBm6/KYXdf5wKmb3dezSLdK/nUq9GHJt3/cPPe0/QunFVXnmoA5OGNmfKiBak1IjzmSMK7qL2vdrVxm53klIjjuwzFnYdOo1OrZHsphAi4spEhvP2228Pef3x48epUqWKz2WVKlXCbDaTlZVFfHz8BR1Xe55ZvtKm0ag9/4/Cyf0D0sgvdFBgsWEy6IiO0uAE8iwOT4Yv3CKeXu3rYDLowj53i8Plk1Eb1DXZL5v55mfuTGXRFcvGKC17/ziJ0WQEfOdthst6RRu1Ydum0ag5kVkQNAhb9tFuHhvSLOC8Ru/aiMrtlXmE+WECcSWg6dT8Ok5kFgTt61/PZmpjL0H9VavLnV0e1DWZu3o04FSOhXkrt5OdZ/VrvyItKRFLoZ3YGD1jBjThRGYB+WYbd95cj9H9Ujl+Oh+Twd3Pp7ItTBjSzDNMfW/fxhRY7f77zfdPRadWAe40X6CqDK0bX0NCrNFnZyAlu6wCv+zy0G71OZNvpXbVCmSesfDo4GbMLBKkhQsiXU6XJ2sZbupAgcXmKZeUnpLIPb3dGUanVUVBoZ3rG1TBoNdg0GsCzvVct+kQsx5oz+K1uz0/UADqVo/l2WVbGdQ1mX1Hs4JuQWqxOpi5fBsvjmvLvqNZtE2rSlpdIzo1cAl/oJwv7/OMOEf6JTjpm6vTFR9w/vXXX3Tp0iXo9d999x2JiYEzIAqLxYJe77soRvnbarVeULvUahVxcaGLQl8uypc3ev7tvUTh+Ol8n8UNL4xuE/Jx8s02UutWJL6CgXKm4MWlT+eaWbJ6h6eAt9XmJEqvoWWjKrz3uZqf9pzw3PbXg5lcd02sXzYH4JWHOgC++1mn1IijQ3o1enesg0mvxWJzYCl0YDJoOXo8lxiTjrhYU9g+OX0sJ+TClYWrAwejSnu8h16VwKHoXNiijFEaZj3YHp1GTb7ZxpQRLfx2lFHkm+3Uqloh5OOdKbCSk1foCdQrxET5vC7/nMpj4Wr/IfvnR7fhyYXfe4JO76CsY3o17rylPharnelj2rJg1Q6/xVEjejXizU9+93kdlSFgi9XO8G4N6NnWgsmgo0KM3q9divGDmlBgsXmqMhijtH6LbZQi7SN6NWJ4jwb8c6oAvU5NbLko3vt8n08b0lP8fxCECyKzzhQyqk9jFq7Zxd6jWTRJTgy4m0/RnY2273PP62ydWtVnXmd6SiJTRrT0zCX1Lpjvcrm3sR3drzFqlQqLzcELo9sQY3Rndb/44QjjB6UDvqvQ05MT6dHWPbyeUiMeg07jXtSmUpGYcPmcg7zPM+Ic6ZfgpG+uLld8wFm5cmU+/fTToNcXJzsZFRXlF1gqfxuNF/aGdzpd5OaGLmkSaRqNmvLljeTmmnEU2f/a6oIFq3y/3MMFTNFGHeZCO/ZCG1mFwYcOz1jsQXdPGdqtPkO718dssaPXa9i885+AwSaA+WzGsN7ZxS2DuibTqHYC/6pXCY1Kza5Dp1m2brfnvukpidS7Lo6srHy/xyraL6GGPhvUdL+nerar7VMGZ92mQwEzgkpQczrHEnJO6jUVY5i/Kvg8Su8+iDZqQz6PQocr6Er5KI2KQmfgBVG/Hshg6drdjBuUzvOvu7N1VROjmT6mLeVjtOg0GuZ9sMMz3zTQfNQla3eTUiPOJ9hTbjeyVyNUaojSa4k2aNFr1UHfL3oV6I1a1CoV42b+L2iZLIvVwfxVO5g2ug0vvvVTwEygQa8h6do4ovQaHh9+PQ6Hk71Hs8g5Uxh0/mlaUiK/H8nkfz//yej+qWTmmGmfXo1l63aHnU8J7oxrz3a+7wXlfiN6NWLZut1+BfOV10nZKUl5zdOSEhk/KJ25K7dzY8uaPqvQY8tF8cSC70mpEe8pf+R0udCqCfteLw2hzjNlmfRLcJHsm/LljZJZLSFXfMCp0+moU8d/jtn5uOaaazh58qTPZSdPnsRkMlGuXLkLfly7/co4iTgcTp+2OlQqTmSZ/eYPhguYjFFaVBTjebuC757y1qfuoKRijJ48mzPoIg1wLwgBsDtcAb+4iwZrSr3Hsf3Dr1QPtnDFoNf4DesWPZZ3RjA9OZHYclHccVMKlkKbu0h4kYVAaUnuMjxL1vovXgmUNU1PScSg1wTt50AlpgCf5x9uyH7Y2aLoyk5WMTo1TrWauWczjL3b1y5WQfKil7twcf+sjT7PPVy5KqNOTdu0a8KWyTKdnWJQdHFPoJ2QwN2PLRtVYWSNeJasdZdGUjKOqXUrenblMTW+hqcXb6ZD02tpUDOeu3s2JONszc1K8SZ+2B38R1GgIfvt+zIYeksDXnmwA4sDvObb92XgdPq+5sptbmxZ0/c5JCdy5831mTCk2bnyR4AGyLXYL6s6sEXPM8JN+iU46ZuryxUfcF4KzZo148cffffl3rJlC02bNkUdwblPkaAEKze1rOl33byV23l+dBuWnJ1jplCCBo2zAGOUKWx9UBcEDXaUoARUYbf0NJ/NilWMNQQsKB4oWNu+LwOz1Q6oyDdbiTYELjVkMugCHrtX+zq8vm53yGMpGc20JPdQ57NLf2DCkOZ88NV+5n2wg17t69CzXW1cQEIFA1t/O47N4fDJCBZ9bCWAU4JTjUpFgc0RMJAIVGJKsX2fe/5nXkHo4M1ssbvLNfVpTKHNjkGr8amPGW7uY7Drix5339FMdh48RUqNOPIKApd+UrlcDOveMGzAadBpSEtK9Dt2oJ2QwN0XLieMGZDK6P6NsTlcqFQqlq7d5ReYPnNPa88uQ6DyzNWcMqJFyB9FwYbsT2YVoNOqg75OgYL2QHVFR/VNJedMIRVjjVxXuRxqpxMH/juHXQ7b1AohyjYJOIEhQ4bQt29fZs6cSd++ffnmm2/4/PPPWbp0aaSbVuqUYKXoUCC46xY+ufB7xg1KZ0Svhj6LeDTOAuwqI9pi1Ig0F4ZePGMutBOj04fc0nPcgFTAvRuRy+UKGcAW/eI+mWnmubPDxRC41FDleFPAY6fWrRgyszewSxImg9Yz/3LG8m30al+HFRv2ex6naDCj1PP0ns9XdKjeGKXl+ftaUznehMblYtZ7v/Db4cyAgUS4KgH5ZlvYuYsxJh1J18bx0CvfYLE6SE9JZFAX9y42sTF6KseHngdb9PGV52YyaJk0tDl6nZoDf2WTfG0ca785FLZIv7nQzqmzGXZlJx7vfjqdY0GvUXNPn0Zknyn0OXagckbefZ2ZW0i0QcsZs5UVXx4IGJguXLOTlLML0hJjz20hufdoFs3rV6ZWtQp+r9vhv3N85nV6iysXRWZuYcDrFIGCdpNBx7TRbTAatJzOsTBp3rfUqlaBcf3TUDmdYbPbpblNrRBCeJOAE0hKSmLBggXMmDGDN998k+rVqzNjxowyUYOzKCVYUQpVF/3yzc6z8vG3fzB+YJr79mfLDJmiTMUKNsFdX7O413tv6VlgtmMyaonSqXG6YNHZoelJQ5uHfLyiX9xF6x0qpYbGFSkKb9CofLYTjTHpyMkLHSTotGqeWPC9z/BqqPqN2/dlcCrHQoUYPROHNudUtm/x78RYIxOHNifGpMNqd5Kda2b55/vp2ymJ3w5vDRhIFGeu7fb9GUGzx+kp7ukRP+w65nke2/dlcFePhsTG6Hl+dBv2HM4MmX32DrSCDWk3SU6kce2K7Dua6XN/pczWuIFpaM6+HvlmG/NWbmfamDZkn7GyYsN+v8A9LakiNruDihUMPvMyi77+wdrz/H2t/eZ9ege2VRJMOF0unl36g2fxzhc/HPFk/YtOsRjVtzFPLPjOv3+TE9HrNGGD/kDXmwxanykJ4H5t8s/Wlw2X3b6QGrlCCHEplLmA8+233w54efv27Wnfvn0pt+byowQr6zYd4rHBzYAiK2LPbvt3MdtsmqK0NElK9NlOUNHk7JxB78dTO50Y9BqWrfuNHQcy/BaFnM8Xd9FgSKGUGipaFN77eebZnFhtgbcuVBijtH5z+ZSAJ1gGM0qnxqTXEqXT8ONvx32yZVUrRlMuWk+0Xsv4mRt45aEO7DiQwfCzcyzBP5Aw6kIX8jdFaTn6Tw69zmaxi2aPR/VJ5T8f/8bEYc0Z+/L/PNfZHE4mDG3GkrW72Xc0kxdGt+GtT/3vf08f9yp1RbAh7V/3Z6ACpo1pww+7j/usxt9xIMM9lK/TUnA2I/7cqNbotRoyMnN9SmXFGHUYorQUWAootKqxO2wMvrk+Lqf7dY0x6HxW/8cYdZwxWzn6T45Pe7yH+4PO+0z2XbwzrHt9ln4UeIrFko92MbRbA45nFnhez3ImHeWi9djPLlpKT04MGCAGep+mJSVi0Ac+Zeeb3aMC4bLbgbapFUKI0lDmAk4RmnewogwHKytiY0w6KkTrL/pNo3Y6GRNkqHzMAHcw673NYly5KF5bc+62RTOGwbKxymMqX9ze5WMCUb60g8k320IeS1nMU5Repw4awKQlJdL5X9diczj5cOPBoKv3x/RPpdO/quF0uTDoNZ4V+grvQELlcjGmXyoL1uz0r3d59sfCyF6NWLput09pqhiTjrhyUTy9ZDMnMs3cVmQrypff+onJd7XwPPcz+Vaf+ysB9JMLv+fGljUZ0q2+Z9pF0Azv2ekb+45m+SzwMug1aFTnFikpmiS7+2Lxh7v8yi6NHZDK2o17+N8vx2jRoDJDu9fnDnsSseX8t5NMC1D+yfuHSdB5n/szcLrOLd6pVyMuYJkkcP8QGN69IZt+/dvv9RzdP5VGtRJom1oV1v/m9zopq9S97zOqb2NcrsABZfTZBVPhstvhrhdCiJIiZx/ho2iwonxRpiUlcmvXZIz6S7NrifdQuVJn0XR2sUjRbRanjGjhkwUqOkQaKhs7qm8q/3f8DFNGtPCUjwm0mjg2Rk9CBQN5Vjv5uYXkmO0YDVqfrbiijbqgx3IHBO7ah0UD0r1HsxjRq1HQlfkrNrj3WB9yS30ycsz0bl/bs4+5xepgx4EMFn24k/v6prL0I3cpHVORou9FAwmNy8W4fqmYbQ4KLHbiYrSo1FpPEF/OpGPwzfXJyDZ7gsWdB0/5ZBmLLtI5kWn2ucxidQQNJFdu2E/tqhV48a2fijXloegCr17t6wRcwf3rfvdwe6CyS8owfJsm15JQwYDZYqd8dBQ7DpwKOGy/ZO1u7r81nb1Hs6hXI47y0VGejGOoaRDe84LDLZ46lW1m75FM7rgphWb1K6NSqdCoVRRaHURFaVCrYUx/d7Y2r8Dm+Rxk5pqZPrYtZov7fWgptBGlUfHEa1v9jpHmNSoQLrvtvU2tEEKUJgk4hZ9zwYqTfLMNQ5SGKJ0GvVp1Sb+sAg3LO9Vqvz29i36pFx1Ct1gdPtlYg16LxWontlwUWpWK9d8f9gzFp9SI9wtiYmP0TBvTNmANTO/FK6YoLSk14v0yv8qiFZ1aBQEWOq3bdIgXx7X1KQCuqBxv5NYb6oXdtWj7vgwKbe6V7Fa7k6Hd63tuGyyQULlcmLRqTDF67CqVX9H02Q918Ky2DqRoaagmSYk+lxV3KkNxb+cdyIVbnBWs7JK50E5WrgW1yv2+UbY6nTi0OS+99ZPPj40dBzIY0bMh+466M8pKFtrpKsYORC7ftgejUrmPHaXT8On3hxnUJRmH00Wh3YHLBTaHi0OHT9OwZjyvrdnJob9zmD62DRVijBTa7KjVKrQaFYmxJlQuF1USYjiReW6er/IeVeYeh8tuy4IhIUSkSMApAnIHKypM5byGmEvhy8q79I6i6Jd6oGFtJduWlpToKUqeUiOO3w6d4oHb0pm/amfQ7OTEoc147cPANTDnr9rJ+LOLibxXzRcdIh07IBXV2S99rcvFuIFp5JttFJzNUBUE2cN90tDmxd61SMku7jiQwZk8d+3ZQPvdF3jNbVQWYBUN4gHPqu9gUxE0mnOrq5okJTKyTyNcLpfPCu3iTGUo7u3AHeilJSWiDVN4OVhAmG+28d2OY37B+61dk+nXqS7vfr6P2Bg94walk1DBQL7Fxt09G6LTqjh2qgCNWkXPdrWpGGsIefzEWCNTRrRwZ0WDZRSTE3G6ICvHzO5Dpxh8cz0ysi0+FQvA/RpWjjfxyO1NuXvaBvLNdn78/QR7j2R6Vp8rc5qDjQp4K5rdvlzqcAohyjYJOMVlJVCtxaIBS6hh7V7tavPfLUc8O7+k1IgnO9vsWWRSYLYxqm9jCq0OMrIL3HvGG/Uhyyp5LyYKNBXAGKUlz2xGqz5XQ1LjdFLOoEWrUVFgsZ+t3+jLoNeg0aiLXdIp2qjz7MNt0GuZ92hHn0Ci6FQEpU9G9GoY8Bjh6qo6XQ5m3t+W8tEGLFY7Z/KtxBh1jBuYxpIPd/m9DkWLphfaHCG3ZAy0Q0/lBBMpNeICzoX1FqzsUrRRxy2t3Dvx7D2axTe//EnPdrUxGbQ0rlOROY9UQa/T+gX5ymKnJxd+T41rKjCse/2gC3rSkxPZ8ts/vPf5Pgx6DbMe7MDiIj9Y0pISGdm7ERlZZhIqGOjXMYkdB0/x7a/H/F6L7fsywAWj+qa6h8cNWv74K5vR/c79iFEUd7Ged3YbkGF0IUTEScApLiuBdvgpGtgoQ+gjezdiZO9GmC02DFFaVKhQqV2M6NWI46fzmTCkGXuPZjFl2Vbq14pnTN9UTFHueYwWq4MqCdFoNCpy86x+x/SWb7ZjijL4LWaKMerQadU8t+wH/u9EHuA7DO/9pe9SqfwyYb3a1wlbgF3J5DVJSiTPbPVkPGNMWveqdK/MZqAs5o4DGe5pEUFWyD+37Aeeuac1VrvDs8DHoNecPTGoKR8d5TfVQKlbWmhzcCKz4OxiFidajYaMbDN5Be7jOZwu0pIq0jSlEga9hqHd6xOla8iJ02ZUKjx1Sr23b9yy6x9WbthP8nVxF112KS0pkWfubc2yj3Z75ns+eXcLPg4yl3bJ2t1MH9OW//3yFx99c4j7+qWyIEBgOrp/KsdPFTBtdBucLhf5BYEXT32/8xh1qsVisztRa1TElzcE/XGxfb97Vf59/VKxO+zc07tx2N2whBDiSiIBp7isBNpdSAkwR/RyB5j5BTZMXsOJ0T5lXlSAi+qVYigotJMepaNtWlXPbQusDu6ftZG5j3bkZKaZdd/+wYheDUO2KdqoZclHu7i7ZyMWrdrhty3l5Ltb8MWWw9zcujbmQjuncyx+O+Y4gYGdk3E6z2X56tWI82zPGYxep/YMZz+58HvuvzU94JzNQFMRFDEmXdCA7N4+qdgcDk9tx6dGtOC6SjHgcgUNYn/1mmrwybeH2espkfSbJ9P52OBmfoGdEozXqBzD/CJzDItmO4NlX5VV6kvW7vZcFmxF+Y4DGSxc7bvAKKFC8KBvx4EMbHYnh//O4fabUnj/i70M7V4fnebcJgc2h5N3P99Lw9oVWbZuN4O6JtG8QZWA800Neg3Tx7Ql64yFfLMt7LxQc6GDifO+4+Xx7STYFEJcdSTgFJeVYLsLpdSIJy2pIlqXi+jy4Wt/Bht69NQp9NrPPerslojBsmk2h5Ma11Twy3bB2f3f1//OsO4N/BbleLKdZxfsKDvkKJkwk0EXcn/69OREqlaMoUe72p7yPS4IuPgj1LaPBp2WTdv/Chg8ffzNIe685dwCpMpxJs+uRaGCWGWqwZgBqZgLHbzxyW8+806DBYDKSvLWjasyrHsDjp8qoEqCic27/mHx2p08OrgZCRUMFFjsOJ1O7u3bCIfD5Ttn0eXi3r6Ni1V2qei0hLC7MFlsJF0Xi9Vqp3+X5IDTDe7p0wiHw0nTlEpc3/CagEXwlYyy0+UitlwUarUKvSX0vNRoo3tRWtE6tEIIcTWQgFNcdgLtLhRoccSFUMoHee/nPnP5Nh4b0ixwXdD+qUxZvJlRfVODBjWdml3nd1/wDbACbWs555GOvPjmj0HnUY7un8qyj3ax9feTnsu9A0JvgaYiKArtNm6/qV7AeYuj+6dic7iDMHfm9NwwfaAg1ntoPt9sI6/ARvloPXuOnCs7FK6kkLnQTvVKMWhUKgx6DcYoLX+dOMNTI1oGnU9q1NnRoPYEYpm5hUyc/x2zHmxPRpHdmYryzixGh8koG6O0dGx6LeAK+pouWbube/s04vYbUzxF8B8b3AyVyl22qegQv0GvYfZDHUL/uEhJxKjX+qw4F0KIq4kEnOKydDE7GYWi1Cn0znQd+CuHGW9v49HBzSi0OXwygFMWu4ughxoODTdMay60n1tQU7siFeMMWO1ODHotNa6p4NmffniPBj51F3G5fIJN74Cw6FxSU5SW6+tX5kev2pQKvVbnNw9TadvC1TsZOyCN9JRExvdLxalSkW91oMJ/C9JAcyUNeg0jezdi+pi2nMwqIEqvCbobjiLfbOPxBd8DMHZAGp9tPsywHg147cPAO/YoQbv3e0CZiqDTqCmyU6n/8z+7wKhyvJFooy5kNluvUzN/1Q6G92gQ8jV1ulzotOqz2eqaqNUq7u3TiJOZZmJMepb/d4/n/harA6vNwbWVYri1a7LnMRSegvyAWhb3CCGuUhJwijJFqVN4Ksfic/mBv3IY9eJXTBnRImBtylD1FsMO05rtzHmkEzl5ZspHG1j60W5qVatAo1oJ3Ne3MYs/2sXzr587ppLV877Mu/xRoNXoykIewCfoTEtKxGINPTRusdoZ3y8VO+fKJ00d2ZJyMXqf4KzoULl3ADrvg3M1Rp+/r3XI/og26pg0tDl6nZrYclEsW7ebQq/i74HaqATt3jVR05MTw+7+5L3AaMLQ5vzn49+4p0+joJnUNz753T1dIMRratBr0KjVvPbhLp/5vMpOVnkFVr/dh15440eeu7c1u/84zbDu9YEGWArtROk1lI/Wu7PWktkUQlzFJOAUZY7G5SIx1hiwfmKw/a1DBTXhhmkNURrmr9rB6P6pvPv5Xp/tK5XMZ/9OSei0aoxRyjxFJxOGNPOro1h0IY/3EPefJ/IY2r0B9/RtjKXQTt7ZHYXOhFkJn2+2Ex2lZcd+93zHbq1rklDBwB9/53Bvn0YsPhucFR0qDzZXc+fBUyEDwO92HPM8Tnqyu8C9pTBc0G5j2brffGqijuqXitXmCFkma+yAcwuMdBo1m3f9w++HT/tllE/nWLDaHWze9Q8QegvIXu3rsGTtLr/3iLLt5RCvObGKE5lmnlq8mQlDm6PVqN3bqJp0Z6eKFDD3wwOMk8LsQoirmAScokxSO50Bd2Q5+k8Oo/qleuY7KgFdk6SKdGxa3S/QSE9JDDtMezrH4hm+Htq9Pm+t9x1uXblhPys37Cc9JZGka91F68cPTMOkxa+OovdCnlDlgJQV348ObkalOGPIvog2arE5XQELpleIiaJP+9oM79HALygMNldTCQDVKvz6qkcb37qbe45k0jq1KpXi4jxZz71Hs3y22HS3UedXE/VMfiGx5QxBd3/KzLVgttm5pXVN7rylnmdOanae1Sd7rJg+po3n36HmW4bbBWl4jwYBrzuRaeaR2ZuY+2hHdFo8lQEUZpvDXepKCCGuQhJwijKr6I4sRoOOfUcz+Tsjj5QacQzoVJeKsUYWr93Fyg37PTvUDOveAHOhHZNBh8mgJSevIODKeu9i4uAORnQa/yLs3llKk0FHu7SqQdvsvZAn1Gpw5fqECgZsDmfIgNgYpQ06x1OtgiHd6rNl1z/UqxHnc32wea1KGavn72tDz3bnAsCKsUYmzP3WE0h6B8ze2356b+tp0GuYMNSdvXxhtDsgdKrVqJ1OjFFaTpx29/2CIrs/Na9fmdtvSiEvz12jVaNWhVxYBb4Lr0IVxVerQs8azTfbQi4O2nvEnS0vqsBiP/cDQwghrjIScIoyTSnObiwXxbzVO9m+P4MpI1q4i4/f3YJFZ3eQMeg1jB+U7hfgKdnEOSu28sDtTdFpGpJXYPMM0yrljBRKwJhUvQIThzTz7FdvjNISpVOj1ah545PddG9bm2ij3meLSrXTScVYI3Me6VjsckAFFjtzVvzCM/e2DrhKfeyAVLLPWPzmHCq278+gZ7va7DuaRctGVXwCqVDzWi1WB7n5hTy7bKsnoG4ba+TZe1tRPlqPzeEkr8CGyaClZ7vaHP0nx9NPyuMP7JJEq8ZV/bYd9axc12vJVln56sejjD27lWi+xU60QYtOq+aNT3731N8EWPx4l7BbeSrXZ+dZPYu5RvRqyLGMfE/2VdmfPZgonYbR/Rrz2tpdfrVGR/VN5cF/b+Tl8e387hdqGF8IIa50coYTAvdwpjL8q8zX9F59Hi6b2LJxVR6ZvSnooiNFtFFHUvUKTBjSjPlnA1yFsuf3sO4NWf7fvXyz/W/PdcqioN2HThEbE4XV5gy7W6HV5iSuvJYTmWZmLfdfhR+l0+B0OTidUxj2cXYcyOC9z9UM7Vaf5f91b8cYbrHOsVNnWDypM05UvLbm3F7273y+zy+AfH50G5/gfMeBDEb0akj2mULu6tGAUzkW5q3cTnae1bNy/fYbkqkaZ6LS9TVYEKAgf692tdl16JQno3oq0xw0Ez12QCoWm90dKJ5dDKQMvT91dwtefOsnz+0HdU0OWTs12qhl2brfSLo2jp5t3RneGJM7G34mrxCL1UG+2U7DWvH8dthdTipQMX8hhLiaSMApBL4rzZXAyOx1Wbjakr3b1yY2Rk+FclE8f19r8gpsfvMRleHrx4Y0Y0GRYFN5HIC2aVXp1aGOT8D564EMFq7ZycAuSTy7dCsWq4MpI1qEfE56nZrTORbapF7D7TfVC7hN49gBqZSPDj3UbNBrGNQ1mQY144nSabi7Z0Pyu9oxRGno/K/qfqu105ISGdgpiUoJRnYcOOWZGzqoa3LQoH3J2t2MG5TuM7fyWEa+J9ArGpQe/SeH+AoG8m12TmaZ6dmuNknXxXn62vuHgvK65Zpt/LEnm9H9GmO1Oz2BN8BTr7nLXyllnob3aMA/pwrQ69TEFBmKX7fpEBOG+M9RTUtKZGCXZHLzrOw6dMonu6pcP7pfY8A9b/bB25pyz/QNPhUIhBDiaiUBpxD4DmcqcxC9hz3DbUvocLqCzvl7bHAzPt9yhKE9GvDyWz8x/tZ0v2BToQSvOo3/cPX2fRn075Tkmd9YnHJAX/xwhBn3t/fbBUk5llLjMtjjNK9fmXLReg7/nUPKdXEs+cj3+bVoUJkx/dNwOJ0U2hyYCx3EnN2Pfe4HO+jdvrbPVp6hgvYRvRq6g9KzQaP3kL13UKrMrwyUqVT6Rgk6vXcZ0uvULP90Hw1rJvLQ7G8Y1DWZw3/nUKtaBUb1TfXZB71qxWhPsFs0o2mxOjj4VzatU6v6zFHdezSLZ5f9QEqNeJ9A1/s5WO3u+bSmKC1mq515j3b0VCAQQoirmQScQnCuILwy585idfhseRlqviLANQnRfsEmnF14o4Yx/dM4nV3A7TfVC7kNJbiD22C3UZ/dnefRwc2Yt3I74wele46jUIaTF6/dybhB6ZjDbFFpsdq5tUsS7ZpUJb68wRNA5eQVUq9mPKdzLPRoVxutWkVKjTj2Hc30DFPvOHiKjGwzK7/a7zMPdPZDHdhxIINbWtX0eV6h5BXY2Hc0i8cGN+O/W4546md6t3V4jwaMG5QetK/BN6tpdzgZ1DWZejXi0GjUPHV3CxxOFwa9hgY140m5Li7gKv8WDavQomFltv52ImDZpbrVY4NOnVACXe/FYEqfOpwuxg1I5UxeAYUONZXK6WUYXQhRJkjAKQTnCsJ7l0ny3vIyXDbR6XQFDeq273MXLp+0YDNpSYmM6NUwZFv0OnXQFdUWq51nl20lPTmR8YPSmbtyOze2rMndPRtQaHVgMmgx6LUU2u08e29rFqzeyW03JIc8ntlsp2KckZVfH/AEjQa9hikjWrI4wHD5hCHNOPhXNo1qJpCYYOJEZgE3t6xJr3a1PVMI8s9OR/AO1MMF7UaD1tOH9/ZpxCOvbvJvq8UedmengZ2S6DCh2tk92G20S6uKXqdh5vJtHPgrh/QUdya0XLSetz7dEzBwfetTGN0vFYvVPX91xvJt9O1Ul6Hd6mO3OwkXItodroAlq5Th89/+L5vGtf1XqgshxNVKAk4hzjpXJsmJxWrHoNeiBcYPTMNstbvrcH7kv/K4V7va5IXJWuabbcx5pKNny8hAReeVxzudY6FCuaiA1ylZP6XI+I0ta7Jyw37aNamGIUrFmQIbLpf7GHPPDqOHC3DLx+j9hqd7ta/Dig37g5ZK6nr9dSTEGwMu1nlscDOije5Ti3egHi5oP31296cdBzKw2Z0+dTgVRoPWZ25tUZXjjVRKMAYebh/SjBlvb2P7vgycThjRs6FPrVXvTOTeo1kU2uyk1Iijd/va2B0uqiVGs+Sj3fx6tpJBKBVjDby53j+Y3b4vgwVrdjKqT2NABWFDVyGEuDpIwCmEF5XLRXmDhhrXlCcrKx+73Yna5SL6bEHusf3TPHuYR58tV5SRVeC3sKSoaKMOtco9dK8KUnReWaWeGGvg3S/2+tzfu5i7Qhm6TU9J5FjGGaa/ee665+9rfa58kVYdMtCzOfyzs6HmW27fn8G9fRuzoEhQp7QJYGj3+qQlJfoMR4faEci7XikE3i5UmfuoCVEHc8LQ5n7BpnK8+at28ujgZox68StPEflQxfM7Na3ON7/8ycoN+xnUNZn13x/2PG6o4Dk9JRGXi5AZb6vdSYxeI/GmEKLMkIBTiPOgdjqJ0akx6A3MX7WTo//kMGlYMwxR2pBBnelsHU1lvp7G5WJc/zQKrA4KzDaiojQYdBp0GjUq4M6b6tOleQ3UKhUWq529R7M8i2G8uYDbuqZQUGjnusoxZ2uBqsk325g6siV2pwuL1cEdN6YwqEsSOw6e8lk1f0+fRpzJt/q1Odx8S6vNEXJY+0xeHe7p04ila3f77AKkUqkY1bcx5kI7doeTKJ0GvU7NEwt865UWrUmprKifMPdbJg1uxqKJnT0rzWOMOtQaFS+9+RM6jTpkuwptDmJj9NzYsiYmgzZkuavFH+1iwtDmPDJ7k18AHip4HtQlmcIA2Vlv+eZzOyYJIURZIAGnEBdA63IxfqA721lgtqN12UPWeFQ7/QM4ldOJSadGhZYCix2tWoVOrQKXCzVQKc7IySxzyLqeleKMPDbnWxrXqcjku1t4so5K5u7jIsFUekoi/36wAza7g4xsd2H6Z0e19nvccPMtzYWhAyqL1cH7X+xlRK+GOF0uLIUOjAYtKpWKfIuVP4/ncSrHzLuf72PS0OY+wWZaUiImg9anwL0xSovVbufxIc2Iiw08ZP7k3S3IyrWEbFe+2cbzo9vw5ie/A6G3qdy+L4O7erinIxQNwJVKBkogHWPQEhOtx2Z3kme2U96k81lxX5Qy5UAIIcoKOesJcYGUbGeMzr0doXcQmm+2E23UnstsBqHsdFR0z3Tl8SrHm0LO9zTotVisDmpVq8AirzqbwTJ32/dlsPjDXYwdmEalOBUThzZHBUwb05qX39rmCfzCzbcMFzDFmHR0bV4DvU7Dog/9pw64V9HvAnyD27SkRMb0T+Wp1zYz+a4WPLP0B8+Q944DGSx5vCvzgmzDOX/VTk+dy2CijTqWrN1NSg13zc6mKZVC3j7fbGP2Qx0CLiS3WB2s3LCfyvFGzwKtUGWaFOkp7ow3Id4XQghxtZGAU4hLqGgQejFBhVOtZu3GA4zuFzxzunTtTsB/zmWoOZi/HsjgRGYBTy7a7PN408a05YkF35GdZ2XdpkNMGdEStRqfYLFJsnvIWKNW0SQpkV+DzGGsHG/isTmbePC2pozs1YisM4U+xfCVIKxJciLXVIxm+pg2RBt12BxOpix2F2HPN9v8AmezNXSJJ5vDFWa+qtMz93Wl1YFBrwn1EmCI0vLgK98wdkBawMDfoNfwzD2t/IJNpT3gW6YpPSWR0f0CZ7yFEOJqJgGnEJepgkI76zcfZf//ZQfcljIn10yNqrHc1KoWUXrfj3Jxal5623Egg9c+3MW0MW34v+N56HVqdv9xika1ExjWvQGncyyogAN/ZeNwOvn/9u48Lqp6/+P4a1ZmBhRBRtRMDQVEBcRQcQHLvC0uuKTWvbkV3cwts36aWdltvRXerHtdKpf7ULvWbXG7Lbdu3cqruWS55gKaS5opCoLADMPMnN8fwxxnmAHMwgU+z8fDR3LOmTNnvgm+/S6f7+qvDjChiikEA3rG8MaqXdx1WwJ7DufTnkhcboWPNx4OuHZgrxhOninlT4s2BTxjqNlAarsmalhrFGassYZpia1cndqw/0i+uvpcUSA60sK3+37GZNSp7XO60H5BK+cXr93NrKw0wD+A3zfYs2tRdSE4K7MD17drgsVkwGzUcc5mJzzEWO3nEEKIukYCpxBXqBJbOS2jw3jwD53VsOldIDP7za08cGdn9h8p4J3PcpiV1c2vvE+jBqZq7x1sjqZnUY3bb99wgHc/z2XOg705caaEVtENcbkUbrz+WhzlLrVskG85IW/v5ej+7Sko+on9PxZw4OjZoNd+uukwrZqHEx1p5tExXdHpNJTYnJhD9Bj1ngVFjSqmGzw7vidud/XLui0mPf/eeJjJw5NxuhVeX7kzYPX5tJGp6LSele4LV+/kqT925/XV/uWuOsVauddn5bzd4eLpxZt4ZWpvHE43J06XEB1pIcSo46fTJdU+U6nNyZy3v+Vkvo0Zo7vQ3Bpa7fVCCFEXSeAU4grVqKHBbyGQl7em5IpP9qnHDxw7y6ysNP75WY5axqembS+DsZc5mTO1N6V2J6EmPacL7SxcvROn63yPqVtRKLG70FB9b+O5Egff/5DP4N5tSGrjWZzjVwQ9zsr425P41/9+4On7evDaSv/95VPirIwd0IE/T+zJyfxSFq7ezX1DOlb7ucwhem5MbYmtzMmitd9XOcw9bkhHGoUZuW9wEovXfk/stREM7OUJw2EWA00iLPxcESR9g3zBuTI0Gg1/XvoNv78lnn2H8hmYHhPwLL4sZj0n822AZ26rzN8UQtRHEjiF+I25tVq1VmdYxQrri5mzZ9CeL97uy7tAZnT/BL787jjgKY/0zufnC7VXVbYnJd4z5O1bz9OXuWLOoleXhGieuq8Hr/uEQZNRx7ghibRoEqb2sHolx1p5ZHQXfjh+lgahBn5/czzmED0hBi2ThiVzpshGfpEDo0HLyTMlaICb01pxLK+YgekxxLaMUFd2b8vJw/2v77l/aCLWCAs7cvN4cek3VYbwicOSeGbxJo6eLOa58T2qHeYuKCpj0ogUdX7oroOnoSJUFpeWo6EUvUHD8xN6kldgZ+WXB9TP+ad70xjRN46u7ZvSKrohYRYjE4cls3jt7oAV6d4Q7P19dKRF5m8KIeolCZxC/IacGg3zKoVEbxjS/8I9s0tr2APdoDu/g1Bsi0a89cl+9evKZXs8q9mdhDcI4f3Pc4PurHPoeCGnC/3LCl13TTgLfBbEeMst5RfZeW1l4EKZ/UfyCTHEsfvgGZZ/fL54vW9R+4WrdwMwbVQqc4P13vqs7N6Rm8eZQjvWRmZMRh1HTxYze/nWgDmt5hA9p86U8odbEjAatDhd1bf1udJymltD/UpIBWxDGWdl+E1xRDQMYVBGDPGtIvjqux+JijCz/6uCgGtnZaXx9OJNauj09uCWO53qYiGd7JsuhKinJHAK8Rtxa7UBYRPO90hOHp78i3q3qlog4x3idSsKM0Z3wWjQ0jA0BJNR59fD5i3bAzBjdBdeWPYN0ZFmnrqvBwveD5zbOP72JJ5842u/96q82t27anxQRoy677qv6rbEBOiV3Jz/q+h5rWpHIK0G/m9kKrMrQqd3gdOQG9uy6osD3Hlzu4BV4Z1irYzql0C5y7MlZtPw6uewWkyeOaKvTO2NTqdlydrdgSWkKrYPHdM/gacXbyY51qq2XbBrFeD5CT05lW/DaNDSPCoUp9tFuVNhwu3JnrApgVMIUU/Vi8B54sQJsrOz2bx5Mw6Hg6SkJGbMmEFsbKx6zcaNG8nOzubAgQM0bdqUCRMmMHjw4Mv30OKqU1OPZGnZhe8u49JoCA2yXWZ1vXHBaj56eRcJ9e58bUBgMhl1xLeK4PRZGxOGdcLlcrPvSAFr1x3E5VYY0TdO7Q2Nbmzhnc9yuK1766DPXTmgVt6nvGljC4qiqOWJgtmWk8ftfWLVz2M0aCkuLadHYlNuSGnBays9K9B9n8to0PLD8UJK7Q6uadIQo15XZf3Sbh2iaRYVysn8UopLy4lubPGbO+rL84zt1d/nF9qCLn5au+4g23PyyEyP4YVl35ASb6VrQlNaN2+IRqOhQYhewqYQol6r84HT4XBw3333ERkZyeuvv05ISAjz5s1jzJgxfPDBB0RGRnLw4EHGjRtHVlYWs2fP5osvvmDmzJlER0fTvXv3y/0RxFWi5pI9zvP1OauhaDTMf38n9w9NClggU2VB94reON+aj16+i4SCBcKq9hKfMboLzaJC+WD9Ib+eUqh6JyLfckxVhuN4K38clBjQI+uruLScjzceJiuzI/uOFNC+dSQGvZ68szb2Hs6v8r73DUrErShoNXBn3zjuGdABU4iek2dK+P5wPh2va4xer+VUfik7K7b5fPDOzkGfwcte5lQ/T8OwEKyNzH7nrY3MPDK6Cy8u+wZHuZvkWCvD+8TRqKERp1NBo0HCphCi3qvzgXPr1q3k5OSwbt06oqOjAXjppZfo2rUr//3vfxk2bBhLly6lXbt2TJkyBYCYmBj27NnDokWLJHCKCxasR9L//IV9u9nKXew9nM+ZAlvAdpnVFXTfkZvH8JtiA4JjZvr5RUKV63NWt5e4Vgs9kpr7nfMGzap2IvINotXtdrRwza6g4dj3Pjty8xjbvz1bvv+Z3p2v4fRZG1qNhsyMNvx74+GgPY1L/vU9110Tzv4jBWSmx/Dkwk3Et4pk4rAkvtnzM49VKnY/bWSqJxBWQ6/zXDD0xrYUFTtYv+Mn/4VYcVb+ODiR6aO7EBUeQvuYSJ5evImXJqdjMurQajSyKl0IUe/V+cAZGxvLG2+8oYZNL0VRKCwsBDyhtG/fvn7n09LSeO6551AUBU1NfyMJAVhC9NWW7PGWw6lpFXup3emZC/nfXAqKbDxxd1cUjQa7w1VjL6pBp+WvD9/gKWtkNrDvcL7fMHuYxT8UX8gQ+Ii+cXy66TA3p7UmzGzgiXu6odNq6NahKcs+8l8Fn19kV4eyqwvH2/bncfuNsUHP+/bIFpyzM2ZAe5Z9sIfNe07y0qRetG8dSXzLiKC9spnpMei0GvW4N9TOe28nk4Yn88nmo2pbeJ97YHpMlf/fUuKtGAw6RvSNIzUhmqUf7g06nL/n0BmS20ZR7nYRf20EUNGjbdRJ2BRCCOpB4LRarfTu3dvv2LJlyygrK6Nnz54A/PzzzzRt2tTvmiZNmmCz2SgoKCAyMvKSPa+4emnd7oAeSTi/Sl3rdgddxd4lIZr7hiRi8wmhqQmeHXb692iJRqNhx4HTRDY01VjQ3WzSo9dpeevT/ZTaHPzfyFRempyurua2hOjpFGdVF/xcyBB4cqyVZ8f3ZOkHe/yOd0mIZkz/BM6ei0FRoEmEmfxCO+OHJrFg5c4adzvSa7UV5YtOq6WQKvfIWhuZWfbhHrbsOel5jV5LwzAjSz/cW+XCpFG3JahfD8qIUX/v3SrT9zPsyM1jSO82ZKbH+N3D+7kH9Izh0XnriW8VSY+kZuw/Enw4PznWSsJ1jTHqdDhcLjIz2nh6tGUoXQghgDoQOI8dO8ZNN91U5fn169djtVrVrz/99FPmzJnDqFGjaNeuHQB2ux2j0X9unfdrh8Nx0c+m11/YApHLRafT+v1XePyadtEDk0ckU2p3UmJzEmrWe1ZEa6DMHRg2TUYdt3ZvzdxKx5/I6gbA7TfE8XOBjf9t9wzj1lTQ3ajXsmDlrvMFzl/43O/88D5tmXC7JxDuPZRPZMMQ9Xx1w+sLV+8mvlUEuw6eJjOjDUkxUTSJNGMvd2EO0as9tQ3MWj5c/wMTb0+mtGLuY1VKy8p5evFmUuKtZD+QwZmzNvb49Mh69z73hk2A02ftNG1sqXZx1tgB7dWvfUNvqd1Ju1YRAa+xO1y88vZ3ZGa0YeyA9pw8U0qYxaCWlvKWZ7JV9DxX1UaLVu+iR1JzOsVZsYR42uMK/xFwScjPmeCkXaombVM3XfWBMzo6mo8++qjK8769k2+99RbPPPMM/fr149FHH1WPh4SEBARL79dms/8CgQul1WqIiLg6trBr2PDiPmNd91u2y5kiGyUlZQFBpaoAowGiI804XIpfmaGqCronx1r54+COlDldNezr3R4tkNHpGsYNTmLXD6fVAFvT/NBBGTHEj0xl3bZjRKVey7z3g/fk9ut5HcX2csocroAeTN9rvcPm2/bnscjtCbTe9/f2dJ4+a/cb5jcZ9dhqCLLnSs5/L/vOKTWb9BSVBP4D0hu691c8j/cZZmV183tmnVZT/TSBnDwGpsdgK3Ni0Gtxup00bRxe7bPWJ/JzJjhpl6pJ29QtV33gNBgMtGnTpsbrZs+ezcKFCxk1ahSPPfaY37zMZs2acerUKb/rT506hcVioUGDBhf1XG63QlFR6UW99lLR6bQ0bGimqMiGyyXzzLxqo12KbE5K7E5aRofxyJguuFyKOnwOnoLpvuHmx1NFPH1fD+wO/1JLlQu6W0wGLCY9pwpsPL5gA9NGdan2OTRomff+TmJbRvDVtuPsq1jxDYELiiqzmAy89el+RvdPCFqLckduHgtW7qRnUnPmvrtDPd4p1spz43tyrsSB3eGiQaiBxuFmnv/7Zr/XZmV2ICXOSqjZQLnLzatvfcdDf7ieP0/oRX6RpyD99tw8OlxX/RQX7zacybFWDhw7y4i+cSS1jcJmd6pzUn2H8I/8fI5ZWWk0amBk/Y6f1NXzldtjW24ebVs0qva9HeVutWfboNNTUFD9Puv1gfycCU7apWqXs20aNjRLz2otueoD54XIzs5m0aJFTJ8+naysrIDzqampbNmyxe/Yxo0b6dy5M1rtxf/Bczqvjh8iLpf7qnnWS+m3bJcSWzkNw/RBt2VMibfyytQb+OlMCX99+zvOFjtIbGNl/vs7+f3N8QH38i3o/sLEXtjsTp5d4glvFlNN39IK23LyGJTh+UdaZnoMTpfCmP4J6Gv4IWsy6tQdjqqsobk/j4G9/PcW356bh/IRfj2YnWKtzLy7G0++8bW6z3iJrZxH528AoEdiMx6/pxsL3vffXz051kqf1GvpFGtlezX7xCfHWhncuw2KorBmXeB8y2kjU/n3xsOMGdCexxdsoHXzcBKui2TPD/l+9T99rfriANkPZFTbRkaDllCz3rMQzOWm+r7Y+kV+zgQn7VI1aZu6pc4Hzs2bN7No0SJGjRpFZmYmeXnn/5KyWCyEhoYyatQohgwZwuzZsxkyZAhfffUVn3zyCYsWLbqMTy6uNopGg63cRandicWkx2zQoalYNBJqNmDS65n7XuBORNv2e3oGeyU359nxPXn1re+whOgZlBFTYymlULPeb13KmUJ7lXM8//C7OAx6Ha881BujXseqrw76BbEpIzpVWSw9OdaqbhdZ00r5YD2lvgt4wBNCF7y/k+mju/DwK+sqPouBOVN7Y7M7aRwewo4DpxmYHsMtaa39CqwvXrObUf0SUCqtkE+Js5KV2RGX4qbP9S0od7pZ8q/vq9zNqH+vGJZ+sIeb01rzzmc5ZKbHqNuDZmV25PCJQh6/pxuNw02U2p2EmfWYjVUXlE+OtXKm0E7L6AayX7oQQlRS5wPnBx98AMDy5ctZvny537lJkyYxefJkYmNjmT9/PtnZ2SxdupQWLVqQnZ0tNTjFBXNVFGv37Y1LibcyYWgSOsCo12FzVL8T0aCMGJZ9sIdpo1KZ//5OtufksWhm3yoDTkq8FbNRj81nKH7uO9t4fkIvXl+1y++9hvZuww2p1zLvvZ0kxTYmsoGJMf0TgPbYK+Yc6vUamkaF4nYHBrmB6TEUl3rmP9ZUb7S6ovCVyy4ZdVpG9I3jyE+FaDQwdc5XmIw6/jIlg/XbfwqYI+rtfexfcp1ah1Ov09KoQQiOcjenz9rY/2MBGqBL+6ZV7iDknW/5zd6T3NajNQB6nVbtxc0a1B6dRssbq/23vOyaEK2uwvf9f+K7V7wMxgkhRKA6HzifeeYZnnnmmRqvy8jIICOj+uEyIYJRgoRN8PRczq+Y07hozW6evDet2vs4yt20viacBRVh02TUsfdQnifgVA6zcVbGD01Cqyi43E5S4qKYMKwTtjInhcVl3Jvp2WGnqNiGVudZRT733R0cOVHIhNuTOFNoDygt5B2Gbh8T6VdQ/UyhncMnCmnZNJxn7+9Bqd3Jc/f3YMeB6hcDVVZd2aVxQxLJ91kgtHBN4N7m3q8zM9pgNOiIae5ZkLP7hzPodBr2/JDv95qW0Q1rbG/f/2q1mvPD6To9C1YGzlPdstezYv7+IYk4nAqltnJCQnSYDDoMOi0akLqbQggRRJ0PnELUNlu5q+qetIo5jXaH64J6Br2roL3h7N8bD2MrUxg3JImycie2MpdnjqBRj81pp9ihJdRkZNyQJBat3kWr5uG0axVBYbGDBhYDTSItLFq9k7tua8+O3Dwev6cbOw+eDug9hPOBLr5VBE8vPr+gx9vjWLm3z7fH0e5wkRLv2dLx6cWbAj5bcqwVN57C8IMyYritu/8w+eurdpHeqTkvP5hBqd0zZJ+ZHrhf+Y7cPMb0b49W4xmWX7vuIPGtIhncuw2xLRqh1aD+v6iqp9UrumIBkcmoIyXOys4Dpz07FGW0wVHNav8te08ysl8CbreCy62gKJB/zk6TcDMGnQaZciaEEIEkcArxK5Xaq18a4u1BCzHoqpxf2SUhGrcCkQ1NzBjdhchwE8dOnuO2Hq1Zs+4H5q/cqV6bHGtldL8EHluwEbvDRac4K3f2jeOW7q3VBTLensKktlEM7ROHosCIvnFEhZvQaqhxaN9XdT2OWi08P6EnZ8+VkXvsLGXlLuJbRQbtOW0SYWbNVwerDK2DMmI4e66MyHAz+48U+O+THne+VqeiKGzZe5KktlF0irVSVu7i9FkbZqOOkf0SGN3fM00gokFI1dMR4jzzLQ8dL6RnUnNG9Utg5vwN2B0uxg5oX+M8Ve9KdK1Gg06roUmEhejIUFmVLoQQVZDAKcSvVNPKcG9P2+w3tzJtVGrA/L8uCdGMGdCehZV6ECcOS2bt/34IupXioZ8KGXpjW1Z8sp/tOXn0TGrOhp2eXsvqhq47xzepsfxR5fM1bVE5sFeM2iPqDbrDb4pFr9PiKHfRLCqU3QdOs2bdwSoX8Dw/oScGvQ6Xyx205NK2nDwWrfHU6sz9sUDtSfUO53dJiObewR15rWLqgcmo45HRXRhxkydsb680t3bUbQm893kut3ZvzYpP9hLXMkK917kSBw1C/TeCqCzUrOeBv3zJXx++gTCDFr3sfiuEENWSwCnEr2Q2VL9y2TunMfdYIdnLPaGzrNzNqfxSNBpwKwSETYDG4Sa1Tmaw8Hjf4I6s/OIAdoeLxuEmDv/kWVXdPCqUhav9Fw2ZjDriW0VgMupqHGqufL6mzRl9A6q3ZNM7n+Xw8oO9mTFvPS2jw5g2KpVX39ke9PXeBTz/Wv8D9wzsWGPv6zuf5eB247dN5XXXhPuVm8rMaMN/Nh9hxO9i6ZXc3G94Pr/IjiVEz/C+sbz7WS6tmoVzXfPzBdobhhnRa7WkxFmDTpVIjrViMurpFGvFEqKXOZtCCHEBJHAK8StpFIUJQ5OYX6nnMticxtxjhdz358/pnXINd/wuDqdLwa0oQUOWo9xd/VaKa3arocutwLPje7Jw9W4GZcT4BSXfHk/w7E/uO7Tvu3JcUSDMYlSLo8e3isTaqPrdPqoKsPaKHYGOnizmXGnNpZS27c/jVK/qN0vwFnWvPPRfuRe2fetIeiQ2Y+kHe4KGxpR4K71TWnB7n1jOlTjQaj1dlMmxVr7eeYK16w4yKysNNASsRh83JBENLiYMS5LyR0IIcYEkcArxG9ApCpOGJgXU4VSAhOsiA0JLRkoLHnplHU/em0ZhceB2i+C/iCgYb88gQPMoC6+v8vSS3ta9tV+IbBgawjuf7Se+VYS6PeQ9AzuQc7SAf/x7L5NHpAT0oKbEWXnloRtwOJwY9dpq9293V9EFqtdp1NeFBVkwZTLqGHJjW1Iq7vHXh27AaNDyzLgeOF1u9h7OD1gF37RxKI3CjJwtdvj1rFaeBhBmMXrCYjWLuUbf1p5lH+3hrlsTMOg16naa3kVQTy/eRNagjtwzoAPnSssxm/TYy8oJ0WnQaLTSsymEEL+ABE4hfiMaRcGi12IJq5j/pyhowC+IOpxudh44rYaaULOhyr3B9x0pIL5lRLXv6Q1a5U7Fr8fSdxj+T/emcUta68BQGW/lxUnpLFkbOJy/LSeP197fyX1DEnG6FTIrgm3lBT+Z6THotIETGJNjrWzLzVNfp9Nq/KYdmIw6po/yPKO32Dp4gu7I2xJ4ZvEmWjUL91sFnxxrJedoAc/c3xOXyzM87g2flXtZdToN9jIX1SmrqC4wpn97tFot8a0i1PcCz/SAee/u4K8P34BbUdDrtEQ1sqBxu/Grti+EEKJGEjiFqGW+QdSt1dIwuTkpcU0INeuxhOir3B1o7bqDvDCpV7X3Nho8vY/eUkIATrfCv3yG4RuEGln+8d7AULk/j9dW7ST22gg27zkZcO/tuXk4XW6KS8v99m/3LVWU/eZWdS92L9+eQoB7B3VEr9MwvE+cWlTeO1Vge+XapTl5uBWYdW8aM+dvYC2e+Zj7jxSo95w+KpWnF28mOdbKs+N78viCDep2lt7PWFTiIDys+oU/phAd4AmeRqiyJ9lW5iQ6woxOo5FeTSGEuEgSOIW4hLRuN2EGLWGGijDkdtMpNoprrGGAfw9ifKtIFAU6xVkDghl4eiibNg5lTP8EtZfRZNQR2TBErXXpXSQU7PUQfO9zXyW2iqFkn/3bK2tgMTArqxsWk8EzlSBEj93hZFZWGmaTHreiUGxz8vTiTWRmtOGuW+Mx6nVV3m9Hbh6l9lh1furYAe0B1N5Hb6/ujtw8Fq7ezaQRKXyy8TAThyUx/72dbM/N8/SA6qsuQ5Uca8Wo9wROc4henRsajMVkkLAphBC/kgROIS4znaLQJMLMPQM7AHC60IYGz5D6Uws3MnlECooSOJw9KL0NP+eXEtEghFK7k78+3ButVsuiNbvVgDmib9wFD8sHE2o2UGJzVBvctu47xTuf5fDEPd0osXl6Qx+8szMvLPsG8ITgp+7rroZWvU5DUltrtc9UXFpOu1ae5z55ptQvnPoOn+/IzSMrswNZmR3IPVLAHb+LZeyA9uh1Ws6V2Lmjb5x6ne8z39E3jp/zSz3bg4bo2XXgdNDnSIm3YjHqJGwKIcSvJIFTiCuA3eFiystfYjLqyH4gg0U+hdaz39zKvYM6MnZAe8ocLpwuN/uP5NMk0qwuFDIZdfzfyFRMRh23prUmMz2GfUcK1NBWnTBL8B2QvHu1uxU3E4Yl8VqQ/cN9h841Gliz7oeKrSe1fguXTEad+rq2LRoRYtAFvJ8vo0GrBmHfgBls68wSm5PTZeWs23Gcm7u15u3/5OJ0uph6ZwrlboVeyc0DtuoMb2Dko0/2efa6VxTaXxcZUNrKu32oRsKmEEL8ahI4hbgCeOdg2h0uzpy1Ed8qwi8kNQwN4amFG/nTH7uTX1ROWsdmfmFz2shU/rXef05kcqyVTrFWtufmVdlDmRJnxWLSB5xPjvWErWeWbOJUgY0Zo7sw/KZYbr8xluLScr85nN4FPfuOFKjlig4cO8usrDT+WVGTc0TfOPU9HOVuFJQaa5e2axXhFzBT4qwM6HU+4HqFmvWs/iqXrIEdKXO6uDezIwoKRbYyGllMJMdGYXe4KLU5sZj1NI8Kw6W4ubt/B7zLnYwaFxNvT6a0zKnuImQJ0UvZIyGE+I1I4BTiCuC7W9Gew/kBWzv+6d40HrgjhaUfeupKzpnaWw2IQ25sGxA2wTOMPPymWNauO6gu7KkcKscNSWLZR3v8Am6YxUCTCAvZy7/hVIGN/xuZil6v5VxJOU0iLfx8poRFb+9WV3NX7un0BEp45/Mc9f18n8Fo0LJ170nu7t8Bt/v7oCvf/73xMNYIM+OGJFJUXEa7VhE0ahCibj/pe705RM89mR0pd7kwG70hUQMGI7jd6MF/3iyKpzvWL0xqg86vFUII8duQwCnEFcB3t6JgAbFhgxCWf7iHvYc921xqNDBjdBdCjDqiIy2s+uJA0PvuPHCa5LZRHDx+ljH9EwDPPuN6nYYTZ0rQaeH3t7TzLA4K0RNi0LL5+xM8u8SzVWWwntOUeCsvP5iBzeHCoNVSYi/HZNTz/ISebN17EpNRR2yLRn7ljuwOl7rSvVGDEA4eO0t8y0juzexAYYnDr9f0082HyRrUEUVR+HrXT7z7WS4JrSPpkdQ8IGxOHJaEDs88WINWamMKIcSVSqMoUlCuNrhcbvLzSy73Y1RLr9cSERFKQUEJTqf8Re11udrFpdGouxV55z8mtY3C2kiLVmvC7nCi02pZuHqXX0Fz36Fm30AG0CjMyAuT0gP2J++SEE3WoI4Bx1PirAxM9wyJpyZEs+yjvQE9pyajjllZabz7eY7/c8R7ekzdiosffy7hz0u/Cfo5TUYdcx7szZJ/fU9sy0Z0bd8UNFBW5sJc0dO7afcJddvOlHgr9w1ORKvR4HC6KCn1DI1fSUPe8r1UNWmb4KRdqnY52yYyMhSdrvrtf8XFkR5OIa4QwXYrshh0OIG57+4gvlUE+yvmSfry1q703Vvc6+a01ry+cmfAa667JjwgbHrvhQZ6JDbn7LmyoOWUMjPa8M/PcoLW9Vzw/k4mDUvm2qZhVX5Ou8OFS1Ho2qEpjcNNlNqduBWFnQdO8+mmw9yc1pqktlEkx3rml54fJlcw6LWENpQhbyGEuNpI4BTiClJ5tyK3RsO8d3eoi3Gqq105KCOGEX3jaNcqQl1sFB4WEvQ11W6ZWVGbs7yKnoXqXrsjN4+TBaVER1qqLaWUX2gjsW0Ur72/k31H8tXV7PGtImgSYcFi1HkW9LjdEiyFEKIOkMApxBWstMyphrbq6mWCp4B55cVGT2R1C3ptTfcqd7ppEmHxO+Yd5jcZq/+xUVxaTsNQJ/cPTeS1lbsCFgX9cXBHHl+wgRCjjsfv7oYCgSvDJWQKIUSdIoFTiCtYie38lpWV9wuvzOVWAnoUA3c5v7B7RTQI4cCxs2ovpe/+7DXV9jQatJTYnGg1CvcP7UiZw42tzEmo2YBOp8HpcjN9VBfCLAZMIXp0ikKY93kkaAohRJ0kM2OFuIKFms8XZffuFx5MSryVnUF2y6nqNdXdKznWil6vZfHa3WSmx3hKFVXsfb4jN499RwpIiav6tfuOFBBq1uN0KWg1WnYcOIWtzInN7mTiS1+wZO0eohqZMBoqArGsWxRCiDpPAqcQVzBLiF4NhmvXHVQDoK+UOCv3DUpk7bqDAa/3viYl3v81h44X8sfBHQPvFe8Z8tZqNGRmtOFv72wjvlUE3ROb+dXUHHlbQsBrvTU0Dx0v5FSBjcde+5r57+8krUNznC4Fi9nAXx++gcnDk9ErCsaK2pdCCCHqPimLVEukLNLV60prF6dGw7z3dqpD295ySQa9FovJgNvlRqOFB/7yVdDXNwoz8vyEnpSVuym1Owkz6zHotcxZ8R3jhyVjszvRaDTqSvG16w6quwd5C7r77o3uveese9MotTv9amgeOl7ImAHteXzBBs4WOwBPiM0a2AGNVoNRr0NXB0PmlfZn5koibROctEvVpCxS3SRzOIW4wukVhcnD/bddNBn16DWw5IPvWb/zBC9PzWDisGQah5vUFer7jhTw6abDTB/VhYWrd/vVzEyOtTLl951Z+sEerrsmPGi5pR25eWg18Oz9PdHp/GeDni12MHP+BnV1ucVkoHtiM+JaRviFTfCselcGarDopDC7EELUVxI4hbgKaN1uwkL0mEP02Mqc6AGb08mdN7ejb9dWWEIMfL3zp4BQ+fS4Hvz9X9/7HQdPmFy8djex10ZUXyIpJ4+B6THqnE/fUGp3uHjnsxySY62Mui2BqXO+UntgfUsz7TtSgM1ejsloQicDKkIIUS9J4BTiCqVoNNjKXZSVO4kINeECdfGNRuNZ7e12Q8MwI6+v3Bk0VBaVOAKOe3nrbdZUIslR7q52P/bM9BhKbeV+K9l9A2xyrJU+qddSVGKnUagJjfRyCiFEvSOBU4grkEurpdReTpnDRVRYCE5FUedxgv/2kgPTY6oMlcWl5UGPe+kvYK6S0aD12ws9K7MDpXYnIUYdG3edIPvNrTw3oSdZmR3Vley+duTm8fqqnUy4PZlSh4tQfVXFmoQQQtRVEjiFuEIoGg12pwtF0eByl2Mvc2Ex6XFpNSxc5Smg3ijMyKQRKVgbmSixORnTvz2ldmeV9zQZdQG7D+07UqAuDGrUIISNu09UuyvQviMFwPkh9KS2Uei0cODHc+w/UoDd4UKn1dC2RSPmvbcj6HNs25+n9s6GNjD+Ng0mhBDiqiGBU4grgEujYdGa3dyc1jqglzAl3sqAnjEcPVnErHvT2PNDPlqNZ6jb7nDSONyMyajD7nD53dNk1NEg1Mj+ioCpbh/ZMoI/T+zFoeOFuNxumjQy0zY9BvAfLk+JszLytgSeWbzJ776OchfNoiw0amDiGqtnz/QSm5PiUgfVKbE5MZt0v6qdhBBCXJ0kcApxGbm12orV5+XcPbADr6/aFdDTuG1/Hm43PDKmC4XnHKzf8ZPfNZ3irMzKSuPpxZv8QmdmRhuWfbSX/Ufyg86tTIm30j6mMfGtIrA7ypk4LJlTBaV+ZY7e/nQ/k0ekkP3mVrVU0r4jBRj1Os4U2Yhr2YjxQxNxON2UOaruaQUINesx6qXciBBC1EcSOIW4TJwaDfPe3aGGx1lZ3aqci7kjNw+LMZGlH+wNCKTbc/LQAPcO6sjcd88PaSe1jeKdz3IY0Tcu6NzKbfvzeG3lTnolNyeprZV57+0IOqzucLrJzGjD/iMFal3OXknNefz1r9Vr5k2/kTOF9iqH5lPirZhD9J6dJmTRkBBC1DsSOIW4hLw9mka9lgXv7/QLZ5VXi1cuMWQvdwUNc+ApX3T3wA789eEbsNmdlNjLcbk9JYiqK3u0PSePsf3bU1buYuyA9hSVOHC7FfYezlfnee7IzSMrswMAf3tnG1mDOgIwY3QXtSf0lRXfMX1UqjrEXnlKwPihSehAwqYQQtRTEjiFuER8ezTnTO0dEB6NhvPDzcFKDL0wsVe19/8pr4QXln2jzvl0uTzhrqayR6V2J//8LIf9R/ID5nlu2fMzq744QHFpOWvXHVRXxs/z6UlNjrVy583teGn5Vh6+63rGD03EXu6irMyFxWzAaNBI2BRCiHquXgTOo0eP8uc//5lvvvFszZeens4jjzxC06ZN1Ws2btxIdnY2Bw4coGnTpkyYMIHBgwdfpicWdY1bq/UbPg+2svzQT4WkxFnZlpNHZkabgGHwEEP1C268gdU753Ngxb7rvkE26LMpSpXzPDvFWZk2MpUGoQZemXoDr63ayfYg9T4BUtpFc/+L/6VTnJXR/RJwuRUURWHXgXxSYq0gRd+FEKLeqvMz+MvKyhg7diwAb731FsuXLycvL49x48bh3Ub+4MGDjBs3jt69e7N69WruuOMOZs6cycaNGy/jk4u6wjuMflv31szK6saIvnGEmv3/rdcozEhaYjOG3xRHcqyVdq0iAnpAv9t/kpR4a9D38C1fBBVzPkN0ZKbHqHMrg0mJs7LzwOmgARc8Q+5r//cDJqMee7kzIGz6vl+7VhEkx1oZ2CuGtz7Zz/G8YoptDpLaRkmxdyGEqOfqfA/nTz/9RGJiIk8++SSRkZEAjB07lokTJ1JQUEBkZCRLly6lXbt2TJkyBYCYmBj27NnDokWL6N69++V8fHGVq7wwCDzhMCPlGrokRPPN3pMATBqRwhurdqvD2haTIeBe736ey4zRXQBPL6bv/byLeeD83M9QswGtRkNza2MS20bx+qqdAa/LyuzI469tYNa9abRrFcFt3VsH1OrckZtHmcOFvcy/7FJlJqOe+FYRfLLpMENuaEtUuAmdViNhUwghRN0PnNdddx2vvvqq+vWxY8dYsWIFHTp0ICIiAoCtW7fSt29fv9elpaXx3HPPoSgKGo3sjCJ+ucrD6F47cvNYuHo3429PwuF0syM3j8bhJvW6dz7LoXtis4D72R0uXlj2Dc9P6MnAXjGeXkeHk31HCtSyRVVtL9mtfTTjBidhczjJL7QTGW5i464T2B3lTB6RwrKP9vr1XibHeobSvfc9WVBKTd8FZpOe9E7N6XN9C3RaLToNMm9TCCEEUA8Cp6977rmHDRs2EB4eztKlS9Ug+fPPP/vN5wRo0qQJNptN7QUV4pcqLXNWuarc02voZOLtSdjLXZTY/LegrKrEkN3hoqCojGeWbGZE3zj2Hynwu6aqofHNe07icLppd10kLpdCn+tbkHu0gBs6X8Pyj/cHDcXe+73zWQ4aYN+RgmrLHoWZDWjcClqtxhM0ZcqmEEKICld94Dx27Bg33XRTlefXr1+P1eqZvzZt2jSmTJnCggULGDt2LKtXr6ZZs2bY7XaMRv/t9rxfOxzV755SHf0VXuRaV7GPtu4C9tOuT36rdikpKqv2/InTpbyw7JuKoe0OfufmvrONZ8f3ZOHq3QHD8U0iLQCsXXeQaSNTgfMBsboSSNty8ri9TyyPv/Y1JqOWPw5OpKyaUks7cvMYlBFDSrxVHWKv/H7gCZtj+3fwrETXadBrAG39+jMl30tVk7YJTtqlatI2ddNVHzijo6P56KOPqjzv2zuZkJAAwJw5c7jhhht4//33mTRpEiEhIQHB0vu12Wy+qOfSajVERIRe1GsvtYYNL+4z1nW/tl3O2qrfece7enxHbh7FNodf7+HZYgePL9jApBEp3D2gvWdfdbMenVaDUa8lJd7Ktv15ZL+5lcyMNgzKiMFR7sYcUv23tEGvJTnWyjuf5dKtYzPKHNXPy1SAcUOSePDlL7E7XAHv1zTKgsmgw6DVYG18dfx5r03yvVQ1aZvgpF2qJm1Tt1z1gdNgMNCmTZsqzx8/fpzdu3dzyy23qMfMZjMtWrTg1KlTADRr1kz9vdepU6ewWCw0aNDgop7L7VYoKiq9qNdeKjqdloYNzRQV2dSajeK3axeLSV/lEHTlVeUvLdvKi5PSme9TDP5ssYNPNh4ma1BHln64R92FqFGYkRcmpauF4709msmxVsb2b1/tM4UYdEy4PYm8szYc5W5CzYGLk3xZG5lxudzqlpl2h8uvB/WVqb3R67UYNFBQUHIBrVI3yfdS1aRtgpN2qdrlbJuGDc3Ss1pLrvrAWZO9e/fywAMP8J///IeWLVsCUFRUxKFDh8jMzAQgNTWVLVu2+L1u48aNdO7cGe2vGBp0Oq+OHyIul/uqedZL6de2ixaYOCyJee/tDBgW911VDp5wWXjOzsD0GMYOaI/N7iTMbMDpdgfsSNQw1IiiuLl3UAdKbE7MIXoMei3f7DnB4ROFau9nZSnxVgwGLTlHC7A7XCS0jsRs1FV7vcmoY9+h/KCfLznW6lkJ73JTfV9u/SHfS1WTtglO2qVq0jZ1S50PnBkZGcTHxzN9+nSeeOIJFEUhOzubiIgIbr/9dgBGjRrFkCFDmD17NkOGDOGrr77ik08+YdGiRZf56cXVTq8oTB6eTGmZU91y0ndVuS+TyYBLAZ1WgzXCzMJVu7i1R2u/sNkyOozH7unG/CAhduKwJFxuF5HhJtzuSvMs46yMG5LE2SI7rZs1xKDX8t5/c/nDTbGMH5rEgpX+JZO821HqgUYNTAE9td7308kqdCGEEBdAoyh1f/uPU6dO8eKLL7JhwwYcDge9evXi0UcfpVmz86Vn1q1bR3Z2NocPH6ZFixZMnjyZfv36XfR7ulxu8vOv7CFGvV5LREQoBQUl8q9IH7XVLm6tlr8FKZMEngA3MD2GZ5dsBiC2RTjTRqXy48linqk4BjB/+o28vmp3lfeYNDyZybO/4PkJPSm1Oym1l9OscSj7jhSweO1uNeQmx1qZMCwJh9NJqN6AW+NZVV9icxJq1mMO0eNyu3jyjU0UFjvIzGhDUtsoDHot5hDPeQmb58n3UtWkbYKTdqna5WybyMhQGVKvJXW+hxM8JY7+8pe/VHtNRkYGGRkZl+iJRH2kdburHGKfOCyJ6X/7n3os91gh2cu38sCdndVjJqMOp0updlW5rcwzuK3XaXn8ta95+r40Fq39PmjZo/nv7WR4n1iWrdvHLd1b0yTi/AR9xa2wbd9pxg9NxmzyBEwNGtAomEMMGDUK8nekEEKIC1UvAqcQVwrfIXZvb6IlRI8WaNUsnLM+wTD3WCEOp0sdzs7MaBNQr7OyEls5Lz+YwdlznioLjRqYqg2oWZkdGNgrhueXbmFWVhozF2wIuG7G6C6YrXpCDRr0eh0REZZ6vUBICCHELyf9xkJcYlq3mzCDluiGRsIMWrRuN1T0flbe83ztVwfV4+1aRdS4qjzUbKDM4cZs8vxbsuaA6qRBqAG7w6W+prIwiwFLDeWWhBBCiOrI3yJCXCGq7P10u5k8PJnThXZ0Ok21pZbMIXoOHitEX1Fvs+aA6vkRkBJv5UyhPeg9oyMtnlAshBBCXCTp4RTiChK097PiuMWk58Wl3zAhSE+odx7o/Pe2YzRomfvONv44uCMmoy7gWt/XhBh0lNjKuW9wIl9+e9TvfEq8rEQXQgjx25AeTiGuEpYQPRENzTy3ZDOPjOmCy6VQYisn1GzAqNey68AptuWcJrZlJK2ahfP4gg1kT0pn3JBEXl+1K2Ch0rghiZw4U0pkwxCcLjd392vPnb+Lp9TuxGLSYzbo0NT9IhZCCCEugXpRFulykLJIV68ruV2cGk3AKveUeCsDesaotT1NRh3TRqay9n8/8POZYp4b14NThTbMIQZsdidmkx5bWTmNGoTwj4/2MapfAnqd9oKGza/ktrmcpF2qJm0TnLRL1aQsUt0kPZxCXEV853mW2pxYzHpCDFq+3Hac6aNScZS7MRq05B47S4eYSAZlxHDOZsfayOK3gKjU7uLD/+1jzID2nh8CMmwuhBCiFkngFOIq453nGWYwAp5ezz0/5PPWJ/vVa7zbZ3789WG6dWxK57YmzzaUWg0lNidNIsyMHdgBHUjYFEIIUeskcApxFXNrtSx4L3D3Iu/XE25PAhRQFDQajd81GpCwKYQQ4pKQwCnEVay0zMm2nKoLux/PK8YcYiC6oTGgZ1TCphBCiEtFZsYKcRWrqbC7o9yt1toUQgghLhcJnEJcxWoq7C67BAkhhLgSSOAU4ipmCdHTqZrC7rJLkBBCiCuBBE4hrmJat5sJw5ICQqfsEiSEEOJKImNtQlzl9IrCpCr2YBdCCCGuBBI4hagDZAW6EEKIK5kMqQshhBBCiFolgVMIIYQQQtQqCZxCCCGEEKJWSeAUQgghhBC1SgKnEEIIIYSoVRI4hRBCCCFErZLAKYQQQgghapUETiGEEEIIUaskcAohhBBCiFolgVMIIYQQQtQqjaIoyuV+iLpIURTc7iu/aXU6LS6XbINYmbRL1aRtgpN2qZq0TXDSLlW7XG2j1WrQaDSX/H3rAwmcQgghhBCiVsmQuhBCCCGEqFUSOIUQQgghRK2SwCmEEEIIIWqVBE4hhBBCCFGrJHAKIYQQQohaJYFTCCGEEELUKgmcQgghhBCiVkngFEIIIYQQtUoCpxBCCCGEqFUSOIUQQgghRK2SwCmEEEIIIWqVBE4hhBBCCFGrJHDWIytXriQ+Pj7or9GjR6vXbdy4kaFDh5KUlMTNN9/M6tWrL99DX2KLFy/mpptuIikpiaFDh7Jp0ya/8/W1bbZs2RL0z83XX3+tXlNf28Zr69atJCQksHnzZr/j9bVdjh49yvjx40lNTSU1NZWpU6fy888/+11TH9vmxIkTPPTQQ/Ts2ZMuXbqQlZVFbm6u3zX1sV0qe+yxx5gxY0bAcWmbq5gi6g2bzaacOnXK79eqVauUdu3aKevWrVMURVEOHDigJCYmKq+88opy8OBBZdGiRUpCQoLy9ddfX+anr33z5s1TkpOTlTVr1iiHDh1SnnzySaVTp07K0aNHFUWp322zbNkypW/fvgF/fsrKyhRFqd9toyiKUlRUpNx4441KXFycsmnTJvV4fW0Xu92u3Hjjjcr999+v5OTkKHv27FHuuusuJTMzU3G73Yqi1M+2KSsrUwYMGKCMHj1a2bVrl5KTk6NMmTJF6d69u3LmzBlFUepnu/hyOp3KCy+8oMTFxSmPPPKI37n63jZXO/3lDrzi0jGZTJhMJvXrwsJCsrOzycrKIj09HYClS5fSrl07pkyZAkBMTAx79uxh0aJFdO/e/bI896VQWlrKwoULmTZtGpmZmQA88cQTfPfdd3z77bdce+219bZtAHJycoiNjcVqtQY9X5/bBuBPf/oT1157LcePH/c7Xl/b5aeffiIxMZEnn3ySyMhIAMaOHcvEiRMpKCggMjKyXrbN1q1bycnJYd26dURHRwPw0ksv0bVrV/773/8ybNiwetkuXgcPHuTRRx/lxx9/pHnz5gHn63Pb1AUypF6PzZ07l5CQECZOnKge27p1K2lpaX7XpaWl8e2336IoyqV+xEtm69at2Gw2+vfvrx7T6XSsXbuWwYMHq9fUx7YB2L9/P23btq3yfH1umzVr1rBt2zZmzpwZcK6+tst1113Hq6++qobNY8eOsWLFCjp06EBERARQP9smNjaWN954Qw2bXoqiUFhYCNTPdvHasmULCQkJfPDBB7Ro0SLgfH1um7pAejjrqZMnT/LWW2/x1FNPYTab1eM///wzTZs29bu2SZMm2Gw2tWeiLjp8+DDh4eHs37+fV155hcOHD9O2bVumTp1K586dgfrbNoqikJubi9VqZejQoZw8eZK4uDimTp1KUlISUH/b5tixYzz33HPMnz+f0NDQgPP1tV183XPPPWzYsIHw8HCWLl2KRqMB6mfbWK1Wevfu7Xds2bJllJWV0bNnT6B+tovX73//+2rP1+e2qQskcNYhx44d46abbqry/Pr169Uh0RUrVhAVFaUOH3vZ7XaMRqPfMe/XDofjN37iS6emtpkyZQp2u51Zs2bx8MMP07x5c/75z38yZswYVq9eTZs2bept27z99tuUlpbicDiYNWsWGo2GZcuWMXLkSFauXEnbtm3rZNvU1C7r1q1j+vTp3HHHHaSmpnLs2LGAa+piu8Av+1kzbdo0pkyZwoIFCxg7diyrV6+mWbNmdbJtfkm7AHz66afMmTOHUaNG0a5dO0D+zFSnrrZNfSGBsw6Jjo7mo48+qvK877/+1qxZw9ChQzEYDH7XhISEBHzjer/27Qm92tTUNp9//jl2u52ZM2eqPRAdOnRg27ZtvPnmmzz55JP1tm1at27N1q1bsVgs6HQ6ALKzsxkwYADLly/nqaeeqpNtU1O7vPvuu5SWljJ58uQqr6mL7QK/7GdNQkICAHPmzOGGG27g/fffZ9KkSXWybX5Ju7z11ls888wz9OvXj0cffVQ9XhfbBX5Z21SlrrZNfSGBsw4xGAy0adOmxut2797NiRMn/OYrejVr1oxTp075HTt16hQWi4UGDRr8Zs96qdXUNnv27AEgPj5ePabRaGjTpo3ac1Vf2wYI+HxarZa2bdty8uRJoG62TU3tsnLlSk6dOkW3bt0A1Dlkf/zjH+natSuLFi2qk+0CNbfN8ePH2b17N7fccot6zGw206JFC7U96mLbXOjP4NmzZ7Nw4UJGjRrFY489pk4zgLrZLnDhbVOduto29YUsGqqHvv32W6xWa9Bv/tTUVLZs2eJ3bOPGjXTu3Bmttu7+cUlNTUWj0bB9+3b1mKIoHDhwgFatWqnX1Me2+fLLL+nUqRMnTpxQjzmdTvbt26cuJKqPbbN8+XI+/PBDVq9ezerVq3njjTcAePbZZ3nuueeA+tkuAHv37uWBBx7g6NGj6rGioiIOHTqk/typr22TnZ3NwoULmT59Oo8//rhf2IT62y4XQtrmKneZyjGJy2jGjBnK3XffHfRcTk6O0qFDByU7O1s5cOCAsnjxYqV9+/b1os7ZzJkzlZ49eypffvmlcujQIeXpp59WkpKSlIMHDyqKUn/b5ty5c0qfPn2UkSNHKrt371b27dunPPTQQ0qXLl2UvLw8RVHqb9v4+vHHHwPqcNbXdikrK1MGDhyo3HHHHcru3buVXbt2KaNHj1b69OmjnDt3TlGU+tk2mzZtUuLi4pRnnnkmoKZtcXGxoij1s12CGTlyZEAdTmmbq5sEznro3nvvVaZOnVrl+a+++koZMGCA0rFjR+XWW29VPvzww0v4dJePw+FQXn75ZaVXr15KYmKicscddyhbt271u6a+ts3Ro0eVyZMnK127dlWSk5OVe+65R9m/f7/fNfW1bbyCBU5Fqb/tcvLkSeWhhx5SunXrpqSkpCiTJ09WfvrpJ79r6lvbPP7440pcXFzQX3/961/V6+pbuwQTLHAqirTN1UyjKFK8SgghhBBC1B6Z9CCEEEIIIWqVBE4hhBBCCFGrJHAKIYQQQohaJYFTCCGEEELUKgmcQgghhBCiVkngFEIIIYQQtUoCpxBCCCGEqFUSOIUQdZaUGRZCiCuDBE4hRJ307rvv8uKLL17ux/jN9OnThxkzZlzuxxBCiIsigVMIUSctWLCAs2fPXu7HEEIIgQROIYQQQghRyyRwCiEuOUVR+Mc//kH//v1JSkrid7/7HQsXLkRRFGbMmEGfPn38rj927Bjx8fGsXLlSPbZ8+XJuvfVWEhMTSU9P509/+hPFxcWAZ/j5+PHjrFq1ivj4eI4dOwbA4cOHeeCBB+jZsyedOnVi1KhRfPvttwHv88knnzBhwgQ6depEjx49mD9/PsXFxcycOZPrr7+eHj16kJ2d7TdHtKysjJdeeonevXvTsWNHBg4cyEcffeT3Ofr06cPzzz/PmDFj6Ny5M7Nmzbqo9vM+58cff8wDDzxASkoKXbp04bHHHqOkpES9rry8nNmzZ5ORkUFSUhJZWVmsXr3ar02EEOJSkMAphLjkXn75ZZ577jl69+7NggULGD58OHPmzGH+/PkX9PoPP/yQF198kbvuuovFixczceJE1qxZw7PPPgvA3LlzsVqt9O7dm3/+8580adKEAwcOMHToUH788Ucef/xxZs+ejUajYcyYMWzZssXv/o899hhxcXEsWLCAtLQ0Xn31VYYNG4bJZOLVV1+lT58+LFq0iH//+9+AJ0BPnDiRt99+m7vvvpsFCxaQkpLC1KlTWb16td+9//GPfxAfH8/f/vY3Bg0a9Kva8cknn+Saa65h/vz53Hvvvbz//vu89tpr6vlZs2axdOlSRo4cybx584iKiuKJJ574Ve8phBAXQ3+5H0AIUb8UFRXx97//nVGjRjF9+nQAevbsSX5+Pt9++y1NmjSp8R6bN2/mmmuu4a677kKr1dK1a1csFgsFBQUAtG/fHqPRSGRkJJ06dQI8IdRgMLBs2TIaNGgAwA033MCAAQPIzs7m3XffVe+fnp7Ogw8+CEDbtm358MMPady4sdoj2bNnTz7++GO+++47brvtNr7++mv+97//MWfOHPr166few2azMXv2bAYMGIBe7/lx26RJE2bMmIFW++v/vd+7d28eeeQRALp3786GDRv48ssvefjhhzl69CirVq3ikUce4e6771af6fTp06xfv/5Xv7cQQvwS0sMphLiktm/fTnl5Ob/73e/8js+YMYMlS5Zc0D3S0tI4fPgwQ4cOZf78+ezZs4eBAwcyZsyYKl+zZcsWbrzxRjVsAuj1evr378+uXbv8hqJTUlLU31utVgCSk5PVYxqNhvDwcM6dOwfAxo0b0Wg09O7dG6fTqf7q06cPeXl55Obmqq9t06bNbxI2ATVMezVt2pTS0lLAE8oVReHWW2/1u2bAgAG/yXsLIcQvIT2cQohLyrtyPDIy8qLv0a9fP9xuNytWrGDu3Lm8+uqrXHPNNTz88MP0798/6GsKCwuJiooKOB4VFYWiKOr8T4CwsLCA68xmc5XPc/bsWRRFoXPnzkHPnzp1ioSEBPX9fiuVn0mr1arzSvPz8wFo3Lix3zW/5fsLIcSFksAphLikGjZsCHgCUUxMjHr8xIkTHDlyBI1Gg8vl8nuNt9fO14ABAxgwYADnzp1j/fr1LFy4kGnTppGamkp0dHTA9eHh4Zw+fTrgeF5eHgARERGcOnXqoj5TgwYNsFgsLFu2LOj5Vq1aXdR9fw1vG5w5c4ZmzZqpx8+cOXPJn0UIIWRIXQhxSSUlJWEwGPj888/9ji9dupQpU6ZgMpkoKCigrKxMPffdd9/5Xfvggw8yadIkwBP2brvtNiZMmIDL5VJDY+Vh6y5duvDFF1+ow+AALpeLDz/8kMTERIxG40V/pq5du1JaWoqiKCQmJqq/cnNzmTdvHk6n86LvfbGuv/56dDodn376qd/xyl8LIcSlID2cQohLKjIyktGjR7N06VKMRiNpaWns2rWLN998k4ceeoj4+HhWrFjBzJkzGT58OLm5uSxZsgSdTqfeIy0tjSeffJIXX3yRjIwMioqKmDt3Lq1bt6Zdu3aApyd1z549bNmyhaSkJCZNmsS6desYPXo09913H0ajkTfffJMff/yRRYsW/arP1Lt3b7p06cKECROYMGECbdq0YefOnfztb3+jV69ev2r6wMW69tpruf3223n55ZcpLy+nXbt2/Oc//+GLL74AAgO5EELUJgmcQohLbtq0aURFRfHWW2+xZMkSWrRowcyZM/nDH/4AwCOPPMLy5cv59NNP6dChA3PnzuXOO+9UX3/nnXdSXl7O22+/zYoVKzCZTHTv3p1p06ZhMBgAuOeee3j++efJysri73//O6mpqaxYsYKXX36ZmTNnotFoSEpKYtmyZaSmpv6qz6PVannjjTd49dVXef311zlz5gzR0dGMHTuWiRMn/qp7/xpPPPEEFouFJUuWUFxcTPfu3Rk/fjzz5s3DYrFctucSQtQ/GsW3crEQQog64ezZs6xbt4709HQiIiLU4y+++CIrV65k8+bNl/HphBD1jfRwCiHEZeJ2u3G73TVe563h+UuYzWaee+45EhISGDNmDBaLhe+++47ly5dz//33X8zjCiHERZMeTiGEuExmzJjBqlWrarzu888/p0WLFr/4/nv37uWVV15h+/bt2Gw2WrZsyZ133sldd92FRqO5mEcWQoiLIoFTCCEuk2PHjqm7I1UnPj7+V62iF0KIy00CpxBCCCGEqFVSF0MIIYQQQtQqCZxCCCGEEKJWSeAUQgghhBC1SgKnEEIIIYSoVRI4hRBCCCFErZLAKYQQQgghapUETiGEEEIIUaskcAohhBBCiFr1/56uuwufFrjwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=customer_loc, y='customer_lat', x='customer_lng')\n",
    "plt.title('Scatter plot:Latitude and Longitudes for customer location after imputation of null values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "dc98d4ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "#let us save it into a file\n",
    "customer_loc.to_csv('data_cleaned/customer_loc_cleaned_dataset.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd27eb46",
   "metadata": {},
   "source": [
    "## 3.3 Sellers dataset\n",
    "\n",
    "<a id = 'seller_dataset' ></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "06a98b34",
   "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>seller_id</th>\n",
       "      <th>seller_zip_code_prefix</th>\n",
       "      <th>seller_city</th>\n",
       "      <th>seller_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3442f8959a84dea7ee197c632cb2df15</td>\n",
       "      <td>13023</td>\n",
       "      <td>campinas</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d1b65fc7debc3361ea86b5f14c68d2e2</td>\n",
       "      <td>13844</td>\n",
       "      <td>mogi guacu</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ce3ad9de960102d0677a81f5d0bb7b2d</td>\n",
       "      <td>20031</td>\n",
       "      <td>rio de janeiro</td>\n",
       "      <td>RJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c0f3eea2e14555b6faeea3dd58c1b1c3</td>\n",
       "      <td>4195</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51a04a8a6bdcb23deccc82b0b80742cf</td>\n",
       "      <td>12914</td>\n",
       "      <td>braganca paulista</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          seller_id  seller_zip_code_prefix  \\\n",
       "0  3442f8959a84dea7ee197c632cb2df15                   13023   \n",
       "1  d1b65fc7debc3361ea86b5f14c68d2e2                   13844   \n",
       "2  ce3ad9de960102d0677a81f5d0bb7b2d                   20031   \n",
       "3  c0f3eea2e14555b6faeea3dd58c1b1c3                    4195   \n",
       "4  51a04a8a6bdcb23deccc82b0b80742cf                   12914   \n",
       "\n",
       "         seller_city seller_state  \n",
       "0           campinas           SP  \n",
       "1         mogi guacu           SP  \n",
       "2     rio de janeiro           RJ  \n",
       "3          sao paulo           SP  \n",
       "4  braganca paulista           SP  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading head and tail of the sellers dataframe\n",
    "sellers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5e66ce60",
   "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>seller_id</th>\n",
       "      <th>seller_zip_code_prefix</th>\n",
       "      <th>seller_city</th>\n",
       "      <th>seller_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3090</th>\n",
       "      <td>98dddbc4601dd4443ca174359b237166</td>\n",
       "      <td>87111</td>\n",
       "      <td>sarandi</td>\n",
       "      <td>PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3091</th>\n",
       "      <td>f8201cab383e484733266d1906e2fdfa</td>\n",
       "      <td>88137</td>\n",
       "      <td>palhoca</td>\n",
       "      <td>SC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3092</th>\n",
       "      <td>74871d19219c7d518d0090283e03c137</td>\n",
       "      <td>4650</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3093</th>\n",
       "      <td>e603cf3fec55f8697c9059638d6c8eb5</td>\n",
       "      <td>96080</td>\n",
       "      <td>pelotas</td>\n",
       "      <td>RS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3094</th>\n",
       "      <td>9e25199f6ef7e7c347120ff175652c3b</td>\n",
       "      <td>12051</td>\n",
       "      <td>taubate</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             seller_id  seller_zip_code_prefix seller_city  \\\n",
       "3090  98dddbc4601dd4443ca174359b237166                   87111     sarandi   \n",
       "3091  f8201cab383e484733266d1906e2fdfa                   88137     palhoca   \n",
       "3092  74871d19219c7d518d0090283e03c137                    4650   sao paulo   \n",
       "3093  e603cf3fec55f8697c9059638d6c8eb5                   96080     pelotas   \n",
       "3094  9e25199f6ef7e7c347120ff175652c3b                   12051     taubate   \n",
       "\n",
       "     seller_state  \n",
       "3090           PR  \n",
       "3091           SC  \n",
       "3092           SP  \n",
       "3093           RS  \n",
       "3094           SP  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sellers.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4f931942",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3095 entries, 0 to 3094\n",
      "Data columns (total 4 columns):\n",
      " #   Column                  Non-Null Count  Dtype \n",
      "---  ------                  --------------  ----- \n",
      " 0   seller_id               3095 non-null   object\n",
      " 1   seller_zip_code_prefix  3095 non-null   int64 \n",
      " 2   seller_city             3095 non-null   object\n",
      " 3   seller_state            3095 non-null   object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 96.8+ KB\n"
     ]
    }
   ],
   "source": [
    "#getting overview of datasets that we have.\n",
    "sellers.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "09e4c246",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seller_id                 0\n",
       "seller_zip_code_prefix    0\n",
       "seller_city               0\n",
       "seller_state              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the null values\n",
    "sellers.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f55e6094",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking duplicates across rows\n",
    "sellers.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1f7ecb8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking duplicates across columns\n",
    "sellers.T.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e1050f1d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3095"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the unique values in column unique id and customer id\n",
    "sellers['seller_id'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2676e2f",
   "metadata": {},
   "source": [
    "#### Quick findings:\n",
    "\n",
    "- Sellers dataset has information about seller location.\n",
    "- We have a total of 3095 unique seller ids which is the primary key for this dataset \n",
    "- This dataset has **three** columns of object datatype and **one** column with numeric datatype.\n",
    "- There are no duplicates across rows or columns.\n",
    "- There is no null value."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fa41c4a",
   "metadata": {},
   "source": [
    "### Merging the coordinates dataframe with the seller dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "895e5864",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merging the dataframes\n",
    "seller_loc=pd.merge(left=sellers, right=coordinates, left_on='seller_zip_code_prefix', right_on='geolocation_zip_code_prefix', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a343fa23",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping unnecessary column\n",
    "seller_loc.drop(columns=['geolocation_zip_code_prefix'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c25191a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3095 entries, 0 to 3094\n",
      "Data columns (total 6 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   seller_id               3095 non-null   object \n",
      " 1   seller_zip_code_prefix  3095 non-null   int64  \n",
      " 2   seller_city             3095 non-null   object \n",
      " 3   seller_state            3095 non-null   object \n",
      " 4   geolocation_lat         3088 non-null   float64\n",
      " 5   geolocation_lng         3088 non-null   float64\n",
      "dtypes: float64(2), int64(1), object(3)\n",
      "memory usage: 169.3+ KB\n"
     ]
    }
   ],
   "source": [
    "seller_loc.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "932c2df4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#renaming column name\n",
    "seller_loc.rename(columns = {'geolocation_lat':'seller_lat', 'geolocation_lng': 'seller_lng' }, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "31aaac73",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seller_id                 0\n",
       "seller_zip_code_prefix    0\n",
       "seller_city               0\n",
       "seller_state              0\n",
       "seller_lat                7\n",
       "seller_lng                7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking null values\n",
    "seller_loc.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b160fc88",
   "metadata": {},
   "source": [
    "We only have 7 rows with null values. Since this is a small number we can find out the cities which are missing these values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0a6122e2",
   "metadata": {
    "scrolled": true
   },
   "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>seller_id</th>\n",
       "      <th>seller_zip_code_prefix</th>\n",
       "      <th>seller_city</th>\n",
       "      <th>seller_state</th>\n",
       "      <th>seller_lat</th>\n",
       "      <th>seller_lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>5962468f885ea01a1b6a97a218797b0a</td>\n",
       "      <td>82040</td>\n",
       "      <td>curitiba</td>\n",
       "      <td>PR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>791</th>\n",
       "      <td>2aafae69bf4c41fbd94053d9413e87ee</td>\n",
       "      <td>91901</td>\n",
       "      <td>porto alegre</td>\n",
       "      <td>RS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1672</th>\n",
       "      <td>2a50b7ee5aebecc6fd0ff9784a4747d6</td>\n",
       "      <td>72580</td>\n",
       "      <td>brasilia</td>\n",
       "      <td>DF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td>2e90cb1677d35cfe24eef47d441b7c87</td>\n",
       "      <td>2285</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2182</th>\n",
       "      <td>0b3f27369a4d8df98f7eb91077e438ac</td>\n",
       "      <td>7412</td>\n",
       "      <td>aruja</td>\n",
       "      <td>SP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2986</th>\n",
       "      <td>42bde9fef835393bb8a8849cb6b7f245</td>\n",
       "      <td>71551</td>\n",
       "      <td>brasilia</td>\n",
       "      <td>DF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3028</th>\n",
       "      <td>870d0118f7a9d85960f29ad89d5d989a</td>\n",
       "      <td>37708</td>\n",
       "      <td>pocos de caldas</td>\n",
       "      <td>MG</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             seller_id  seller_zip_code_prefix  \\\n",
       "473   5962468f885ea01a1b6a97a218797b0a                   82040   \n",
       "791   2aafae69bf4c41fbd94053d9413e87ee                   91901   \n",
       "1672  2a50b7ee5aebecc6fd0ff9784a4747d6                   72580   \n",
       "1931  2e90cb1677d35cfe24eef47d441b7c87                    2285   \n",
       "2182  0b3f27369a4d8df98f7eb91077e438ac                    7412   \n",
       "2986  42bde9fef835393bb8a8849cb6b7f245                   71551   \n",
       "3028  870d0118f7a9d85960f29ad89d5d989a                   37708   \n",
       "\n",
       "          seller_city seller_state  seller_lat  seller_lng  \n",
       "473          curitiba           PR         NaN         NaN  \n",
       "791      porto alegre           RS         NaN         NaN  \n",
       "1672         brasilia           DF         NaN         NaN  \n",
       "1931        sao paulo           SP         NaN         NaN  \n",
       "2182            aruja           SP         NaN         NaN  \n",
       "2986         brasilia           DF         NaN         NaN  \n",
       "3028  pocos de caldas           MG         NaN         NaN  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cities which are missing coordinates.\n",
    "seller_loc.loc[seller_loc['seller_lat'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35272209",
   "metadata": {},
   "source": [
    "There are seven cities which are missing the values: Curitiba, Porto Alegre, Brasilia, Sao Paulo, Aruja, Pocos de caldas.\n",
    "We will calaculate the mean latitude and longitude values for these cities and fill the missing values.  I am proceeding with this step on the assumption that none of the cities from different states have same name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "1f214652",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sao paulo              694\n",
       "curitiba               127\n",
       "rio de janeiro          96\n",
       "belo horizonte          68\n",
       "ribeirao preto          52\n",
       "                      ... \n",
       "taruma                   1\n",
       "s jose do rio preto      1\n",
       "domingos martins         1\n",
       "messias targino          1\n",
       "leme                     1\n",
       "Name: seller_city, Length: 611, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seller_loc['seller_city'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "44062b51",
   "metadata": {
    "scrolled": false
   },
   "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>seller_zip_code_prefix</th>\n",
       "      <th>seller_lat</th>\n",
       "      <th>seller_lng</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seller_city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>aruja</th>\n",
       "      <td>7404.666667</td>\n",
       "      <td>-23.396249</td>\n",
       "      <td>-46.319178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brasilia</th>\n",
       "      <td>71520.928571</td>\n",
       "      <td>-15.805680</td>\n",
       "      <td>-47.962067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>curitiba</th>\n",
       "      <td>81194.370079</td>\n",
       "      <td>-25.452713</td>\n",
       "      <td>-49.277135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pocos de caldas</th>\n",
       "      <td>37705.600000</td>\n",
       "      <td>-21.811821</td>\n",
       "      <td>-46.557979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>porto alegre</th>\n",
       "      <td>90875.392857</td>\n",
       "      <td>-30.036305</td>\n",
       "      <td>-51.190150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sao paulo</th>\n",
       "      <td>3951.201729</td>\n",
       "      <td>-23.548343</td>\n",
       "      <td>-46.629935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 seller_zip_code_prefix  seller_lat  seller_lng\n",
       "seller_city                                                    \n",
       "aruja                       7404.666667  -23.396249  -46.319178\n",
       "brasilia                   71520.928571  -15.805680  -47.962067\n",
       "curitiba                   81194.370079  -25.452713  -49.277135\n",
       "pocos de caldas            37705.600000  -21.811821  -46.557979\n",
       "porto alegre               90875.392857  -30.036305  -51.190150\n",
       "sao paulo                   3951.201729  -23.548343  -46.629935"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking if we have mean latitude and longitude values for these cities\n",
    "seller_loc[(seller_loc['seller_city']== 'sao paulo') | (seller_loc['seller_city']== 'curitiba') |\n",
    "(seller_loc['seller_city']== 'porto alegre') | (seller_loc['seller_city']== 'brasilia') |\n",
    "(seller_loc['seller_city']== 'aruja') | (seller_loc['seller_city']== 'pocos de caldas')]. \\\n",
    "groupby('seller_city')[['seller_zip_code_prefix', 'seller_lat', 'seller_lng']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "67f8dd7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#saving the calaculation from above in separate dataframe\n",
    "sdb=seller_loc.groupby('seller_city')[['seller_zip_code_prefix', 'seller_lat', 'seller_lng']].mean().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2895528",
   "metadata": {},
   "source": [
    "We can impute the missing latitude and longitude values with the mean latitude and longitude values at these cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ec9f6989",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in seller_loc[seller_loc['seller_lat'].isnull()].index:\n",
    "    seller_loc.loc[i, 'seller_lat']=sdb[sdb['seller_city']==seller_loc.loc[i, 'seller_city']]['seller_lat'].values[0]\n",
    "    seller_loc.loc[i, 'seller_lng']=sdb[sdb['seller_city']==seller_loc.loc[i, 'seller_city']]['seller_lng'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c5c561b5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "seller_id                 0\n",
       "seller_zip_code_prefix    0\n",
       "seller_city               0\n",
       "seller_state              0\n",
       "seller_lat                0\n",
       "seller_lng                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for null\n",
    "seller_loc.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbfb50dd",
   "metadata": {},
   "source": [
    "Save the cleaned seller location data in seller_loc_cleaned csv file and similary save the cleaned customer location data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "1f2aa2e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the cleaned data to file\n",
    "seller_loc.to_csv('data_cleaned/seller_loc_cleaned_dataset.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1001f54",
   "metadata": {},
   "source": [
    "## 3.4 Payments dataset\n",
    "\n",
    "<a id = 'payment_dataset' ></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "45146a45",
   "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>order_id</th>\n",
       "      <th>payment_sequential</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>payment_installments</th>\n",
       "      <th>payment_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b81ef226f3fe1789b1e8b2acac839d17</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>8</td>\n",
       "      <td>99.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a9810da82917af2d9aefd1278f1dcfa0</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>1</td>\n",
       "      <td>24.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25e8ea4e93396b6fa0d3dd708e76c1bd</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>1</td>\n",
       "      <td>65.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ba78997921bbcdc1373bb41e913ab953</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>8</td>\n",
       "      <td>107.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42fdf880ba16b47b59251dd489d4441a</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2</td>\n",
       "      <td>128.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id  payment_sequential payment_type  \\\n",
       "0  b81ef226f3fe1789b1e8b2acac839d17                   1  credit_card   \n",
       "1  a9810da82917af2d9aefd1278f1dcfa0                   1  credit_card   \n",
       "2  25e8ea4e93396b6fa0d3dd708e76c1bd                   1  credit_card   \n",
       "3  ba78997921bbcdc1373bb41e913ab953                   1  credit_card   \n",
       "4  42fdf880ba16b47b59251dd489d4441a                   1  credit_card   \n",
       "\n",
       "   payment_installments  payment_value  \n",
       "0                     8          99.33  \n",
       "1                     1          24.39  \n",
       "2                     1          65.71  \n",
       "3                     8         107.78  \n",
       "4                     2         128.45  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading head and tail of the payments dataframe\n",
    "payments.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "772c3d8f",
   "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>order_id</th>\n",
       "      <th>payment_sequential</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>payment_installments</th>\n",
       "      <th>payment_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>103881</th>\n",
       "      <td>0406037ad97740d563a178ecc7a2075c</td>\n",
       "      <td>1</td>\n",
       "      <td>boleto</td>\n",
       "      <td>1</td>\n",
       "      <td>363.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103882</th>\n",
       "      <td>7b905861d7c825891d6347454ea7863f</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2</td>\n",
       "      <td>96.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103883</th>\n",
       "      <td>32609bbb3dd69b3c066a6860554a77bf</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>1</td>\n",
       "      <td>47.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103884</th>\n",
       "      <td>b8b61059626efa996a60be9bb9320e10</td>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>5</td>\n",
       "      <td>369.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103885</th>\n",
       "      <td>28bbae6599b09d39ca406b747b6632b1</td>\n",
       "      <td>1</td>\n",
       "      <td>boleto</td>\n",
       "      <td>1</td>\n",
       "      <td>191.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                order_id  payment_sequential payment_type  \\\n",
       "103881  0406037ad97740d563a178ecc7a2075c                   1       boleto   \n",
       "103882  7b905861d7c825891d6347454ea7863f                   1  credit_card   \n",
       "103883  32609bbb3dd69b3c066a6860554a77bf                   1  credit_card   \n",
       "103884  b8b61059626efa996a60be9bb9320e10                   1  credit_card   \n",
       "103885  28bbae6599b09d39ca406b747b6632b1                   1       boleto   \n",
       "\n",
       "        payment_installments  payment_value  \n",
       "103881                     1         363.31  \n",
       "103882                     2          96.80  \n",
       "103883                     1          47.77  \n",
       "103884                     5         369.54  \n",
       "103885                     1         191.58  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "payments.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "904f50e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 103886 entries, 0 to 103885\n",
      "Data columns (total 5 columns):\n",
      " #   Column                Non-Null Count   Dtype  \n",
      "---  ------                --------------   -----  \n",
      " 0   order_id              103886 non-null  object \n",
      " 1   payment_sequential    103886 non-null  int64  \n",
      " 2   payment_type          103886 non-null  object \n",
      " 3   payment_installments  103886 non-null  int64  \n",
      " 4   payment_value         103886 non-null  float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 4.0+ MB\n"
     ]
    }
   ],
   "source": [
    "#getting overview of datasets that we have.\n",
    "payments.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "b94bdcba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id                0\n",
       "payment_sequential      0\n",
       "payment_type            0\n",
       "payment_installments    0\n",
       "payment_value           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the null values\n",
    "payments.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "50b72710",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking duplicates across rows\n",
    "payments.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "fd3a69d3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking duplicates across columns\n",
    "payments.T.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "adc8dbad",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99440"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking if we have payment details for all 99441 customers and their orders.\n",
    "payments['order_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "18696d0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['credit_card', 'boleto', 'voucher', 'debit_card', 'not_defined']"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "payments['payment_type'].value_counts(normalize=True).index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "1a12145b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(payments['payment_type'].value_counts(normalize=True)*100).plot(kind='barh')\n",
    "plt.title('Different payment methods used')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61ce477a",
   "metadata": {},
   "source": [
    "#### Quick findings:\n",
    "\n",
    "- Payments dataset has information about the way customer made payment for each order.\n",
    "- We have a total of 99441 customer id which is equal to the total order ids but we have payment information for 99440 orders.\n",
    "- This dataset has **three** columns of object datatype and **two** column with numeric datatype.\n",
    "- There are no duplicates across rows or columns.\n",
    "- There is no null value.\n",
    "- order_id is the foreign key in this table.\n",
    "\n",
    "We will keep this table aside beacuse we are not interested in this table for our present scope of work."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b33bc2e",
   "metadata": {},
   "source": [
    "## 3.5 Order item dataset\n",
    "\n",
    "<a id = 'order_item_dataset' ></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "73fbe39f",
   "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>order_id</th>\n",
       "      <th>order_item_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>shipping_limit_date</th>\n",
       "      <th>price</th>\n",
       "      <th>freight_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00010242fe8c5a6d1ba2dd792cb16214</td>\n",
       "      <td>1</td>\n",
       "      <td>4244733e06e7ecb4970a6e2683c13e61</td>\n",
       "      <td>48436dade18ac8b2bce089ec2a041202</td>\n",
       "      <td>2017-09-19 09:45:35</td>\n",
       "      <td>58.90</td>\n",
       "      <td>13.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00018f77f2f0320c557190d7a144bdd3</td>\n",
       "      <td>1</td>\n",
       "      <td>e5f2d52b802189ee658865ca93d83a8f</td>\n",
       "      <td>dd7ddc04e1b6c2c614352b383efe2d36</td>\n",
       "      <td>2017-05-03 11:05:13</td>\n",
       "      <td>239.90</td>\n",
       "      <td>19.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000229ec398224ef6ca0657da4fc703e</td>\n",
       "      <td>1</td>\n",
       "      <td>c777355d18b72b67abbeef9df44fd0fd</td>\n",
       "      <td>5b51032eddd242adc84c38acab88f23d</td>\n",
       "      <td>2018-01-18 14:48:30</td>\n",
       "      <td>199.00</td>\n",
       "      <td>17.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00024acbcdf0a6daa1e931b038114c75</td>\n",
       "      <td>1</td>\n",
       "      <td>7634da152a4610f1595efa32f14722fc</td>\n",
       "      <td>9d7a1d34a5052409006425275ba1c2b4</td>\n",
       "      <td>2018-08-15 10:10:18</td>\n",
       "      <td>12.99</td>\n",
       "      <td>12.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00042b26cf59d7ce69dfabb4e55b4fd9</td>\n",
       "      <td>1</td>\n",
       "      <td>ac6c3623068f30de03045865e4e10089</td>\n",
       "      <td>df560393f3a51e74553ab94004ba5c87</td>\n",
       "      <td>2017-02-13 13:57:51</td>\n",
       "      <td>199.90</td>\n",
       "      <td>18.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id  order_item_id  \\\n",
       "0  00010242fe8c5a6d1ba2dd792cb16214              1   \n",
       "1  00018f77f2f0320c557190d7a144bdd3              1   \n",
       "2  000229ec398224ef6ca0657da4fc703e              1   \n",
       "3  00024acbcdf0a6daa1e931b038114c75              1   \n",
       "4  00042b26cf59d7ce69dfabb4e55b4fd9              1   \n",
       "\n",
       "                         product_id                         seller_id  \\\n",
       "0  4244733e06e7ecb4970a6e2683c13e61  48436dade18ac8b2bce089ec2a041202   \n",
       "1  e5f2d52b802189ee658865ca93d83a8f  dd7ddc04e1b6c2c614352b383efe2d36   \n",
       "2  c777355d18b72b67abbeef9df44fd0fd  5b51032eddd242adc84c38acab88f23d   \n",
       "3  7634da152a4610f1595efa32f14722fc  9d7a1d34a5052409006425275ba1c2b4   \n",
       "4  ac6c3623068f30de03045865e4e10089  df560393f3a51e74553ab94004ba5c87   \n",
       "\n",
       "   shipping_limit_date   price  freight_value  \n",
       "0  2017-09-19 09:45:35   58.90          13.29  \n",
       "1  2017-05-03 11:05:13  239.90          19.93  \n",
       "2  2018-01-18 14:48:30  199.00          17.87  \n",
       "3  2018-08-15 10:10:18   12.99          12.79  \n",
       "4  2017-02-13 13:57:51  199.90          18.14  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading head and tail of the order item dataframe\n",
    "order_items.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "7f305ce6",
   "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>order_id</th>\n",
       "      <th>order_item_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>shipping_limit_date</th>\n",
       "      <th>price</th>\n",
       "      <th>freight_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>112645</th>\n",
       "      <td>fffc94f6ce00a00581880bf54a75a037</td>\n",
       "      <td>1</td>\n",
       "      <td>4aa6014eceb682077f9dc4bffebc05b0</td>\n",
       "      <td>b8bc237ba3788b23da09c0f1f3a3288c</td>\n",
       "      <td>2018-05-02 04:11:01</td>\n",
       "      <td>299.99</td>\n",
       "      <td>43.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112646</th>\n",
       "      <td>fffcd46ef2263f404302a634eb57f7eb</td>\n",
       "      <td>1</td>\n",
       "      <td>32e07fd915822b0765e448c4dd74c828</td>\n",
       "      <td>f3c38ab652836d21de61fb8314b69182</td>\n",
       "      <td>2018-07-20 04:31:48</td>\n",
       "      <td>350.00</td>\n",
       "      <td>36.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112647</th>\n",
       "      <td>fffce4705a9662cd70adb13d4a31832d</td>\n",
       "      <td>1</td>\n",
       "      <td>72a30483855e2eafc67aee5dc2560482</td>\n",
       "      <td>c3cfdc648177fdbbbb35635a37472c53</td>\n",
       "      <td>2017-10-30 17:14:25</td>\n",
       "      <td>99.90</td>\n",
       "      <td>16.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112648</th>\n",
       "      <td>fffe18544ffabc95dfada21779c9644f</td>\n",
       "      <td>1</td>\n",
       "      <td>9c422a519119dcad7575db5af1ba540e</td>\n",
       "      <td>2b3e4a2a3ea8e01938cabda2a3e5cc79</td>\n",
       "      <td>2017-08-21 00:04:32</td>\n",
       "      <td>55.99</td>\n",
       "      <td>8.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112649</th>\n",
       "      <td>fffe41c64501cc87c801fd61db3f6244</td>\n",
       "      <td>1</td>\n",
       "      <td>350688d9dc1e75ff97be326363655e01</td>\n",
       "      <td>f7ccf836d21b2fb1de37564105216cc1</td>\n",
       "      <td>2018-06-12 17:10:13</td>\n",
       "      <td>43.00</td>\n",
       "      <td>12.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                order_id  order_item_id  \\\n",
       "112645  fffc94f6ce00a00581880bf54a75a037              1   \n",
       "112646  fffcd46ef2263f404302a634eb57f7eb              1   \n",
       "112647  fffce4705a9662cd70adb13d4a31832d              1   \n",
       "112648  fffe18544ffabc95dfada21779c9644f              1   \n",
       "112649  fffe41c64501cc87c801fd61db3f6244              1   \n",
       "\n",
       "                              product_id                         seller_id  \\\n",
       "112645  4aa6014eceb682077f9dc4bffebc05b0  b8bc237ba3788b23da09c0f1f3a3288c   \n",
       "112646  32e07fd915822b0765e448c4dd74c828  f3c38ab652836d21de61fb8314b69182   \n",
       "112647  72a30483855e2eafc67aee5dc2560482  c3cfdc648177fdbbbb35635a37472c53   \n",
       "112648  9c422a519119dcad7575db5af1ba540e  2b3e4a2a3ea8e01938cabda2a3e5cc79   \n",
       "112649  350688d9dc1e75ff97be326363655e01  f7ccf836d21b2fb1de37564105216cc1   \n",
       "\n",
       "        shipping_limit_date   price  freight_value  \n",
       "112645  2018-05-02 04:11:01  299.99          43.41  \n",
       "112646  2018-07-20 04:31:48  350.00          36.53  \n",
       "112647  2017-10-30 17:14:25   99.90          16.95  \n",
       "112648  2017-08-21 00:04:32   55.99           8.72  \n",
       "112649  2018-06-12 17:10:13   43.00          12.79  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_items.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "ee4874e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 112650 entries, 0 to 112649\n",
      "Data columns (total 7 columns):\n",
      " #   Column               Non-Null Count   Dtype  \n",
      "---  ------               --------------   -----  \n",
      " 0   order_id             112650 non-null  object \n",
      " 1   order_item_id        112650 non-null  int64  \n",
      " 2   product_id           112650 non-null  object \n",
      " 3   seller_id            112650 non-null  object \n",
      " 4   shipping_limit_date  112650 non-null  object \n",
      " 5   price                112650 non-null  float64\n",
      " 6   freight_value        112650 non-null  float64\n",
      "dtypes: float64(2), int64(1), object(4)\n",
      "memory usage: 6.0+ MB\n"
     ]
    }
   ],
   "source": [
    "#getting overview of datasets that we have.\n",
    "order_items.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "c3ef01f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id               0\n",
       "order_item_id          0\n",
       "product_id             0\n",
       "seller_id              0\n",
       "shipping_limit_date    0\n",
       "price                  0\n",
       "freight_value          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the null values\n",
    "order_items.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "8b28b796",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking duplicates across rows\n",
    "order_items.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "76a2a6ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking duplicates across columns\n",
    "order_items.T.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "1c008302",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98666"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the unique values in column order_id and order_item_id\n",
    "order_items['order_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "e84ff01a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "775"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#difference in order number as compared to total orders\n",
    "99441-98666"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "4077ebf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_items['order_item_id'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18580f44",
   "metadata": {},
   "source": [
    "#### Quick findings:\n",
    "\n",
    "- Order item dataset has information about order item. It tells us about number of items in each order, shipping limit and fright value\n",
    "- We have a total of 98666 order ids which is less than 99441. \n",
    "- This dataset has **four** columns of object datatype and **three** column with numeric datatype.\n",
    "- The column shipping limit date is of date time format so we need to convert it into correct format.\n",
    "- There are no duplicates across rows or columns.\n",
    "- There is no null values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84d72ca6",
   "metadata": {},
   "source": [
    "## 3.6 Order dataset\n",
    "<a id = 'order_dataset' ></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "216b4dc0",
   "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>order_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>order_purchase_timestamp</th>\n",
       "      <th>order_approved_at</th>\n",
       "      <th>order_delivered_carrier_date</th>\n",
       "      <th>order_delivered_customer_date</th>\n",
       "      <th>order_estimated_delivery_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>e481f51cbdc54678b7cc49136f2d6af7</td>\n",
       "      <td>9ef432eb6251297304e76186b10a928d</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-10-02 10:56:33</td>\n",
       "      <td>2017-10-02 11:07:15</td>\n",
       "      <td>2017-10-04 19:55:00</td>\n",
       "      <td>2017-10-10 21:25:13</td>\n",
       "      <td>2017-10-18 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53cdb2fc8bc7dce0b6741e2150273451</td>\n",
       "      <td>b0830fb4747a6c6d20dea0b8c802d7ef</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-07-24 20:41:37</td>\n",
       "      <td>2018-07-26 03:24:27</td>\n",
       "      <td>2018-07-26 14:31:00</td>\n",
       "      <td>2018-08-07 15:27:45</td>\n",
       "      <td>2018-08-13 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47770eb9100c2d0c44946d9cf07ec65d</td>\n",
       "      <td>41ce2a54c0b03bf3443c3d931a367089</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-08-08 08:38:49</td>\n",
       "      <td>2018-08-08 08:55:23</td>\n",
       "      <td>2018-08-08 13:50:00</td>\n",
       "      <td>2018-08-17 18:06:29</td>\n",
       "      <td>2018-09-04 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>949d5b44dbf5de918fe9c16f97b45f8a</td>\n",
       "      <td>f88197465ea7920adcdbec7375364d82</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-11-18 19:28:06</td>\n",
       "      <td>2017-11-18 19:45:59</td>\n",
       "      <td>2017-11-22 13:39:59</td>\n",
       "      <td>2017-12-02 00:28:42</td>\n",
       "      <td>2017-12-15 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ad21c59c0840e6cb83a9ceb5573f8159</td>\n",
       "      <td>8ab97904e6daea8866dbdbc4fb7aad2c</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-02-13 21:18:39</td>\n",
       "      <td>2018-02-13 22:20:29</td>\n",
       "      <td>2018-02-14 19:46:34</td>\n",
       "      <td>2018-02-16 18:17:02</td>\n",
       "      <td>2018-02-26 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id                       customer_id  \\\n",
       "0  e481f51cbdc54678b7cc49136f2d6af7  9ef432eb6251297304e76186b10a928d   \n",
       "1  53cdb2fc8bc7dce0b6741e2150273451  b0830fb4747a6c6d20dea0b8c802d7ef   \n",
       "2  47770eb9100c2d0c44946d9cf07ec65d  41ce2a54c0b03bf3443c3d931a367089   \n",
       "3  949d5b44dbf5de918fe9c16f97b45f8a  f88197465ea7920adcdbec7375364d82   \n",
       "4  ad21c59c0840e6cb83a9ceb5573f8159  8ab97904e6daea8866dbdbc4fb7aad2c   \n",
       "\n",
       "  order_status order_purchase_timestamp    order_approved_at  \\\n",
       "0    delivered      2017-10-02 10:56:33  2017-10-02 11:07:15   \n",
       "1    delivered      2018-07-24 20:41:37  2018-07-26 03:24:27   \n",
       "2    delivered      2018-08-08 08:38:49  2018-08-08 08:55:23   \n",
       "3    delivered      2017-11-18 19:28:06  2017-11-18 19:45:59   \n",
       "4    delivered      2018-02-13 21:18:39  2018-02-13 22:20:29   \n",
       "\n",
       "  order_delivered_carrier_date order_delivered_customer_date  \\\n",
       "0          2017-10-04 19:55:00           2017-10-10 21:25:13   \n",
       "1          2018-07-26 14:31:00           2018-08-07 15:27:45   \n",
       "2          2018-08-08 13:50:00           2018-08-17 18:06:29   \n",
       "3          2017-11-22 13:39:59           2017-12-02 00:28:42   \n",
       "4          2018-02-14 19:46:34           2018-02-16 18:17:02   \n",
       "\n",
       "  order_estimated_delivery_date  \n",
       "0           2017-10-18 00:00:00  \n",
       "1           2018-08-13 00:00:00  \n",
       "2           2018-09-04 00:00:00  \n",
       "3           2017-12-15 00:00:00  \n",
       "4           2018-02-26 00:00:00  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading head and tail of the order dataframe\n",
    "orders.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "408b72dc",
   "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>order_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>order_purchase_timestamp</th>\n",
       "      <th>order_approved_at</th>\n",
       "      <th>order_delivered_carrier_date</th>\n",
       "      <th>order_delivered_customer_date</th>\n",
       "      <th>order_estimated_delivery_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99436</th>\n",
       "      <td>9c5dedf39a927c1b2549525ed64a053c</td>\n",
       "      <td>39bd1228ee8140590ac3aca26f2dfe00</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-03-09 09:54:05</td>\n",
       "      <td>2017-03-09 09:54:05</td>\n",
       "      <td>2017-03-10 11:18:03</td>\n",
       "      <td>2017-03-17 15:08:01</td>\n",
       "      <td>2017-03-28 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99437</th>\n",
       "      <td>63943bddc261676b46f01ca7ac2f7bd8</td>\n",
       "      <td>1fca14ff2861355f6e5f14306ff977a7</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-02-06 12:58:58</td>\n",
       "      <td>2018-02-06 13:10:37</td>\n",
       "      <td>2018-02-07 23:22:42</td>\n",
       "      <td>2018-02-28 17:37:56</td>\n",
       "      <td>2018-03-02 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99438</th>\n",
       "      <td>83c1379a015df1e13d02aae0204711ab</td>\n",
       "      <td>1aa71eb042121263aafbe80c1b562c9c</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-08-27 14:46:43</td>\n",
       "      <td>2017-08-27 15:04:16</td>\n",
       "      <td>2017-08-28 20:52:26</td>\n",
       "      <td>2017-09-21 11:24:17</td>\n",
       "      <td>2017-09-27 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99439</th>\n",
       "      <td>11c177c8e97725db2631073c19f07b62</td>\n",
       "      <td>b331b74b18dc79bcdf6532d51e1637c1</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-01-08 21:28:27</td>\n",
       "      <td>2018-01-08 21:36:21</td>\n",
       "      <td>2018-01-12 15:35:03</td>\n",
       "      <td>2018-01-25 23:32:54</td>\n",
       "      <td>2018-02-15 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99440</th>\n",
       "      <td>66dea50a8b16d9b4dee7af250b4be1a5</td>\n",
       "      <td>edb027a75a1449115f6b43211ae02a24</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-03-08 20:57:30</td>\n",
       "      <td>2018-03-09 11:20:28</td>\n",
       "      <td>2018-03-09 22:11:59</td>\n",
       "      <td>2018-03-16 13:08:30</td>\n",
       "      <td>2018-04-03 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               order_id                       customer_id  \\\n",
       "99436  9c5dedf39a927c1b2549525ed64a053c  39bd1228ee8140590ac3aca26f2dfe00   \n",
       "99437  63943bddc261676b46f01ca7ac2f7bd8  1fca14ff2861355f6e5f14306ff977a7   \n",
       "99438  83c1379a015df1e13d02aae0204711ab  1aa71eb042121263aafbe80c1b562c9c   \n",
       "99439  11c177c8e97725db2631073c19f07b62  b331b74b18dc79bcdf6532d51e1637c1   \n",
       "99440  66dea50a8b16d9b4dee7af250b4be1a5  edb027a75a1449115f6b43211ae02a24   \n",
       "\n",
       "      order_status order_purchase_timestamp    order_approved_at  \\\n",
       "99436    delivered      2017-03-09 09:54:05  2017-03-09 09:54:05   \n",
       "99437    delivered      2018-02-06 12:58:58  2018-02-06 13:10:37   \n",
       "99438    delivered      2017-08-27 14:46:43  2017-08-27 15:04:16   \n",
       "99439    delivered      2018-01-08 21:28:27  2018-01-08 21:36:21   \n",
       "99440    delivered      2018-03-08 20:57:30  2018-03-09 11:20:28   \n",
       "\n",
       "      order_delivered_carrier_date order_delivered_customer_date  \\\n",
       "99436          2017-03-10 11:18:03           2017-03-17 15:08:01   \n",
       "99437          2018-02-07 23:22:42           2018-02-28 17:37:56   \n",
       "99438          2017-08-28 20:52:26           2017-09-21 11:24:17   \n",
       "99439          2018-01-12 15:35:03           2018-01-25 23:32:54   \n",
       "99440          2018-03-09 22:11:59           2018-03-16 13:08:30   \n",
       "\n",
       "      order_estimated_delivery_date  \n",
       "99436           2017-03-28 00:00:00  \n",
       "99437           2018-03-02 00:00:00  \n",
       "99438           2017-09-27 00:00:00  \n",
       "99439           2018-02-15 00:00:00  \n",
       "99440           2018-04-03 00:00:00  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "a095a723",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99441 entries, 0 to 99440\n",
      "Data columns (total 8 columns):\n",
      " #   Column                         Non-Null Count  Dtype \n",
      "---  ------                         --------------  ----- \n",
      " 0   order_id                       99441 non-null  object\n",
      " 1   customer_id                    99441 non-null  object\n",
      " 2   order_status                   99441 non-null  object\n",
      " 3   order_purchase_timestamp       99441 non-null  object\n",
      " 4   order_approved_at              99281 non-null  object\n",
      " 5   order_delivered_carrier_date   97658 non-null  object\n",
      " 6   order_delivered_customer_date  96476 non-null  object\n",
      " 7   order_estimated_delivery_date  99441 non-null  object\n",
      "dtypes: object(8)\n",
      "memory usage: 6.1+ MB\n"
     ]
    }
   ],
   "source": [
    "#getting overview of datasets that we have.\n",
    "orders.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "0b453856",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id                            0\n",
       "customer_id                         0\n",
       "order_status                        0\n",
       "order_purchase_timestamp            0\n",
       "order_approved_at                 160\n",
       "order_delivered_carrier_date     1783\n",
       "order_delivered_customer_date    2965\n",
       "order_estimated_delivery_date       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the null values\n",
    "orders.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "602d589b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4908"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "b0325f1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking duplicates across rows\n",
    "orders.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "e53188a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking duplicates across columns\n",
    "orders.T.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "af068f5a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99441"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the unique values in order id and customer id\n",
    "orders['order_id'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b3bbc48",
   "metadata": {},
   "source": [
    "#### Quick findings:\n",
    "\n",
    "- Orders dataset has information about the orders. Each order contains a customer id, order status, purchase timestamp and actual and estimated delivery information.\n",
    "- We have a total of 99441 unique orders which is the primary key for this table. \n",
    "- This dataset has **eight** columns of object datatype.\n",
    "- **There are a total of five columns of date time values but saved in object format. We need to convert them into date time format.**\n",
    "- There are no duplicates across rows or columns.\n",
    "- There are null values in the order_approved_at, order_delivered_carrier_date, and order_delivered_customer_date."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7fb5531",
   "metadata": {},
   "source": [
    "We have null values in 3 columns: order_approved_at, order_delivered_carrier_date, and order_delivered_customer_date. Does it have to do anything with the Order status? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "18323bb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert all the rows with date time data to date-time format.\n",
    "orders['order_purchase_timestamp']=pd.to_datetime(orders['order_purchase_timestamp'])\n",
    "orders['order_approved_at']=pd.to_datetime(orders['order_approved_at'])\n",
    "orders['order_delivered_carrier_date']=pd.to_datetime(orders['order_delivered_carrier_date'])\n",
    "orders['order_delivered_customer_date']=pd.to_datetime(orders['order_delivered_customer_date'])\n",
    "orders['order_estimated_delivery_date']=pd.to_datetime(orders['order_estimated_delivery_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "e0159c0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id                         0.000000\n",
       "customer_id                      0.000000\n",
       "order_status                     0.000000\n",
       "order_purchase_timestamp         0.000000\n",
       "order_approved_at                0.160899\n",
       "order_delivered_carrier_date     1.793023\n",
       "order_delivered_customer_date    2.981668\n",
       "order_estimated_delivery_date    0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#percentage of values that are missing\n",
    "orders.isnull().mean()*100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c9e29d4",
   "metadata": {},
   "source": [
    "The percentage of missing values is very less. We want to get the best out of available data. So we will try to find a way to impute these values. There is a chance that order status can be a reason for these missing values so let us explore it further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "5a9371d6",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#percentage of order according to different order status\n",
    "order_st_per= round(orders['order_status'].value_counts(normalize=True)*100, 2)\n",
    "\n",
    "#ploting the orders to visually check what all status do we have.\n",
    "plt.figure()\n",
    "graph=plt.bar(order_st_per.index, order_st_per.values)\n",
    "plt.title(\"Percentage with respect to total orders\")\n",
    "plt.xticks(rotation=45)\n",
    "for p in graph:\n",
    "    height=p.get_height()\n",
    "    plt.annotate( \"{}%\".format(height),(p.get_x() + p.get_width()/2, height+.05),ha=\"center\",va=\"bottom\",fontsize=9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "343b6af9",
   "metadata": {},
   "source": [
    "We have a total of eight order status. Our data has 97% of orders that were delivered. Only 0.63% of orders were cancelled. There are diferent order status that specify at which stage our order is."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd72678e",
   "metadata": {},
   "source": [
    "We want to understand if there is any relationship with missing values and the order status. We will filter the rows with missing values and check what is the status of that order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "284a7a40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "canceled    141\n",
       "created       5\n",
       "Name: order_status, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the status where all the values are missing\n",
    "orders.loc[orders['order_delivered_customer_date'].isnull() & \\\n",
    "           orders['order_delivered_carrier_date'].isnull() & \\\n",
    "           orders['order_approved_at'].isnull() ]['order_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "003ec78e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "unavailable    609\n",
       "canceled       550\n",
       "invoiced       314\n",
       "processing     301\n",
       "created          5\n",
       "approved         2\n",
       "delivered        1\n",
       "Name: order_status, dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the status where both order delivered customer and delivered carrier are missing\n",
    "orders.loc[orders['order_delivered_customer_date'].isnull() & \\\n",
    "           orders['order_delivered_carrier_date'].isnull()]['order_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "83fd38be",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "shipped        1107\n",
       "canceled        619\n",
       "unavailable     609\n",
       "invoiced        314\n",
       "processing      301\n",
       "delivered         8\n",
       "created           5\n",
       "approved          2\n",
       "Name: order_status, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the status where only order delivered customer is missing\n",
    "orders.loc[orders['order_delivered_customer_date'].isnull()]['order_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "04fbcabb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "unavailable    609\n",
       "canceled       550\n",
       "invoiced       314\n",
       "processing     301\n",
       "created          5\n",
       "approved         2\n",
       "delivered        2\n",
       "Name: order_status, dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the status where only order delivered carrier is missing\n",
    "orders.loc[orders['order_delivered_carrier_date'].isnull()]['order_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "39eae998",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "canceled     141\n",
       "delivered     14\n",
       "created        5\n",
       "Name: order_status, dtype: int64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the status where only order approved is missing\n",
    "orders.loc[orders['order_approved_at'].isnull()]['order_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "ec2db3cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "canceled    141\n",
       "created       5\n",
       "Name: order_status, dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the status where both order approved and order delivered cautomer are missing\n",
    "orders.loc[orders['order_approved_at'].isnull() & \n",
    "           orders['order_delivered_customer_date'].isnull() ]['order_status'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2cbc4fb",
   "metadata": {},
   "source": [
    "#### Different kind of order status and my understanding:\n",
    "\n",
    "\n",
    "| Order_status | Number | Description | Findings about missing values |\n",
    "|--- | --- | --- | --- |\n",
    "| delivered | 96478 | End stage, order has been delivered to end customer |  Some are missing but not a clear understanding about them|\n",
    "| shipped | 1107 | Order has been shipped to warehouse and handled to the logistic partner for delivery to end customer | order_delivered_customer_date (1107 Nos) |\n",
    "| canceled | 625 | Orders cancelled by customer after making payment.  |Some are missing but not a clear understanding about them|\n",
    "| unavailable | 609 | We don't know the status, we have exactly the same number of missing records for order delivered carrier date and order delivered customer date as the number of rows with unavailable status | Both order delivered carrier date and order delivered customer date (609 Nos) |\n",
    "| invoiced | 314 | A bill has been created and is sent out with the order  | Both order delivered carrier date and order delivered customer date (314 Nos) |\n",
    "| processing | 301 | A step before invoicing | Both order delivered carrier date and order delivered customer date (301 Nos) |\n",
    "| created | 5 | These orders are created recently, their payments have been made but seller has not approved the order yet. | All of order approved at, order delivered carrier date, and  order delivered customer date (5 Nos) |\n",
    "| approved | 2 | These orders are recently approved by the seller and need further process like invoicing and shipping | Both order delivered carrier date and order delivered customer date (2 Nos) |\n",
    "\n",
    "\n",
    "Customer made payment for the order &rarr; Order created\n",
    "\n",
    "Seller approved the order &rarr; Order approved\n",
    "\n",
    "Seller preparing the order &rarr; Order processing\n",
    "\n",
    "Seller invoiced the order &rarr; Order invoiced\n",
    "\n",
    "Seller shipped the order and was handed over to logistic partner &rarr; Order shipped\n",
    "\n",
    "Logistic partner delivered the product to end customer &rarr; Order delivered\n",
    "\n",
    "\n",
    "**We might not need the rows with order status as `unavailable`, `invoiced`, `processing`, `created`, `approved`, `shipped` so we will go ahead and delete these rows.** Logically these rows are missing the correct values as per the status. If we were to impute the missing values then we will have to change the status also. These rows have very less number compared to the total rows and there should not be much effect after removing these rows.  \n",
    "\n",
    "\n",
    "**Need to impute the missing values for `delivered` and `canceled` order status**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "825dd7ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "#deleting the redundant values.\n",
    "index_redundant_status= orders[(orders['order_status']=='invoiced') | (orders['order_status']=='processing') | \n",
    "       (orders['order_status']=='created') | (orders['order_status']=='approved') | (orders['order_status']=='unavailable') |\n",
    "       (orders['order_status']=='shipped')].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "bce1fe9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop the redundant rows\n",
    "orders.drop(index_redundant_status, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "d026d9b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id                           0\n",
       "customer_id                        0\n",
       "order_status                       0\n",
       "order_purchase_timestamp           0\n",
       "order_approved_at                155\n",
       "order_delivered_carrier_date     552\n",
       "order_delivered_customer_date    627\n",
       "order_estimated_delivery_date      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the null values\n",
    "orders.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d0e4402",
   "metadata": {},
   "source": [
    "#### How to impute the missing values?\n",
    "- We can find the difference in days between the known values i.e order_purchase_timestamp and the columns with NANs.\n",
    "- We can see the distribution of days that we will get from above step by plotting the box plot.\n",
    "- Then we can decide if we want to impute the missing values with mean, median or mode of the days difference.\n",
    "- Once we are clear of that, we can determine the missing value by adding the days difference to the order purchase date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "a8e16305",
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculating difference in days between the purchase date and the columns with NANS\n",
    "diff_est=((orders['order_estimated_delivery_date']-orders['order_purchase_timestamp'])/np.timedelta64(1, 'D')).astype(int)\n",
    "diff_approved=((orders['order_approved_at']-orders['order_purchase_timestamp'])/np.timedelta64(1, 'D'))\n",
    "diff_logist=((orders['order_delivered_carrier_date']-orders['order_purchase_timestamp'])/np.timedelta64(1, 'D'))\n",
    "diff_del=((orders['order_delivered_customer_date']-orders['order_purchase_timestamp'] )/np.timedelta64(1, 'D'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "0b915683",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.subplot(2, 2, 1)\n",
    "diff_est.plot.box(title='No. of days between estimated delivery and purchase time')\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "diff_approved.plot.box(title='No. of days between date approved and purchase date')\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "diff_logist.plot.box(title='No. of days between purchase date and carrier date')\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "diff_del.plot.box(title='No. of days between purchase date and actual delivery date')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea5f7429",
   "metadata": {},
   "source": [
    "#### Observations:\n",
    "\n",
    "- We can see that there are a bunch of outliers in these plots.\n",
    "- The number of days between purchase date and carrier date has a negative observation. If we are assuming that the data is recorded at Olist server where time zone factor has been removed. This is an incorrect observation and can be removed.\n",
    "- Upon looking at the box plots we have a bunch of outliers and therefore imputation using median is the right approach.\n",
    "\n",
    "We will simply drop these columns as we do not want to deal with them in our current scope. If we were to impute then we would calaculate the median number of days from each of diff_approved, diff_logist, diff_del and add it to order_purchase_timestamp to get the order_approved_at, order_delivered_carrier_date, order_delivered_customer_date respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "d19393c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "orders.drop(columns=['order_approved_at', 'order_delivered_carrier_date', 'order_delivered_customer_date'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "6f4895b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 97103 entries, 0 to 99440\n",
      "Data columns (total 5 columns):\n",
      " #   Column                         Non-Null Count  Dtype         \n",
      "---  ------                         --------------  -----         \n",
      " 0   order_id                       97103 non-null  object        \n",
      " 1   customer_id                    97103 non-null  object        \n",
      " 2   order_status                   97103 non-null  object        \n",
      " 3   order_purchase_timestamp       97103 non-null  datetime64[ns]\n",
      " 4   order_estimated_delivery_date  97103 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](2), object(3)\n",
      "memory usage: 4.4+ MB\n"
     ]
    }
   ],
   "source": [
    "orders.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e178f53",
   "metadata": {},
   "source": [
    "## 3.7 Product dataset\n",
    "<a id = 'product_dataset' ></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "c02b1599",
   "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_id</th>\n",
       "      <th>product_category_name</th>\n",
       "      <th>product_name_lenght</th>\n",
       "      <th>product_description_lenght</th>\n",
       "      <th>product_photos_qty</th>\n",
       "      <th>product_weight_g</th>\n",
       "      <th>product_length_cm</th>\n",
       "      <th>product_height_cm</th>\n",
       "      <th>product_width_cm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1e9e8ef04dbcff4541ed26657ea517e5</td>\n",
       "      <td>perfumaria</td>\n",
       "      <td>40.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3aa071139cb16b67ca9e5dea641aaa2f</td>\n",
       "      <td>artes</td>\n",
       "      <td>44.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>96bd76ec8810374ed1b65e291975717f</td>\n",
       "      <td>esporte_lazer</td>\n",
       "      <td>46.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cef67bcfe19066a932b7673e239eb23d</td>\n",
       "      <td>bebes</td>\n",
       "      <td>27.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9dc1a7de274444849c219cff195d0b71</td>\n",
       "      <td>utilidades_domesticas</td>\n",
       "      <td>37.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>625.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         product_id  product_category_name  \\\n",
       "0  1e9e8ef04dbcff4541ed26657ea517e5             perfumaria   \n",
       "1  3aa071139cb16b67ca9e5dea641aaa2f                  artes   \n",
       "2  96bd76ec8810374ed1b65e291975717f          esporte_lazer   \n",
       "3  cef67bcfe19066a932b7673e239eb23d                  bebes   \n",
       "4  9dc1a7de274444849c219cff195d0b71  utilidades_domesticas   \n",
       "\n",
       "   product_name_lenght  product_description_lenght  product_photos_qty  \\\n",
       "0                 40.0                       287.0                 1.0   \n",
       "1                 44.0                       276.0                 1.0   \n",
       "2                 46.0                       250.0                 1.0   \n",
       "3                 27.0                       261.0                 1.0   \n",
       "4                 37.0                       402.0                 4.0   \n",
       "\n",
       "   product_weight_g  product_length_cm  product_height_cm  product_width_cm  \n",
       "0             225.0               16.0               10.0              14.0  \n",
       "1            1000.0               30.0               18.0              20.0  \n",
       "2             154.0               18.0                9.0              15.0  \n",
       "3             371.0               26.0                4.0              26.0  \n",
       "4             625.0               20.0               17.0              13.0  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading head and tail of the customer dataframe\n",
    "products.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "21e79ea3",
   "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_id</th>\n",
       "      <th>product_category_name</th>\n",
       "      <th>product_name_lenght</th>\n",
       "      <th>product_description_lenght</th>\n",
       "      <th>product_photos_qty</th>\n",
       "      <th>product_weight_g</th>\n",
       "      <th>product_length_cm</th>\n",
       "      <th>product_height_cm</th>\n",
       "      <th>product_width_cm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32946</th>\n",
       "      <td>a0b7d5a992ccda646f2d34e418fff5a0</td>\n",
       "      <td>moveis_decoracao</td>\n",
       "      <td>45.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12300.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32947</th>\n",
       "      <td>bf4538d88321d0fd4412a93c974510e6</td>\n",
       "      <td>construcao_ferramentas_iluminacao</td>\n",
       "      <td>41.0</td>\n",
       "      <td>971.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32948</th>\n",
       "      <td>9a7c6041fa9592d9d9ef6cfe62a71f8c</td>\n",
       "      <td>cama_mesa_banho</td>\n",
       "      <td>50.0</td>\n",
       "      <td>799.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32949</th>\n",
       "      <td>83808703fc0706a22e264b9d75f04a2e</td>\n",
       "      <td>informatica_acessorios</td>\n",
       "      <td>60.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32950</th>\n",
       "      <td>106392145fca363410d287a815be6de4</td>\n",
       "      <td>cama_mesa_banho</td>\n",
       "      <td>58.0</td>\n",
       "      <td>309.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2083.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             product_id              product_category_name  \\\n",
       "32946  a0b7d5a992ccda646f2d34e418fff5a0                   moveis_decoracao   \n",
       "32947  bf4538d88321d0fd4412a93c974510e6  construcao_ferramentas_iluminacao   \n",
       "32948  9a7c6041fa9592d9d9ef6cfe62a71f8c                    cama_mesa_banho   \n",
       "32949  83808703fc0706a22e264b9d75f04a2e             informatica_acessorios   \n",
       "32950  106392145fca363410d287a815be6de4                    cama_mesa_banho   \n",
       "\n",
       "       product_name_lenght  product_description_lenght  product_photos_qty  \\\n",
       "32946                 45.0                        67.0                 2.0   \n",
       "32947                 41.0                       971.0                 1.0   \n",
       "32948                 50.0                       799.0                 1.0   \n",
       "32949                 60.0                       156.0                 2.0   \n",
       "32950                 58.0                       309.0                 1.0   \n",
       "\n",
       "       product_weight_g  product_length_cm  product_height_cm  \\\n",
       "32946           12300.0               40.0               40.0   \n",
       "32947            1700.0               16.0               19.0   \n",
       "32948            1400.0               27.0                7.0   \n",
       "32949             700.0               31.0               13.0   \n",
       "32950            2083.0               12.0                2.0   \n",
       "\n",
       "       product_width_cm  \n",
       "32946              40.0  \n",
       "32947              16.0  \n",
       "32948              27.0  \n",
       "32949              20.0  \n",
       "32950               7.0  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "4b8756c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32951 entries, 0 to 32950\n",
      "Data columns (total 9 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   product_id                  32951 non-null  object \n",
      " 1   product_category_name       32341 non-null  object \n",
      " 2   product_name_lenght         32341 non-null  float64\n",
      " 3   product_description_lenght  32341 non-null  float64\n",
      " 4   product_photos_qty          32341 non-null  float64\n",
      " 5   product_weight_g            32949 non-null  float64\n",
      " 6   product_length_cm           32949 non-null  float64\n",
      " 7   product_height_cm           32949 non-null  float64\n",
      " 8   product_width_cm            32949 non-null  float64\n",
      "dtypes: float64(7), object(2)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "#getting overview of datasets that we have.\n",
    "products.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "959511d1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "product_id                      0\n",
       "product_category_name         610\n",
       "product_name_lenght           610\n",
       "product_description_lenght    610\n",
       "product_photos_qty            610\n",
       "product_weight_g                2\n",
       "product_length_cm               2\n",
       "product_height_cm               2\n",
       "product_width_cm                2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the null values\n",
    "products.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "cedfe4be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2448"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "1f629c4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking duplicates across rows\n",
    "products.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "8a16f44d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking duplicates across columns\n",
    "products.T.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "5e1b390c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the unique values in column unique id and customer id\n",
    "products['product_category_name'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "17c2bdaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32951"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products['product_id'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4936ade9",
   "metadata": {},
   "source": [
    "#### Quick findings:\n",
    "\n",
    "- Products dataset has information about product categories, and their attributes.\n",
    "- We have a total of 32951 products ids which is the primary key for this table. There are a total of 73 product categories.\n",
    "- This dataset has **two** columns of object datatype and **seven** column with numeric datatype.\n",
    "- There are no duplicates across rows or columns.\n",
    "- There are few null values and we need to impute these values.   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc59875a",
   "metadata": {},
   "source": [
    "#### How can we impute these missing values?\n",
    "\n",
    "- We have seen that the rows which are missing values, a majority are missing a categorical data ie. product category name and respective, description, name length and photo quantities which are numerical.\n",
    "- We can find out the rows which are exactly matching the columns (weight, lenght, height, width) and we can fill the null value with the product category name of the matching category. We can fill rest of the values ie product description lenght, product name lenght and photo quantity with either mean, median or mode of that known category.\n",
    "    - If there are multiple matches for product category, we can filter out the category with most occuring match.\n",
    "    - If there is no match we can create a separate category **'other'** and fill rest of the values with either of mean, median or mode (determined after making a boxplot).\n",
    "- We will separate the rows from products dataframe with the missing values in separate dataframe (missing) and create another dataframe (all_values) where there is no null values.\n",
    "- We will find the element wise match of missing with all_values.\n",
    "- We only have two rows with missing product weight, height, lenght and width. We will use mean to fill these values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "91066a9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#quickly checking the categories\n",
    "products['product_category_name'].value_counts().plot(kind='bar', figsize=(20, 10),)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "01820e75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x800 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# making box plots for all the numeric columns\n",
    "fig, axes= plt.subplots(2,4, figsize=(12,8))\n",
    "numeric=products.select_dtypes(include=np.number).columns\n",
    "for i, val in enumerate(numeric):\n",
    "    products[val].plot.box(title=f'Box: {val}', ax=axes.flat[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2f2a280",
   "metadata": {},
   "source": [
    "#### Observations:\n",
    "\n",
    "- We can see from the box plot that there are a bunch of outliers in every box plot. We can use median to impute the missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "8fc12865",
   "metadata": {},
   "outputs": [],
   "source": [
    "#separating the missing values and known values\n",
    "missing=products[products['product_category_name'].isnull()]\n",
    "all_values= products[products['product_category_name'].notnull()]\n",
    "\n",
    "#getting the column names for 'product_weight_g', 'product_length_cm', 'product_height_cm', 'product_width_cm'\n",
    "col=missing.columns[5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "d897d76c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['product_weight_g', 'product_length_cm', 'product_height_cm',\n",
       "       'product_width_cm'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "7bfe623c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#checking for match and saving the index and values in a dictionary\n",
    "match=dict()\n",
    "for i in missing.index:\n",
    "    if ((all_values.iloc[:, 5]==missing.loc[i, col[0]]) & (all_values.iloc[:, 6]==missing.loc[i, col[1]]) & (all_values.iloc[:, 7]==missing.loc[i, col[2]]) & (all_values.iloc[:, 8]==missing.loc[i, col[3]])).sum()>0:\n",
    "        #saving the product category which is most occuring\n",
    "        match[i]=all_values[(all_values.iloc[:, 5]==missing.loc[i, col[0]]) & (all_values.iloc[:, 6]==missing.loc[i, col[1]]) & (all_values.iloc[:, 7]==missing.loc[i, col[2]]) & (all_values.iloc[:, 8]==missing.loc[i, col[3]])]['product_category_name'].value_counts().head(1).index\n",
    "    else:\n",
    "        match[i]='empty'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "4a3c6d5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#imputing the missing values\n",
    "for i in match.keys():\n",
    "    if match[i]!='empty':\n",
    "        #where there is a match we are filling the value with median of columns for the category we are assigning\n",
    "        products.loc[i, 'product_category_name']=match[i][0]\n",
    "        products.loc[i, 'product_name_lenght']=round(products[products['product_category_name']== match[i][0]]['product_name_lenght'].median())\n",
    "        products.loc[i, 'product_description_lenght']=round(products[products['product_category_name']== match[i][0]]['product_description_lenght'].median())\n",
    "        products.loc[i, 'product_photos_qty']=round(products[products['product_category_name']== match[i][0]]['product_photos_qty'].median())\n",
    "    else:\n",
    "        #for no match we are filling with other\n",
    "        products.loc[i, 'product_category_name']='Other'\n",
    "        products.loc[i, 'product_name_lenght']=round(products['product_name_lenght'].median())\n",
    "        products.loc[i, 'product_description_lenght']=round(products['product_description_lenght'].median())\n",
    "        products.loc[i, 'product_photos_qty']=round(products['product_photos_qty'].median())\n",
    "\n",
    "products['product_weight_g']=products['product_weight_g'].fillna(round(products['product_weight_g'].mean()))\n",
    "products['product_length_cm']=products['product_length_cm'].fillna(round(products['product_length_cm'].mean()))\n",
    "products['product_height_cm']=products['product_height_cm'].fillna(round(products['product_height_cm'].mean()))\n",
    "products['product_width_cm']=products['product_width_cm'].fillna(round(products['product_width_cm'].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "ed4ea1c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "product_id                    0\n",
       "product_category_name         0\n",
       "product_name_lenght           0\n",
       "product_description_lenght    0\n",
       "product_photos_qty            0\n",
       "product_weight_g              0\n",
       "product_length_cm             0\n",
       "product_height_cm             0\n",
       "product_width_cm              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8eab2ad",
   "metadata": {},
   "source": [
    "## 3.8 Review dataset\n",
    "\n",
    "<a id = 'review_dataset' ></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "c06a6186",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>review_score</th>\n",
       "      <th>review_comment_title</th>\n",
       "      <th>review_comment_message</th>\n",
       "      <th>review_creation_date</th>\n",
       "      <th>review_answer_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7bc2406110b926393aa56f80a40eba40</td>\n",
       "      <td>73fc7af87114b39712e6da79b0a377eb</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-01-18 00:00:00</td>\n",
       "      <td>2018-01-18 21:46:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80e641a11e56f04c1ad469d5645fdfde</td>\n",
       "      <td>a548910a1c6147796b98fdf73dbeba33</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-03-10 00:00:00</td>\n",
       "      <td>2018-03-11 03:05:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>228ce5500dc1d8e020d8d1322874b6f0</td>\n",
       "      <td>f9e4b658b201a9f2ecdecbb34bed034b</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-02-17 00:00:00</td>\n",
       "      <td>2018-02-18 14:36:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>e64fb393e7b32834bb789ff8bb30750e</td>\n",
       "      <td>658677c97b385a9be170737859d3511b</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Recebi bem antes do prazo estipulado.</td>\n",
       "      <td>2017-04-21 00:00:00</td>\n",
       "      <td>2017-04-21 22:02:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f7c4243c7fe1938f181bec41a392bdeb</td>\n",
       "      <td>8e6bfb81e283fa7e4f11123a3fb894f1</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Parabéns lojas lannister adorei comprar pela I...</td>\n",
       "      <td>2018-03-01 00:00:00</td>\n",
       "      <td>2018-03-02 10:26:53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          review_id                          order_id  \\\n",
       "0  7bc2406110b926393aa56f80a40eba40  73fc7af87114b39712e6da79b0a377eb   \n",
       "1  80e641a11e56f04c1ad469d5645fdfde  a548910a1c6147796b98fdf73dbeba33   \n",
       "2  228ce5500dc1d8e020d8d1322874b6f0  f9e4b658b201a9f2ecdecbb34bed034b   \n",
       "3  e64fb393e7b32834bb789ff8bb30750e  658677c97b385a9be170737859d3511b   \n",
       "4  f7c4243c7fe1938f181bec41a392bdeb  8e6bfb81e283fa7e4f11123a3fb894f1   \n",
       "\n",
       "   review_score review_comment_title  \\\n",
       "0             4                  NaN   \n",
       "1             5                  NaN   \n",
       "2             5                  NaN   \n",
       "3             5                  NaN   \n",
       "4             5                  NaN   \n",
       "\n",
       "                              review_comment_message review_creation_date  \\\n",
       "0                                                NaN  2018-01-18 00:00:00   \n",
       "1                                                NaN  2018-03-10 00:00:00   \n",
       "2                                                NaN  2018-02-17 00:00:00   \n",
       "3              Recebi bem antes do prazo estipulado.  2017-04-21 00:00:00   \n",
       "4  Parabéns lojas lannister adorei comprar pela I...  2018-03-01 00:00:00   \n",
       "\n",
       "  review_answer_timestamp  \n",
       "0     2018-01-18 21:46:59  \n",
       "1     2018-03-11 03:05:13  \n",
       "2     2018-02-18 14:36:24  \n",
       "3     2017-04-21 22:02:06  \n",
       "4     2018-03-02 10:26:53  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading head and tail of the review dataframe\n",
    "reviews.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "0ddc52d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>review_score</th>\n",
       "      <th>review_comment_title</th>\n",
       "      <th>review_comment_message</th>\n",
       "      <th>review_creation_date</th>\n",
       "      <th>review_answer_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99219</th>\n",
       "      <td>574ed12dd733e5fa530cfd4bbf39d7c9</td>\n",
       "      <td>2a8c23fee101d4d5662fa670396eb8da</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-07-07 00:00:00</td>\n",
       "      <td>2018-07-14 17:18:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99220</th>\n",
       "      <td>f3897127253a9592a73be9bdfdf4ed7a</td>\n",
       "      <td>22ec9f0669f784db00fa86d035cf8602</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-12-09 00:00:00</td>\n",
       "      <td>2017-12-11 20:06:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99221</th>\n",
       "      <td>b3de70c89b1510c4cd3d0649fd302472</td>\n",
       "      <td>55d4004744368f5571d1f590031933e4</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Excelente mochila, entrega super rápida. Super...</td>\n",
       "      <td>2018-03-22 00:00:00</td>\n",
       "      <td>2018-03-23 09:10:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99222</th>\n",
       "      <td>1adeb9d84d72fe4e337617733eb85149</td>\n",
       "      <td>7725825d039fc1f0ceb7635e3f7d9206</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-07-01 00:00:00</td>\n",
       "      <td>2018-07-02 12:59:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99223</th>\n",
       "      <td>efe49f1d6f951dd88b51e6ccd4cc548f</td>\n",
       "      <td>90531360ecb1eec2a1fbb265a0db0508</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>meu produto chegou e ja tenho que devolver, po...</td>\n",
       "      <td>2017-07-03 00:00:00</td>\n",
       "      <td>2017-07-03 21:01:49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              review_id                          order_id  \\\n",
       "99219  574ed12dd733e5fa530cfd4bbf39d7c9  2a8c23fee101d4d5662fa670396eb8da   \n",
       "99220  f3897127253a9592a73be9bdfdf4ed7a  22ec9f0669f784db00fa86d035cf8602   \n",
       "99221  b3de70c89b1510c4cd3d0649fd302472  55d4004744368f5571d1f590031933e4   \n",
       "99222  1adeb9d84d72fe4e337617733eb85149  7725825d039fc1f0ceb7635e3f7d9206   \n",
       "99223  efe49f1d6f951dd88b51e6ccd4cc548f  90531360ecb1eec2a1fbb265a0db0508   \n",
       "\n",
       "       review_score review_comment_title  \\\n",
       "99219             5                  NaN   \n",
       "99220             5                  NaN   \n",
       "99221             5                  NaN   \n",
       "99222             4                  NaN   \n",
       "99223             1                  NaN   \n",
       "\n",
       "                                  review_comment_message review_creation_date  \\\n",
       "99219                                                NaN  2018-07-07 00:00:00   \n",
       "99220                                                NaN  2017-12-09 00:00:00   \n",
       "99221  Excelente mochila, entrega super rápida. Super...  2018-03-22 00:00:00   \n",
       "99222                                                NaN  2018-07-01 00:00:00   \n",
       "99223  meu produto chegou e ja tenho que devolver, po...  2017-07-03 00:00:00   \n",
       "\n",
       "      review_answer_timestamp  \n",
       "99219     2018-07-14 17:18:30  \n",
       "99220     2017-12-11 20:06:42  \n",
       "99221     2018-03-23 09:10:43  \n",
       "99222     2018-07-02 12:59:13  \n",
       "99223     2017-07-03 21:01:49  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "2a8ad996",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99224 entries, 0 to 99223\n",
      "Data columns (total 7 columns):\n",
      " #   Column                   Non-Null Count  Dtype \n",
      "---  ------                   --------------  ----- \n",
      " 0   review_id                99224 non-null  object\n",
      " 1   order_id                 99224 non-null  object\n",
      " 2   review_score             99224 non-null  int64 \n",
      " 3   review_comment_title     11568 non-null  object\n",
      " 4   review_comment_message   40977 non-null  object\n",
      " 5   review_creation_date     99224 non-null  object\n",
      " 6   review_answer_timestamp  99224 non-null  object\n",
      "dtypes: int64(1), object(6)\n",
      "memory usage: 5.3+ MB\n"
     ]
    }
   ],
   "source": [
    "#getting overview of datasets that we have.\n",
    "reviews.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "55d91492",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "review_id                      0\n",
       "order_id                       0\n",
       "review_score                   0\n",
       "review_comment_title       87656\n",
       "review_comment_message     58247\n",
       "review_creation_date           0\n",
       "review_answer_timestamp        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the null values\n",
    "reviews.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "2b686859",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "145903"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "bfdc96f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking duplicates across rows\n",
    "reviews.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "9fdc316d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking duplicates across columns\n",
    "reviews.T.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "16575151",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98410"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the unique values in column unique id and customer id\n",
    "reviews['review_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "9ab369fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "814"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews.shape[0]-reviews['review_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "9bc37318",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98673"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews['order_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "ee1f297a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "263"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "98673-98410"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b697f733",
   "metadata": {},
   "source": [
    "#### Quick findings:\n",
    "\n",
    "- Reviews dataset has information about reviews given by the customers. It consists of review score, comment, review creation date and review submission timestamp.\n",
    "- **We have a total of 99224 review ids of which 98410 are the unique review ids. It means there are 814 reviews which have been resubmitted. These are the ones that need to be tackled.**\n",
    "- **We have 98673 unique order ids and 98410 unique review ids. It means that there are 263 reviews with same order id. It is possible that these reviews are for different products ordered under same order id.**\n",
    "- This dataset has **six** columns of object datatype and **one** column with numeric datatype.\n",
    "- There are no duplicates across rows or columns.\n",
    "- There are 145903 null values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbc190b1",
   "metadata": {},
   "source": [
    "## 3.9 Product_eng dataset\n",
    "\n",
    "<a id = 'eng_dataset' ></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "e8cab8e6",
   "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_category_name</th>\n",
       "      <th>product_category_name_english</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>beleza_saude</td>\n",
       "      <td>health_beauty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>informatica_acessorios</td>\n",
       "      <td>computers_accessories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>automotivo</td>\n",
       "      <td>auto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cama_mesa_banho</td>\n",
       "      <td>bed_bath_table</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>moveis_decoracao</td>\n",
       "      <td>furniture_decor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    product_category_name product_category_name_english\n",
       "0            beleza_saude                 health_beauty\n",
       "1  informatica_acessorios         computers_accessories\n",
       "2              automotivo                          auto\n",
       "3         cama_mesa_banho                bed_bath_table\n",
       "4        moveis_decoracao               furniture_decor"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading head and tail of the customer dataframe\n",
    "product_eng.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "26bf55e8",
   "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_category_name</th>\n",
       "      <th>product_category_name_english</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>flores</td>\n",
       "      <td>flowers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>artes_e_artesanato</td>\n",
       "      <td>arts_and_craftmanship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>fraldas_higiene</td>\n",
       "      <td>diapers_and_hygiene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>fashion_roupa_infanto_juvenil</td>\n",
       "      <td>fashion_childrens_clothes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>seguros_e_servicos</td>\n",
       "      <td>security_and_services</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            product_category_name product_category_name_english\n",
       "66                         flores                       flowers\n",
       "67             artes_e_artesanato         arts_and_craftmanship\n",
       "68                fraldas_higiene           diapers_and_hygiene\n",
       "69  fashion_roupa_infanto_juvenil     fashion_childrens_clothes\n",
       "70             seguros_e_servicos         security_and_services"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_eng.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "28f9e09a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 71 entries, 0 to 70\n",
      "Data columns (total 2 columns):\n",
      " #   Column                         Non-Null Count  Dtype \n",
      "---  ------                         --------------  ----- \n",
      " 0   product_category_name          71 non-null     object\n",
      " 1   product_category_name_english  71 non-null     object\n",
      "dtypes: object(2)\n",
      "memory usage: 1.2+ KB\n"
     ]
    }
   ],
   "source": [
    "#getting overview of datasets that we have.\n",
    "product_eng.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "a633de70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "product_category_name            0\n",
       "product_category_name_english    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the null values\n",
    "product_eng.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "049e66ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking duplicates across rows\n",
    "product_eng.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "bccb33d0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking duplicates across columns\n",
    "product_eng.T.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cb9f013",
   "metadata": {},
   "source": [
    "### Merging Product_eng with products"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "488847a4",
   "metadata": {},
   "source": [
    "We are now joining the product category with english names of the products and dropping the original product category name with names in portugese."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "44f6a29f",
   "metadata": {},
   "outputs": [],
   "source": [
    "products_final=pd.merge(left=products, right=product_eng, on='product_category_name', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "2aea29d9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "product_id                         0\n",
       "product_category_name              0\n",
       "product_name_lenght                0\n",
       "product_description_lenght         0\n",
       "product_photos_qty                 0\n",
       "product_weight_g                   0\n",
       "product_length_cm                  0\n",
       "product_height_cm                  0\n",
       "product_width_cm                   0\n",
       "product_category_name_english    406\n",
       "dtype: int64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products_final.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "6c5e9b6f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Other                                            393\n",
       "portateis_cozinha_e_preparadores_de_alimentos     10\n",
       "pc_gamer                                           3\n",
       "Name: product_category_name, dtype: int64"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the categores where we don't have english name\n",
    "products_final[products_final['product_category_name_english'].isnull()]['product_category_name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "42549eac",
   "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_id</th>\n",
       "      <th>product_category_name</th>\n",
       "      <th>product_name_lenght</th>\n",
       "      <th>product_description_lenght</th>\n",
       "      <th>product_photos_qty</th>\n",
       "      <th>product_weight_g</th>\n",
       "      <th>product_length_cm</th>\n",
       "      <th>product_height_cm</th>\n",
       "      <th>product_width_cm</th>\n",
       "      <th>product_category_name_english</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1e9e8ef04dbcff4541ed26657ea517e5</td>\n",
       "      <td>perfumaria</td>\n",
       "      <td>40.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>perfumery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3aa071139cb16b67ca9e5dea641aaa2f</td>\n",
       "      <td>artes</td>\n",
       "      <td>44.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>art</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>96bd76ec8810374ed1b65e291975717f</td>\n",
       "      <td>esporte_lazer</td>\n",
       "      <td>46.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>sports_leisure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cef67bcfe19066a932b7673e239eb23d</td>\n",
       "      <td>bebes</td>\n",
       "      <td>27.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>baby</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9dc1a7de274444849c219cff195d0b71</td>\n",
       "      <td>utilidades_domesticas</td>\n",
       "      <td>37.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>625.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>housewares</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         product_id  product_category_name  \\\n",
       "0  1e9e8ef04dbcff4541ed26657ea517e5             perfumaria   \n",
       "1  3aa071139cb16b67ca9e5dea641aaa2f                  artes   \n",
       "2  96bd76ec8810374ed1b65e291975717f          esporte_lazer   \n",
       "3  cef67bcfe19066a932b7673e239eb23d                  bebes   \n",
       "4  9dc1a7de274444849c219cff195d0b71  utilidades_domesticas   \n",
       "\n",
       "   product_name_lenght  product_description_lenght  product_photos_qty  \\\n",
       "0                 40.0                       287.0                 1.0   \n",
       "1                 44.0                       276.0                 1.0   \n",
       "2                 46.0                       250.0                 1.0   \n",
       "3                 27.0                       261.0                 1.0   \n",
       "4                 37.0                       402.0                 4.0   \n",
       "\n",
       "   product_weight_g  product_length_cm  product_height_cm  product_width_cm  \\\n",
       "0             225.0               16.0               10.0              14.0   \n",
       "1            1000.0               30.0               18.0              20.0   \n",
       "2             154.0               18.0                9.0              15.0   \n",
       "3             371.0               26.0                4.0              26.0   \n",
       "4             625.0               20.0               17.0              13.0   \n",
       "\n",
       "  product_category_name_english  \n",
       "0                     perfumery  \n",
       "1                           art  \n",
       "2                sports_leisure  \n",
       "3                          baby  \n",
       "4                    housewares  "
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products_final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04bf6e3b",
   "metadata": {},
   "source": [
    "We know we can set other to other, we can use google translate to convert rest to english.\n",
    "\n",
    "**portateis cozinha e preparadores de alimentos** : kitchen and food preparation racks <br/>\n",
    "**Pc gamer** : PC games"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "728bbe18",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#filling english name of three categories.\n",
    "products_final.loc[products_final['product_category_name']=='Other', 'product_category_name_english']='Other'\n",
    "products_final.loc[products_final['product_category_name']=='portateis_cozinha_e_preparadores_de_alimentos', 'product_category_name_english']='kitchen_and_food_preparation_racks'\n",
    "products_final.loc[products_final['product_category_name']=='pc_gamer', 'product_category_name_english']='PC_games'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "4e6e88a2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "product_id                       0\n",
       "product_category_name            0\n",
       "product_name_lenght              0\n",
       "product_description_lenght       0\n",
       "product_photos_qty               0\n",
       "product_weight_g                 0\n",
       "product_length_cm                0\n",
       "product_height_cm                0\n",
       "product_width_cm                 0\n",
       "product_category_name_english    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products_final.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "1d7ac8a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping the original product category name which is in portugese\n",
    "products_final.drop(columns=['product_category_name'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "d34c67ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Saving the final dataset to csv\n",
    "products_final.to_csv('data_cleaned/products_final_dataset.csv', index=False)\n",
    "orders.to_csv('data_cleaned/orders_cleaned_dataset.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1df5e08d",
   "metadata": {},
   "source": [
    "## 4. Joining all the tables\n",
    "\n",
    "<a id = 'joining_tables' ></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8ac0f61",
   "metadata": {},
   "source": [
    "We will be joining the tables to get a master table for addressing the business problem of Sales prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ae5395d",
   "metadata": {},
   "source": [
    "Starting from the orders dataset, we will first join the order_items and then the products dataset. \n",
    "\n",
    "Since we have already cleaned the orders dataset and saved it in a csv file, we will load that data set and start from there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "b1142d47",
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading the cleaned orders dataset.\n",
    "order_fin=pd.read_csv('data_cleaned/orders_cleaned_dataset.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43284e9f",
   "metadata": {},
   "source": [
    "### Merging Orders and Order_items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "070af336",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We already have our orders_items data set loaded. Let us join the order_items to order_fin\n",
    "order_comp= pd.merge(left=order_fin, right= order_items, on= 'order_id', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "f0200347",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 110739 entries, 0 to 110738\n",
      "Data columns (total 11 columns):\n",
      " #   Column                         Non-Null Count   Dtype  \n",
      "---  ------                         --------------   -----  \n",
      " 0   order_id                       110739 non-null  object \n",
      " 1   customer_id                    110739 non-null  object \n",
      " 2   order_status                   110739 non-null  object \n",
      " 3   order_purchase_timestamp       110739 non-null  object \n",
      " 4   order_estimated_delivery_date  110739 non-null  object \n",
      " 5   order_item_id                  110739 non-null  int64  \n",
      " 6   product_id                     110739 non-null  object \n",
      " 7   seller_id                      110739 non-null  object \n",
      " 8   shipping_limit_date            110739 non-null  object \n",
      " 9   price                          110739 non-null  float64\n",
      " 10  freight_value                  110739 non-null  float64\n",
      "dtypes: float64(2), int64(1), object(8)\n",
      "memory usage: 10.1+ MB\n"
     ]
    }
   ],
   "source": [
    "#basic overview\n",
    "order_comp.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f37dec86",
   "metadata": {},
   "source": [
    "okay, let us proceed with joining the products table."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbce1c9d",
   "metadata": {},
   "source": [
    "### Merging Products table with Order_comp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "ec724041",
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading the final product dataset.\n",
    "product_fin=pd.read_csv('data_cleaned/products_final_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "6aa7fc9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#let us join it with order_comp\n",
    "Order_cons=pd.merge(left=order_comp, right=product_fin, on='product_id', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "a16e1a4c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 110739 entries, 0 to 110738\n",
      "Data columns (total 19 columns):\n",
      " #   Column                         Non-Null Count   Dtype  \n",
      "---  ------                         --------------   -----  \n",
      " 0   order_id                       110739 non-null  object \n",
      " 1   customer_id                    110739 non-null  object \n",
      " 2   order_status                   110739 non-null  object \n",
      " 3   order_purchase_timestamp       110739 non-null  object \n",
      " 4   order_estimated_delivery_date  110739 non-null  object \n",
      " 5   order_item_id                  110739 non-null  int64  \n",
      " 6   product_id                     110739 non-null  object \n",
      " 7   seller_id                      110739 non-null  object \n",
      " 8   shipping_limit_date            110739 non-null  object \n",
      " 9   price                          110739 non-null  float64\n",
      " 10  freight_value                  110739 non-null  float64\n",
      " 11  product_name_lenght            110739 non-null  float64\n",
      " 12  product_description_lenght     110739 non-null  float64\n",
      " 13  product_photos_qty             110739 non-null  float64\n",
      " 14  product_weight_g               110739 non-null  float64\n",
      " 15  product_length_cm              110739 non-null  float64\n",
      " 16  product_height_cm              110739 non-null  float64\n",
      " 17  product_width_cm               110739 non-null  float64\n",
      " 18  product_category_name_english  110739 non-null  object \n",
      "dtypes: float64(9), int64(1), object(9)\n",
      "memory usage: 16.9+ MB\n"
     ]
    }
   ],
   "source": [
    "#basic overview\n",
    "Order_cons.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc3b0e1f",
   "metadata": {},
   "source": [
    "### Merging Sellers datset with the Order_cons"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "581ec6fc",
   "metadata": {},
   "source": [
    "Let us add the seller and customer details one by one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "33e62f3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#load the cleaned seller data form cap_data\\seller_loc_cleaned_dataset.csv\n",
    "seller_fin= pd.read_csv('data_cleaned/seller_loc_cleaned_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "3b809f11",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge the data\n",
    "order_with_seller=pd.merge(left=Order_cons, right=seller_fin, on='seller_id', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "40ec320a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 110739 entries, 0 to 110738\n",
      "Data columns (total 24 columns):\n",
      " #   Column                         Non-Null Count   Dtype  \n",
      "---  ------                         --------------   -----  \n",
      " 0   order_id                       110739 non-null  object \n",
      " 1   customer_id                    110739 non-null  object \n",
      " 2   order_status                   110739 non-null  object \n",
      " 3   order_purchase_timestamp       110739 non-null  object \n",
      " 4   order_estimated_delivery_date  110739 non-null  object \n",
      " 5   order_item_id                  110739 non-null  int64  \n",
      " 6   product_id                     110739 non-null  object \n",
      " 7   seller_id                      110739 non-null  object \n",
      " 8   shipping_limit_date            110739 non-null  object \n",
      " 9   price                          110739 non-null  float64\n",
      " 10  freight_value                  110739 non-null  float64\n",
      " 11  product_name_lenght            110739 non-null  float64\n",
      " 12  product_description_lenght     110739 non-null  float64\n",
      " 13  product_photos_qty             110739 non-null  float64\n",
      " 14  product_weight_g               110739 non-null  float64\n",
      " 15  product_length_cm              110739 non-null  float64\n",
      " 16  product_height_cm              110739 non-null  float64\n",
      " 17  product_width_cm               110739 non-null  float64\n",
      " 18  product_category_name_english  110739 non-null  object \n",
      " 19  seller_zip_code_prefix         110739 non-null  int64  \n",
      " 20  seller_city                    110739 non-null  object \n",
      " 21  seller_state                   110739 non-null  object \n",
      " 22  seller_lat                     110739 non-null  float64\n",
      " 23  seller_lng                     110739 non-null  float64\n",
      "dtypes: float64(11), int64(2), object(11)\n",
      "memory usage: 21.1+ MB\n"
     ]
    }
   ],
   "source": [
    "order_with_seller.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5d31147",
   "metadata": {},
   "source": [
    "### Merging Customer dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "c364c5b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#load the cleaned customer data form cap_data\\customer_loc_cleaned_dataset.csv\n",
    "customer_fin=pd.read_csv('data_cleaned/customer_loc_cleaned_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "0586d10c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merging the data\n",
    "order_with_customer=pd.merge(left=order_with_seller, right=customer_fin, on='customer_id', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "7c220836",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 110739 entries, 0 to 110738\n",
      "Data columns (total 30 columns):\n",
      " #   Column                         Non-Null Count   Dtype  \n",
      "---  ------                         --------------   -----  \n",
      " 0   order_id                       110739 non-null  object \n",
      " 1   customer_id                    110739 non-null  object \n",
      " 2   order_status                   110739 non-null  object \n",
      " 3   order_purchase_timestamp       110739 non-null  object \n",
      " 4   order_estimated_delivery_date  110739 non-null  object \n",
      " 5   order_item_id                  110739 non-null  int64  \n",
      " 6   product_id                     110739 non-null  object \n",
      " 7   seller_id                      110739 non-null  object \n",
      " 8   shipping_limit_date            110739 non-null  object \n",
      " 9   price                          110739 non-null  float64\n",
      " 10  freight_value                  110739 non-null  float64\n",
      " 11  product_name_lenght            110739 non-null  float64\n",
      " 12  product_description_lenght     110739 non-null  float64\n",
      " 13  product_photos_qty             110739 non-null  float64\n",
      " 14  product_weight_g               110739 non-null  float64\n",
      " 15  product_length_cm              110739 non-null  float64\n",
      " 16  product_height_cm              110739 non-null  float64\n",
      " 17  product_width_cm               110739 non-null  float64\n",
      " 18  product_category_name_english  110739 non-null  object \n",
      " 19  seller_zip_code_prefix         110739 non-null  int64  \n",
      " 20  seller_city                    110739 non-null  object \n",
      " 21  seller_state                   110739 non-null  object \n",
      " 22  seller_lat                     110739 non-null  float64\n",
      " 23  seller_lng                     110739 non-null  float64\n",
      " 24  customer_unique_id             110739 non-null  object \n",
      " 25  customer_zip_code_prefix       110739 non-null  int64  \n",
      " 26  customer_city                  110739 non-null  object \n",
      " 27  customer_state                 110739 non-null  object \n",
      " 28  customer_lat                   110739 non-null  float64\n",
      " 29  customer_lng                   110739 non-null  float64\n",
      "dtypes: float64(13), int64(3), object(14)\n",
      "memory usage: 26.2+ MB\n"
     ]
    }
   ],
   "source": [
    "order_with_customer.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff733bb1",
   "metadata": {},
   "source": [
    "### Merging the Reviews dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "e42660c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merging the review dataset\n",
    "olist_cons=pd.merge(left=order_with_customer, right=reviews, on='order_id', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "b20b1f2d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 110542 entries, 0 to 110541\n",
      "Data columns (total 36 columns):\n",
      " #   Column                         Non-Null Count   Dtype  \n",
      "---  ------                         --------------   -----  \n",
      " 0   order_id                       110542 non-null  object \n",
      " 1   customer_id                    110542 non-null  object \n",
      " 2   order_status                   110542 non-null  object \n",
      " 3   order_purchase_timestamp       110542 non-null  object \n",
      " 4   order_estimated_delivery_date  110542 non-null  object \n",
      " 5   order_item_id                  110542 non-null  int64  \n",
      " 6   product_id                     110542 non-null  object \n",
      " 7   seller_id                      110542 non-null  object \n",
      " 8   shipping_limit_date            110542 non-null  object \n",
      " 9   price                          110542 non-null  float64\n",
      " 10  freight_value                  110542 non-null  float64\n",
      " 11  product_name_lenght            110542 non-null  float64\n",
      " 12  product_description_lenght     110542 non-null  float64\n",
      " 13  product_photos_qty             110542 non-null  float64\n",
      " 14  product_weight_g               110542 non-null  float64\n",
      " 15  product_length_cm              110542 non-null  float64\n",
      " 16  product_height_cm              110542 non-null  float64\n",
      " 17  product_width_cm               110542 non-null  float64\n",
      " 18  product_category_name_english  110542 non-null  object \n",
      " 19  seller_zip_code_prefix         110542 non-null  int64  \n",
      " 20  seller_city                    110542 non-null  object \n",
      " 21  seller_state                   110542 non-null  object \n",
      " 22  seller_lat                     110542 non-null  float64\n",
      " 23  seller_lng                     110542 non-null  float64\n",
      " 24  customer_unique_id             110542 non-null  object \n",
      " 25  customer_zip_code_prefix       110542 non-null  int64  \n",
      " 26  customer_city                  110542 non-null  object \n",
      " 27  customer_state                 110542 non-null  object \n",
      " 28  customer_lat                   110542 non-null  float64\n",
      " 29  customer_lng                   110542 non-null  float64\n",
      " 30  review_id                      110542 non-null  object \n",
      " 31  review_score                   110542 non-null  int64  \n",
      " 32  review_comment_title           13215 non-null   object \n",
      " 33  review_comment_message         46485 non-null   object \n",
      " 34  review_creation_date           110542 non-null  object \n",
      " 35  review_answer_timestamp        110542 non-null  object \n",
      "dtypes: float64(13), int64(4), object(19)\n",
      "memory usage: 31.2+ MB\n"
     ]
    }
   ],
   "source": [
    "olist_cons.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42767882",
   "metadata": {},
   "source": [
    "We will be dropping some of the rows from the reveiw table since we don't need them in our current scope of work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "0bb42e74",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping not needed column.\n",
    "olist_cons.drop(columns=['review_comment_title','review_comment_message'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "d93a10f6",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id                         0\n",
       "customer_id                      0\n",
       "order_status                     0\n",
       "order_purchase_timestamp         0\n",
       "order_estimated_delivery_date    0\n",
       "order_item_id                    0\n",
       "product_id                       0\n",
       "seller_id                        0\n",
       "shipping_limit_date              0\n",
       "price                            0\n",
       "freight_value                    0\n",
       "product_name_lenght              0\n",
       "product_description_lenght       0\n",
       "product_photos_qty               0\n",
       "product_weight_g                 0\n",
       "product_length_cm                0\n",
       "product_height_cm                0\n",
       "product_width_cm                 0\n",
       "product_category_name_english    0\n",
       "seller_zip_code_prefix           0\n",
       "seller_city                      0\n",
       "seller_state                     0\n",
       "seller_lat                       0\n",
       "seller_lng                       0\n",
       "customer_unique_id               0\n",
       "customer_zip_code_prefix         0\n",
       "customer_city                    0\n",
       "customer_state                   0\n",
       "customer_lat                     0\n",
       "customer_lng                     0\n",
       "review_id                        0\n",
       "review_score                     0\n",
       "review_creation_date             0\n",
       "review_answer_timestamp          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for null values\n",
    "olist_cons.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "6f2e2ed0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for duplicate values\n",
    "olist_cons.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "d6073274",
   "metadata": {
    "scrolled": false
   },
   "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>order_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>order_purchase_timestamp</th>\n",
       "      <th>order_estimated_delivery_date</th>\n",
       "      <th>order_item_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>shipping_limit_date</th>\n",
       "      <th>price</th>\n",
       "      <th>...</th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>customer_zip_code_prefix</th>\n",
       "      <th>customer_city</th>\n",
       "      <th>customer_state</th>\n",
       "      <th>customer_lat</th>\n",
       "      <th>customer_lng</th>\n",
       "      <th>review_id</th>\n",
       "      <th>review_score</th>\n",
       "      <th>review_creation_date</th>\n",
       "      <th>review_answer_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>e481f51cbdc54678b7cc49136f2d6af7</td>\n",
       "      <td>9ef432eb6251297304e76186b10a928d</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-10-02 10:56:33</td>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>1</td>\n",
       "      <td>87285b34884572647811a353c7ac498a</td>\n",
       "      <td>3504c0cb71d7fa48d967e0e4c94d59d9</td>\n",
       "      <td>2017-10-06 11:07:15</td>\n",
       "      <td>29.99</td>\n",
       "      <td>...</td>\n",
       "      <td>7c396fd4830fd04220f754e42b4e5bff</td>\n",
       "      <td>3149</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "      <td>-23.577482</td>\n",
       "      <td>-46.587077</td>\n",
       "      <td>a54f0611adc9ed256b57ede6b6eb5114</td>\n",
       "      <td>4</td>\n",
       "      <td>2017-10-11 00:00:00</td>\n",
       "      <td>2017-10-12 03:43:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>128e10d95713541c87cd1a2e48201934</td>\n",
       "      <td>a20e8105f23924cd00833fd87daa0831</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-08-15 18:29:31</td>\n",
       "      <td>2017-08-28</td>\n",
       "      <td>1</td>\n",
       "      <td>87285b34884572647811a353c7ac498a</td>\n",
       "      <td>3504c0cb71d7fa48d967e0e4c94d59d9</td>\n",
       "      <td>2017-08-21 20:05:16</td>\n",
       "      <td>29.99</td>\n",
       "      <td>...</td>\n",
       "      <td>3a51803cc0d012c3b5dc8b7528cb05f7</td>\n",
       "      <td>3366</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "      <td>-23.564636</td>\n",
       "      <td>-46.534401</td>\n",
       "      <td>b46f1e34512b0f4c74a72398b03ca788</td>\n",
       "      <td>4</td>\n",
       "      <td>2017-08-19 00:00:00</td>\n",
       "      <td>2017-08-20 15:16:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0e7e841ddf8f8f2de2bad69267ecfbcf</td>\n",
       "      <td>26c7ac168e1433912a51b924fbd34d34</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-08-02 18:24:47</td>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>1</td>\n",
       "      <td>87285b34884572647811a353c7ac498a</td>\n",
       "      <td>3504c0cb71d7fa48d967e0e4c94d59d9</td>\n",
       "      <td>2017-08-08 18:37:31</td>\n",
       "      <td>29.99</td>\n",
       "      <td>...</td>\n",
       "      <td>ef0996a1a279c26e7ecbd737be23d235</td>\n",
       "      <td>2290</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "      <td>-23.600462</td>\n",
       "      <td>-46.655318</td>\n",
       "      <td>dc90f19c2806f1abba9e72ad3c350073</td>\n",
       "      <td>5</td>\n",
       "      <td>2017-08-08 00:00:00</td>\n",
       "      <td>2017-08-08 23:26:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bfc39df4f36c3693ff3b63fcbea9e90a</td>\n",
       "      <td>53904ddbea91e1e92b2b3f1d09a7af86</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2017-10-23 23:26:46</td>\n",
       "      <td>2017-11-13</td>\n",
       "      <td>1</td>\n",
       "      <td>87285b34884572647811a353c7ac498a</td>\n",
       "      <td>3504c0cb71d7fa48d967e0e4c94d59d9</td>\n",
       "      <td>2017-10-31 02:14:11</td>\n",
       "      <td>29.99</td>\n",
       "      <td>...</td>\n",
       "      <td>e781fdcc107d13d865fc7698711cc572</td>\n",
       "      <td>88032</td>\n",
       "      <td>florianopolis</td>\n",
       "      <td>SC</td>\n",
       "      <td>-27.546553</td>\n",
       "      <td>-48.497691</td>\n",
       "      <td>1bafb430e498b939f258b9c9dbdff9b1</td>\n",
       "      <td>3</td>\n",
       "      <td>2017-11-08 00:00:00</td>\n",
       "      <td>2017-11-10 19:52:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53cdb2fc8bc7dce0b6741e2150273451</td>\n",
       "      <td>b0830fb4747a6c6d20dea0b8c802d7ef</td>\n",
       "      <td>delivered</td>\n",
       "      <td>2018-07-24 20:41:37</td>\n",
       "      <td>2018-08-13</td>\n",
       "      <td>1</td>\n",
       "      <td>595fac2a385ac33a80bd5114aec74eb8</td>\n",
       "      <td>289cdb325fb7e7f891c38608bf9e0962</td>\n",
       "      <td>2018-07-30 03:24:27</td>\n",
       "      <td>118.70</td>\n",
       "      <td>...</td>\n",
       "      <td>af07308b275d755c9edb36a90c618231</td>\n",
       "      <td>47813</td>\n",
       "      <td>barreiras</td>\n",
       "      <td>BA</td>\n",
       "      <td>-12.186877</td>\n",
       "      <td>-44.540232</td>\n",
       "      <td>8d5266042046a06655c8db133d120ba5</td>\n",
       "      <td>4</td>\n",
       "      <td>2018-08-08 00:00:00</td>\n",
       "      <td>2018-08-08 18:37:50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id                       customer_id  \\\n",
       "0  e481f51cbdc54678b7cc49136f2d6af7  9ef432eb6251297304e76186b10a928d   \n",
       "1  128e10d95713541c87cd1a2e48201934  a20e8105f23924cd00833fd87daa0831   \n",
       "2  0e7e841ddf8f8f2de2bad69267ecfbcf  26c7ac168e1433912a51b924fbd34d34   \n",
       "3  bfc39df4f36c3693ff3b63fcbea9e90a  53904ddbea91e1e92b2b3f1d09a7af86   \n",
       "4  53cdb2fc8bc7dce0b6741e2150273451  b0830fb4747a6c6d20dea0b8c802d7ef   \n",
       "\n",
       "  order_status order_purchase_timestamp order_estimated_delivery_date  \\\n",
       "0    delivered      2017-10-02 10:56:33                    2017-10-18   \n",
       "1    delivered      2017-08-15 18:29:31                    2017-08-28   \n",
       "2    delivered      2017-08-02 18:24:47                    2017-08-15   \n",
       "3    delivered      2017-10-23 23:26:46                    2017-11-13   \n",
       "4    delivered      2018-07-24 20:41:37                    2018-08-13   \n",
       "\n",
       "   order_item_id                        product_id  \\\n",
       "0              1  87285b34884572647811a353c7ac498a   \n",
       "1              1  87285b34884572647811a353c7ac498a   \n",
       "2              1  87285b34884572647811a353c7ac498a   \n",
       "3              1  87285b34884572647811a353c7ac498a   \n",
       "4              1  595fac2a385ac33a80bd5114aec74eb8   \n",
       "\n",
       "                          seller_id  shipping_limit_date   price  ...  \\\n",
       "0  3504c0cb71d7fa48d967e0e4c94d59d9  2017-10-06 11:07:15   29.99  ...   \n",
       "1  3504c0cb71d7fa48d967e0e4c94d59d9  2017-08-21 20:05:16   29.99  ...   \n",
       "2  3504c0cb71d7fa48d967e0e4c94d59d9  2017-08-08 18:37:31   29.99  ...   \n",
       "3  3504c0cb71d7fa48d967e0e4c94d59d9  2017-10-31 02:14:11   29.99  ...   \n",
       "4  289cdb325fb7e7f891c38608bf9e0962  2018-07-30 03:24:27  118.70  ...   \n",
       "\n",
       "                 customer_unique_id  customer_zip_code_prefix  customer_city  \\\n",
       "0  7c396fd4830fd04220f754e42b4e5bff                      3149      sao paulo   \n",
       "1  3a51803cc0d012c3b5dc8b7528cb05f7                      3366      sao paulo   \n",
       "2  ef0996a1a279c26e7ecbd737be23d235                      2290      sao paulo   \n",
       "3  e781fdcc107d13d865fc7698711cc572                     88032  florianopolis   \n",
       "4  af07308b275d755c9edb36a90c618231                     47813      barreiras   \n",
       "\n",
       "   customer_state  customer_lat  customer_lng  \\\n",
       "0              SP    -23.577482    -46.587077   \n",
       "1              SP    -23.564636    -46.534401   \n",
       "2              SP    -23.600462    -46.655318   \n",
       "3              SC    -27.546553    -48.497691   \n",
       "4              BA    -12.186877    -44.540232   \n",
       "\n",
       "                          review_id  review_score review_creation_date  \\\n",
       "0  a54f0611adc9ed256b57ede6b6eb5114             4  2017-10-11 00:00:00   \n",
       "1  b46f1e34512b0f4c74a72398b03ca788             4  2017-08-19 00:00:00   \n",
       "2  dc90f19c2806f1abba9e72ad3c350073             5  2017-08-08 00:00:00   \n",
       "3  1bafb430e498b939f258b9c9dbdff9b1             3  2017-11-08 00:00:00   \n",
       "4  8d5266042046a06655c8db133d120ba5             4  2018-08-08 00:00:00   \n",
       "\n",
       "   review_answer_timestamp  \n",
       "0      2017-10-12 03:43:48  \n",
       "1      2017-08-20 15:16:36  \n",
       "2      2017-08-08 23:26:23  \n",
       "3      2017-11-10 19:52:38  \n",
       "4      2018-08-08 18:37:50  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading the head of our consolidated table\n",
    "olist_cons.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05716ae0",
   "metadata": {},
   "source": [
    "**Since we will be focusing on the Sales predictions we only want observations which have 'delivered' status.** Therefore will be dropping rows that have status not equal to delivered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "847644da",
   "metadata": {},
   "outputs": [],
   "source": [
    "#this is the final dataset with only delivered orders.\n",
    "final=olist_cons.loc[olist_cons['order_status']=='delivered']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "112b1f56",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 110013 entries, 0 to 110541\n",
      "Data columns (total 34 columns):\n",
      " #   Column                         Non-Null Count   Dtype  \n",
      "---  ------                         --------------   -----  \n",
      " 0   order_id                       110013 non-null  object \n",
      " 1   customer_id                    110013 non-null  object \n",
      " 2   order_status                   110013 non-null  object \n",
      " 3   order_purchase_timestamp       110013 non-null  object \n",
      " 4   order_estimated_delivery_date  110013 non-null  object \n",
      " 5   order_item_id                  110013 non-null  int64  \n",
      " 6   product_id                     110013 non-null  object \n",
      " 7   seller_id                      110013 non-null  object \n",
      " 8   shipping_limit_date            110013 non-null  object \n",
      " 9   price                          110013 non-null  float64\n",
      " 10  freight_value                  110013 non-null  float64\n",
      " 11  product_name_lenght            110013 non-null  float64\n",
      " 12  product_description_lenght     110013 non-null  float64\n",
      " 13  product_photos_qty             110013 non-null  float64\n",
      " 14  product_weight_g               110013 non-null  float64\n",
      " 15  product_length_cm              110013 non-null  float64\n",
      " 16  product_height_cm              110013 non-null  float64\n",
      " 17  product_width_cm               110013 non-null  float64\n",
      " 18  product_category_name_english  110013 non-null  object \n",
      " 19  seller_zip_code_prefix         110013 non-null  int64  \n",
      " 20  seller_city                    110013 non-null  object \n",
      " 21  seller_state                   110013 non-null  object \n",
      " 22  seller_lat                     110013 non-null  float64\n",
      " 23  seller_lng                     110013 non-null  float64\n",
      " 24  customer_unique_id             110013 non-null  object \n",
      " 25  customer_zip_code_prefix       110013 non-null  int64  \n",
      " 26  customer_city                  110013 non-null  object \n",
      " 27  customer_state                 110013 non-null  object \n",
      " 28  customer_lat                   110013 non-null  float64\n",
      " 29  customer_lng                   110013 non-null  float64\n",
      " 30  review_id                      110013 non-null  object \n",
      " 31  review_score                   110013 non-null  int64  \n",
      " 32  review_creation_date           110013 non-null  object \n",
      " 33  review_answer_timestamp        110013 non-null  object \n",
      "dtypes: float64(13), int64(4), object(17)\n",
      "memory usage: 29.4+ MB\n"
     ]
    }
   ],
   "source": [
    "final.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa07ce94",
   "metadata": {},
   "source": [
    "**Remioving few more columns that we don't need**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "968d48c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#removing the redundant columns from the final dataset\n",
    "final_cleaned=final.drop(columns=['order_status', 'seller_zip_code_prefix', 'customer_zip_code_prefix', 'review_creation_date', 'review_answer_timestamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "df464c53",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 110013 entries, 0 to 110541\n",
      "Data columns (total 29 columns):\n",
      " #   Column                         Non-Null Count   Dtype  \n",
      "---  ------                         --------------   -----  \n",
      " 0   order_id                       110013 non-null  object \n",
      " 1   customer_id                    110013 non-null  object \n",
      " 2   order_purchase_timestamp       110013 non-null  object \n",
      " 3   order_estimated_delivery_date  110013 non-null  object \n",
      " 4   order_item_id                  110013 non-null  int64  \n",
      " 5   product_id                     110013 non-null  object \n",
      " 6   seller_id                      110013 non-null  object \n",
      " 7   shipping_limit_date            110013 non-null  object \n",
      " 8   price                          110013 non-null  float64\n",
      " 9   freight_value                  110013 non-null  float64\n",
      " 10  product_name_lenght            110013 non-null  float64\n",
      " 11  product_description_lenght     110013 non-null  float64\n",
      " 12  product_photos_qty             110013 non-null  float64\n",
      " 13  product_weight_g               110013 non-null  float64\n",
      " 14  product_length_cm              110013 non-null  float64\n",
      " 15  product_height_cm              110013 non-null  float64\n",
      " 16  product_width_cm               110013 non-null  float64\n",
      " 17  product_category_name_english  110013 non-null  object \n",
      " 18  seller_city                    110013 non-null  object \n",
      " 19  seller_state                   110013 non-null  object \n",
      " 20  seller_lat                     110013 non-null  float64\n",
      " 21  seller_lng                     110013 non-null  float64\n",
      " 22  customer_unique_id             110013 non-null  object \n",
      " 23  customer_city                  110013 non-null  object \n",
      " 24  customer_state                 110013 non-null  object \n",
      " 25  customer_lat                   110013 non-null  float64\n",
      " 26  customer_lng                   110013 non-null  float64\n",
      " 27  review_id                      110013 non-null  object \n",
      " 28  review_score                   110013 non-null  int64  \n",
      "dtypes: float64(13), int64(2), object(14)\n",
      "memory usage: 25.2+ MB\n"
     ]
    }
   ],
   "source": [
    "#checking info\n",
    "final_cleaned.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e60f3de9",
   "metadata": {},
   "source": [
    "**Renaming the order_item_id as 'qty' as it tells us about the number of items bought**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "26ea719c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#rename the order_item_id to qty as it hold the quantity of items in each category.\n",
    "final_cleaned.rename(columns = {'order_item_id':'qty' }, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "8355b94d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#removing another column\n",
    "final_cleaned.drop(columns='review_id', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "b87ac479",
   "metadata": {
    "scrolled": false
   },
   "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>order_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>order_purchase_timestamp</th>\n",
       "      <th>order_estimated_delivery_date</th>\n",
       "      <th>qty</th>\n",
       "      <th>product_id</th>\n",
       "      <th>seller_id</th>\n",
       "      <th>shipping_limit_date</th>\n",
       "      <th>price</th>\n",
       "      <th>freight_value</th>\n",
       "      <th>...</th>\n",
       "      <th>seller_city</th>\n",
       "      <th>seller_state</th>\n",
       "      <th>seller_lat</th>\n",
       "      <th>seller_lng</th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>customer_city</th>\n",
       "      <th>customer_state</th>\n",
       "      <th>customer_lat</th>\n",
       "      <th>customer_lng</th>\n",
       "      <th>review_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>e481f51cbdc54678b7cc49136f2d6af7</td>\n",
       "      <td>9ef432eb6251297304e76186b10a928d</td>\n",
       "      <td>2017-10-02 10:56:33</td>\n",
       "      <td>2017-10-18</td>\n",
       "      <td>1</td>\n",
       "      <td>87285b34884572647811a353c7ac498a</td>\n",
       "      <td>3504c0cb71d7fa48d967e0e4c94d59d9</td>\n",
       "      <td>2017-10-06 11:07:15</td>\n",
       "      <td>29.99</td>\n",
       "      <td>8.72</td>\n",
       "      <td>...</td>\n",
       "      <td>maua</td>\n",
       "      <td>SP</td>\n",
       "      <td>-23.680862</td>\n",
       "      <td>-46.444311</td>\n",
       "      <td>7c396fd4830fd04220f754e42b4e5bff</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "      <td>-23.577482</td>\n",
       "      <td>-46.587077</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>128e10d95713541c87cd1a2e48201934</td>\n",
       "      <td>a20e8105f23924cd00833fd87daa0831</td>\n",
       "      <td>2017-08-15 18:29:31</td>\n",
       "      <td>2017-08-28</td>\n",
       "      <td>1</td>\n",
       "      <td>87285b34884572647811a353c7ac498a</td>\n",
       "      <td>3504c0cb71d7fa48d967e0e4c94d59d9</td>\n",
       "      <td>2017-08-21 20:05:16</td>\n",
       "      <td>29.99</td>\n",
       "      <td>7.78</td>\n",
       "      <td>...</td>\n",
       "      <td>maua</td>\n",
       "      <td>SP</td>\n",
       "      <td>-23.680862</td>\n",
       "      <td>-46.444311</td>\n",
       "      <td>3a51803cc0d012c3b5dc8b7528cb05f7</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "      <td>-23.564636</td>\n",
       "      <td>-46.534401</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0e7e841ddf8f8f2de2bad69267ecfbcf</td>\n",
       "      <td>26c7ac168e1433912a51b924fbd34d34</td>\n",
       "      <td>2017-08-02 18:24:47</td>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>1</td>\n",
       "      <td>87285b34884572647811a353c7ac498a</td>\n",
       "      <td>3504c0cb71d7fa48d967e0e4c94d59d9</td>\n",
       "      <td>2017-08-08 18:37:31</td>\n",
       "      <td>29.99</td>\n",
       "      <td>7.78</td>\n",
       "      <td>...</td>\n",
       "      <td>maua</td>\n",
       "      <td>SP</td>\n",
       "      <td>-23.680862</td>\n",
       "      <td>-46.444311</td>\n",
       "      <td>ef0996a1a279c26e7ecbd737be23d235</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "      <td>-23.600462</td>\n",
       "      <td>-46.655318</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bfc39df4f36c3693ff3b63fcbea9e90a</td>\n",
       "      <td>53904ddbea91e1e92b2b3f1d09a7af86</td>\n",
       "      <td>2017-10-23 23:26:46</td>\n",
       "      <td>2017-11-13</td>\n",
       "      <td>1</td>\n",
       "      <td>87285b34884572647811a353c7ac498a</td>\n",
       "      <td>3504c0cb71d7fa48d967e0e4c94d59d9</td>\n",
       "      <td>2017-10-31 02:14:11</td>\n",
       "      <td>29.99</td>\n",
       "      <td>14.10</td>\n",
       "      <td>...</td>\n",
       "      <td>maua</td>\n",
       "      <td>SP</td>\n",
       "      <td>-23.680862</td>\n",
       "      <td>-46.444311</td>\n",
       "      <td>e781fdcc107d13d865fc7698711cc572</td>\n",
       "      <td>florianopolis</td>\n",
       "      <td>SC</td>\n",
       "      <td>-27.546553</td>\n",
       "      <td>-48.497691</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53cdb2fc8bc7dce0b6741e2150273451</td>\n",
       "      <td>b0830fb4747a6c6d20dea0b8c802d7ef</td>\n",
       "      <td>2018-07-24 20:41:37</td>\n",
       "      <td>2018-08-13</td>\n",
       "      <td>1</td>\n",
       "      <td>595fac2a385ac33a80bd5114aec74eb8</td>\n",
       "      <td>289cdb325fb7e7f891c38608bf9e0962</td>\n",
       "      <td>2018-07-30 03:24:27</td>\n",
       "      <td>118.70</td>\n",
       "      <td>22.76</td>\n",
       "      <td>...</td>\n",
       "      <td>belo horizonte</td>\n",
       "      <td>SP</td>\n",
       "      <td>-19.807885</td>\n",
       "      <td>-43.980818</td>\n",
       "      <td>af07308b275d755c9edb36a90c618231</td>\n",
       "      <td>barreiras</td>\n",
       "      <td>BA</td>\n",
       "      <td>-12.186877</td>\n",
       "      <td>-44.540232</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           order_id                       customer_id  \\\n",
       "0  e481f51cbdc54678b7cc49136f2d6af7  9ef432eb6251297304e76186b10a928d   \n",
       "1  128e10d95713541c87cd1a2e48201934  a20e8105f23924cd00833fd87daa0831   \n",
       "2  0e7e841ddf8f8f2de2bad69267ecfbcf  26c7ac168e1433912a51b924fbd34d34   \n",
       "3  bfc39df4f36c3693ff3b63fcbea9e90a  53904ddbea91e1e92b2b3f1d09a7af86   \n",
       "4  53cdb2fc8bc7dce0b6741e2150273451  b0830fb4747a6c6d20dea0b8c802d7ef   \n",
       "\n",
       "  order_purchase_timestamp order_estimated_delivery_date  qty  \\\n",
       "0      2017-10-02 10:56:33                    2017-10-18    1   \n",
       "1      2017-08-15 18:29:31                    2017-08-28    1   \n",
       "2      2017-08-02 18:24:47                    2017-08-15    1   \n",
       "3      2017-10-23 23:26:46                    2017-11-13    1   \n",
       "4      2018-07-24 20:41:37                    2018-08-13    1   \n",
       "\n",
       "                         product_id                         seller_id  \\\n",
       "0  87285b34884572647811a353c7ac498a  3504c0cb71d7fa48d967e0e4c94d59d9   \n",
       "1  87285b34884572647811a353c7ac498a  3504c0cb71d7fa48d967e0e4c94d59d9   \n",
       "2  87285b34884572647811a353c7ac498a  3504c0cb71d7fa48d967e0e4c94d59d9   \n",
       "3  87285b34884572647811a353c7ac498a  3504c0cb71d7fa48d967e0e4c94d59d9   \n",
       "4  595fac2a385ac33a80bd5114aec74eb8  289cdb325fb7e7f891c38608bf9e0962   \n",
       "\n",
       "   shipping_limit_date   price  freight_value  ...     seller_city  \\\n",
       "0  2017-10-06 11:07:15   29.99           8.72  ...            maua   \n",
       "1  2017-08-21 20:05:16   29.99           7.78  ...            maua   \n",
       "2  2017-08-08 18:37:31   29.99           7.78  ...            maua   \n",
       "3  2017-10-31 02:14:11   29.99          14.10  ...            maua   \n",
       "4  2018-07-30 03:24:27  118.70          22.76  ...  belo horizonte   \n",
       "\n",
       "   seller_state  seller_lat  seller_lng                customer_unique_id  \\\n",
       "0            SP  -23.680862  -46.444311  7c396fd4830fd04220f754e42b4e5bff   \n",
       "1            SP  -23.680862  -46.444311  3a51803cc0d012c3b5dc8b7528cb05f7   \n",
       "2            SP  -23.680862  -46.444311  ef0996a1a279c26e7ecbd737be23d235   \n",
       "3            SP  -23.680862  -46.444311  e781fdcc107d13d865fc7698711cc572   \n",
       "4            SP  -19.807885  -43.980818  af07308b275d755c9edb36a90c618231   \n",
       "\n",
       "   customer_city  customer_state customer_lat customer_lng review_score  \n",
       "0      sao paulo              SP   -23.577482   -46.587077            4  \n",
       "1      sao paulo              SP   -23.564636   -46.534401            4  \n",
       "2      sao paulo              SP   -23.600462   -46.655318            5  \n",
       "3  florianopolis              SC   -27.546553   -48.497691            3  \n",
       "4      barreiras              BA   -12.186877   -44.540232            4  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_cleaned.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "536fd735",
   "metadata": {},
   "source": [
    "**We have learnt from the Kaggel website that the total order value is calculated using qty and price. Since price tells us about the unit price, the total order value= qty* price.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5056c185",
   "metadata": {},
   "source": [
    "#### Creating column total_amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "de4d09f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating column total_amount\n",
    "final_cleaned['total_amount']=final_cleaned['qty']*final_cleaned['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "b6d61bae",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 110013 entries, 0 to 110541\n",
      "Data columns (total 29 columns):\n",
      " #   Column                         Non-Null Count   Dtype  \n",
      "---  ------                         --------------   -----  \n",
      " 0   order_id                       110013 non-null  object \n",
      " 1   customer_id                    110013 non-null  object \n",
      " 2   order_purchase_timestamp       110013 non-null  object \n",
      " 3   order_estimated_delivery_date  110013 non-null  object \n",
      " 4   qty                            110013 non-null  int64  \n",
      " 5   product_id                     110013 non-null  object \n",
      " 6   seller_id                      110013 non-null  object \n",
      " 7   shipping_limit_date            110013 non-null  object \n",
      " 8   price                          110013 non-null  float64\n",
      " 9   freight_value                  110013 non-null  float64\n",
      " 10  product_name_lenght            110013 non-null  float64\n",
      " 11  product_description_lenght     110013 non-null  float64\n",
      " 12  product_photos_qty             110013 non-null  float64\n",
      " 13  product_weight_g               110013 non-null  float64\n",
      " 14  product_length_cm              110013 non-null  float64\n",
      " 15  product_height_cm              110013 non-null  float64\n",
      " 16  product_width_cm               110013 non-null  float64\n",
      " 17  product_category_name_english  110013 non-null  object \n",
      " 18  seller_city                    110013 non-null  object \n",
      " 19  seller_state                   110013 non-null  object \n",
      " 20  seller_lat                     110013 non-null  float64\n",
      " 21  seller_lng                     110013 non-null  float64\n",
      " 22  customer_unique_id             110013 non-null  object \n",
      " 23  customer_city                  110013 non-null  object \n",
      " 24  customer_state                 110013 non-null  object \n",
      " 25  customer_lat                   110013 non-null  float64\n",
      " 26  customer_lng                   110013 non-null  float64\n",
      " 27  review_score                   110013 non-null  int64  \n",
      " 28  total_amount                   110013 non-null  float64\n",
      "dtypes: float64(14), int64(2), object(13)\n",
      "memory usage: 25.2+ MB\n"
     ]
    }
   ],
   "source": [
    "final_cleaned.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5c40e19",
   "metadata": {},
   "source": [
    "Now that we have our final data wireframe ready, we will go ahead and save this to folder data_cleaned for furthr processing in our next notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "aa8c3bf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_cleaned.to_csv('data_cleaned/master_dataset.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed63ce5b",
   "metadata": {},
   "source": [
    "## 5. Scraping holiday data\n",
    "\n",
    "<a id = 'scraping' ></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "926c16f6",
   "metadata": {},
   "source": [
    "Since, we will be predicting sales amount we need to collect Holiday information to help our model in understanding impacts of holidays.\n",
    "We will be doing a simple scape to collect the Brazilian National Holiday information from this website: https://www.officeholidays.com/countries/brazil/\n",
    "\n",
    "We can append the above URL with 2017 and 2018 to get the pages with holiday information for year 2017 and 2018."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "322ba7a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For web scraping (the requests package allows you to send HTTP requests using Python)\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "# For performing regex operations\n",
    "import re\n",
    "\n",
    "# For adding delays so that we don't spam requests\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "aa2248d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#define empty dictionary to save content\n",
    "content={}\n",
    "\n",
    "#scaping holiday information for pages 2017 and 2018\n",
    "for i in [2017, 2018]:\n",
    "    url = 'https://www.officeholidays.com/countries/brazil/'\n",
    "    url = url+str(i)\n",
    "    response = requests.get(url)\n",
    "    soup = BeautifulSoup(response.content)\n",
    "    content[i]=soup.find_all('time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "0bda65ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "#extracting Holiday information from the scarpped data\n",
    "#empty list\n",
    "holidays=[]\n",
    "for key in content:\n",
    "    dict_size=len(content[key])\n",
    "    dict_val=content[key]\n",
    "    for j in range(0,dict_size):\n",
    "        holidays.append(dict_val[j].attrs['datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "5dd877f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a dataframe for the holiday information\n",
    "\n",
    "holidays_df=pd.DataFrame(index=[holidays], data=np.ones(len(holidays)), columns=['is_holiday'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "5c5a4bd3",
   "metadata": {
    "scrolled": true
   },
   "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>is_holiday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-27</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-28</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-01</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-14</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            is_holiday\n",
       "2017-01-01         1.0\n",
       "2017-02-27         1.0\n",
       "2017-02-28         1.0\n",
       "2017-03-01         1.0\n",
       "2017-04-14         1.0"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holidays_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "f94067fd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 36 entries, ('2017-01-01',) to ('2018-12-25',)\n",
      "Data columns (total 1 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   is_holiday  36 non-null     float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 1.7+ KB\n"
     ]
    }
   ],
   "source": [
    "holidays_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba86584c",
   "metadata": {},
   "source": [
    "This dataframe has only one column 'is_holiday' which is one meaning it is an holiday. The index are the dates of the holiday.\n",
    "\n",
    "**These dates are for year 2017 and 2018. The index is not continuous, these are just the holiday dates. We have saved the data like this so that we can use it for time series.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "0093ffd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#saving the holiday dataframe as csv file\n",
    "holidays_df.to_csv('data_cleaned/holiday.csv')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}