instacart-market-basket-Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "029fc593",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "color = sns.color_palette()\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4a68246d",
   "metadata": {},
   "outputs": [],
   "source": [
    "order_products_train_df = pd.read_csv(\"D:/Instacart-Market-Basket-Analysis/order_products__train.csv\")\n",
    "order_products_prior_df = pd.read_csv(\"D:/Instacart-Market-Basket-Analysis/order_products__prior.csv\")\n",
    "orders_df = pd.read_csv(\"D:/Instacart-Market-Basket-Analysis/orders.csv\")\n",
    "products_df = pd.read_csv(\"D:/Instacart-Market-Basket-Analysis/products.csv\")\n",
    "aisles_df = pd.read_csv(\"D:/Instacart-Market-Basket-Analysis/aisles.csv\")\n",
    "departments_df = pd.read_csv(\"D:/Instacart-Market-Basket-Analysis/departments.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "aa4b0843",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>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",
       "      <th>0</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2398795</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1e98673c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>33120</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>28985</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>9327</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>45918</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_products_prior_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5e080380",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        vertical-align: top;\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>49302</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>11109</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>10246</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>49683</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>43633</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  product_id  add_to_cart_order  reordered\n",
       "0         1       49302                  1          1\n",
       "1         1       11109                  2          1\n",
       "2         1       10246                  3          0\n",
       "3         1       49683                  4          0\n",
       "4         1       43633                  5          1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_products_train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d86e9f60",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TalhaPC\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnt_srs = orders_df.eval_set.value_counts()\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.barplot(cnt_srs.index, cnt_srs.values, alpha=0.8, color=color[1])\n",
    "plt.ylabel('Number of Occurrences', fontsize=12)\n",
    "plt.xlabel('Eval set type', fontsize=12)\n",
    "plt.title('Count of rows in each dataset', fontsize=15)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ec331b8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "eval_set\n",
       "prior    206209\n",
       "test      75000\n",
       "train    131209\n",
       "Name: user_id, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_unique_count(x):\n",
    "    return len(np.unique(x))\n",
    "\n",
    "cnt_srs = orders_df.groupby(\"eval_set\")[\"user_id\"].aggregate(get_unique_count)\n",
    "cnt_srs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3ba58255",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.countplot(x=\"order_dow\", data=orders_df, color=color[0])\n",
    "plt.ylabel('Count', fontsize=12)\n",
    "plt.xlabel('Day of week', fontsize=12)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.title(\"Frequency of order by week day\", fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c6740f92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yqEuSJEkd8mJSSdoITOviVS9claTpcURdkiRJ6pAj6pIkdc5PRKSNk0FdkqS1ZHCWtBCc+iJJkiR1yKAuSZIkdcigLkmSJHXIoC5JkiR1yKAuSZIkdcigLkmSJHXI5RklSXd6LpcoaUPkiLokSZLUIYO6JEmS1CGDuiRJktQhg7okSZLUIYO6JEmS1CGDuiRJktQhl2eUJE2cyyVK0vozqEuSpNX4Rkvqg1NfJEmSpA4Z1CVJkqQOGdQlSZKkDhnUJUmSpA4Z1CVJkqQOGdQlSZKkDhnUJUmSpA4Z1CVJkqQOGdQlSZKkDhnUJUmSpA4Z1CVJkqQOGdQlSZKkDhnUJUmSpA4Z1CVJkqQOGdQlSZKkDhnUJUmSpA4Z1CVJkqQOGdQlSZKkDhnUJUmSpA4Z1CVJkqQOLVnsDkhSDy47Zq+p1b30yBVTq1uStOFyRF2SJEnqkEFdkiRJ6pBBXZIkSeqQQV2SJEnqkEFdkiRJ6pBBXZIkSerQggT1JLsl+UySC5NckOQlrfzoJN9Pcn67/d7IMa9MckmSi5IcMFL+0CQr2rY3JUkr3yzJB1r5OUl2HznmsCQXt9thC3HOkiRJ0vpYqHXUbwFeVlXnJbkb8JUkZ7Ztb6yqN4zunOT+wDLgAcA9gU8luU9V3Qq8DTgC+BLwceBA4HTgcOC6qrp3kmXA64GnJdkWOArYB6jW9mlVdd2Uz1mSJElaZwsyol5VV1bVee3xjcCFwC5zHHIQcHJV3VxV3wUuAfZNsjOwVVV9saoKeDdw8MgxJ7XHHwIe3UbbDwDOrKpVLZyfyRDuJUmSpG4t+Bz1NiXlwcA5reiFSb6e5IQk27SyXYDLRw5b2cp2aY/Hy1c7pqpuAW4AtpujLkmSJKlbCxrUk2wJ/Bvw0qr6EcM0ll8D9gauBP5hZtdZDq85ytf1mNG+HZFkeZLl11577VynIUmSJE3dggX1JHdhCOnvraoPA1TV1VV1a1X9AngHsG/bfSWw28jhuwJXtPJdZylf7ZgkS4CtgVVz1LWaqjquqvapqn122GGH9TlVSZIkab0t1KovAY4HLqyqfxwp33lktycC32iPTwOWtZVc9gD2BL5cVVcCNybZr9V5KPDRkWNmVnQ5BDirzWM/A9g/yTZtas3+rUySJEnq1kKt+vJw4JnAiiTnt7K/BJ6eZG+GqSiXAs8BqKoLkpwCfJNhxZgXtBVfAJ4HnAhszrDay+mt/HjgPUkuYRhJX9bqWpXktcC5bb9jqmrVVM5SkiRJmpAFCepV9QVmnyv+8TmOORY4dpby5cADZym/CXjKGuo6AThhvv2VJEmSFpvfTCpJkiR1yKAuSZIkdcigLkmSJHXIoC5JkiR1yKAuSZIkdcigLkmSJHXIoC5JkiR1yKAuSZIkdcigLkmSJHXIoC5JkiR1yKAuSZIkdcigLkmSJHXIoC5JkiR1yKAuSZIkdcigLkmSJHXIoC5JkiR1yKAuSZIkdcigLkmSJHXIoC5JkiR1yKAuSZIkdcigLkmSJHXIoC5JkiR1yKAuSZIkdcigLkmSJHXIoC5JkiR1aMlid0CSJG3cLjtmr6nUu/TIFVOpV1oojqhLkiRJHTKoS5IkSR0yqEuSJEkdMqhLkiRJHTKoS5IkSR0yqEuSJEkdMqhLkiRJHTKoS5IkSR0yqEuSJEkdMqhLkiRJHTKoS5IkSR0yqEuSJEkdMqhLkiRJHTKoS5IkSR0yqEuSJEkdMqhLkiRJHTKoS5IkSR0yqEuSJEkdMqhLkiRJHTKoS5IkSR0yqEuSJEkdMqhLkiRJHTKoS5IkSR0yqEuSJEkdMqhLkiRJHTKoS5IkSR0yqEuSJEkdMqhLkiRJHTKoS5IkSR0yqEuSJEkdMqhLkiRJHTKoS5IkSR0yqEuSJEkdMqhLkiRJHTKoS5IkSR0yqEuSJEkdMqhLkiRJHVqQoJ5ktySfSXJhkguSvKSVb5vkzCQXt/ttRo55ZZJLklyU5ICR8ocmWdG2vSlJWvlmST7Qys9JsvvIMYe1Ni5OcthCnLMkSZK0PhZqRP0W4GVVdT9gP+AFSe4PvAL4dFXtCXy6/Uzbtgx4AHAg8NYkm7a63gYcAezZbge28sOB66rq3sAbgde3urYFjgIeBuwLHDX6hkCSJEnq0YIE9aq6sqrOa49vBC4EdgEOAk5qu50EHNweHwScXFU3V9V3gUuAfZPsDGxVVV+sqgLePXbMTF0fAh7dRtsPAM6sqlVVdR1wJreFe0mSJKlLCz5HvU1JeTBwDrBTVV0JQ5gHdmy77QJcPnLYyla2S3s8Xr7aMVV1C3ADsN0cdUmSJEndWtCgnmRL4N+Al1bVj+badZaymqN8XY8Z7dsRSZYnWX7ttdfO0TVJkiRp+hYsqCe5C0NIf29VfbgVX92ms9Dur2nlK4HdRg7fFbiile86S/lqxyRZAmwNrJqjrtVU1XFVtU9V7bPDDjus62lKkiRJE7FQq74EOB64sKr+cWTTacDMKiyHAR8dKV/WVnLZg+Gi0S+36TE3Jtmv1Xno2DEzdR0CnNXmsZ8B7J9km3YR6f6tTJIkSerWkgVq5+HAM4EVSc5vZX8JvA44JcnhwGXAUwCq6oIkpwDfZFgx5gVVdWs77nnAicDmwOntBsMbgfckuYRhJH1Zq2tVktcC57b9jqmqVVM6T0mSJGkiFiSoV9UXmH2uOMCj13DMscCxs5QvBx44S/lNtKA/y7YTgBPm219JkiRpsfnNpJIkSVKHDOqSJElShwzqkiRJUocM6pIkSVKHDOqSJElShwzqkiRJUocM6pIkSVKHDOqSJElShwzqkiRJUocM6pIkSVKHDOqSJElShwzqkiRJUocM6pIkSVKHDOqSJElShwzqkiRJUocM6pIkSVKHDOqSJElShwzqkiRJUocM6pIkSVKHlix2ByRJkhbKZcfsNbW6lx65Ymp1a+PkiLokSZLUIYO6JEmS1CGDuiRJktQhg7okSZLUIYO6JEmS1CGDuiRJktQhl2eU1CWXUJMkbewcUZckSZI6ZFCXJEmSOmRQlyRJkjpkUJckSZI6ZFCXJEmSOmRQlyRJkjpkUJckSZI6ZFCXJEmSOmRQlyRJkjpkUJckSZI6ZFCXJEmSOmRQlyRJkjpkUJckSZI6ZFCXJEmSOmRQlyRJkjpkUJckSZI6ZFCXJEmSOmRQlyRJkjpkUJckSZI6ZFCXJEmSOmRQlyRJkjo076Ce5ClrKD9kct2RJEmSBGs3on78GsqPm0RHJEmSJN1myR3tkORX28NNkuwBZGTzrwI3TaNjkiRJ0sbsDoM6cAlQDAH922PbrgKOnnCfJEmSpI3eHQb1qtoEIMlnq+p3pt8lSZIkSfOeo25IlyRJkhbOfKa+ANDmpx8L7A1sObqtqpZOtluSJEnSxm3eQR14H8Mc9ZcBP51OdyRJkiTB2gX1BwAPr6pfTKszkiRJkgZrs47654AHT6sjkiRJkm6zNiPqlwJnJPkww7KMv1RVR06yU5IkSdLGbm2C+l2BjwF3AXabTnckSZIkwVoE9ar6o2l2RJIkSdJt1mZ5xl9d07aq+s5kuiNJkiQJ1m7qyyVAARkpq3a/6cR6JEmSJGmtvpl0k6ratN1vAtwTOA545h0dm+SEJNck+cZI2dFJvp/k/Hb7vZFtr0xySZKLkhwwUv7QJCvatjclSSvfLMkHWvk5SXYfOeawJBe322HzPV9JkiRpMa3N8oyrqaqrgJcCfzuP3U8EDpyl/I1VtXe7fRwgyf2BZQzrth8IvDXJzIj924AjgD3bbabOw4HrqurewBuB17e6tgWOAh4G7AsclWSbtTtTSZIkaeGtc1Bvfh3Y4o52qqrPAavmWedBwMlVdXNVfZdhys2+SXYGtqqqL1ZVAe8GDh455qT2+EPAo9to+wHAmVW1qqquA85k9jcMkiRJUlfW5mLSz3PbnHQYAvoDgGPWo/0XJjkUWA68rIXpXYAvjeyzspX9T3s8Xk67vxygqm5JcgOw3Wj5LMdIkiRJ3Vqbi0nfOfbzT4CvVdXF69j224DXMoT/1wL/ADyb1S9WnTF+EetoOet4zGqSHMEwrYalS5fO1W9JkiRp6tZmHfWT7niv+auqq2ceJ3kH8O/tx5Ws/oVKuwJXtPJdZykfPWZlkiXA1gxTbVYCjxw75uw19Oc4hotj2WeffWYN85IkSdJCmfcc9SR3SfKaJN9JclO7f02SX1mXhtuc8xlPBGZWhDkNWNZWctmD4aLRL1fVlcCNSfZr888PBT46cszMii6HAGe1eexnAPsn2aZdRLp/K5MkSZK6tjZTX/6OYeWU5wLfA+4F/BWwFfCncx2Y5P0MI9vbJ1nJsBLLI5PszTAV5VLgOQBVdUGSU4BvArcAL6iqW1tVz2NYQWZz4PR2AzgeeE+SSxhG0pe1ulYleS1wbtvvmKqa70WtkiRJ0qJZm6D+FOBBVfXD9vNFSc4DvsYdBPWqevosxcfPsf+xwLGzlC8HHjhL+U2tf7PVdQJwwlz9kyRJknqzNsszznZh5lzlkiRJktbR2gT1DwIfS3JAkvslORA4tZVLkiRJmqC1mfrycuDVwFuAewLfB94P/PUU+iVJkiRt1O5wRD3Jw5O8vqp+XlVHVtW9q2qLqtoT2Ax4yPS7KUmSJG1c5jP15S+Bz61h22eAV02uO5IkSZJgfkF9b+ATa9j2KeChE+uNJEmSJGB+QX0rYE1fanQX4G6T644kSZIkmF9Q/xbDN3rOZv+2XZIkSdIEzWfVlzcCb0+yKXBqVf0iySbAwQwrwPzZFPsnSZJ0p3XZMXtNre6lR66YWt3qwx0G9ap6X5J7ACcBmyX5AbA9cBNwVFW9f8p9lCRJkjY681pHvar+Mck7gd8CtgN+CHyxqn40zc5JkiRJG6t5f+FRC+VnTLEvkiRJkpr5XEwqSZIkaYEZ1CVJkqQOGdQlSZKkDhnUJUmSpA4Z1CVJkqQOGdQlSZKkDhnUJUmSpA4Z1CVJkqQOGdQlSZKkDhnUJUmSpA4Z1CVJkqQOGdQlSZKkDhnUJUmSpA4Z1CVJkqQOGdQlSZKkDhnUJUmSpA4Z1CVJkqQOGdQlSZKkDhnUJUmSpA4Z1CVJkqQOGdQlSZKkDhnUJUmSpA4Z1CVJkqQOGdQlSZKkDhnUJUmSpA4Z1CVJkqQOGdQlSZKkDhnUJUmSpA4Z1CVJkqQOGdQlSZKkDhnUJUmSpA4Z1CVJkqQOGdQlSZKkDhnUJUmSpA4Z1CVJkqQOGdQlSZKkDhnUJUmSpA4Z1CVJkqQOGdQlSZKkDhnUJUmSpA4Z1CVJkqQOGdQlSZKkDhnUJUmSpA4Z1CVJkqQOGdQlSZKkDhnUJUmSpA4Z1CVJkqQOGdQlSZKkDi1Z7A5IkiRpMi47Zq+p1b30yBVTq1uzc0RdkiRJ6tCCBPUkJyS5Jsk3Rsq2TXJmkovb/TYj216Z5JIkFyU5YKT8oUlWtG1vSpJWvlmSD7Tyc5LsPnLMYa2Ni5McthDnK0mSJK2vhRpRPxE4cKzsFcCnq2pP4NPtZ5LcH1gGPKAd89Ykm7Zj3gYcAezZbjN1Hg5cV1X3Bt4IvL7VtS1wFPAwYF/gqNE3BJIkSVKvFiSoV9XngFVjxQcBJ7XHJwEHj5SfXFU3V9V3gUuAfZPsDGxVVV+sqgLePXbMTF0fAh7dRtsPAM6sqlVVdR1wJrd/wyBJkiR1ZzHnqO9UVVcCtPsdW/kuwOUj+61sZbu0x+Plqx1TVbcANwDbzVGXJEmS1LUeLybNLGU1R/m6HrN6o8kRSZYnWX7ttdfOq6OSJEnStCxmUL+6TWeh3V/TylcCu43stytwRSvfdZby1Y5JsgTYmmGqzZrqup2qOq6q9qmqfXbYYYf1OC1JkiRp/S1mUD8NmFmF5TDgoyPly9pKLnswXDT65TY95sYk+7X554eOHTNT1yHAWW0e+xnA/km2aReR7t/KJEmSpK4tyBceJXk/8Ehg+yQrGVZieR1wSpLDgcuApwBU1QVJTgG+CdwCvKCqbm1VPY9hBZnNgdPbDeB44D1JLmEYSV/W6lqV5LXAuW2/Y6pq/KJWSZIkqTsLEtSr6ulr2PToNex/LHDsLOXLgQfOUn4TLejPsu0E4IR5d1aSJEnqQI8Xk0qSJEkbPYO6JEmS1CGDuiRJktQhg7okSZLUIYO6JEmS1CGDuiRJktQhg7okSZLUoQVZR13ShuGyY/aaSr1Lj1wxlXolSbozc0RdkiRJ6pBBXZIkSeqQQV2SJEnqkEFdkiRJ6pBBXZIkSeqQQV2SJEnqkEFdkiRJ6pBBXZIkSeqQQV2SJEnqkEFdkiRJ6pBBXZIkSeqQQV2SJEnqkEFdkiRJ6pBBXZIkSeqQQV2SJEnqkEFdkiRJ6pBBXZIkSeqQQV2SJEnqkEFdkiRJ6tCSxe6AJEmS7pwuO2avqdS79MgVU6n3zsYRdUmSJKlDBnVJkiSpQwZ1SZIkqUMGdUmSJKlDBnVJkiSpQwZ1SZIkqUMGdUmSJKlDBnVJkiSpQwZ1SZIkqUMGdUmSJKlDBnVJkiSpQwZ1SZIkqUMGdUmSJKlDBnVJkiSpQwZ1SZIkqUMGdUmSJKlDBnVJkiSpQwZ1SZIkqUMGdUmSJKlDBnVJkiSpQwZ1SZIkqUMGdUmSJKlDBnVJkiSpQ0sWuwOSJEnSfFx2zF5TqXfpkSumUu/6ckRdkiRJ6pBBXZIkSeqQQV2SJEnqkEFdkiRJ6pBBXZIkSeqQQV2SJEnqkEFdkiRJ6pBBXZIkSeqQQV2SJEnqkEFdkiRJ6pBBXZIkSerQogf1JJcmWZHk/CTLW9m2Sc5McnG732Zk/1cmuSTJRUkOGCl/aKvnkiRvSpJWvlmSD7Tyc5LsvuAnKUmSJK2lRQ/qzaOqau+q2qf9/Arg01W1J/Dp9jNJ7g8sAx4AHAi8Ncmm7Zi3AUcAe7bbga38cOC6qro38Ebg9QtwPpIkSdJ66SWojzsIOKk9Pgk4eKT85Kq6uaq+C1wC7JtkZ2CrqvpiVRXw7rFjZur6EPDomdF2SZIkqVc9BPUCPpnkK0mOaGU7VdWVAO1+x1a+C3D5yLErW9ku7fF4+WrHVNUtwA3AdlM4D0mSJGlilix2B4CHV9UVSXYEzkzyrTn2nW0kvOYon+uY1Sse3iQcAbB06dK5eyxJkiRN2aKPqFfVFe3+GuAjwL7A1W06C+3+mrb7SmC3kcN3Ba5o5bvOUr7aMUmWAFsDq2bpx3FVtU9V7bPDDjtM5uQkSZKkdbSoQT3JXZPcbeYxsD/wDeA04LC222HAR9vj04BlbSWXPRguGv1ymx5zY5L92vzzQ8eOmanrEOCsNo9dkiRJ6tZiT33ZCfhIu7ZzCfC+qvpEknOBU5IcDlwGPAWgqi5IcgrwTeAW4AVVdWur63nAicDmwOntBnA88J4klzCMpC9biBOTJEmS1seiBvWq+g7woFnKfwg8eg3HHAscO0v5cuCBs5TfRAv6kiRJ0p3Fos9RlyRJknR7BnVJkiSpQwZ1SZIkqUMGdUmSJKlDBnVJkiSpQwZ1SZIkqUMGdUmSJKlDBnVJkiSpQwZ1SZIkqUMGdUmSJKlDSxa7A5IkSVKPLjtmr6nUu/TIFfPazxF1SZIkqUMGdUmSJKlDBnVJkiSpQwZ1SZIkqUMGdUmSJKlDBnVJkiSpQwZ1SZIkqUMGdUmSJKlDBnVJkiSpQwZ1SZIkqUMGdUmSJKlDBnVJkiSpQ0sWuwOS1t1lx+w1lXqXHrliKvVKkqT5c0RdkiRJ6pBBXZIkSeqQQV2SJEnqkEFdkiRJ6pBBXZIkSeqQQV2SJEnqkEFdkiRJ6pBBXZIkSeqQQV2SJEnqkEFdkiRJ6tCSxe5A76b1Fe3g17RLkiRpzRxRlyRJkjpkUJckSZI6ZFCXJEmSOmRQlyRJkjpkUJckSZI6ZFCXJEmSOmRQlyRJkjpkUJckSZI6ZFCXJEmSOmRQlyRJkjpkUJckSZI6ZFCXJEmSOmRQlyRJkjpkUJckSZI6tGSxO6DVXXbMXlOpd+mRK6ZSryRJkqbDEXVJkiSpQwZ1SZIkqUMGdUmSJKlDzlGXJshrDCRJ0qQ4oi5JkiR1yKAuSZIkdcipLxs5p2pIkiT1yRF1SZIkqUMGdUmSJKlDTn3RglroqTZO7ZEkSXdWjqhLkiRJHTKoS5IkSR0yqEuSJEkd2miCepIDk1yU5JIkr1js/kiSJElz2SiCepJNgbcAjwPuDzw9yf0Xt1eSJEnSmm0UQR3YF7ikqr5TVT8HTgYOWuQ+SZIkSWu0sQT1XYDLR35e2cokSZKkLqWqFrsPU5fkKcABVfXH7ednAvtW1YtG9jkCOKL9+OvARevQ1PbAD9azu7Zne7Zne4vZlu3Znu1tPO1tyOd2Z2rvXlW1w2wbNpYvPFoJ7Dby867AFaM7VNVxwHHr00iS5VW1z/rUYXu2Z3u2t5ht2Z7t2d7G096GfG4bSnsby9SXc4E9k+yR5FeAZcBpi9wnSZIkaY02ihH1qrolyQuBM4BNgROq6oJF7pYkSZK0RhtFUAeoqo8DH59yM+s1dcb2bM/2bK+DtmzP9mxv42lvQz63DaK9jeJiUkmSJOnOZmOZoy5JkiTdqRjUJUmSpA5tNHPUpyHJfRm+4XQXoBiWfDytqi5c1I5NSDu/XYBzqurHI+UHVtUnptDevkBV1blJ7g8cCHyrXV8wVUneXVWHTrudkfYewfCNud+oqk9Oof6HARdW1Y+SbA68AngI8E3gb6rqhgm392LgI1V1+R3uvP5tzazcdEVVfSrJM4DfBi4Ejquq/5lCm78GPJFhmddbgIuB90/6eZQkaZRz1NdRkr8Ang6czLBOOwzrsy8DTq6q1y1gX/6oqt414TpfDLyAIfzsDbykqj7atp1XVQ+ZcHtHAY9jePN4JvAw4GzgMcAZVXXsBNsaX5ozwKOAswCq6gmTamukzS9X1b7t8Z8wPLcfAfYHPjbp10uSC4AHtRWPjgN+CnwIeHQrf9KE27sB+AnwbeD9wAer6tpJtjHS1nsZXidbANcDWwIfZji3VNVhE27vxcDjgc8CvwecD1zHENyfX1VnT7I9SZJmGNTXUZL/Bh4wPnrXRvsuqKo9F7Avl1XV0gnXuQL4rar6cZLdGULee6rqn5N8taoePIX29gY2A64Cdh0ZDT6nqn5jgm2dxzCy/E6GT0LCEC6XAVTVZyfV1kibv3zOkpwL/F5VXZvkrsCXqmqvCbd3YVXdrz1e7Y1VkvOrau8Jt/dV4KEMb6yeBjwB+ArD8/rhqrpxgm19vap+I8kS4PvAPavq1iQBvjbJ10prbwWwd2tjC+DjVfXIJEuBj07634I2PEl2rKprFrsf05Jku6r64WL3Q3NLsjXwSuBgYOZbMK8BPgq8rqquX8C+nF5Vj5twnVsxnN+uwOlV9b6RbW+tqudPuL17AEcBvwCOBF4EPJlhgPMlVXXlJNpxjvq6+wVwz1nKd27bJirJ19dwWwHsNOn2gE1nprtU1aXAI4HHJflHhmA7abdU1a1V9VPg21X1o9b2z5j887kPQ4h8FXBDGxH9WVV9dhohvdkkyTZJtmN4g3wtQFX9hGEqxaR9I8kftcdfS7IPQJL7ABOfGsIwZekXVfXJqjqc4d/GWxmmL31nwm1t0t4Q341hVH3rVr4ZcJcJtzVjZprgZq1dquqyabWXZOskr0vyrSQ/bLcLW9ndp9HmGvpx+hTq3CrJ3yZ5T5u2NLrtrVNo7x5J3pbkLUm2S3J0khVJTkmy8xTa23bsth3w5fbvf9sptHfgyOOtkxzf/ja8L8nE/za01+D27fE+Sb4DnJPke0l+ZwrtnZfk1W362dS1c/pMkn9NsluSM5PckOTcJBN/U55kyyTHJLmgtXNtki8ledak2wJOYfg08JFVtV1VbcfwafJ1wAcn3ViSh6zh9lCGgblJexdDPvk3YFmSf0uyWdu23xTaO5Fh0O9y4DPAz4DfBz4P/H+TasQ56uvupcCnk1zM8EsCWArcG3jhFNrbCTiA4R/UqAD/NYX2rkqyd1WdD9BG1v8AOAGY6Ohv8/MkW7Sg/tCZwjYCMNGgXlW/AN6Y5IPt/mqm/29ha4Y3BwEqyT2q6qokWzKdNz5/DPxzklcDPwC+mORyhtfqH0+hvdXOoX3SdBpwWvtUZJKOB77F8OVlrwI+2MLCfgxT0SbtncC5Sb4E/B/g9QBJdgBWTaE9GP6gnsXwB/Wq1t49gMMY/qA+dlINJVnTNLYwvT+mFzP8MX12kicDz6iqm5neH9P/AO7K8Mf0vQx/TA9i+GN60ITb+wHwvbGyXYDzGD7B+9UJt/c3wMw1Q/8AXMkwVetJwNsZRk8n6fer6hXt8d8DT2vXFd0HeB/DQMgkbQPcHfhMkqsYPqX7QFVdMeF2ZryVYZT07gx/W/+0qh6b5NFt229NuL33MkyDPAB4KsPr9GTg1UnuU1V/OcG2dq+q148WtP9fXp/k2RNsZ8a5DFMGZ/sbd/cptPdrVfXk9vjUJK8Czkoy8emszU5V9WaAJM8feW7fnOTwibVSVd7W8cbwicR+DB91HNIebzqlto4HHrGGbe+bQnu7AvdYw7aHT6G9zdZQvj2w15R/j7/PcIHlYryGtgD2mGL9dwMexPDmZ6cptnOfBX7e7skw5QWG//APAfadYnsPaG3cd4HO76J12baObd3K8KbgM7PcfjaFczt/7OdXAf8JbAecN4X2vjry+LK5+jKh9v4fQ3Dea6Tsu1N8rZw38nj8uZ3G+X0LWNIef2ls24opn9//ZgjLV7XX5xEL/Hr56hTa+9rYz+e2+00YFlOYZFufBF4++reAYRDwL4BPTeHcvgHsuYZtl0+hvQuBTcbKDgMuAL43zd8d8Ndj2yb2b8ER9fVQw8jslxaorTW+O6uqZ6xp23q0t3KObf85hfZuXkP5DxhGqKamqv6DYcRtwdXwCcJ3p1j/jcDXplX/SDv/Pe02xtq7YuTx9QzXUEyzvQsY/rNfKN9L8nLgpKq6GqBNY3gWt32CNykXAs+pqovHN7RPYSZtsySbtP8/qapjk6wEPsdwYfCkjU7xfPfYtk0n3VhVvSHJyQyf1l3OMDo7zYvBdkzyZwyjllslSbWkwHSmt74F+HiS1wGfSPJP3HYx9/lTaO+XqurzwOeTvIjhU6WnMflvgrwpyf4Mn4JWkoOr6tQ2refWCbcF8JMkj6iqLyR5PO1Tuqr6RZJJf9r6NIYVwD7b/j8p4GqGTz+fOuG2AI5mza/BF02hvY8Bvwt8aqagqk5qn5q/eQrtfTTJllX146p69UxhknsDF02qEYO6JPVn9A/qjq1s5g/qUybc1tH4x3Si2kDHU1rwOpPhk7NpeQftugngJIZPIa9tU6XOn3RjVfXmDNdGPQ+4D0OOuA9wKvDaSbcH3G4QoKpuZfjUYuLLBAPPBf6OYcrlAcDzkpzIcOH6n0ypvXe2qUPfAJ4Nv5xa95ZJNlRV1yV5F8Nr8ks1tuwyE34+q+pDSe7bpg2ttswzcNMk22rtvXy29qrqExlW75p0e0e29lZbxrqqLknyzkm146ovknQnkiksx9pDWxtqe+0ajV+rqm9siOdne3ee9rLwyy4vdHsvYrhGcINqz6AuSXcimcJyrD20ZXu2Z3vTbS+Ls+yy7a0np75IUmeSfH1Nm5jwcqwL2Zbt2Z7tLWp7qy27nOSRwIeS3IvprD5mexNgUJek/izkcqwLvfSr7dme7S1Oewu97LLtTYBBXZL68+/AljN/AEYlOftO3Jbt2Z7tLV57hzL2BXtVdQtwaJK3T7gt25sQ56hLkiRJHZrGGquSJEmS1pNBXZIkSeqQQV2SNFVJfj3JV5PcOJ8vHklydJJ/XYi+SVLPDOqSdCeX5NIkjxkre1aSLyxWn8a8HDi7qu5WVW9a7M5I0p2FQV2SNG9J1mW1sHsBF0y6L5K0oTOoS9JGIMn9kpyd5PokFyR5wsi2s5P88cjPq43GJ6kkL0hyMXDxGup/Qqv3+lbf/Vr5WcCjgH9J8uMk95nl2D2SfLZNjTkT2H5s+weTXJXkhiSfS/KAVv6bSa4effOQ5MlJzl/Hp0mSumJQl6QNXJK7AB8DPgnsCLwIeG+SX1+Lag4GHgbcf5b67wO8H3gpsAPwceBjSX6lqn4X+Dzwwqrasqr+e5a63wd8hSGgvxY4bGz76cCere/nAe8FqKpzgR8Cjx3Z9w+B96zFeUlStwzqkrRhOLWNZl+f5HrgrSPb9gO2BF5XVT+vqrMYvmjl6WtR/99W1aqq+tks254G/EdVnVlV/wO8Adgc+O07qjTJUuA3gb+qqpur6nMMbyp+qapOqKobq+pm4GjgQUm2bptPYgjnJNmW4Vse37cW5yVJ3TKoS9KG4eCquvvMDXj+yLZ7ApdX1S9Gyr4H7LIW9V8+x7Z7tvoAaO1cPs/67wlcV1U/GesbAEk2TfK6JN9O8iPg0rZpZnrMvwKPT7Il8FTg81V15TzalaTuGdQlacN3BbBbktH/85cC32+PfwJsMbLtHrPUMdfXWF/BcMEoAEkC7DZS/1yuBLZJctexvs14BnAQ8Bhga2D3mWYAqur7wBeBJwLPxGkvkjYgBnVJ2vCdwxDGX57kLkkeCTweOLltPx94UpItktwbOHwt6z8F+P0kj27z4V8G3Az81x0dWFXfA5YDr0nyK0ke0fo2426trh8yvJn4m1mqeTfDEpB7AR9Zy75LUrcM6pK0gauqnwNPAB4H/IBh/vqhVfWttssbgZ8DVzPM+X7vWtZ/EcM88Te3+h8PPL61Ox/PYLhQdRVwFEPwnvFuhqkw3we+CXxpluM/wjCi/5GxKTSSdKeWqrk+zZQkqX9Jvg08p6o+tdh9kaRJcURdknSnluTJDHPoz1rsvkjSJK3LN8xJktSFJGczrO3+zLFVbSTpTs+pL5IkSVKHnPoiSZIkdcigLkmSJHXIoC5JkiR1yKAuSZIkdcigLkmSJHXIoC5JkiR16P8HEPrYzWTsQIgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.countplot(x=\"order_hour_of_day\", data=orders_df, color=color[1])\n",
    "plt.ylabel('Count', fontsize=12)\n",
    "plt.xlabel('Hour of day', fontsize=12)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.title(\"Frequency of order by hour of day\", fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "13ef0318",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SnWbyZax/Fo+N6Z7LN9N9S/BR4KnDz7WB7T8AfIeBM++09qPoTnm4rt3vlwHPnyS399KNgP+Irlj+N+Dx470OuP+sL5sPxboWeMtQ2+/QvcbuoitYPwU8cmD9Y+me0+vonuer6U6tuctk7zHAL9MdCHs33YfafSfLZ4LX9RPpvh37Ed3c+9OBHSZ63U/ymG9PV0zf2vZrJfCUKeY36fOlbTeV98wJn/suLouxpMrpWNKGoo0mHVpVuy12LpNJciawY1VN9Qd41ENJ/pTuw85ONfFBxPN1+8voPridVFV/s9C3v9jatysvrqrtJttWUv849UVSryR5PN0p036HGf5AkxZfkt3opke9mm5qxoIW6e3Hd55A9yM529KNPEvSkmKhLqlvPkl3OrV3VdVZi52MZuxYuiJ5Jd0vhy60nejmdN8M/HFVrZlke0nqHae+SJIkST3k6RklSZKkHrJQlyRJknrIOeojbLfddrXbbrstdhqSJEn6GfeVr3zl+1W1/ah1Fuoj7LbbbqxatWqx05AkSdLPuCTXjbfOqS+SJElSD1moS5IkST1koS5JkiT1kIW6JEmS1EMW6pIkSVIPWahLkiRJPWShLkmSJPWQhbokSZLUQxbqkiRJUg9ZqEuSJEk9ZKEuSZIk9ZCFuiRJktRDFuqSJElSD1moS5IkST1koS5JkiT1kIW6JEmS1EMW6pIkSVIPWahLkiRJPbRssROQJEmSlpKV+66YdYwVF6ycdBtH1CVJkqQeslCXJEmSeshCXZIkSeohC3VJkiSphyzUJUmSpB6yUJckSZJ6yEJdkiRJ6iELdUmSJKmHLNQlSZKkHrJQlyRJknrIQl2SJEnqIQt1SZIkqYcs1CVJkqQeslCXJEmSeshCXZIkSeohC3VJkiSphyzUJUmSpB6yUJckSZJ6aEEK9SQ/l+TiJF9PckWS17f2Y5N8N8mlbXnWQJ9XJVmd5KokBw60PznJ5W3dO5KktW+a5MOt/aIkuw30OTLJ1W05ciH2WZIkSZqNZQt0O3cDv1pVdybZBPhSknPburdX1VsGN07yOOBwYE9gJ+CzSR5TVfcC7waOBr4MfBo4CDgXeD5wa1XtnuRw4M3Ac5JsAxwDLAcK+EqSc6rq1nneZ0mSJGnGFmREvTp3tqubtKUm6HIwcEZV3V1V1wCrgX2S7AhsWVUXVlUBpwKHDPQ5pV0+C9i/jbYfCJxfVetacX4+XXEvSZIk9daCzVFPsnGSS4Gb6Qrni9qqFye5LMlJSbZubTsD1w90X9Padm6Xh9vX61NV9wC3A9tOEGs4v6OTrEqyau3atTPfUUmSJGkOLFihXlX3VtXewC50o+N70U1jeTSwN3Aj8Na2eUaFmKB9pn0G8zuhqpZX1fLtt99+gj2RJEmS5t+Cn/Wlqm4DvggcVFU3tQL+PuC9wD5tszXArgPddgFuaO27jGhfr0+SZcBWwLoJYkmSJEm9tVBnfdk+yUPb5c2AXwO+1eacj3k28I12+Rzg8HYml0cBewAXV9WNwB1Jntbmnx8BnD3QZ+yMLocCn2/z2M8DDkiydZtac0BrkyRJknproc76siNwSpKN6T4cnFlV/5bktCR7001FuRb4Y4CquiLJmcA3gXuAF7UzvgC8EDgZ2IzubC9jZ485ETgtyWq6kfTDW6x1Sd4IXNK2e0NVrZvHfZUkSZJmLd2gswYtX768Vq1atdhpSJIkqYdW7rti1jFWXLASgCRfqarlo7bxl0klSZKkHrJQlyRJknrIQl2SJEnqIQt1SZIkqYcs1CVJkqQeslCXJEmSeshCXZIkSeohC3VJkiSphyzUJUmSpB6yUJckSZJ6yEJdkiRJ6iELdUmSJKmHLNQlSZKkHrJQlyRJknrIQl2SJEnqIQt1SZIkqYcs1CVJkqQeslCXJEmSeshCXZIkSeohC3VJkiSphyzUJUmSpB6yUJckSZJ6yEJdkiRJ6iELdUmSJKmHLNQlSZKkHrJQlyRJknrIQl2SJEnqoWWLnYC0oVq574pZx1hxwco5yESSJPWRI+qSJElSD1moS5IkST1koS5JkiT1kIW6JEmS1EMW6pIkSVIPWahLkiRJPWShLkmSJPWQhbokSZLUQxbqkiRJUg9ZqEuSJEk9ZKEuSZIk9ZCFuiRJktRDFuqSJElSD1moS5IkST1koS5JkiT1kIW6JEmS1EMW6pIkSVIPWahLkiRJPWShLkmSJPXQghTqSX4uycVJvp7kiiSvb+3bJDk/ydXt79YDfV6VZHWSq5IcOND+5CSXt3XvSJLWvmmSD7f2i5LsNtDnyHYbVyc5ciH2WZIkSZqNhRpRvxv41ap6ArA3cFCSpwGvBD5XVXsAn2vXSfI44HBgT+Ag4F1JNm6x3g0cDezRloNa+/OBW6tqd+DtwJtbrG2AY4CnAvsAxwx+IJAkSZL6aEEK9erc2a5u0pYCDgZOae2nAIe0ywcDZ1TV3VV1DbAa2CfJjsCWVXVhVRVw6lCfsVhnAfu30fYDgfOral1V3Qqcz/3FvSRJktRLCzZHPcnGSS4FbqYrnC8CdqiqGwHa34e1zXcGrh/ovqa17dwuD7ev16eq7gFuB7adINZwfkcnWZVk1dq1a2exp5IkSdLsLVihXlX3VtXewC50o+N7TbB5RoWYoH2mfQbzO6GqllfV8u23336C1CRJkqT5t+Bnfamq24Av0k0/ualNZ6H9vblttgbYdaDbLsANrX2XEe3r9UmyDNgKWDdBLEmSJKm3FuqsL9sneWi7vBnwa8C3gHOAsbOwHAmc3S6fAxzezuTyKLqDRi9u02PuSPK0Nv/8iKE+Y7EOBT7f5rGfBxyQZOt2EOkBrU2SJEnqrWULdDs7Aqe0M7dsBJxZVf+W5ELgzCTPB74DHAZQVVckORP4JnAP8KKqurfFeiFwMrAZcG5bAE4ETkuymm4k/fAWa12SNwKXtO3eUFXr5nVvJUmSpFlakEK9qi4Dnjii/RZg/3H6HAccN6J9FfCA+e1VdRet0B+x7iTgpOllLUmSJC0ef5lUkiRJ6iELdUmSJKmHLNQlSZKkHrJQlyRJknrIQl2SJEnqIQt1SZIkqYcs1CVJkqQeslCXJEmSeshCXZIkSeohC3VJkiSphyzUJUmSpB6yUJckSZJ6yEJdkiRJ6iELdUmSJKmHLNQlSZKkHrJQlyRJknrIQl2SJEnqIQt1SZIkqYcs1CVJkqQeslCXJEmSeshCXZIkSeohC3VJkiSphyzUJUmSpB6yUJckSZJ6yEJdkiRJ6iELdUmSJKmHLNQlSZKkHrJQlyRJknrIQl2SJEnqIQt1SZIkqYcs1CVJkqQeslCXJEmSeshCXZIkSeohC3VJkiSphyzUJUmSpB6yUJckSZJ6yEJdkiRJ6iELdUmSJKmHLNQlSZKkHrJQlyRJknrIQl2SJEnqIQt1SZIkqYcs1CVJkqQeslCXJEmSeshCXZIkSeohC3VJkiSphyzUJUmSpB5akEI9ya5JvpDkyiRXJHlJaz82yXeTXNqWZw30eVWS1UmuSnLgQPuTk1ze1r0jSVr7pkk+3NovSrLbQJ8jk1zdliMXYp8lSZKk2Vi2QLdzD/Dyqvpqki2AryQ5v617e1W9ZXDjJI8DDgf2BHYCPpvkMVV1L/Bu4Gjgy8CngYOAc4HnA7dW1e5JDgfeDDwnyTbAMcByoNptn1NVt87zPkuSJEkztiAj6lV1Y1V9tV2+A7gS2HmCLgcDZ1TV3VV1DbAa2CfJjsCWVXVhVRVwKnDIQJ9T2uWzgP3baPuBwPlVta4V5+fTFfeSJElSby34HPU2JeWJwEWt6cVJLktyUpKtW9vOwPUD3da0tp3b5eH29fpU1T3A7cC2E8QazuvoJKuSrFq7du3Md1CSJEmaAwtaqCfZHPgo8BdV9QO6aSyPBvYGbgTeOrbpiO41QftM+9zfUHVCVS2vquXbb7/9RLshSZIkzbsFK9STbEJXpH+wqj4GUFU3VdW9VXUf8F5gn7b5GmDXge67ADe09l1GtK/XJ8kyYCtg3QSxJEmSpN5aqLO+BDgRuLKq3jbQvuPAZs8GvtEunwMc3s7k8ihgD+DiqroRuCPJ01rMI4CzB/qMndHlUODzbR77ecABSbZuU2sOaG2SJElSby3UWV+eDvwBcHmSS1vbq4HnJtmbbirKtcAfA1TVFUnOBL5Jd8aYF7UzvgC8EDgZ2IzubC/ntvYTgdOSrKYbST+8xVqX5I3AJW27N1TVunnZS0mSJGmOLEihXlVfYvRc8U9P0Oc44LgR7auAvUa03wUcNk6sk4CTppqvJEmStNj8ZVJJkiSphyzUJUmSpB6yUJckSZJ6yEJdkiRJ6iELdUmSJKmHFur0jNKStXLfFbOOseKClXOQiSRJ2pA4oi5JkiT1kIW6JEmS1EMW6pIkSVIPWahLkiRJPWShLkmSJPWQhbokSZLUQxbqkiRJUg9ZqEuSJEk9ZKEuSZIk9ZCFuiRJktRDFuqSJElSD1moS5IkST1koS5JkiT1kIW6JEmS1EMW6pIkSVIPWahLkiRJPWShLkmSJPWQhbokSZLUQxbqkiRJUg9ZqEuSJEk9tGyxE9CGaeW+K2YdY8UFK+cgE0mSpH5yRF2SJEnqIQt1SZIkqYcs1CVJkqQeslCXJEmSesiDSTWhuTjoEzzwU5IkabocUZckSZJ6yEJdkiRJ6iGnvuhniudnlyRJPyscUZckSZJ6yEJdkiRJ6iELdUmSJKmHLNQlSZKkHrJQlyRJknrIQl2SJEnqoSkX6kkOG6f90LlLR5IkSRJMb0T9xHHaT5iLRCRJkiTdb9IfPEry8+3iRkkeBWRg9c8Dd81HYpIkSdKGbCq/TLoaKLoC/X+G1n0POHaOc5IkSZI2eJMW6lW1EUCSlVU1+99nlyRJkjSpKc9Rt0iXJEmSFs50zvryqCSnJ/lmku8MLlPou2uSLyS5MskVSV7S2rdJcn6Sq9vfrQf6vCrJ6iRXJTlwoP3JSS5v696RJK190yQfbu0XJdltoM+R7TauTnLkVPdZkiRJWixTmaM+5nS6OeovB340zdu5B3h5VX01yRbAV5KcDxwFfK6qjk/ySuCVwCuSPA44HNgT2An4bJLHVNW9wLuBo4EvA58GDgLOBZ4P3FpVuyc5HHgz8Jwk2wDHAMvp5tp/Jck5VXXrNPdBkiRJWjDTKdT3BJ5eVfdN90aq6kbgxnb5jiRXAjsDBwP7tc1OAb4IvKK1n1FVdwPXJFkN7JPkWmDLqroQIMmpwCF0hfrB3H9g61nAv7TR9gOB86tqXetzPl1x/6Hp7ockSZK0UKZzHvULgCfO9gbblJQnAhcBO7QifqyYf1jbbGfg+oFua1rbzu3ycPt6farqHuB2YNsJYg3ndXSSVUlWrV27dhZ7KEmSJM3edEbUrwXOS/IxutMy/lRVvW4qAZJsDnwU+Iuq+kGbXj5y0xFtNUH7TPvc31B1Au3Hm5YvX/6A9ZIkSdJCms6I+kOATwKbALsOLZNKsgldkf7BqvpYa74pyY5t/Y7Aza19zVDcXYAbWvsuI9rX65NkGbAVsG6CWJIkSVJvTXlEvar+cKY30uaKnwhcWVVvG1h1DnAkcHz7e/ZA++lJ3kZ3MOkewMVVdW+SO5I8jW7qzBHAPw/FuhA4FPh8VVWS84C/GzijzAHAq2a6L5IkSdJCmHKhnuTnx1tXVd+epPvTgT8ALk9yaWt7NV2BfmaS5wPfAQ5r8a5IcibwTbozxryonfEF4IXAycBmdAeRntvaTwROaweerqM7awxVtS7JG4FL2nZvGDuwVJIkSeqr6cxRX80D53yPzeXeeKKOVfUlRs8VB9h/nD7HAceNaF8F7DWi/S5aoT9i3UnASRPlKEmSJPXJdKa+rDefPcnD6c5P/h9znZQkSZK0oZvOwaTrqarvAX8BvGnOspEkSZIETG/qyyi/ADx4LhLR7K3cd8WsY6y4YOUcZCJJkqTZms7BpP/B+ucffzDdr5W+Ya6TkiRJkjZ00xlRf9/Q9R8CX6+qq+cwH0mSJElM72DSU+YzEUmSJEn3m/LBpEk2SfL6JN9Oclf7+/okD5rPBCVJkqQN0XSmvvw9sA/wJ8B1wCOBvwG2BF4696lJkiRJG67pFOqHAU+oqlva9auSfBX4OhbqkiRJ0pyaznnUx/tl0fHaJUmSJM3QdAr1jwCfTHJgkl9MchDwidYuSZIkaQ5NZ+rLXwOvBd4J7AR8F/gQ8LfzkJckSZK0QZt0RD3J05O8uap+UlWvq6rdq+rBVbUHsCnwpPlPU5IkSdqwTGXqy6uBC8ZZ9wXgNXOXjiRJkiSYWqG+N/CZcdZ9FnjynGUjSZIkCZjaHPUtgQcBPx6xbhNgiznNSNKMrdx3xZzEWXHByjmJI0mSZm4qI+rfAg4YZ90Bbb0kSZKkOTSVEfW3A+9JsjHwiaq6L8lGwCF0Z4B52TzmJ0mSJG2QJi3Uq+r0JA8HTgE2TfJ9YDvgLuCYqvrQPOcoSZIkbXCmdB71qnpbkvcBvwRsC9wCXFhVP5jP5CRJkqQN1ZR/8KgV5efNYy6SJEmSmqkcTCpJkiRpgVmoS5IkST1koS5JkiT1kIW6JEmS1EMW6pIkSVIPWahLkiRJPWShLkmSJPWQhbokSZLUQxbqkiRJUg9ZqEuSJEk9ZKEuSZIk9ZCFuiRJktRDFuqSJElSD1moS5IkST1koS5JkiT1kIW6JEmS1EMW6pIkSVIPWahLkiRJPWShLkmSJPWQhbokSZLUQxbqkiRJUg9ZqEuSJEk9ZKEuSZIk9ZCFuiRJktRDFuqSJElSD1moS5IkST20IIV6kpOS3JzkGwNtxyb5bpJL2/KsgXWvSrI6yVVJDhxof3KSy9u6dyRJa980yYdb+0VJdhvoc2SSq9ty5ELsryRJkjRbCzWifjJw0Ij2t1fV3m35NECSxwGHA3u2Pu9KsnHb/t3A0cAebRmL+Xzg1qraHXg78OYWaxvgGOCpwD7AMUm2nvvdkyRJkubWghTqVXUBsG6Kmx8MnFFVd1fVNcBqYJ8kOwJbVtWFVVXAqcAhA31OaZfPAvZvo+0HAudX1bqquhU4n9EfGCRJkqReWew56i9OclmbGjM20r0zcP3ANmta287t8nD7en2q6h7gdmDbCWI9QJKjk6xKsmrt2rWz2ytJkiRplhazUH838Ghgb+BG4K2tPSO2rQnaZ9pn/caqE6pqeVUt33777SdIW5IkSZp/i1aoV9VNVXVvVd0HvJduDjl0o967Dmy6C3BDa99lRPt6fZIsA7aim2ozXixJkiSp1xatUG9zzsc8Gxg7I8w5wOHtTC6Pojto9OKquhG4I8nT2vzzI4CzB/qMndHlUODzbR77ecABSbZuU2sOaG2SJElSry1biBtJ8iFgP2C7JGvozsSyX5K96aaiXAv8MUBVXZHkTOCbwD3Ai6rq3hbqhXRnkNkMOLctACcCpyVZTTeSfniLtS7JG4FL2nZvqKqpHtQqSZIkLZoFKdSr6rkjmk+cYPvjgONGtK8C9hrRfhdw2DixTgJOmnKykiRJUg8sSKGu9a3cd8WsY6y4YOUcZCJJkqS+WuzTM0qSJEkawUJdkiRJ6iELdUmSJKmHLNQlSZKkHrJQlyRJknrIQl2SJEnqIQt1SZIkqYcs1CVJkqQeslCXJEmSeshCXZIkSeohC3VJkiSphyzUJUmSpB6yUJckSZJ6yEJdkiRJ6iELdUmSJKmHLNQlSZKkHrJQlyRJknrIQl2SJEnqIQt1SZIkqYcs1CVJkqQeslCXJEmSeshCXZIkSeohC3VJkiSphyzUJUmSpB6yUJckSZJ6yEJdkiRJ6iELdUmSJKmHLNQlSZKkHrJQlyRJknpo2WInIKn/Vu67YtYxVlywcg4ykSRpw+GIuiRJktRDFuqSJElSD1moS5IkST1koS5JkiT1kIW6JEmS1EMW6pIkSVIPWahLkiRJPWShLkmSJPWQhbokSZLUQxbqkiRJUg9ZqEuSJEk9ZKEuSZIk9ZCFuiRJktRDFuqSJElSD1moS5IkST1koS5JkiT10IIU6klOSnJzkm8MtG2T5PwkV7e/Ww+se1WS1UmuSnLgQPuTk1ze1r0jSVr7pkk+3NovSrLbQJ8j221cneTIhdhfSZIkabYWakT9ZOCgobZXAp+rqj2Az7XrJHkccDiwZ+vzriQbtz7vBo4G9mjLWMznA7dW1e7A24E3t1jbAMcATwX2AY4Z/EAgSZIk9dWCFOpVdQGwbqj5YOCUdvkU4JCB9jOq6u6qugZYDeyTZEdgy6q6sKoKOHWoz1iss4D922j7gcD5VbWuqm4FzueBHxgkSZKk3lm2iLe9Q1XdCFBVNyZ5WGvfGfjywHZrWtv/tsvD7WN9rm+x7klyO7DtYPuIPutJcjTdaD2PeMQjZr5XkrTIVu67YtYxVlywcg4ykSTNRh8PJs2ItpqgfaZ91m+sOqGqllfV8u23335KiUqSJEnzZTEL9ZvadBba35tb+xpg14HtdgFuaO27jGhfr0+SZcBWdFNtxoslSZIk9dpiFurnAGNnYTkSOHug/fB2JpdH0R00enGbJnNHkqe1+edHDPUZi3Uo8Pk2j/084IAkW7eDSA9obZIkSVKvLcgc9SQfAvYDtkuyhu5MLMcDZyZ5PvAd4DCAqroiyZnAN4F7gBdV1b0t1AvpziCzGXBuWwBOBE5LsppuJP3wFmtdkjcCl7Tt3lBVwwe1SpIkSb2zIIV6VT13nFX7j7P9ccBxI9pXAXuNaL+LVuiPWHcScNKUk5UkSZJ6YDHP+iJpA+fZSSRJGl8fz/oiSZIkbfAs1CVJkqQeslCXJEmSeshCXZIkSeohC3VJkiSphyzUJUmSpB6yUJckSZJ6yEJdkiRJ6iELdUmSJKmHLNQlSZKkHrJQlyRJknrIQl2SJEnqoWWLnYAkbahW7rtiTuKsuGDlnMSRJPWLI+qSJElSD1moS5IkST3k1BdJP1PmYjqJU0kkSX3giLokSZLUQxbqkiRJUg9ZqEuSJEk9ZKEuSZIk9ZAHk0rSFHiQqiRpoTmiLkmSJPWQhbokSZLUQxbqkiRJUg9ZqEuSJEk9ZKEuSZIk9ZCFuiRJktRDFuqSJElSD1moS5IkST3kDx5Nwh85kSRJ0mJwRF2SJEnqIUfUJUlT4jeMkrSwHFGXJEmSeshCXZIkSeohC3VJkiSphyzUJUmSpB6yUJckSZJ6yEJdkiRJ6iELdUmSJKmHLNQlSZKkHrJQlyRJknrIQl2SJEnqIQt1SZIkqYcs1CVJkqQeslCXJEmSeshCXZIkSeqhRS/Uk1yb5PIklyZZ1dq2SXJ+kqvb360Htn9VktVJrkpy4ED7k1uc1UnekSStfdMkH27tFyXZbcF3UpIkSZqmRS/Um1+pqr2ranm7/krgc1W1B/C5dp0kjwMOB/YEDgLelWTj1ufdwNHAHm05qLU/H7i1qnYH3g68eQH2R5IkSZqVvhTqww4GTmmXTwEOGWg/o6rurqprgNXAPkl2BLasqgurqoBTh/qMxToL2H9stF2SJEnqqz4U6gX8e5KvJDm6te1QVTcCtL8Pa+07A9cP9F3T2nZul4fb1+tTVfcAtwPbDieR5Ogkq5KsWrt27ZzsmCRJkjRTyxY7AeDpVXVDkocB5yf51gTbjhoJrwnaJ+qzfkPVCcAJAMuXL3/AekmSJGkhLfqIelXd0P7eDHwc2Ae4qU1nof29uW2+Bth1oPsuwA2tfZcR7ev1SbIM2ApYNx/7IkmSJM2VRS3UkzwkyRZjl4EDgG8A5wBHts2OBM5ul88BDm9ncnkU3UGjF7fpMXckeVqbf37EUJ+xWIcCn2/z2CVJkqTeWuypLzsAH2/Hdi4DTq+qzyS5BDgzyfOB7wCHAVTVFUnOBL4J3AO8qKrubbFeCJwMbAac2xaAE4HTkqymG0k/fCF2TJI0uZX7rph1jBUXrJyDTCSpfxa1UK+qbwNPGNF+C7D/OH2OA44b0b4K2GtE+120Ql+SJM2cH6ykhbXoc9QlSZIkPZCFuiRJktRDFuqSJElSD1moS5IkST1koS5JkiT1kIW6JEmS1EOLfR51SZLmnKcRlPSzwEJdkqSfQX5YkZY+p75IkiRJPWShLkmSJPWQU18kSZqCuZhKAk4nkTR1jqhLkiRJPeSIuiRJi8iDPiWNxxF1SZIkqYcs1CVJkqQecuqLJElaVE7/kUZzRF2SJEnqIQt1SZIkqYcs1CVJkqQeslCXJEmSesiDSSVJkrSo/OXf0RxRlyRJknrIQl2SJEnqIQt1SZIkqYecoy5Jkn7mOOdZPwscUZckSZJ6yBF1SZIkTdlcfFvhNxVTY6EuSZI0RRapWkhOfZEkSZJ6yBF1SZKkRTYfI/WO/i/9+8ARdUmSJKmHLNQlSZKkHrJQlyRJknrIQl2SJEnqIQt1SZIkqYcs1CVJkqQeslCXJEmSeshCXZIkSeohC3VJkiSphyzUJUmSpB6yUJckSZJ6yEJdkiRJ6iELdUmSJKmHLNQlSZKkHrJQlyRJknrIQl2SJEnqIQt1SZIkqYc2mEI9yUFJrkqyOskrFzsfSZIkaSIbRKGeZGPgncAzgccBz03yuMXNSpIkSRrfBlGoA/sAq6vq21X1E+AM4OBFzkmSJEka17LFTmCB7AxcP3B9DfDUwQ2SHA0c3a7emeSqKcbeDvj+hFskUww1jbjzEXOpxTXXpRXXXJdWXHNdWnHNdWnFNdelFXf+c33keJtsKIX6qHu41rtSdQJwwrQDJ6uqavlME1vIuEsp1/mKa65LK665Lq245rq04prr0oprrksr7lzF3FCmvqwBdh24vgtwwyLlIkmSJE1qQynULwH2SPKoJA8CDgfOWeScJEmSpHFtEFNfquqeJC8GzgM2Bk6qqivmKPy0p8ssYtyllOt8xTXXpRXXXJdWXHNdWnHNdWnFNdelFXdOYqaqJt9KkiRJ0oLaUKa+SJIkSUuKhbokSZLUQxbqkiRJUg9ZqEuSJEk9tEGc9WWuJdmB7tdOC7ihqm5a5JTGNV+5zkfcpZTrfMU1V0nzJcljgYMZeN0C51TVlX2La65LK665zmNcz/oydUn2Bv4V2Ar4bmveBbgN+NOq+uos489Z4TNfuc5H3KWU63zFNdefxl5ab6Ab+D+RpZTrfMVdKrkmeQXwXOAMuh8BhO51ezhwRlUd35e45rq04prr/MUFoKpcprgAlwJPHdH+NODrs4i7N/Bl4Ergs235Vmt7Us9ynfO4SylX74N5zfUVLfYrgd9vyyvH2jaEuOa6tOIusVz/G9hkRPuDgKtnkeucxzXXpRXXXOcvblVZqE/zgRj3zgZWzyLupcx9MTVfuc553KWUq/fBvOa6pN5AN/R/IkspV++Dgm7w55Ej2h8JXDWLXOc8rrkurbjmOn9xq8o56tN0bpJPAacC17e2XYEjgM/MIu5Dquqi4caq+nKSh8ww5nzlOh9xl1Ku8xXXXOE+YCfguqH2Hdu6DSGuuS6tuEsp178APpfkau5/3T4C2B148Qxjzlfc+Yg5X3HnI+ZSizsfMecr7nzEnM+4zlGfriTP5P55g6Gbi3ROVX16FjHfATya0YXPNVU1owd5PnKdr7hLKdf5iruh55rkIOBfgJFvdFU1ow8BSymuuS6tuEsp1xZ3I2Af1n/dXlJV984k3nzGNdelFddc5zGuhXo/zFeRJi0lS+4NdAP/J7KUcp2vuEspV0lLj4X6HElydFWdsNh5TMV85TofcZdSrvMV11wlzack/1ZVv7kU4prr0oprrrOP6w8ezZ3MS9Dk6PkIOw8x5yvuUsp1vuJu8Lkm+bcNPa65Lq24SylX4I/mIeZ8xTXXpRXXXGcZ1xH1aZqvc+ZOcHt/XFXvmWHfx9LleVFV3TnQftBM5zi2/vsAVVWXJHkccBDwrbmcppPk1Ko6Yq7itZjPoPsq+RtV9e+ziPNU4Mqq+kGSzehOm/Yk4JvA31XV7TOI+efAx6vq+kk3nl7cB9Gdx/WGqvpskt8D/i/dqUBPqKr/nWHcRwPPpjuW4h66ubQfmsm+T/H2dqyqGzfkuOa6tOIupVw1t5I8rKpuXuw8pirJtlV1y2LnodEcUZ+GdkL7M+hGDS8GLmmXP5TklfN0sz+ZSadW+J0N/BnwjSQHD6z+u5kmk+QY4B3Au5O8ie6Ap82BVyZ5zQxjnjO0fBL4nbHrs8j14oHLf9Ry3QI4ZpaP10nAj9rlf6L74Z83t7b3zzDmG4GLkvxHkj9Nsv0s8hv0fuA3gJckOQ04DLgIeArw3pkEbM+tfwV+rsXZjK5gvzDJfrNP+YHmqzBZSnHnoZB82HzEnY+YSbadj7hjlsJ9MNu4SbZKcnySbyW5pS1XtraHznGaY7d57gz7bZnkTUlOa4MLg+veNYt8Hp7k3UnemWTbJMcmuTzJmUl2nGHMbYaWbYGLk2ydZJtZ5HrQwOWtkpyY5LIkp6f7ccSZxj0+yXbt8vIk36b733NdkhUzjPnVJK9tAzhzpuX3hSQfSLJrkvOT3J7kkiRPnGHMzZO8IckVLdbaJF9OctQsc52/19dszu24oS3M4wntJ7jN78yw3+XA5u3ybsAq4CXt+tdmkc/lwMbAg4EfAFu29s2Ay2YY86vAB4D9gBXt743t8opZ5Pq1gcuXANu3yw8BLp9F3CsHcx9ad+lMc6X74HwAcCKwlu5Uh0cCW8wi18va32XATcDG7Xpm8XhdPhDnwcAX2+VHzPK5tRVwPN35aG9py5Wt7aEzjTvJbZ47i75bAm8CTgN+b2jdu2YY8+HAu4F3AtsCx7b7+0xgx1nkus3Qsi1wLbA1sM0MYx409NidCFwGnA7sMItcjwe2a5eXA98GVtOdqnDFLOJ+FXgt8Og5fP4sB77Q3r92Bc4Hbm/vN0+cRdzNgTcAV7R4a+l+AO+oWcQ8j+6HlB4+9Hx7BXD+LOI+aZzlycCNM4z50fY8OAQ4p13fdOxxnEWun6EbvHple66+or1v/Rlw9gxj3gdcM7T8b/v77dk8Xwcuvw/4W7pzcr8U+MQs4l4+cPkLwFPa5ccAq2YY8xrgLcB36AYxXwrsNNMcB+JeDDyT7hc/rwcObe37AxfOMObZwFF0vxr6MuBvgD2AU+i+EZ9prvPy+qoqC/VpPhDzdaL8y8ZZLgfunmHMbw5d37y9Sb2NGRaTLc7XRl1u12cUl65AfSndP7m9W9uM3+AG4n6drhDZdvgNaDj3acb9CPCH7fL7geXt8mPozsowk5jDBf8mwG8DHwLWziLXb9B9kNwauINWlNGNhl85w5iXc/8/za2Brwze3ixyXTKFRIs758UE81BItLhzXkywhAqJ1n/OiwnmoZBo/ee8mGCC/1ETrZtC3HuBz7fHanj58QxjXjp0/TXAf9K9l8+mUP/awOXvTHSb04j5l+11+/jB59psnlctxuDra/j+mFGure+3gGXt8peH1s1oAGso118G3gV8rz0Hjp6nx+trM4z59aHrl7S/G9FN4Z1prvPy+qqyUJ/uA3EQ3cjOucAJbflMaztoFnFvAvZu/+QGl93o5hbPJObnaUXvQNsyunO13zuLXC8CHtwubzTQvtVs3kBbjF3oiuB/GX5RzjDetXSjcde0vw9v7ZvP8o1uK+Bk4H/a/fG/Lf5K4AkzjPm1CdZtNotcX9pyuw74c+BzdFNeLgeOmWHMl9AVkSe0N/2xDy3bAxfMItclU0i0uJcOXZ91MTHJP6bZPGfnvJhgCRUSI/Kdk2JiksfrazOJ2frOeTEB/Dvw1wx82wHsQPdh8LOzyPUbwB7jrLt+hjGvZOD/S2s7ku4bhuvm4n4F/nYOn1tj/7veRje9ci4GmtbQfUh7eXsPz8C6GX0b2vr+WXsu/CrdN3b/COwLvB44bYYxH/B+R/fN+0HA+2eR64V03zIfRvc/7JDWvoKZj/7/F/CMdvm3gPMG1s3m/8y8vL6qLNRn8mBsBDwN+H/Aoe3yxrOMeeLYE2fEutNnGHMXBkYmh9Y9fRa5bjpO+3YMFAGzvD9+g1l8BTWF+A8GHjUHcbYAnkA3Mjvjr/pbrMfM4/7uRBs5BB7anrf7zDLmni3OY+cwzyVTSLS+c15MME+FROs/p8UES6iQaHHnvJhgHgqJ1n/Oiwm6b7/eTPdB6FZgXXsOv5kZTn9qcQ8FfmGcdYfMMObfA782ov0gZjHNlG460eYj2ncHzppp3IE4v0U3Rel7cxDrmKFlbOrmw4FTZxl7P+DDdFMuLwc+DRzNiKm9U4x3xmz3d5y4T6D7pvVc4LF0x4Td1t5j/+8sYl7c4nxp7LlLN9D057PIdV5eX1UW6i4uLj1Zht7o1g290W09i7hzXki0vnNeTMx3IdFizUkxsUiFxLJZxJzzYmI+CokW9/8MFROPae2zLSYeC/za8HOMWXwjPBB3/7mMO0HMZ/Y5V7rjtfbq6/26CI/XbHP9xXnI9Rfn6XWwD/dP1duTbhDjWbOJWWWh7uLisgQW2vSaDTnuXMYcKiZ6netSjdu3XOmmvl0FfIJuWuDBA+tmM+97zuPSfasyH7nOedx5vF/n6z5YSo/Xn9MN3Mz14zWnMVvfY+gGQFbRnWTgc8DrgAuA18w0bpWFuouLyxJYmINjFpZ6XHNdWnH7livzeyawOY1rrksrrrn+NO6cnhFvbFmGJPVAksvGW0U3V/1nPq65Lq24SylXumOp7gSoqmvbbx6cleSRzO4XhecjrrkurbjmCvdU1b3Aj5L8T1X9oN3Gj5PcN4u4FuqSemMH4EC6A3EGhe7gug0hrrkurbhLKdfvJdm7qi4FqKo7k/wm3Q+4PX6mic5TXHNdWnHNFX6S5MFV9SO6E0wA3Q8h0Z0ed8Ys1CX1xb/RfSV56fCKJF/cQOKa69KKu5RyPQK4Z7Chqu4BjkjynhnGnK+45rq04por7FtVd7d4g4X5JnRnA5uxtDk0kiRJknpko8VOQJIkSdIDWahLkiRJPWShLkkbgCTPS/Lvi53HZJLcmeTnFzsPgCT7JVmz2HlI2nBZqEvSHEhybZIfJ7kjyW1J/ivJnyTpxftsVX2wqg5Y7DwmU1WbV9W3FzsPSeqDXvwDkaSfEb9VVVsAjwSOB14BnLi4KS0NSWZ1FrLF7j/f8SRtmCzUJWmOVdXtVXUO8BzgyCR7AST5jSRfS/KDJNcnOXasT5JPJfmzwThJLktySDpvT3Jzkttb+16jbjvJUUm+3Ub2r0nyvIH2Lw1sV23E/+oktyZ5Z5IMrP+jJFe2ON9M8qTWvlOSjyZZ2+L/+Xj3Q5KTk/xrkvNbnJXth0UGc3hRkquBqwfadm+Xt0pyarut65K8duwbirY//9nul3XAsSNuf9Mk/5jkhrb8Y5JN27r9kqxJ8ook3wPen2SzlvOtSb4JPGUo3rj7nuTYJGcl+UCSHwBHjXe/SNJUWahL0jypqouBNcAvt6Yf0p3H96HAbwAvTHJIW3cK8PtjfZM8AdgZ+DRwALAv8JjW9znALcO3l+QhwDuAZ7aR/f8LXDpBir9JV4w+Afhduh/ZIclhdIXvEcCWwG8Dt7Qi+ZPA11tu+wN/keTACW7jecAbge1aLh8cWn8I8FTgcSP6/jOwFfDzwIqWzx8OrH8q8G3gYcBxI/q/BngasHfbx32A1w6sfziwDd03IEcDxwCPbsuBDJz/eIr7fjBwFt1jNLyfkjRtFuqSNL9uoCsGqaovVtXlVXVfVV0GfIiuAAU4G9gjyR7t+h8AH66qnwD/C2wBPJbu9y+urKobx7m9+4C9kmxWVTdW1RUT5HZ8Vd1WVd8BvkBX0AK8APj7qrqkOqur6jq6on77qnpDVf2kzSV/L3D4BLfxqaq6oP0YyGuAX0qy68D6N1XVuqr68WCnJBvTfSB5VVXdUVXXAm9t98uYG6rqn6vqnuH+zfOAN1TVzVW1Fnj9UP/7gGOq6u7W/3eB41o+19N96BkzlX2/sKo+0R7fUflI0rRYqEvS/NoZWAeQ5KlJvtCmTtwO/AndSDOtkD0T+P02evtc4LS27vPAvwDvBG5KckKSLYdvqKp+SFfc/glwY5tO89gJcvvewOUfAZu3y7sC/zNi+0cCO7WDZW9LchvwarqfvB/P9QP53dnui51GrR+yHfAg4LqBtuvo7s/J+o7ZaUT/wdteW1V3DW1//dD2Y6ay75PlI0nTYqEuSfMkyVPoCsuxueGnA+cAu1bVVsC/AhnocgrdKPD+wI+q6sKxFVX1jqp6MrAn3RSYvxp1m1V1XlX9OrAj8C26Ud/pup5u+seo9muq6qEDyxZV9awJYv109DzJ5nTfLtwwmPI4/b5P903CIwfaHgF8dwp9x9wwov9Et33jYL5t+zFT2Xd/6lvSnLJQl6Q5lmTLJL8JnAF8oKoub6u2ANZV1V1J9gF+b7BfK8zvo5vicdpAvKe00fhN6Oa53wXcO+J2d0jy222u+t3AnaO2m4L3AX+Z5MntQNbd20GgFwM/aAdgbpZk4yR7tQ8k43lWkmckeRDdXPWL2rSSCVXVvXTfMByXZIt2+y8DPjCN/fgQ8Nok2yfZDnjdJP3PBF6VZOskuwCDB/fOZN8laVYs1CVp7nwyyR10o6+vAd7G+gc//inwhrbN6+gKw2GnAo9n/YJyS7qR8VvppmPcArxlRN+NgJfTjRqvo5v//qfT3Ymq+gjdwZmnA3cAnwC2acXzb9HNZb+GbtT7fXQHfI7ndLqDNNcBT6b7xmCq/ozug8m36b6VOB04aRr9/xZYBVwGXA58tbWN5/V09+81wL8z8GFphvsuSbOSKr+pk6S+SHIEcHRVPWOxc5mtJCcDa6rqtZNtK0l6IEfUJaknkjyYbgT8hMXORZK0+CzUJakH2vm41wI30U3xkCRt4Jz6IkmSJPWQI+qSJElSD1moS5IkST1koS5JkiT1kIW6JEmS1EMW6pIkSVIPWahLkiRJPfT/AxA9GXhauH/eAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.countplot(x=\"days_since_prior_order\", data=orders_df, color=color[3])\n",
    "plt.ylabel('Count', fontsize=12)\n",
    "plt.xlabel('Days since prior order', fontsize=12)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.title(\"Frequency distribution by days since prior order\", fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "39f16714",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5896974667922161"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# percentage of re-orders in prior set #\n",
    "order_products_prior_df.reordered.sum() / order_products_prior_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1bfe7a56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5985944127509629"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# percentage of re-orders in train set #\n",
    "order_products_train_df.reordered.sum() / order_products_train_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "58cc4805",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TalhaPC\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped_df = order_products_train_df.groupby(\"order_id\")[\"add_to_cart_order\"].aggregate(\"max\").reset_index()\n",
    "cnt_srs = grouped_df.add_to_cart_order.value_counts()\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.barplot(cnt_srs.index, cnt_srs.values, alpha=0.8)\n",
    "plt.ylabel('Number of Occurrences', fontsize=12)\n",
    "plt.xlabel('Number of products in the given order', fontsize=12)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0acbbf31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_name</th>\n",
       "      <th>aisle_id</th>\n",
       "      <th>department_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Chocolate Sandwich Cookies</td>\n",
       "      <td>61</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>All-Seasons Salt</td>\n",
       "      <td>104</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3e55cb69",
   "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>aisle_id</th>\n",
       "      <th>aisle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>prepared soups salads</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>specialty cheeses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>energy granola bars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>instant foods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>marinades meat preparation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   aisle_id                       aisle\n",
       "0         1       prepared soups salads\n",
       "1         2           specialty cheeses\n",
       "2         3         energy granola bars\n",
       "3         4               instant foods\n",
       "4         5  marinades meat preparation"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aisles_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ebb61e1a",
   "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>department_id</th>\n",
       "      <th>department</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>frozen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>bakery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>produce</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>alcohol</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   department_id department\n",
       "0              1     frozen\n",
       "1              2      other\n",
       "2              3     bakery\n",
       "3              4    produce\n",
       "4              5    alcohol"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "departments_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4a77e5b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>aisle_id</th>\n",
       "      <th>department_id</th>\n",
       "      <th>aisle</th>\n",
       "      <th>department</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>33120</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Organic Egg Whites</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "      <td>eggs</td>\n",
       "      <td>dairy eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>28985</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Michigan Organic Kale</td>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "      <td>fresh vegetables</td>\n",
       "      <td>produce</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>9327</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Garlic Powder</td>\n",
       "      <td>104</td>\n",
       "      <td>13</td>\n",
       "      <td>spices seasonings</td>\n",
       "      <td>pantry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>45918</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Coconut Butter</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "      <td>oils vinegars</td>\n",
       "      <td>pantry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>30035</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Natural Sweetener</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>baking ingredients</td>\n",
       "      <td>pantry</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  product_id  add_to_cart_order  reordered           product_name  \\\n",
       "0         2       33120                  1          1     Organic Egg Whites   \n",
       "1         2       28985                  2          1  Michigan Organic Kale   \n",
       "2         2        9327                  3          0          Garlic Powder   \n",
       "3         2       45918                  4          1         Coconut Butter   \n",
       "4         2       30035                  5          0      Natural Sweetener   \n",
       "\n",
       "   aisle_id  department_id               aisle  department  \n",
       "0        86             16                eggs  dairy eggs  \n",
       "1        83              4    fresh vegetables     produce  \n",
       "2       104             13   spices seasonings      pantry  \n",
       "3        19             13       oils vinegars      pantry  \n",
       "4        17             13  baking ingredients      pantry  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_products_prior_df = pd.merge(order_products_prior_df, products_df, on='product_id', how='left')\n",
    "order_products_prior_df = pd.merge(order_products_prior_df, aisles_df, on='aisle_id', how='left')\n",
    "order_products_prior_df = pd.merge(order_products_prior_df, departments_df, on='department_id', how='left')\n",
    "order_products_prior_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "aeaad7b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_name</th>\n",
       "      <th>frequency_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Banana</td>\n",
       "      <td>472565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bag of Organic Bananas</td>\n",
       "      <td>379450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Organic Strawberries</td>\n",
       "      <td>264683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Organic Baby Spinach</td>\n",
       "      <td>241921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Organic Hass Avocado</td>\n",
       "      <td>213584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Organic Avocado</td>\n",
       "      <td>176815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Large Lemon</td>\n",
       "      <td>152657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Strawberries</td>\n",
       "      <td>142951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Limes</td>\n",
       "      <td>140627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Organic Whole Milk</td>\n",
       "      <td>137905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Organic Raspberries</td>\n",
       "      <td>137057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Organic Yellow Onion</td>\n",
       "      <td>113426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Organic Garlic</td>\n",
       "      <td>109778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Organic Zucchini</td>\n",
       "      <td>104823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Organic Blueberries</td>\n",
       "      <td>100060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Cucumber Kirby</td>\n",
       "      <td>97315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Organic Fuji Apple</td>\n",
       "      <td>89632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Organic Lemon</td>\n",
       "      <td>87746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Apple Honeycrisp Organic</td>\n",
       "      <td>85020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Organic Grape Tomatoes</td>\n",
       "      <td>84255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                product_name  frequency_count\n",
       "0                     Banana           472565\n",
       "1     Bag of Organic Bananas           379450\n",
       "2       Organic Strawberries           264683\n",
       "3       Organic Baby Spinach           241921\n",
       "4       Organic Hass Avocado           213584\n",
       "5            Organic Avocado           176815\n",
       "6                Large Lemon           152657\n",
       "7               Strawberries           142951\n",
       "8                      Limes           140627\n",
       "9         Organic Whole Milk           137905\n",
       "10       Organic Raspberries           137057\n",
       "11      Organic Yellow Onion           113426\n",
       "12            Organic Garlic           109778\n",
       "13          Organic Zucchini           104823\n",
       "14       Organic Blueberries           100060\n",
       "15            Cucumber Kirby            97315\n",
       "16        Organic Fuji Apple            89632\n",
       "17             Organic Lemon            87746\n",
       "18  Apple Honeycrisp Organic            85020\n",
       "19    Organic Grape Tomatoes            84255"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnt_srs = order_products_prior_df['product_name'].value_counts().reset_index().head(20)\n",
    "cnt_srs.columns = ['product_name', 'frequency_count']\n",
    "cnt_srs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "00c63cae",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TalhaPC\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnt_srs = order_products_prior_df['aisle'].value_counts().head(20)\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.barplot(cnt_srs.index, cnt_srs.values, alpha=0.8, color=color[5])\n",
    "plt.ylabel('Number of Occurrences', fontsize=12)\n",
    "plt.xlabel('Aisle', fontsize=12)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84f1201e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}