final project -Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: unrecognized arguments: # we want the plot to be appear as in line with the code\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline   # we want the plot to be appear as in line with the code "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "products=pd.read_csv(\"products.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49688, 4)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_name</th>\n",
       "      <th>aisle_id</th>\n",
       "      <th>department_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Chocolate Sandwich Cookies</td>\n",
       "      <td>61</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>All-Seasons Salt</td>\n",
       "      <td>104</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Robust Golden Unsweetened Oolong Tea</td>\n",
       "      <td>94</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Smart Ones Classic Favorites Mini Rigatoni Wit...</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Green Chile Anytime Sauce</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_id                                       product_name  aisle_id  \\\n",
       "0           1                         Chocolate Sandwich Cookies        61   \n",
       "1           2                                   All-Seasons Salt       104   \n",
       "2           3               Robust Golden Unsweetened Oolong Tea        94   \n",
       "3           4  Smart Ones Classic Favorites Mini Rigatoni Wit...        38   \n",
       "4           5                          Green Chile Anytime Sauce         5   \n",
       "\n",
       "   department_id  \n",
       "0             19  \n",
       "1             13  \n",
       "2              7  \n",
       "3              1  \n",
       "4             13  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders=pd.read_csv(\"orders.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3421083, 7)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>eval_set</th>\n",
       "      <th>order_number</th>\n",
       "      <th>order_dow</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2539329</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2398795</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
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       "      <td>7</td>\n",
       "      <td>15.0</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>473747</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2254736</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>431534</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  user_id eval_set  order_number  order_dow  order_hour_of_day  \\\n",
       "0   2539329        1    prior             1          2                  8   \n",
       "1   2398795        1    prior             2          3                  7   \n",
       "2    473747        1    prior             3          3                 12   \n",
       "3   2254736        1    prior             4          4                  7   \n",
       "4    431534        1    prior             5          4                 15   \n",
       "\n",
       "   days_since_prior_order  \n",
       "0                     NaN  \n",
       "1                    15.0  \n",
       "2                    21.0  \n",
       "3                    29.0  \n",
       "4                    28.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#orders.drop(['eval_set','days_since_prior_order'],axis=1,inplace= False )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>eval_set</th>\n",
       "      <th>order_number</th>\n",
       "      <th>order_dow</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2539329</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>1</td>\n",
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       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2398795</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
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       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>15.0</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>473747</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2254736</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>431534</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  user_id eval_set  order_number  order_dow  order_hour_of_day  \\\n",
       "0   2539329        1    prior             1          2                  8   \n",
       "1   2398795        1    prior             2          3                  7   \n",
       "2    473747        1    prior             3          3                 12   \n",
       "3   2254736        1    prior             4          4                  7   \n",
       "4    431534        1    prior             5          4                 15   \n",
       "\n",
       "   days_since_prior_order  \n",
       "0                     NaN  \n",
       "1                    15.0  \n",
       "2                    21.0  \n",
       "3                    29.0  \n",
       "4                    28.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "opp=pd.read_csv(\"order_products__prior.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>add_to_cart_order</th>\n",
       "      <th>reordered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>33120</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>28985</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>9327</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>45918</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>30035</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  product_id  add_to_cart_order  reordered\n",
       "0         2       33120                  1          1\n",
       "1         2       28985                  2          1\n",
       "2         2        9327                  3          0\n",
       "3         2       45918                  4          1\n",
       "4         2       30035                  5          0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_orders = set(opp.order_id.sample(100000).tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "sampled_opp = opp[opp.order_id.isin(selected_orders)].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt=pd.read_csv(\"order_products__train.csv\")\n",
    "department=pd.read_csv(\"departments.csv\")\n",
    "ailes=pd.read_csv(\"aisles.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "opp_product = sampled_opp.merge(products, on='product_id')\n",
    "opp_prod_dept = opp_product.merge(department, on='department_id')\n",
    "opp_prod_dept_aisle = opp_prod_dept.merge(ailes, on='aisle_id')\n",
    "fdata = opp_prod_dept_aisle.merge(orders, on='order_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>add_to_cart_order</th>\n",
       "      <th>reordered</th>\n",
       "      <th>product_name</th>\n",
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       "      <th>eval_set</th>\n",
       "      <th>order_number</th>\n",
       "      <th>order_dow</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>17330</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Light</td>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "      <td>alcohol</td>\n",
       "      <td>beers coolers</td>\n",
       "      <td>45082</td>\n",
       "      <td>prior</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>35419</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Handmade Vodka From Austin, Texas</td>\n",
       "      <td>124</td>\n",
       "      <td>5</td>\n",
       "      <td>alcohol</td>\n",
       "      <td>spirits</td>\n",
       "      <td>45082</td>\n",
       "      <td>prior</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>27407</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Chunky Salsa Medium</td>\n",
       "      <td>51</td>\n",
       "      <td>13</td>\n",
       "      <td>pantry</td>\n",
       "      <td>preserved dips spreads</td>\n",
       "      <td>45082</td>\n",
       "      <td>prior</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>44635</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Salsa Con Queso Medium Dip</td>\n",
       "      <td>51</td>\n",
       "      <td>13</td>\n",
       "      <td>pantry</td>\n",
       "      <td>preserved dips spreads</td>\n",
       "      <td>45082</td>\n",
       "      <td>prior</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>196</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Soda</td>\n",
       "      <td>77</td>\n",
       "      <td>7</td>\n",
       "      <td>beverages</td>\n",
       "      <td>soft drinks</td>\n",
       "      <td>45082</td>\n",
       "      <td>prior</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  product_id  add_to_cart_order  reordered  \\\n",
       "0        13       17330                  1          0   \n",
       "1        13       35419                  3          0   \n",
       "2        13       27407                  2          0   \n",
       "3        13       44635                  5          0   \n",
       "4        13         196                  4          0   \n",
       "\n",
       "                        product_name  aisle_id  department_id department  \\\n",
       "0                              Light        27              5    alcohol   \n",
       "1  Handmade Vodka From Austin, Texas       124              5    alcohol   \n",
       "2                Chunky Salsa Medium        51             13     pantry   \n",
       "3         Salsa Con Queso Medium Dip        51             13     pantry   \n",
       "4                               Soda        77              7  beverages   \n",
       "\n",
       "                    aisle  user_id eval_set  order_number  order_dow  \\\n",
       "0           beers coolers    45082    prior             2          6   \n",
       "1                 spirits    45082    prior             2          6   \n",
       "2  preserved dips spreads    45082    prior             2          6   \n",
       "3  preserved dips spreads    45082    prior             2          6   \n",
       "4             soft drinks    45082    prior             2          6   \n",
       "\n",
       "   order_hour_of_day  days_since_prior_order  \n",
       "0                 17                     1.0  \n",
       "1                 17                     1.0  \n",
       "2                 17                     1.0  \n",
       "3                 17                     1.0  \n",
       "4                 17                     1.0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1522612, 15)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "fdata=fdata[['user_id','order_hour_of_day','order_dow','order_number','order_id','product_id','product_name','reordered','days_since_prior_order','department_id','department','aisle']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>user_id</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>order_dow</th>\n",
       "      <th>order_number</th>\n",
       "      <th>order_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_name</th>\n",
       "      <th>reordered</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "      <th>department_id</th>\n",
       "      <th>department</th>\n",
       "      <th>aisle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>45082</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>17330</td>\n",
       "      <td>Light</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>alcohol</td>\n",
       "      <td>beers coolers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>45082</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>35419</td>\n",
       "      <td>Handmade Vodka From Austin, Texas</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>alcohol</td>\n",
       "      <td>spirits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>45082</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>27407</td>\n",
       "      <td>Chunky Salsa Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13</td>\n",
       "      <td>pantry</td>\n",
       "      <td>preserved dips spreads</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>45082</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>44635</td>\n",
       "      <td>Salsa Con Queso Medium Dip</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13</td>\n",
       "      <td>pantry</td>\n",
       "      <td>preserved dips spreads</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>45082</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>196</td>\n",
       "      <td>Soda</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7</td>\n",
       "      <td>beverages</td>\n",
       "      <td>soft drinks</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  order_hour_of_day  order_dow  order_number  order_id  product_id  \\\n",
       "0    45082                 17          6             2        13       17330   \n",
       "1    45082                 17          6             2        13       35419   \n",
       "2    45082                 17          6             2        13       27407   \n",
       "3    45082                 17          6             2        13       44635   \n",
       "4    45082                 17          6             2        13         196   \n",
       "\n",
       "                        product_name  reordered  days_since_prior_order  \\\n",
       "0                              Light          0                     1.0   \n",
       "1  Handmade Vodka From Austin, Texas          0                     1.0   \n",
       "2                Chunky Salsa Medium          0                     1.0   \n",
       "3         Salsa Con Queso Medium Dip          0                     1.0   \n",
       "4                               Soda          0                     1.0   \n",
       "\n",
       "   department_id department                   aisle  \n",
       "0              5    alcohol           beers coolers  \n",
       "1              5    alcohol                 spirits  \n",
       "2             13     pantry  preserved dips spreads  \n",
       "3             13     pantry  preserved dips spreads  \n",
       "4              7  beverages             soft drinks  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1522612, 12)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "produce       440385\n",
       "dairy eggs    251811\n",
       "snacks        140271\n",
       "Name: department, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdata['department'].value_counts().head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>index</th>\n",
       "      <th>product_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Banana</td>\n",
       "      <td>19741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Bag of Organic Bananas</td>\n",
       "      <td>15707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Organic Strawberries</td>\n",
       "      <td>11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Organic Baby Spinach</td>\n",
       "      <td>10686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Organic Hass Avocado</td>\n",
       "      <td>10126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Organic Avocado</td>\n",
       "      <td>7878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Large Lemon</td>\n",
       "      <td>7374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Limes</td>\n",
       "      <td>6943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Organic Raspberries</td>\n",
       "      <td>6606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Strawberries</td>\n",
       "      <td>6033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    index  product_name\n",
       "0                  Banana         19741\n",
       "1  Bag of Organic Bananas         15707\n",
       "2    Organic Strawberries         11890\n",
       "3    Organic Baby Spinach         10686\n",
       "4    Organic Hass Avocado         10126\n",
       "5         Organic Avocado          7878\n",
       "6             Large Lemon          7374\n",
       "7                   Limes          6943\n",
       "8     Organic Raspberries          6606\n",
       "9            Strawberries          6033"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#top 5 orders --->\n",
    "fdata.product_name.value_counts(ascending=False).reset_index().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>product_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>38044</td>\n",
       "      <td>Gluten Free Seeds &amp; Grains Bread</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38045</td>\n",
       "      <td>100 Calorie Right Bites Crackers</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38046</td>\n",
       "      <td>Spicy Spinach Blend</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38047</td>\n",
       "      <td>Snacks Apple</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38048</td>\n",
       "      <td>Brownie Crunch High Protein Bars</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  index  product_name\n",
       "38044  Gluten Free Seeds & Grains Bread             1\n",
       "38045  100 Calorie Right Bites Crackers             1\n",
       "38046               Spicy Spinach Blend             1\n",
       "38047                      Snacks Apple             1\n",
       "38048  Brownie Crunch High Protein Bars             1"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdata.product_name.value_counts(ascending=False).reset_index().tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1bd94150>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fdata['department'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1cd61590>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fdata['order_hour_of_day'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# users oders are the highest from 9 am untill 6 pm. By 10 amd and 11 am  order the most and after 6 pm it slows down. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11    130559\n",
       "10    129654\n",
       "12    126488\n",
       "14    126012\n",
       "13    124608\n",
       "15    121394\n",
       "9     119481\n",
       "16    114529\n",
       "17     93590\n",
       "8      81598\n",
       "18     72898\n",
       "19     53844\n",
       "20     46453\n",
       "7      43611\n",
       "21     40366\n",
       "22     32488\n",
       "23     20509\n",
       "6      14146\n",
       "0      10644\n",
       "1       5811\n",
       "5       5064\n",
       "4       3255\n",
       "2       3040\n",
       "3       2570\n",
       "Name: order_hour_of_day, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdata['order_hour_of_day'].value_counts().head(24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1d36de10>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAD1CAYAAAClSgmzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAT40lEQVR4nO3df6zd9X3f8ecrJjDaNECCw5hNa9Z4W0i2OGCBJ/ZHGiowZJrpBJqZFKyIzFUEaiJVU5xsElUSJvJHi4KUILnFjYnauIw2wlucuhYhi7IlxBfCAONm3BIaXBMwsUPIaKAm7/1xPlYPl/O59/raPucyng/p6HzP+/v5fr7vw4/7ut8f59xUFZIkjfKGSTcgSVq8DAlJUpchIUnqMiQkSV2GhCSpy5CQJHWdNOkGjrczzzyzVqxYMek2JOk15f7773+2qpbOrP9/FxIrVqxgampq0m1I0mtKkr8eVfd0kySpy5CQJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldc36YLsk/AL4BnNLG31VVNyY5F9gGvAV4APhAVb2U5BTgDuAC4EfAv6uqJ9pcHweuA14Gfquqdrb6WuCzwBLgD6rq5lYfuY/j9N4BWLHpK8dzuld54ub3n9D5JelEms+RxIvA+6rq3cAqYG2SNcBngFuqaiVwiMEPf9rzoap6O3BLG0eS84D1wDuBtcDnkyxJsgT4HHA5cB5wTRvLLPuQJI3BnCFRAz9tL9/YHgW8D7ir1bcCV7blde01bf0lSdLq26rqxar6PjANXNge01X1eDtK2Aasa9v09iFJGoN5XZNov/E/CDwD7AL+CvhxVR1uQ/YBy9ryMuBJgLb+OeCtw/UZ2/Tqb51lH5KkMZhXSFTVy1W1CljO4Df/d4wa1p7TWXe86q+SZGOSqSRTBw4cGDVEkrQAR3V3U1X9GPg6sAY4PcmRC9/Lgf1teR9wDkBbfxpwcLg+Y5te/dlZ9jGzr81VtbqqVi9d+qpvupUkLdCcIZFkaZLT2/KpwK8De4F7gavasA3A3W15e3tNW/+1qqpWX5/klHbX0krgO8BuYGWSc5OczODi9va2TW8fkqQxmM/fkzgb2NruQnoDcGdV/fckjwLbknwa+C5wext/O/DFJNMMjiDWA1TVniR3Ao8Ch4Hrq+plgCQ3ADsZ3AK7par2tLk+1tmHJGkM5gyJqnoIeM+I+uMMrk/MrP8MuLoz103ATSPqO4Ad892HJGk8/MS1JKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUpchIUnqMiQkSV2GhCSpy5CQJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldhoQkqcuQkCR1zedvXGsRW7HpKyd0/idufv8JnV/S4uaRhCSpy5CQJHUZEpKkLkNCktRlSEiSugwJSVLXnCGR5Jwk9ybZm2RPko+0+u8k+ZskD7bHFUPbfDzJdJLvJblsqL621aaTbBqqn5vkviSPJfmTJCe3+int9XRbv+J4vnlJ0uzmcyRxGPjtqnoHsAa4Psl5bd0tVbWqPXYAtHXrgXcCa4HPJ1mSZAnwOeBy4DzgmqF5PtPmWgkcAq5r9euAQ1X1duCWNk6SNCZzhkRVPVVVD7Tl54G9wLJZNlkHbKuqF6vq+8A0cGF7TFfV41X1ErANWJckwPuAu9r2W4Erh+ba2pbvAi5p4yVJY3BU1yTa6Z73APe10g1JHkqyJckZrbYMeHJos32t1qu/FfhxVR2eUX/FXG39c228JGkM5h0SSd4E/Cnw0ar6CXAb8KvAKuAp4HePDB2xeS2gPttcM3vbmGQqydSBAwdmfR+SpPmbV0gkeSODgPijqvozgKp6uqperqqfA7/P4HQSDI4EzhnafDmwf5b6s8DpSU6aUX/FXG39acDBmf1V1eaqWl1Vq5cuXTqftyRJmof53N0U4HZgb1X93lD97KFhvwE80pa3A+vbnUnnAiuB7wC7gZXtTqaTGVzc3l5VBdwLXNW23wDcPTTXhrZ8FfC1Nl6SNAbz+RbYi4EPAA8nebDVPsHg7qRVDE7/PAH8JkBV7UlyJ/Aogzujrq+qlwGS3ADsBJYAW6pqT5vvY8C2JJ8GvssglGjPX0wyzeAIYv0xvFdJ0lGaMySq6puMvjawY5ZtbgJuGlHfMWq7qnqcvz9dNVz/GXD1XD1Kkk4MP3EtSeoyJCRJXYaEJKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUpchIUnqMiQkSV2GhCSpy5CQJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6prPny+VTpgVm75ywuZ+4ub3n7C5pdcLjyQkSV2GhCSpy5CQJHUZEpKkLkNCktRlSEiSugwJSVLXnCGR5Jwk9ybZm2RPko+0+luS7EryWHs+o9WT5NYk00keSnL+0Fwb2vjHkmwYql+Q5OG2za1JMts+JEnjMZ8jicPAb1fVO4A1wPVJzgM2AfdU1UrgnvYa4HJgZXtsBG6DwQ984EbgIuBC4MahH/q3tbFHtlvb6r19SJLGYM6QqKqnquqBtvw8sBdYBqwDtrZhW4Er2/I64I4a+DZwepKzgcuAXVV1sKoOAbuAtW3dm6vqW1VVwB0z5hq1D0nSGBzVNYkkK4D3APcBZ1XVUzAIEuBtbdgy4Mmhzfa12mz1fSPqzLIPSdIYzDskkrwJ+FPgo1X1k9mGjqjVAurzlmRjkqkkUwcOHDiaTSVJs5jXF/wleSODgPijqvqzVn46ydlV9VQ7ZfRMq+8DzhnafDmwv9XfO6P+9VZfPmL8bPt4haraDGwGWL169VEFjLRQJ/LLCcEvKNTiMJ+7mwLcDuytqt8bWrUdOHKH0gbg7qH6te0upzXAc+1U0U7g0iRntAvWlwI727rnk6xp+7p2xlyj9iFJGoP5HElcDHwAeDjJg632CeBm4M4k1wE/AK5u63YAVwDTwAvABwGq6mCSTwG727hPVtXBtvxh4AvAqcBX24NZ9iHpGHkkpPmYMySq6puMvm4AcMmI8QVc35lrC7BlRH0KeNeI+o9G7UOSNB5+4lqS1GVISJK6DAlJUpchIUnqMiQkSV2GhCSpy5CQJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldhoQkqWtef5lOkhYb/x7GeBgSkjQBr5WQ83STJKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUpchIUnqMiQkSV2GhCSpa86QSLIlyTNJHhmq/U6Sv0nyYHtcMbTu40mmk3wvyWVD9bWtNp1k01D93CT3JXksyZ8kObnVT2mvp9v6FcfrTUuS5mc+RxJfANaOqN9SVavaYwdAkvOA9cA72zafT7IkyRLgc8DlwHnANW0swGfaXCuBQ8B1rX4dcKiq3g7c0sZJksZozpCoqm8AB+c53zpgW1W9WFXfB6aBC9tjuqoer6qXgG3AuiQB3gfc1bbfClw5NNfWtnwXcEkbL0kak2O5JnFDkofa6agzWm0Z8OTQmH2t1qu/FfhxVR2eUX/FXG39c228JGlMFhoStwG/CqwCngJ+t9VH/aZfC6jPNterJNmYZCrJ1IEDB2brW5J0FBYUElX1dFW9XFU/B36fwekkGBwJnDM0dDmwf5b6s8DpSU6aUX/FXG39aXROe1XV5qpaXVWrly5dupC3JEkaYUEhkeTsoZe/ARy582k7sL7dmXQusBL4DrAbWNnuZDqZwcXt7VVVwL3AVW37DcDdQ3NtaMtXAV9r4yVJYzLnHx1K8iXgvcCZSfYBNwLvTbKKwemfJ4DfBKiqPUnuBB4FDgPXV9XLbZ4bgJ3AEmBLVe1pu/gYsC3Jp4HvAre3+u3AF5NMMziCWH/M71aSdFTmDImqumZE+fYRtSPjbwJuGlHfAewYUX+cvz9dNVz/GXD1XP1Jkk4cP3EtSeoyJCRJXYaEJKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUpchIUnqMiQkSV2GhCSpy5CQJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldhoQkqcuQkCR1GRKSpC5DQpLUZUhIkroMCUlSlyEhSeqaMySSbEnyTJJHhmpvSbIryWPt+YxWT5Jbk0wneSjJ+UPbbGjjH0uyYah+QZKH2za3Jsls+5Akjc98jiS+AKydUdsE3FNVK4F72muAy4GV7bERuA0GP/CBG4GLgAuBG4d+6N/Wxh7Zbu0c+5AkjcmcIVFV3wAOziivA7a25a3AlUP1O2rg28DpSc4GLgN2VdXBqjoE7ALWtnVvrqpvVVUBd8yYa9Q+JEljstBrEmdV1VMA7fltrb4MeHJo3L5Wm62+b0R9tn1IksbkeF+4zohaLaB+dDtNNiaZSjJ14MCBo91cktSx0JB4up0qoj0/0+r7gHOGxi0H9s9RXz6iPts+XqWqNlfV6qpavXTp0gW+JUnSTAsNie3AkTuUNgB3D9WvbXc5rQGea6eKdgKXJjmjXbC+FNjZ1j2fZE27q+naGXON2ockaUxOmmtAki8B7wXOTLKPwV1KNwN3JrkO+AFwdRu+A7gCmAZeAD4IUFUHk3wK2N3GfbKqjlwM/zCDO6hOBb7aHsyyD0nSmMwZElV1TWfVJSPGFnB9Z54twJYR9SngXSPqPxq1D0nS+PiJa0lSlyEhSeoyJCRJXYaEJKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUpchIUnqMiQkSV2GhCSpy5CQJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldhoQkqcuQkCR1GRKSpC5DQpLUZUhIkroMCUlS1zGFRJInkjyc5MEkU632liS7kjzWns9o9SS5Ncl0koeSnD80z4Y2/rEkG4bqF7T5p9u2OZZ+JUlH53gcSfxaVa2qqtXt9SbgnqpaCdzTXgNcDqxsj43AbTAIFeBG4CLgQuDGI8HSxmwc2m7tcehXkjRPJ+J00zpga1veClw5VL+jBr4NnJ7kbOAyYFdVHayqQ8AuYG1b9+aq+lZVFXDH0FySpDE41pAo4C+S3J9kY6udVVVPAbTnt7X6MuDJoW33tdps9X0j6q+SZGOSqSRTBw4cOMa3JEk64qRj3P7iqtqf5G3AriR/OcvYUdcTagH1VxerNgObAVavXj1yjCTp6B3TkURV7W/PzwBfZnBN4el2qoj2/Ewbvg84Z2jz5cD+OerLR9QlSWOy4JBI8otJfunIMnAp8AiwHThyh9IG4O62vB24tt3ltAZ4rp2O2glcmuSMdsH6UmBnW/d8kjXtrqZrh+aSJI3BsZxuOgv4crsr9STgj6vqz5PsBu5Mch3wA+DqNn4HcAUwDbwAfBCgqg4m+RSwu437ZFUdbMsfBr4AnAp8tT0kSWOy4JCoqseBd4+o/wi4ZES9gOs7c20BtoyoTwHvWmiPkqRj4yeuJUldhoQkqcuQkCR1GRKSpC5DQpLUZUhIkroMCUlSlyEhSeoyJCRJXYaEJKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUpchIUnqMiQkSV2GhCSpy5CQJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldiz4kkqxN8r0k00k2TbofSXo9WdQhkWQJ8DngcuA84Jok5022K0l6/VjUIQFcCExX1eNV9RKwDVg34Z4k6XUjVTXpHrqSXAWsraoPtdcfAC6qqhtmjNsIbGwv/ynwvRPY1pnAsydw/hPN/ifntdw72P+knej+f6Wqls4snnQCd3g8ZETtValWVZuBzSe+HUgyVVWrx7GvE8H+J+e13DvY/6RNqv/FfrppH3DO0OvlwP4J9SJJrzuLPSR2AyuTnJvkZGA9sH3CPUnS68aiPt1UVYeT3ADsBJYAW6pqz4TbGstprRPI/ifntdw72P+kTaT/RX3hWpI0WYv9dJMkaYIMCUlSlyEhSepa1BeuJy3JP2PwCe9lDD6fsR/YXlV7J9rY60CSi4C9VfWTJKcCm4DzgUeB/1JVz020waOU5F8x+AaBR6rqLybdz3y0//6XAfdV1U+H6mur6s8n19nRS3JHVV076T6ORpILgaqq3e3riNYCf1lVO8bahxeuR0vyMeAaBl8Fsq+VlzO4DXdbVd08qd6OhyQfrKo/nHQfPUn2AO9ud7htBl4A7gIuafV/O9EG55DkO1V1YVv+D8D1wJeBS4H/ttj/+0nyWwx63gusAj5SVXe3dQ9U1fmT7G82SWbeJh/g14CvAVTVvxl7U0cpyY0MvrPuJGAXcBHwdeDXgZ1VddPYejEkRkvyf4B3VtXfzaifDOypqpWT6ez4SPKDqvrlSffRk2RvVb2jLb/ih1KSB6tq1eS6m1uS71bVe9rybuCKqjqQ5BeBb1fVP59sh7NL8jDwL6vqp0lWMAjoL1bVZ4ff22KU5AEGR5x/wOAMQIAvMfgFj6r6H5Prbn7aP/9VwCnAD4HlQ0fV91XVvxhXL55u6vs58I+Av55RP7utW/SSPNRbBZw1zl4W4JGho53/nWR1VU0l+SfA38218SLwhiRnMLjul6o6AFBV/zfJ4cm2Ni9LjpxiqqonkrwXuCvJrzD663IWk9XAR4D/BPzHqnowyd++FsJhyOGqehl4IclfVdVPAKrqb5OM9eePIdH3UeCeJI8BT7baLwNvB27obrW4nAVcBhyaUQ/wv8bfzlH5EPDZJP+ZwZeafSvJkwz+XXxoop3Nz2nA/Qz+WVeSf1hVP0zyJhb/D1mAHyZZVVUPArQjin8NbAEW9VFQVf0cuCXJf23PT/Pa+1n3UpJfqKoXgAuOFJOcxph/SfV00yySvIHBxcZlDP7H3gfsbgm/6CW5HfjDqvrmiHV/XFX/fgJtHZUkvwT8Ywb/k++rqqcn3NIxSfILwFlV9f1J9zKbJMsZ/Db7wxHrLq6q/zmBthYkyfuBi6vqE5PuZb6SnFJVL46onwmcXVUPj60XQ0KS1OPnJCRJXYaEJKnLkJAkdRkSkqQuQ0KS1PX/AKgBkZ0hl/iRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fdata['order_dow'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sunday,saturday , monday has the most orders\n",
    "#wednesday , thursday \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id                   62765\n",
       "order_hour_of_day            24\n",
       "order_dow                     7\n",
       "order_number                 99\n",
       "order_id                  97799\n",
       "product_id                38049\n",
       "product_name              38049\n",
       "reordered                     2\n",
       "days_since_prior_order       31\n",
       "department_id                21\n",
       "department                   21\n",
       "aisle                       134\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdata.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "users=fdata.user_id.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_count=len(users)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62765"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders_amount_of_users = fdata.groupby('user_id')['order_number'].count().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Amount of Orders')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vtb46yatLKf+m1voXh7BMAACw4a2nj/gppZSTSvN7pZTzSynPWHrJAABgA1tPEP+eWusdSZ6R5BFJXpDkl5ZaKgAA2ODWE8RH3/BnJfn9WusFC8MAAIADsJ4gfl4p5c1pQfxNpZQTk+xZbrEAAGBjW88X+nxvkiclubLWurWU8rC07ikAAMABWk8Q/4r++wtK0SMFAAAOhvUE8f+88PenJXlKkvOSfN1SSgQAAJvAfQbxWuuzF/8vpTwmyf9eWokAAGATWM+bNfd2bZLPO9gFAQCAzeQ+W8RLKb+ZpPZ/j0p74+YFyywUAABsdOvpI37uwt+7kvxprfVdSyoPAABsCvsM4qWUk5OcXGt99V7Dn1hKObnWetPSSwcAABvUWn3EfzPJyasMPyXJry+nOAAAsDmsFcQ/v9Z6zt4Da61vSvIFyysSAABsfGsF8WMP8DUAAOA+rBXELyulPGvvgaWUb0hy5fKKxMrKlqysbJm7GAAALNFan5ryo0n+tpTyb9O+STNJTk/yZUn+9bILBgAAG9k+W8RrrR9N8vlJzklyav85J8kX9NcAAIADtObniNdadyT5/UNUFgAA2DQO5CvuAQCA+0kQBwCAGewziJdS3tp//69DVxwAANgc1uoj/qhSylcneU4p5TVJyuKLtdbzl1oyAADYwNYK4j+T5EVpX2n/q3u9VpN83bIKBQAAG90+g3it9XVJXldK+W+11p8/hGUCAIANb82PL0ySWuvPl1Kek+Sr+qB31Fr/drnFAgCAje0+PzWllPKLSV6Y5MP954V9GAAAcIDus0U8yTcmeVKtdU+SlFJeneQDSV68zIIBAMBGtt7PEX/wwt+fvoyCAADAZrKeFvFfTPKBUsrb0z7C8KuiNRwAAO6X9bxZ809LKe9I8iVpQfynaq0fX3bBAABgI1tPi3hqrTckOWvJZQEAgE1jvX3EAQCAg0gQBwCAGawZxEspR5VSLjpUhQEAgM1izSDePzv8glLKYw9ReQAAYFNYz5s1H5Xk4lLK+5LcPQbWWp+ztFIBAMAGt54g/t+XXgoAANhk1vM54ueUUh6X5LRa61tKKQ9McvTyiwYAABvXfX5qSinlPyR5XZKX90GPTvL6ZRaKT7WysiUrK1vmLgYAAAfJej6+8AeTfHmSO5Kk1npZkkcss1BMhG8AgI1pPUF8R631nvFPKeWYJHV5RQIAgI1vPUH8nFLKTyd5QCnl6Un+PMnfLLdYAACwsa0niL8oyU1JPpTk+5K8Mcl/XWahAABgo1vPp6bsKaW8Osl707qkXFpr1TUFAADuh/sM4qWUb0zyO0muSFKSrJRSvq/W+nfLLhwAAGxU6/lCn5cm+dpa6+VJUkp5QpI3JBHEAQDgAK2nj/iNI4R3Vya5cUnlAQCATWGfLeKllG/uf15cSnljktem9RH/1iTvPwRlAwCADWutrinPXvj7E0m+uv99U5KHLK1EAACwCewziNdaX3AoCwIAAJvJej41ZSXJf0py6uL4tdbnLK9YAACwsa3nU1Nen+SVad+muWe5xQEAgM1hPUF8e631N5ZekiVbWdmSJLnqqlNnLQcAACTrC+K/Xkp5SZI3J9kxBtZaz19aqQAAYINbTxD//CTfleTrMnVNqf1/AADgAKwniD8vyeNrrfcsuzAAALBZrOebNS9I8uBlFwQAADaT9bSIPzLJJaWU9+fefcR9fCEAAByg9QTxlyy9FAAAsMncZxCvtZ5zKAoCAACbyXq+WfPOtE9JSZLjkhyb5O5a60nLLBgAAGxk62kRP3Hx/1LKNyV5ytJKBAAAm8B6PjXlXmqtr4/PEAcAgPtlPV1Tvnnh36OSnJ6pqwoAAHAA1vOpKc9e+HtXki1JnruU0gAAwCaxnj7iLzgUBQEAgM1kn0G8lPIza0xXa60/v4TyAADAprBWi/jdqww7Icn3JnlYEkEcAAAO0D6DeK31pePvUsqJSV6Y5AVJXpPkpfuaDgAAuG9r9hEvpTw0yY8l+c4kr07y5FrrrYeiYAAAsJGt1Uf8l5N8c5JXJPn8Wutdh6xU7NPKypYkyVVXnTprOQAAuH/W+kKfH0/ymUn+a5LrSyl39J87Syl3HJriAQDAxrRWH/H9/tZNAABgfYRtAACYgSAOAAAzEMQBAGAGgjgAAMxAEAcAgBkI4gAAMANBHAAAZiCIH8FWVrb88zdtAgBwZBHEAQBgBoI4AADMYGlBvJRyZinlxlLKRQvDHlpKObuUcln//ZA+vJRSfqOUcnkp5cJSypMXpnl+H/+yUsrzl1VeAAA4lJbZIv6qJM/ca9iLkry11npakrf2/5PkG5Kc1n/OSPKypAX3JC9J8qVJnpLkJSO8AwDAkWxpQbzW+g9Jbtlr8HOTvLr//eok37Qw/A9q854kDy6lPCrJ1yc5u9Z6S6311iRn51PDPQAAHHEOdR/xR9Zab0iS/vsRffijk1yzMN61fdi+hgMAwBHtcHmzZlllWF1j+KfOoJQzSinnllLOvemmmw5q4QAA4GA71EH8E73LSfrvG/vwa5M8ZmG8U5Jcv8bwT1FrfUWt9fRa6+knn3zyQS84AAAcTIc6iJ+VZHzyyfOT/PXC8O/un57y1CS3964rb0ryjFLKQ/qbNJ/RhwEAwBHtmGXNuJTyp0m+JsnDSynXpn36yS8leW0p5XuTXJ3kW/vob0zyrCSXJ9ma5AVJUmu9pZTy80ne38f7uVrr3m8ABQCAI87Sgnit9dv38dLTVhm3JvnBfcznzCRnHsSiAQDA7A6XN2sCAMCmIogDAMAMBPENYGVlS1ZWtsxdDAAA9oMgDgAAMxDEAQBgBoI4AADMQBAHAIAZCOIbiDdtAgAcOQRxAACYgSAOAAAzEMQBAGAGgjgAAMxAEAcAgBkI4gAAMANBHAAAZiCIAwDADARxAACYgSAOAAAzEMQBAGAGgjgAAMxAEAcAgBkI4gAAMANBHAAAZiCIAwDADARxAACYgSAOAAAzEMQBAGAGgjgAAMxAEN+gVla2ZGVly9zFAABgHwRxAACYgSAOAAAzEMQBAGAGgjgAAMxAEAcAgBkI4gAAMANBHAAAZiCIAwDADARxAACYgSAOAAAzOGbuAiybr3kHAOBwpEUcAABmIIhvcCsrWzwVAAA4DAniAAAwA0EcAABmIIgDAMAMBHEAAJiBIA4AADMQxAEAYAaCOAAAzEAQBwCAGQjiAAAwA0F8E/ENmwAAhw9BHAAAZiCIAwDADARxAACYgSAOAAAzEMQBAGAGgjgAAMxAEAcAgBkI4gAAMANBHAAAZiCIAwDADARxAACYgSC+Ca2sbMnKypa5iwEAsKkJ4gAAMANBHAAAZiCIAwDADARxAACYgSAOAAAzEMQBAGAGgjgAAMxAEAcAgBkI4gAAMANBHAAAZiCIAwDADARxAACYgSC+ia2sbMnKypa5iwEAsCkJ4gAAMANBHAAAZiCIk0Q3FQCAQ00QBwCAGQjiAAAwA0EcAABmIIhzL/qKAwAcGoI4AADMQBAHAIAZCOIAADADQRwAAGYgiAMAwAwEcQAAmIEgDgAAMxDEAQBgBrME8VLKllLKh0opHyylnNuHPbSUcnYp5bL++yF9eCml/EYp5fJSyoWllCfPUWYAADiY5mwR/9pa65Nqraf3/1+U5K211tOSvLX/nyTfkOS0/nNGkpcd8pICAMBBdjh1TXluklf3v1+d5JsWhv9Bbd6T5MGllEfNUcDNxFfdAwAs11xBvCZ5cynlvFLKGX3YI2utNyRJ//2IPvzRSa5ZmPbaPgwAAI5Yx8y03C+vtV5fSnlEkrNLKZesMW5ZZVj9lJFaoD8jSR772McenFICAMCSzNIiXmu9vv++MclfJXlKkk+MLif994199GuTPGZh8lOSXL/KPF9Raz291nr6ySefvMziAwDA/XbIg3gp5YRSyonj7yTPSHJRkrOSPL+P9vwkf93/PivJd/dPT3lqkttHFxYAADhSzdE15ZFJ/qqUMpb/J7XWvy+lvD/Ja0sp35vk6iTf2sd/Y5JnJbk8ydYkLzj0Rd7cxps2r7rq1FnLAQCwkRzyIF5rvTLJF64y/OYkT1tleE3yg4egaAAAcMgcTh9fCAAAm4YgDgAAMxDEAQBgBoI4AADMQBAHAIAZCOIAADADQRwAAGYgiLNuKytb/vnLfQAAuH8EcQAAmIEgDgAAMxDEAQBgBoI494t+4wAAB0YQ54AI3wAA948gDgAAMxDEAQBgBoI4B4W+4gAA+0cQBwCAGQjiAAAwA0Gcg043FQCA+yaIAwDADARxAACYgSAOAAAzEMQBAGAGgjgAAMxAEAcAgBkI4iyNjzEEANg3QRwAAGYgiAMAwAwEcQAAmMExcxeAzWXvPuNXXXXqLOUAAJibFnEAAJiBIA4AADMQxFk6H2MIAPCpNmwfccEPAIDDmRZxAACYgSAOAAAzEMSZnW5EAMBmJIgDAMAMBHEAAJiBIA4AADMQxAEAYAaCOAAAzEAQ57Cx2jdw+lZOAGCjEsQBAGAGgjgAAMxAEAcAgBkI4hx29AsHADYDQRwAAGYgiHPE0nIOABzJBHEAAJiBIM4RQes3ALDRCOIAADADQZwjipZxAGCjEMQ54gnnAMCRSBAHAIAZCOIAADADQZwNabXuKrqwAACHE0GcDU34BgAOV4I4AADMQBBnQ9H6DQAcKQRxAACYgSAOAAAzEMTZdLyBEwA4HAjiAAAwA0GcTWuxZVwrOQBwqAniAAAwA0Ec7oNv6QQAlkEQhwUCNgBwqAjicD8I7gDAgRLEAQBgBoI4HARaxgGA/SWIAwDADARx2Act3ADAMgnisCS6qwAAaxHEYZ2WFawFdgDYnI6ZuwCw0QjVAMB6aBGHw4SWcQDYXARxOARGwF5G2BbgAeDIJIjDDO5PeBa6AWBj0EccDmN7h+6rrjp1lnIAAAefFnHYQHRTAYAjhxZxOMyMIH1frd/rCdzrnRcAcOhpEYdNYrXWcq3nADAfLeKwAd3fN4JqQQeA5RPEAS3jADADXVNgkzkYb+j0plAAuP8EcWBd7u9nnwvuAHBvgjiwqvWE58VxtLIDwP7RRxxY07Lf+Lna/FdWtnjDKAAbniAOHFTL+Hzz/Q31ByPE+wQZAJZNEAcOmYPR9WTvgLxaYF7vsINVBgA4EII4cNhaK7ivN9Tv3c1lMUTvPY/1tLgvK9zva3nLnD8A8zpigngp5ZlJfj3J0Ul+r9b6SzMXCdhg7k/w3Z9Qv9rNwP6Ov57lAHB4OyKCeCnl6CS/neTpSa5N8v5Sylm11g/PWzJgozvQcL5WeF7vtPtrPSF9Wd129veG4v7OC2AjOCKCeJKnJLm81nplkpRSXpPkuUkEcWBDOBiBf63X7+9rqwX31V5brVz3dzkHe5usZ/wDfVKx9zwOVreoubgpguU6UoL4o5Ncs/D/tUm+dKayAGxI9yfU7884+zPefU23v0H/vqbbn/C81vLWunG5r+n2tY5rBf7VrHdei6+tNc/9ndfBfEJzoOu4mgO5Ubs/3cjWujm7P/anXPd3OYvzP1RP0DaLIyWIl1WG1XuNUMoZSc7o/96VrFya5OFJPrn4u5R8so/zKcNKWd/4R/C81lyOeZmXeR2aea1jOea1yrbv/y+9XIfp+fte81prmxyu67i/+3EZ67g4r73mv9Zy7te233v8+yrXfry23+Vaa173Nf7e22uN+rLuZS+7zIdwXo/Lgaq1HvY/Sb4syZsW/n9xkhevY7pzV/u93mEH+trhOq8jsczmZV7mZV7mZV7mtTnndSSV+UB/jpSvuH9/ktNKKSullOOSfFuSs2YuEwAAHLAjomtKrXVXKeWHkrwp7eMLz6y1XjxzsQAA4IAdEUE8SWqtb0zyxv2c7BX7+L3eYQf62uE6ryOxzOZlXuZlXuZlXua1Oed1JJX5gJTevwUAADiEjpQ+4gAAsKEcMV1T9kcp5cwk/zrJLUluSPKoJI9NcmuS25L8RZJnJ/nsJFf0yR7dX/vMJFcnOaFPN+xJclOStyb5t5m2XUmyPe2mZk+S4/vv3f33pyXZleSOJA/OdPOzu/8cm2Rn/72rvz6Gj3kc18cZy6x93GuTfEaSB/bxRnlGeWtan/rS/97Zf+9J8oD+d0myo/8+trtd2/IAACAASURBVL92VJJ7kmxN8pD+97EL45e+/Dv7Ou3p2+DYhfJtT/tonyf0+Yz1OrqX481p++j4JNv6sJ19eTv78LEepZfh+IX1OHavYcOOPuzutH1YFl6/JcnDFrbB0f3v2ss21u+o/lrS9tsxfZ6j7Ecl+Xhf9xP6Mo9bmOco8+L+GOt+Tx93GMs+ZqGcZaH8Y16LH+G5OGyUeZR/V5/XUZnq5NinY//vTKszR6ftp9rX4dP7vPYszHtb7l1XxvZarJejjo36uqVvm0f08izWwcVtsrj+R+feFpe1WoPBYv3ee/je22vY2td7tXmttoztff7HrvLa2B6rDR/1YO9x9zXNvqy1jKzy2lrba61y3dOHH51716ej9xpv2J7kqiSfs8q8Fvf3fZX3QNclvXxHrTLd3sfXvua3uE77u18OxL7q2L6Oib331eJrW9PODeuZ11j2asfEWnViX//vPe3e+2DxXLza9l3r+NzXslY7P6xVb/a1L/f3tbWO233tz8WyLZZvX+f/pF1Hx/l472XflOTkfSxncVn7sx3W60DOV8PidOOatC+r7d/F+e3PflltHov1cO9zyt7z2Fdd23s5Y7wxz+1Jrkzy0bQvoHx4Wo7cnuRna61/tVYhN2qL+KuSPLP//eNpF4zHp4Wbb0/y/CQ39te/Nsn5SX6y1vr4JCelfWvnPWkh7Oq0E9//SHJiWlj5nSTXJfmjtEr260kuS/K8JH+Q5K4kZ6ZdwO9KC4A/l+TctJDyxWkXspuT/HGSS5LcnuTitE+I2Z3ku9IO0LemhZ3/1Nfp9rS+8rf3eRzXx/svaWHqBUkuT/INfR3eknYzcm2f/64k703y1b1cd6R9WdLXJflgkrf1bXZX30Y1yc/25d+S5OuTvK+v64l92J4k7+rl3NHnszWtUm7v8/+rvl4fSvIPSVYyHUyv78u7I8nfJfnpPvzmJN/T/35Lf/0fk/zHvs6v7ct8Zt9PL0/yG/3v3+779/a+7s/r5fpYWkD8ZNp+vyDtZHdJ34cXp92cnd2nu6SX57Vpbxa+PMnrejn2pAXVf0pyadoN3ven3eC9NcnfJrm+r9v7+/44N8nn9e0zXvucvq1fnuRXFrZFTfJ/kjy9T/svknwi7QamJDmnl+Ej/fcdfZvv7OtVezluS6sDf9P3weWZwtaL004oV/Zt85a+jN192gcm+fO0enNX3yc3JHl7X+Z/7ev+8b5PPpZ2bJzcx7++D/+JXp4fTqt/te+PcQN2flpdubkv+/y+znf2bbO7b+e7+8/2vo9GIL+07+t7+mvfnVbXal/fXWnH44/2YVf0ce/q++Kyvp/v6fPZ0afZ2Zf9hj7dr/bxX57kv/Xxti8s964+/mv6/9v7PP4h7Vh5aZ9+ax/vnv7zB315Z/Xlvy6tbm3r+2RnHz6WdXsvy7b+/y/37XJTWj28oc+v9rLf3vfLy/s2fXWa3b0s41ivfX47+/Tn9N83LQy/KcmpmW7sFrfXdWn7fEd//Zq0BpA9aefL3WnH1Y19XXakHVu/lVZ/70o7fsf87knyzj7dH6Wd836sv3Zp/z3mc31fpwvT6vyuvk2+r79+Z9q565O93Hf3ZX6wj/uhvuyv6+u4LdNxe1f/2dWnf8nCvvujvo7b0q4NV2e6MT6/z2tL3w87+/psy9Rgc0vfB1nYzrt72XYm+b1M9Wvc+I7PL35zHz6WOco5GkfuTjvW7+j7Zkcv16v6NkraOX+EiZv7vO9MO5eNurYn7dje3cv+3l6Oi5O8O9MxuL2Pt6ePc2Vf/q19nA/33x9IOz/e0afZnXZ+HfPf0Zd9aZ/+r3t5P7mwDt/ff/922jF+XZLf7+t0bt93dWF7vLn/viitrtzW90VNu+7s6su+rZfrvX17fyjtGBzT7kk7X1ybdr0Z87i9z+PsTPXp7r5Ot/Zy/U6mc/t1C2Xd2dft8v7alf21US+2JfmTtLoyrj2/m6keXNDn8Zo+/YVpGWDs121p56C7+7qO6/bZfZwL+rzuTtv3SauLuxfWZ0/fbnVhHca14fYkf9n31TvTrpu70/bxMWn1qvZ98sE+/R/1ed7Sl/eBPp9Lell2p52v/qxPe27//Y6+zDf3aWvfL7uS/HzfrtvS6sWetGvTKMtodLokbT9flelYGdeYJNlTay1p9fsDaeeNW9Pq3O6+LbelHdOXpdXVLUn+Vdp1+l/1+T8zyctLKWs2em/IIF5rHRe9XbXW82vz8bTA8kVpra5/3Ud/UJKvSvLKPu0I4CekVcT3pu2oK9Iq6Zcm+cO0CvObaSejd/Vp/z7TjvzaPv64q/vytIP27l62Hb0c56aF0ivSdvQj+vJuTNv5n9/Lc3St9c1pQeuBaRX1c9NOBkny1LQKc31fzyyU96S0ynNzWgv9qX25N6e19G7NdBd4cdqTgJP6uo+w/IJeph1pgeb4tPpzQtoB8IRMrbol07ef3pkWyl7fl/HRXoYnpoWF0l97RJKf6a99Y98Gn55W4ZNW2R+YdvL5N32b/MtMF7OkXRBO6X8/J1MQSS//L/W/H9TX79S0O9eT0g7Mz0rywr4/np/Wen5K2sn9y9LC0mPTTlpP6fPa3tfzp/qwV9RaL+/l+pdJHtr3ybjwnpz29OXiPv8r024SL087sXxx366X9fFv79vlsrQvDLgirbW5JvnCPu5onTqxz7OkXdSSqQX4rrT9dnRfv92ZWlyOTQtvx/TyXdz/HsH2s/s8Lkzb73/at8fWJF+TVuevyNQa/iVp9fO4Xra/TKv/9/TyjCcwF2Z68vPwXrbxJGSsxwPTTuxHp90MH5tWR47p230Ejdel7dcdacfKH/bpSpK/79vp1rQnXrsy1dubM11gxsl8PEkYT4tG6/+etItA0s4Xt/dxxo3CHX28a9JO8Dv6/EvaDfcxSf5z7t3qnL4On9V/v6EPO7vP67q+bcYNwVFpx9RRtdYf7+OO0Lcz7enF9j7tqP+7+v/jvLUz7XwxXjsxLdSXXt5jMzVUnNrX60Fpx+tRfd9szfT05AG97NvS6vvfZGp5+lCmc8wpvWwnpJ0D7+rzuKQvY0faft6R6QnaKN+uTI0iT+vjn5LpG5dvzfSU7HV9+hv6PJ6TdjF+QJL/29dl58J2eksv7//q2/aqtDp2Xdo59rb+/z19WdvSLvjjacLT0ur78ZmOo1GHTuvLeneSJ/e/P6+/tqvP7xN9mj2ZbkxHy/d46rN9YZyjFvbPsX17jadPJ/T5HZXpfF/6up/U16mkvblstMI+IVNdP25hmhEGH9i34zV9/PPSrkt70p7IPqwv+1GZngIflXbePLVvo/FU7TGZAtZn9e1yfN/uD+3rPI7HsY1uz3TDfEWm890j+7w/3NfrirQQtL2X5cy+rE/26R/Wt8moe6PRYndf9va+3rel1ckT+zY5qe/XcaO5u2+n89Pq0oMz3VSP7XBVX9eP9214Wy/3/5vkf/bl/G5/7bQk7+nz+pH+2jgPHJfpPHFdpif840nmsZmeRl+fdn0aDQEn97/HNfvDfb2/qr8+rr017eZpW98XF2W6Id/dyzduKK7I1Aug9PkdnZYVntb3y6lp17htadfA9OWXtJvKJ/bt8rRMT+PSl7GnDxvHVk2rT+P1cWN1VJLTM93MfGYv3860/Xx1WoNVMl1vxk3h8WnngZK238dxWNMaxfYkOaqUclJfjwelXXtf1bfT0WnH29+n1a1PT7vuPSntGHlirfVdafXiMbn3k4LV3Z8PIT+cf3pluGjh/8dnOlBelRYgtqaF1rvTwsQHekU5Ie3AGOPfnnaAb+079dS0ynpq38jPHstKOwBHK8dH0i4Y96RVyov77/f0CnRRptaf3WkVdHuvCJ/XXxutYF+dqRVqtPb8eaa78NFSdlH/+9t6JRkthnty75a4uzK1DN3U//+V3PviPVr3xkE/DszRWnVBpoNlhM3r0ir8J9LC5fa0k+05aSfb9/SyXN3HqQvLObPP712ZDrox/49muhPf1ss+7tJv6GXa2rfNOIGPdRlh/eN9+93d57tn4efj/feH91rerWknxbtz764dt2U6cYy77NG6+NG0wDaG3Z12Vz9aeM9M8kN92v+98P+4s74rrfVja99Ou9IO8E/2cc5Ku0mrCz9je+1Ju8ka/4/9uFjGccEfr+/e67VxIzguHmP/jBaS9y+Mf3Yv06i3O/r/OxfmNW4+a9qTkZ9cmO+ol6PVbZTnjkwn1j/p45+6V9lr2k1KTbvxW6wv56W1WNeFZb89rSV4HNfb0o6XO/v+enamY3XMc9wQjbJe2sv1G0n+n0xhsWY6l9yddmyM1uDRqn1NkhctjP+hvuzdmY7pcfNzWabWwVHHRhnG9rkmUxe30YJ0V5/HGH/xuB31ftSXER7GNt21sK3GjcRlfR3uzHRsjYaJrQv/L7bYXp/p5mCU/bZM9WjUxXH8fGKv18bri/u5Zqqf29Lq4uL8b12Y/p2Z6uJY7zH/sf43L+yHuxeWOc5NO9POVWOe1y7sq6vTwswIhxf2dbi7b6/R2jm2/Y6043VcC3YtLG/v9RvH5Difjfo1tvPi/hzTje1/Q+59fPzDXuP8U6YnH3+2sI3GfLctDKtpT2/25N77pC5sn1HH7lgo89g/NVNo/vjC+GN+52a6ER7rNPbDu9Lq9mjpHE+gPt73+9gWo4X+fb0cd2Z6MrAt07lk3Bj+UR9vS982WzO1yo76tKOvzzgXjW1b9xrv8tz72F/cbmP9x3V58bx0ddo5qCb53v770j7f0Qo/rnM17by0uP0XyzXqwFWZbihWK8Mo47kL8xjX8OdnujF6Z+69n+9ZWPb4GfXnI3stZ/HaMcq2a5Xpx/a4ba9l7Fl4bdSRdy4MGz9jPWvufUzs/TOGL55v3rrw+t1pOWXU4ev22qZjHovLuyVTZtqR9qTtjL4OV6fV2TvTGgq/NNP54Xkb5Qt97pdSyoPSQusvp4Xwx6W1Cr4rrQvI8Wl3jy9M20E/l9Z68aS0i/mD0sLYqBT7Ws5/STtAd6S1nj4sbcdc0X/GgfmLaS0Jt6Z1DbgsbSe+O+0O+O60g/VFaTcFo4X1XWk3C1f2ab8y7Q56nPSv7vP9H336u3o5npKpdeXYXqZT0gLjeMT79CTfkRaU3pN2Yfk/fdVOz9Qd4JVpJ/zb0m5O3pd2ghytmVv773syteAc37f5TWmPQccNw4399bel3Z1+RdpF/KV9O1zSyzn6do8T7PW9fNvSTi53pp1sd6Z1Sdie1vr3h33YD2c6+Xy0r9exmYLEB9PuXkump0TnZ2qV35V2V7540diS1sqzrY+/rU/7j2l16fP6dr0j7YLxrZmCwHPSWiJK2on2OWl39Jf0YS9P8k19vb+sz//mtHr4o2lPDJ6Y1krzvr7+n5bpJuNxaTdVtZdnZy/vj/V1enf/f3ef31Fp9fMf0urXPWktBbf1/0cL1wmZAvGoR1+8sO1uS9vnn5HpRmFnpla27X1dX9LX6c/S9uOnZerzuiefahxzT+3LGSf/bWmPX5PkFzLV7z1pXah+or/2/L7sp6SF7dEVZ+9j+bG9/D+cqWV8Z6ZuXUmrh9vTbuR/qpfnbb0sn9XXddxEjCcCo3V3e1qL+MfTjs3H9NcuTXuMOlprbk9r7duT9jTh1/q042nBDWmPTh+Ttg92ZHrE/Gt9m16fVmd2pHVlG+vzO5ku5KNsV/UyH51W30eL1LFpdfKEvvwHZGpJfmSmYH5rpvdrbElr6Upfzst6GR+QqfV2PFkb2+kLM31B2x9lCvV3p3VZ2ZJpf4/3bDykL/vv+nQnZnpvy+mZbnpHvTwhrY6PFvTRt/nWTC1yiyHnmLRj4Jd7WR+Z1jI8gti39+17TFrAfXhaF8W3Z+pOsD1TA8TpaefkcWO6qy97R6YWyNLLtzPTft2e1vKWvpw9aeeFP+zTvGVhWz6yz/eGPv5X9N9jO5zUf8ZrJa1ldHTBOqYPOyZtv/xIpmNka9rTgbHtxrk9fbuMJyk70o7nrWk3o+nbbDzpGNv5IQvzStr54qN9PZ6adj68Nu06PbbPQ9POGUl7gjPOS7+cKVw9um/XUzK1hD4zrQ48NVODy5PT6sO39flfk/ZEcndaXRnnzB1px+Bo1Dqpb8/PTDunjy5x46nYuzOF962ZnviNblMPSfKdfZv/VC/3w3qZt6c9Tbw505POk/s8P9nLPrbhlr6M8ZTv6F6G4zK9Z+1j/fWj0homxlPHczPd+PxqphumL+vb/Li+XuNa9/FMDToP7P9/dqYuacn0BHHcJF6aad+Om7Rdfd3He5Rqpu47o/vR6I51S9q15af7euxIO7aOyXQT9OZMXewu7OX73UzH8bj+jPl/dR/342nX99FafWPfPsn0FDNpT8iOWZjHzkxPo4/uw87s835k2jnoorSeGO9Nu6n6riQvLqWMVv3Vzd1yvewW8bQD4U1pIeQX0yrarX2Hb017jLklrR/0T6SF2yuSXLswn2vTTnw3pV3kvqTP+0v6Tn92H+fdmbpELD5auyftYv2f0g6SJ/SdeedC2cabNW/uZfuJtIp3Y9rJ/PJezrPTQtZoWdmS6c7t4rST8JsynThO6evxkr7srUmu7sN+t0/3v/o0H+nbYfT3Ha1ln0y7IP95WovxuHNevEtfvHves9fvvYfv62fc4Y6gNVosRri4tO+vD/ZtcGNakH173w6/kHbjdHfaiXeU/9a0E+3Nff1+rs//VWkH/FdmahEcrb3flKmlcTxl+NZMfQgXL9p7MvVH+8pMLX0XpJ0sntvHvbL/vDnJv+vz/7G0k9G70/rm78jU6jP65t6cdrG5s89rtFh8X9qN0W9mag29La2+vKdPf15afXp+34bj4j7KPm6wbu7z+o+ZTk7/N+0x6nj/wV/1+f16n9fH0vrdvTutZf+FfZ/t6uUYN05/18e/aWHddqfVu9HnckumC8viRXB0Nahpx9S4CRp1frS0jRbR3Unu6fV71KNn9/V8f9qN6T2ZWjC3pF1Q3p12DG/r44/WpVEvtmV6j8O2tIA5nka9KNO5ZDw1G33JR1/tr+7zuiitDiweM7eknbR3pZ17tme6IfqutG492zP18b86yY6+jqMl/dq+3Mdl6ru8c2H+o9X9BzK1lC0ee7uz72NyrWN2tZ9rFqZ9Ti/7P/V99PG+Tbb3dd2dFpqu7mX+ity7dfG2TDfMH+jDx/xvSAtSe9JuOEd9G0/4rs50PtmV1iI2WvLG/Hf2+e9MO7ftTDu/1LTj5sv7tH+T6enbZZme+I0W9F19PcYN6Gi0uT1T3Rzvd7kw05OX3X2cEYbOy1SPx7l1tPyOVtKaqfFlnOfHE6H/ntYtcmta2NjVp9vW68vY5tf0Mn5WX9+LMj3J+ljauebvMz2xujTTjcClmd6bsC3tXLEzU4v6LZn6iy+e10er5ycXfo9pLko7F57TXxtPgq9MO5ddkelp6LjWjmP5wr4vxn4+I+043dKHjfPiG/rvGzPd6NyQdk39k76ccY28pW+Xv+vj/UQfdwTm0dVvLHPxydC2TK37qx1He7fW7t0Su69jcDzVGXV0x8L2W5zPxZmue4uBdDQGXtfX4YY+/TmZ6t+OTMfBuEEbrdfXZgrwu9KebF61sK9u7/visrRjYZRn8fc5mZ767sn0lGlsj09kOiffmlZH3tn//pm0a8DHFqbdujDtn/Wy35R7P+lZ7Wfvlu/FPDPW9+ZMLeKXpXXJHPv6rrQGzx/u2/DstOvjR5N8bj/WLk3rRvT2JKdv9hbxV6bt2DNrrS9O6+90UVqQO6fW+i1pFfM5ffjT0ltLSikPTLtbHf3FT0rb2d/S5/0taTvmyWl3my9Mq4iXZHpT1I1plfy1ffztaY8uRmW4My1sviFtxx+dVhm/IO1Af1X/e/TdvTitf9K70lpV359WKS7I1HI1WkS2JfniUsrn9WWOPp4fLaV8Rl/nbb1cj8/UYvyktANh9J384bSK9vS0E96nZ3pE9Zt9vB/I9CafH0+7OP1Q2snztrQWynekXSTekNbqfVbfBl/bl/fetNa73+vzfm9ai+3z+jZ8W1pr8F+nfXLNaH3+srTA+A1plf9DfXu/I+2g/8lM/VifmXbnmrT+a9enPZW4ub++knag/2T//6i0VpaPpD2Ovrlv68vSQsUIc/+6b/szMn16xIPTguqPZAp5x6Yd0D/at8t3prX0vDDtxu77+7Y7r++vd/Z5/H9pJ5kzMp1cj08LCk/J1I9wS1oL4wN7OR6fdrL65rSbydEdY7RGvixTf/Cn9n22Na2u35MWdO7oZfnGtIvft/d9+Mi0lv/fTqtLH0zb/29Lu5Bdlxbmv7KXfXwqy2hFOb4v5+5MLVXjAjb+vzntMV8ytSR/PNObl8ZJ/vo+bZLs7PV7fIJR+nIfl1Z3junlKZn6d35+Wn0c4+/oy7oqbd/9RR//M/s4X5tWj+5J8u/7OE/N1Jf8OWn7d7xp9Q/7fH8hUzep0SL7l2nnjz1prfLXpLU+bku7IXxm2j7/0v775CTbSynfkna+OCotNN2T9rj7LZkusqN/8o60Y+AzMrWQ7up/f7Rvm8WWvI/1v/8k0/tLLknbp1v69r4hrd7cnKnF75K0epJMLcH3pD3BGe+LGG9I/IxehjelHbPbkzyjb69xsX5lpsf1T8jU4pq+zGf31z6vDzsq06cmPaKv51v67/EEafEm+m2Zjp3H9d+fk6kh4HN7Wb6oT3NS2j7esjD/12dqOfztvt3Hze5v9f9LWn2paU8SR4DalXa+f1hf9sMzdScY0403jI7Wwl1p57dkOsc9oP9/Utq5ZbwJfPT7vaWU8iWZ3kNzXVr9/B+9/A9IO/ftSDvfnZd2HkmmbjwjXD2ol2m8CW/09T82U/e5XX17jEaLO9Lq+oVp56ZjMvWrH0HozL69a6b3JNyYdmx+rA+7oE97VdrN7rFp14aHpdWLN6Q1tjw6rX4cl1YndqXddByfdp0cT23PS6srX7ywDuNp1llpb7q7Oe14PylTy++1fR+8J+1cN25EtmR6I9+4ydqTdp6vaTeD430ab8/0FGu8x+K1acfNeML1xLTrwba+7kel1a2HpQW/EbpHfbo77bjamekmoqadA87r01+edt44oe+rJ2d6g+YFaef1bZmeFo1g+pBMjSOf7Ou0J9M56BV9/70hLXCPfXpP315b0651V/Rl/21a3Xl7X/ZVffs/qv9/Ytq+vjWtztzWy7ajL/+ctHND0s4fX5/pjZnjfVY7M51vRreSd6Y9GUzf3h/qr300LUuMLn0/kOmTUcaHJNyZKbR/R1rD31+mnZvOTzt+t5VSvjxtPx+X9vRgS9awIb/Qp5Typ2mPjk/OFGxPSjuAPpFWOS5LC8tXpR3gx6cdUB9LCzevSGsdfFDu/dFL440P6/nox7ow7djQZa/XdubejwPHx98tvglvtY/22ZNWwR65yvLGnd148+T4+LXROn9U2klrsXx7Fv4eb8a4PlNAP25h/HGheF9acHh0pkdXJ6YdOA9Kq7SPztT6f0KmR8t/k3aS+cK0Snp82sH6LxbW5UF9vsf036PbwTjQknvvh8W72219eWM7jlbCB2Tq3jAuAuPkuyvTSWXx4wTHvEcXgz1p9eaktO0/HuuOcm1Lq2enpu2jz0gLV2eltWJ/sg+7J9OjxB1pQXVsuy9Ie4T4aWkniM9Oax1aSbv4PiDt4nFspjeGjT6R4+Kzd10b63hPn+94lDzeCPZpmT5ia3RlOCZT/Rv1c1emOru4fcZ2vSotsI7HeFkoy+K4zOv+7IdxTjjc9uPBrFvjPLw/89/X6+P8ejDKdqiPn3Hcr1aG/S3Lsst+OJxbVts2Yxuuti3Xckem7kT7Ws5iPR3zX207jPFWG3+ZRnBd7VgaN8H781Hai9ezfX3E6uK447o+br6T6WZ+S9p1el/LGdfB0W113HBdkZZdxjJ3pd00vjWt4eTT07Lk3Ul+rtb6+rVWaEMGcQAAONxthq4pAABw2BHEAQBgBoI4AADMQBAHAIAZCOIAADADQRxgyUopzyul1FLKv7jvsZdajh/p34+wP9N8ZSnl4lLKB0spD9jrtVNKKX9dSrmslHJFKeXXSynHrXO+r+qfhQ6waQniAMv37WlfQPVtM5fjR9K+HGN/fGeSX6m1PqnWum0MLKWUtC+zeH2t9bS0L0x5UNqXFt1LKWV/Pid4VQdjHgCHG0EcYIlKKQ9K+5Kw781CEC+lfE0p5ZxSymtLKR8tpfxSKeU7SynvK6V8qJTyhD7e40opby2lXNh/P7YPv1eLcinlroX5vqOU8rpSyiWllD8uzQ+nfdHS20spb89eSilPK6V8oC/7zFLK8aWUf5/2LbY/U0r5470m+bok22utv58ktdbdad/q+D2llAeWUv5dKeXPSyl/k+TNvQy/VUr5cCnlDWnfTDmW/cV9W5xXSnlTKeVRffg7Sin/s5RyTpIXllK+tZRyUSnlglLKP9y/PQMwPy0MAMv1TUn+vtb60VLKLaWUJ9daz++vfWHaV5HfkvbNqb9Xa31KKeWFyf/f3v2EWFWGcRz//pABCQwh3CkJBi60UFzImCjiokUL8R9lYaAQ1CKFFi1E3EsEooJSIKOCf1BxEwhKQoGCthk1xXRhSNnCPwtF0dD5uXjfO96me++MwfWO8PusznnnnOc9dxaX5z48h4evKBXsncA+23slrQe215idzKVMrr0FnAHet71d0tfAEtt3mi+WNBEYAJbW59wHfGl7m6SFwI+2j47YYxZlbPYw2/cl3QTeqUv9wHu270laQZkQ+y5lIu0VYI+kPmAHsMz2bUkfUarq62uMybYX1+e8BHxg+y9Jk0f5H0REjHupiEdEdNca4FA9PlTPG361/bftJ5SxySfr+iVejF7uBw7U4/3AwjHsed72n7aHgEHaj3FumAncsH2tnu8FFo1yT2OMd6f1U7bv1eNFwEHbz2zf3af1ugAAAcNJREFUAk437T0bOCVpENgMTG2Kd7jp+AwwIOlzuj+aOyKi61IRj4joEklvUVo4ZksyJXm0pG/qJU+aLh9qOh+i/fdzI8l9Si2m1H7t5pckm+M+6xBr+FFH+Xsrl4GV/woivQlMo/yomAc8HHFPu8T9su3+NvsMx7D9haT5wIfAoKQ5tu/+j2ePiBgXUhGPiOieVZS2krdtT7c9DbjB2KraDWd50Vv+KeWlT4A/KMkuwDKgbwyxHgCTWqxfBaZLarSUrAV+HiXWT8Abkj4DkDQB+A4YsP2oxfW/AB9LmlB7wJfU9d+BKZL6a5w+SbNabShphu1ztrcAdyhJf0TEayuJeERE96wBjo9YOwZ88hIxNgDrJF2kJMgb6/oPwGJJ54H5/Lf63Mr3wImRL2vafgysA47UPuwhYHenQLYNLAdWS7oOXAMeA5va3HIcuE5pu9lFTfRt/0P5wbJV0gVKK82CNjG+rS+T/kZJ7C90/rgREeObyndpRERERES8SqmIR0RERET0QBLxiIiIiIgeSCIeEREREdEDScQjIiIiInogiXhERERERA8kEY+IiIiI6IEk4hERERERPZBEPCIiIiKiB54D4pvShFbt2sMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distirbution of orders among users \n",
    "plt.figure(figsize=(12,10))\n",
    "sns.barplot(x= orders_amount_of_users.index, y=orders_amount_of_users.values,color='blue')\n",
    "plt.ylabel('Number of Customers')\n",
    "plt.xlabel('Amount of Orders')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#average, customers order about 4-5 times . "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1522612, 12)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id  order_number\n",
       "25070    13              109\n",
       "60694    2               108\n",
       "72377    7               104\n",
       "129928   30              104\n",
       "         55              101\n",
       "163724   7                99\n",
       "60694    6                96\n",
       "         62               96\n",
       "201268   9                94\n",
       "166743   32               92\n",
       "Name: order_number, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdata.groupby('user_id').order_number.value_counts().sort_values(ascending=False).head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62765"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdata.user_id.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "hmap1 = fdata.groupby([\"order_hour_of_day\",\"order_dow\"])[\"order_number\"].aggregate(\"count\").reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a211d7790>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hmap1 = hmap1.pivot('order_dow', 'order_hour_of_day', 'order_number')\n",
    "sns.heatmap(hmap1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#most oreders are saturday morning and sunday morning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "134"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(fdata['aisle'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fresh vegetables                 167363\n",
       "fresh fruits                     164027\n",
       "packaged vegetables fruits        82661\n",
       "yogurt                            71798\n",
       "packaged cheese                   48732\n",
       "milk                              37376\n",
       "chips pretzels                    35193\n",
       "water seltzer sparkling water     33343\n",
       "soy lactosefree                   28245\n",
       "bread                             26918\n",
       "Name: aisle, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdata['aisle'].value_counts()[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_items_per_order=fdata.groupby('order_id')['product_id'].count().value_counts().sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9     5129\n",
       "8     5058\n",
       "10    5047\n",
       "11    4843\n",
       "12    4818\n",
       "7     4721\n",
       "13    4529\n",
       "14    4454\n",
       "15    4233\n",
       "6     4126\n",
       "Name: product_id, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "number_of_items_per_order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Majority of order consisted of 9-10 orders. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2454bad0>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fdata.days_since_prior_order.value_counts().sort_index().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "#most orders are being ordered the most after 6-7 days. it appears to peak by 14 days and 21-22 days which indicates users habbit of ordering weekly,biweekly,triweekly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>department</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>babies</th>\n",
       "      <th>bakery</th>\n",
       "      <th>beverages</th>\n",
       "      <th>breakfast</th>\n",
       "      <th>bulk</th>\n",
       "      <th>canned goods</th>\n",
       "      <th>dairy eggs</th>\n",
       "      <th>deli</th>\n",
       "      <th>dry goods pasta</th>\n",
       "      <th>...</th>\n",
       "      <th>household</th>\n",
       "      <th>international</th>\n",
       "      <th>meat seafood</th>\n",
       "      <th>missing</th>\n",
       "      <th>other</th>\n",
       "      <th>pantry</th>\n",
       "      <th>personal care</th>\n",
       "      <th>pets</th>\n",
       "      <th>produce</th>\n",
       "      <th>snacks</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>119</td>\n",
       "      <td>340</td>\n",
       "      <td>865</td>\n",
       "      <td>224</td>\n",
       "      <td>7</td>\n",
       "      <td>378</td>\n",
       "      <td>1641</td>\n",
       "      <td>328</td>\n",
       "      <td>287</td>\n",
       "      <td>...</td>\n",
       "      <td>229</td>\n",
       "      <td>75</td>\n",
       "      <td>214</td>\n",
       "      <td>21</td>\n",
       "      <td>7</td>\n",
       "      <td>705</td>\n",
       "      <td>147</td>\n",
       "      <td>57</td>\n",
       "      <td>3149</td>\n",
       "      <td>944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>64</td>\n",
       "      <td>188</td>\n",
       "      <td>465</td>\n",
       "      <td>128</td>\n",
       "      <td>7</td>\n",
       "      <td>183</td>\n",
       "      <td>956</td>\n",
       "      <td>176</td>\n",
       "      <td>161</td>\n",
       "      <td>...</td>\n",
       "      <td>106</td>\n",
       "      <td>54</td>\n",
       "      <td>92</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>368</td>\n",
       "      <td>106</td>\n",
       "      <td>43</td>\n",
       "      <td>1671</td>\n",
       "      <td>548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>44</td>\n",
       "      <td>105</td>\n",
       "      <td>235</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>116</td>\n",
       "      <td>456</td>\n",
       "      <td>84</td>\n",
       "      <td>89</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>26</td>\n",
       "      <td>72</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>219</td>\n",
       "      <td>43</td>\n",
       "      <td>8</td>\n",
       "      <td>880</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>36</td>\n",
       "      <td>89</td>\n",
       "      <td>219</td>\n",
       "      <td>54</td>\n",
       "      <td>3</td>\n",
       "      <td>94</td>\n",
       "      <td>393</td>\n",
       "      <td>73</td>\n",
       "      <td>85</td>\n",
       "      <td>...</td>\n",
       "      <td>51</td>\n",
       "      <td>26</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>141</td>\n",
       "      <td>49</td>\n",
       "      <td>25</td>\n",
       "      <td>702</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>48</td>\n",
       "      <td>113</td>\n",
       "      <td>258</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>147</td>\n",
       "      <td>489</td>\n",
       "      <td>124</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>79</td>\n",
       "      <td>39</td>\n",
       "      <td>79</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>178</td>\n",
       "      <td>41</td>\n",
       "      <td>24</td>\n",
       "      <td>879</td>\n",
       "      <td>249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>73</td>\n",
       "      <td>157</td>\n",
       "      <td>365</td>\n",
       "      <td>111</td>\n",
       "      <td>2</td>\n",
       "      <td>195</td>\n",
       "      <td>803</td>\n",
       "      <td>170</td>\n",
       "      <td>134</td>\n",
       "      <td>...</td>\n",
       "      <td>147</td>\n",
       "      <td>40</td>\n",
       "      <td>112</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>273</td>\n",
       "      <td>73</td>\n",
       "      <td>15</td>\n",
       "      <td>1552</td>\n",
       "      <td>408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>257</td>\n",
       "      <td>501</td>\n",
       "      <td>935</td>\n",
       "      <td>372</td>\n",
       "      <td>18</td>\n",
       "      <td>475</td>\n",
       "      <td>2500</td>\n",
       "      <td>439</td>\n",
       "      <td>401</td>\n",
       "      <td>...</td>\n",
       "      <td>317</td>\n",
       "      <td>130</td>\n",
       "      <td>312</td>\n",
       "      <td>33</td>\n",
       "      <td>15</td>\n",
       "      <td>829</td>\n",
       "      <td>162</td>\n",
       "      <td>28</td>\n",
       "      <td>4214</td>\n",
       "      <td>1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>45</td>\n",
       "      <td>1000</td>\n",
       "      <td>1712</td>\n",
       "      <td>3001</td>\n",
       "      <td>1081</td>\n",
       "      <td>41</td>\n",
       "      <td>1392</td>\n",
       "      <td>7561</td>\n",
       "      <td>1437</td>\n",
       "      <td>1297</td>\n",
       "      <td>...</td>\n",
       "      <td>900</td>\n",
       "      <td>337</td>\n",
       "      <td>960</td>\n",
       "      <td>117</td>\n",
       "      <td>46</td>\n",
       "      <td>2354</td>\n",
       "      <td>552</td>\n",
       "      <td>117</td>\n",
       "      <td>13040</td>\n",
       "      <td>3704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>159</td>\n",
       "      <td>1518</td>\n",
       "      <td>3093</td>\n",
       "      <td>5936</td>\n",
       "      <td>2115</td>\n",
       "      <td>79</td>\n",
       "      <td>2744</td>\n",
       "      <td>14339</td>\n",
       "      <td>2589</td>\n",
       "      <td>2254</td>\n",
       "      <td>...</td>\n",
       "      <td>1797</td>\n",
       "      <td>652</td>\n",
       "      <td>1772</td>\n",
       "      <td>168</td>\n",
       "      <td>90</td>\n",
       "      <td>4530</td>\n",
       "      <td>926</td>\n",
       "      <td>203</td>\n",
       "      <td>23870</td>\n",
       "      <td>7491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>314</td>\n",
       "      <td>2289</td>\n",
       "      <td>4388</td>\n",
       "      <td>9625</td>\n",
       "      <td>2959</td>\n",
       "      <td>139</td>\n",
       "      <td>4096</td>\n",
       "      <td>20475</td>\n",
       "      <td>3880</td>\n",
       "      <td>3249</td>\n",
       "      <td>...</td>\n",
       "      <td>2393</td>\n",
       "      <td>949</td>\n",
       "      <td>2510</td>\n",
       "      <td>266</td>\n",
       "      <td>117</td>\n",
       "      <td>6480</td>\n",
       "      <td>1407</td>\n",
       "      <td>298</td>\n",
       "      <td>34286</td>\n",
       "      <td>11796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "department         alcohol  babies  bakery  beverages  breakfast  bulk  \\\n",
       "order_hour_of_day                                                        \n",
       "0                       17     119     340        865        224     7   \n",
       "1                       11      64     188        465        128     7   \n",
       "2                       11      44     105        235         61     1   \n",
       "3                       11      36      89        219         54     3   \n",
       "4                        8      48     113        258         69     1   \n",
       "5                        7      73     157        365        111     2   \n",
       "6                       25     257     501        935        372    18   \n",
       "7                       45    1000    1712       3001       1081    41   \n",
       "8                      159    1518    3093       5936       2115    79   \n",
       "9                      314    2289    4388       9625       2959   139   \n",
       "\n",
       "department         canned goods  dairy eggs  deli  dry goods pasta  ...  \\\n",
       "order_hour_of_day                                                   ...   \n",
       "0                           378        1641   328              287  ...   \n",
       "1                           183         956   176              161  ...   \n",
       "2                           116         456    84               89  ...   \n",
       "3                            94         393    73               85  ...   \n",
       "4                           147         489   124              100  ...   \n",
       "5                           195         803   170              134  ...   \n",
       "6                           475        2500   439              401  ...   \n",
       "7                          1392        7561  1437             1297  ...   \n",
       "8                          2744       14339  2589             2254  ...   \n",
       "9                          4096       20475  3880             3249  ...   \n",
       "\n",
       "department         household  international  meat seafood  missing  other  \\\n",
       "order_hour_of_day                                                           \n",
       "0                        229             75           214       21      7   \n",
       "1                        106             54            92       11      5   \n",
       "2                         80             26            72       13      2   \n",
       "3                         51             26            60        7      3   \n",
       "4                         79             39            79        4      4   \n",
       "5                        147             40           112       16     12   \n",
       "6                        317            130           312       33     15   \n",
       "7                        900            337           960      117     46   \n",
       "8                       1797            652          1772      168     90   \n",
       "9                       2393            949          2510      266    117   \n",
       "\n",
       "department         pantry  personal care  pets  produce  snacks  \n",
       "order_hour_of_day                                                \n",
       "0                     705            147    57     3149     944  \n",
       "1                     368            106    43     1671     548  \n",
       "2                     219             43     8      880     240  \n",
       "3                     141             49    25      702     233  \n",
       "4                     178             41    24      879     249  \n",
       "5                     273             73    15     1552     408  \n",
       "6                     829            162    28     4214    1200  \n",
       "7                    2354            552   117    13040    3704  \n",
       "8                    4530            926   203    23870    7491  \n",
       "9                    6480           1407   298    34286   11796  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_dep = pd.crosstab(fdata['order_hour_of_day'] ,fdata['department'])\n",
    "time_dep.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "produce       440385\n",
       "dairy eggs    251811\n",
       "snacks        140271\n",
       "beverages     113859\n",
       "frozen        107880\n",
       "pantry         89667\n",
       "bakery         55596\n",
       "Name: department, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdata['department'].value_counts().head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "personal care    18998\n",
       "international    13423\n",
       "alcohol           4707\n",
       "pets              4609\n",
       "missing           3438\n",
       "bulk              1622\n",
       "other             1550\n",
       "Name: department, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdata['department'].value_counts().tail(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24, 21)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_dep.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for col in time_dep:\n",
    "    #print(col)\n",
    "    #print(time_dep[col].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#time_dep['produce'] = time_dep.produce.apply(lambda x: int(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a29c6da90>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_dep.sort_index().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_dep.sort_values(by='produce', ascending=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>department</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>babies</th>\n",
       "      <th>bakery</th>\n",
       "      <th>beverages</th>\n",
       "      <th>breakfast</th>\n",
       "      <th>bulk</th>\n",
       "      <th>canned goods</th>\n",
       "      <th>dairy eggs</th>\n",
       "      <th>deli</th>\n",
       "      <th>dry goods pasta</th>\n",
       "      <th>...</th>\n",
       "      <th>household</th>\n",
       "      <th>international</th>\n",
       "      <th>meat seafood</th>\n",
       "      <th>missing</th>\n",
       "      <th>other</th>\n",
       "      <th>pantry</th>\n",
       "      <th>personal care</th>\n",
       "      <th>pets</th>\n",
       "      <th>produce</th>\n",
       "      <th>snacks</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>36</td>\n",
       "      <td>89</td>\n",
       "      <td>219</td>\n",
       "      <td>54</td>\n",
       "      <td>3</td>\n",
       "      <td>94</td>\n",
       "      <td>393</td>\n",
       "      <td>73</td>\n",
       "      <td>85</td>\n",
       "      <td>...</td>\n",
       "      <td>51</td>\n",
       "      <td>26</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>141</td>\n",
       "      <td>49</td>\n",
       "      <td>25</td>\n",
       "      <td>702</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>48</td>\n",
       "      <td>113</td>\n",
       "      <td>258</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>147</td>\n",
       "      <td>489</td>\n",
       "      <td>124</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>79</td>\n",
       "      <td>39</td>\n",
       "      <td>79</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>178</td>\n",
       "      <td>41</td>\n",
       "      <td>24</td>\n",
       "      <td>879</td>\n",
       "      <td>249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>44</td>\n",
       "      <td>105</td>\n",
       "      <td>235</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>116</td>\n",
       "      <td>456</td>\n",
       "      <td>84</td>\n",
       "      <td>89</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>26</td>\n",
       "      <td>72</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>219</td>\n",
       "      <td>43</td>\n",
       "      <td>8</td>\n",
       "      <td>880</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>73</td>\n",
       "      <td>157</td>\n",
       "      <td>365</td>\n",
       "      <td>111</td>\n",
       "      <td>2</td>\n",
       "      <td>195</td>\n",
       "      <td>803</td>\n",
       "      <td>170</td>\n",
       "      <td>134</td>\n",
       "      <td>...</td>\n",
       "      <td>147</td>\n",
       "      <td>40</td>\n",
       "      <td>112</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>273</td>\n",
       "      <td>73</td>\n",
       "      <td>15</td>\n",
       "      <td>1552</td>\n",
       "      <td>408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>64</td>\n",
       "      <td>188</td>\n",
       "      <td>465</td>\n",
       "      <td>128</td>\n",
       "      <td>7</td>\n",
       "      <td>183</td>\n",
       "      <td>956</td>\n",
       "      <td>176</td>\n",
       "      <td>161</td>\n",
       "      <td>...</td>\n",
       "      <td>106</td>\n",
       "      <td>54</td>\n",
       "      <td>92</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>368</td>\n",
       "      <td>106</td>\n",
       "      <td>43</td>\n",
       "      <td>1671</td>\n",
       "      <td>548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "department         alcohol  babies  bakery  beverages  breakfast  bulk  \\\n",
       "order_hour_of_day                                                        \n",
       "3                       11      36      89        219         54     3   \n",
       "4                        8      48     113        258         69     1   \n",
       "2                       11      44     105        235         61     1   \n",
       "5                        7      73     157        365        111     2   \n",
       "1                       11      64     188        465        128     7   \n",
       "\n",
       "department         canned goods  dairy eggs  deli  dry goods pasta  ...  \\\n",
       "order_hour_of_day                                                   ...   \n",
       "3                            94         393    73               85  ...   \n",
       "4                           147         489   124              100  ...   \n",
       "2                           116         456    84               89  ...   \n",
       "5                           195         803   170              134  ...   \n",
       "1                           183         956   176              161  ...   \n",
       "\n",
       "department         household  international  meat seafood  missing  other  \\\n",
       "order_hour_of_day                                                           \n",
       "3                         51             26            60        7      3   \n",
       "4                         79             39            79        4      4   \n",
       "2                         80             26            72       13      2   \n",
       "5                        147             40           112       16     12   \n",
       "1                        106             54            92       11      5   \n",
       "\n",
       "department         pantry  personal care  pets  produce  snacks  \n",
       "order_hour_of_day                                                \n",
       "3                     141             49    25      702     233  \n",
       "4                     178             41    24      879     249  \n",
       "2                     219             43     8      880     240  \n",
       "5                     273             73    15     1552     408  \n",
       "1                     368            106    43     1671     548  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_dep.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#number_of_items_per_order, order_hour_of_day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Order size analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>user_id</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>order_dow</th>\n",
       "      <th>order_number</th>\n",
       "      <th>order_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_name</th>\n",
       "      <th>reordered</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "      <th>department_id</th>\n",
       "      <th>department</th>\n",
       "      <th>aisle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>45082</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>17330</td>\n",
       "      <td>Light</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>alcohol</td>\n",
       "      <td>beers coolers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>45082</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>35419</td>\n",
       "      <td>Handmade Vodka From Austin, Texas</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>alcohol</td>\n",
       "      <td>spirits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>45082</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>27407</td>\n",
       "      <td>Chunky Salsa Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13</td>\n",
       "      <td>pantry</td>\n",
       "      <td>preserved dips spreads</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>45082</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>44635</td>\n",
       "      <td>Salsa Con Queso Medium Dip</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13</td>\n",
       "      <td>pantry</td>\n",
       "      <td>preserved dips spreads</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>45082</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>196</td>\n",
       "      <td>Soda</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7</td>\n",
       "      <td>beverages</td>\n",
       "      <td>soft drinks</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  order_hour_of_day  order_dow  order_number  order_id  product_id  \\\n",
       "0    45082                 17          6             2        13       17330   \n",
       "1    45082                 17          6             2        13       35419   \n",
       "2    45082                 17          6             2        13       27407   \n",
       "3    45082                 17          6             2        13       44635   \n",
       "4    45082                 17          6             2        13         196   \n",
       "\n",
       "                        product_name  reordered  days_since_prior_order  \\\n",
       "0                              Light          0                     1.0   \n",
       "1  Handmade Vodka From Austin, Texas          0                     1.0   \n",
       "2                Chunky Salsa Medium          0                     1.0   \n",
       "3         Salsa Con Queso Medium Dip          0                     1.0   \n",
       "4                               Soda          0                     1.0   \n",
       "\n",
       "   department_id department                   aisle  \n",
       "0              5    alcohol           beers coolers  \n",
       "1              5    alcohol                 spirits  \n",
       "2             13     pantry  preserved dips spreads  \n",
       "3             13     pantry  preserved dips spreads  \n",
       "4              7  beverages             soft drinks  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# temp_df contains the data that we will use in modeling.\n",
    "temp_df = fdata.groupby(['order_hour_of_day', 'order_dow', 'order_id']).order_number.count().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     41986\n",
       "2     12599\n",
       "3      4761\n",
       "4      1930\n",
       "5       835\n",
       "6       363\n",
       "7       170\n",
       "8        61\n",
       "9        36\n",
       "10       13\n",
       "11        7\n",
       "13        2\n",
       "12        2\n",
       "Name: user_id, dtype: int64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we want to know whether user_id can be added to the X dataframe to improve prediction. However, the user_id can only\n",
    "# be added if the user has ordered more than once in the dataset. The following lines count the number of users who\n",
    "# have ordered N times. For example, the first row is the number of poeple who have ordered once, the second row is\n",
    "# the number of people who have ordered 2 times, etc.\n",
    "order_user_df = fdata.groupby(['order_id','user_id']).order_number.count().reset_index()\n",
    "order_user_df.drop(\n",
    "    'order_id', axis=1).user_id.value_counts().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we see that 41,986 users have only ordered once, and can't be used in a model that includes user_id as a feature.\n",
    "# However, we're going to stll create the feature, and drop those user_ids from it when using a model that contains\n",
    "# user_id as a feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we are going to merge the order_user_df with temp_df, and then we will drop those order_id rows where the\n",
    "# corresponding user_id only has one order\n",
    "temp_df_with_userid = temp_df.merge(order_user_df[['order_id', 'user_id']], on='order_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_order_count = temp_df_with_userid.user_id.value_counts()\n",
    "users_ordering_once = set(user_order_count[user_order_count == 1].index)\n",
    "temp_df_with_userid = temp_df_with_userid[~temp_df_with_userid.user_id.isin(users_ordering_once)].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>order_dow</th>\n",
       "      <th>order_id</th>\n",
       "      <th>order_number</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>71735</td>\n",
       "      <td>18</td>\n",
       "      <td>72220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>152561</td>\n",
       "      <td>24</td>\n",
       "      <td>132305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>183636</td>\n",
       "      <td>17</td>\n",
       "      <td>107393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>210244</td>\n",
       "      <td>6</td>\n",
       "      <td>138485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>214833</td>\n",
       "      <td>12</td>\n",
       "      <td>80853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55808</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>3364129</td>\n",
       "      <td>38</td>\n",
       "      <td>61895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55809</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>3407955</td>\n",
       "      <td>24</td>\n",
       "      <td>129282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55810</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>3416251</td>\n",
       "      <td>39</td>\n",
       "      <td>37592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55811</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>3418243</td>\n",
       "      <td>20</td>\n",
       "      <td>196596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55812</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>3419335</td>\n",
       "      <td>26</td>\n",
       "      <td>174536</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>55813 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       order_hour_of_day  order_dow  order_id  order_number  user_id\n",
       "0                      0          0     71735            18    72220\n",
       "1                      0          0    152561            24   132305\n",
       "2                      0          0    183636            17   107393\n",
       "3                      0          0    210244             6   138485\n",
       "4                      0          0    214833            12    80853\n",
       "...                  ...        ...       ...           ...      ...\n",
       "55808                 23          6   3364129            38    61895\n",
       "55809                 23          6   3407955            24   129282\n",
       "55810                 23          6   3416251            39    37592\n",
       "55811                 23          6   3418243            20   196596\n",
       "55812                 23          6   3419335            26   174536\n",
       "\n",
       "[55813 rows x 5 columns]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df_with_userid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a44f49e90>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# there doesn't seem to be a linear relationship between order_hour_of_day and order_size. So, we will use a\n",
    "# non-linear model.\n",
    "sns.boxplot(x='order_hour_of_day', y='order_number', data=temp_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import model_selection\n",
    "from sklearn import metrics\n",
    "\n",
    "#lr = LinearRegression()\n",
    "model = DecisionTreeRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6211029788403895"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_cols=['order_hour_of_day', 'order_dow', 'user_id']\n",
    "X=temp_df_with_userid[feature_cols]\n",
    "y=temp_df_with_userid['order_number']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)\n",
    "\n",
    "\n",
    "\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "y_pred=model.predict(X_test)\n",
    "model.score(X, y)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#def train_test(x,y):\n",
    "#X = temp_df_with_userid['order_hour_of_day', 'order_dow', 'user_id']\n",
    "#y = temp_df_with_userid['order_number']\n",
    "    \n",
    "    \n",
    "#X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=123)\n",
    "    \n",
    "    \n",
    "\n",
    "#model.fit(X, y)\n",
    "#model.score(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X = temp_df_with_userid[['order_hour_of_day', 'order_dow', 'user_id']]\n",
    "#y = temp_df_with_userid['order_number']\n",
    "#model.fit(X, y)\n",
    "#model.score(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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