{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2fef6e14-7298-423c-ac0f-aa636a65d6a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0010c602-e7e3-4c63-99a5-fb6f4ca8d180",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>order_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  order_amount\n",
       "0         1           900\n",
       "1         2          1100\n",
       "2         3          1200\n",
       "3         4          1250\n",
       "4         5          1300\n",
       "5         6          1400\n",
       "6         7          1500\n",
       "7         8          1600\n",
       "8         9          1800\n",
       "9        10         50000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders = pd.DataFrame({\n",
    "    'order_id': range(1, 11),\n",
    "    'order_amount': [\n",
    "        900, 1100, 1200, 1250, 1300,\n",
    "        1400, 1500, 1600, 1800, 50000\n",
    "    ]\n",
    "})\n",
    "\n",
    "orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f680a9a1-9b99-4d5a-bd43-7e932ee535a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Средний чек: 6 205.00 ₽\n",
      "Медианный чек: 1 350.00 ₽\n"
     ]
    }
   ],
   "source": [
    "mean_value = orders['order_amount'].mean()\n",
    "median_value = orders['order_amount'].median()\n",
    "\n",
    "def format_currency(value):\n",
    "    return f'{value:_.2f}'.replace('_', ' ')\n",
    "\n",
    "print(f'Средний чек: {format_currency(mean_value)} ₽')\n",
    "print(f'Медианный чек: {format_currency(median_value)} ₽')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d3e0ec53-674e-4bbb-b9cf-08772e2ec233",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "sns.set_theme(style='whitegrid')\n",
    "\n",
    "sns.barplot(\n",
    "    data=orders,\n",
    "    x='order_id',\n",
    "    y='order_amount',\n",
    "    color='#4C78A8'\n",
    ")\n",
    "\n",
    "plt.axhline(\n",
    "    mean_value,\n",
    "    color='#E45756',\n",
    "    linestyle='--',\n",
    "    label=f'Среднее: {format_currency(mean_value)} ₽'\n",
    ")\n",
    "\n",
    "plt.axhline(\n",
    "    median_value,\n",
    "    color='#54A24B',\n",
    "    linestyle='--',\n",
    "    label=f'Медиана: {format_currency(median_value)} ₽'\n",
    ")\n",
    "\n",
    "plt.title('Чеки заказов: среднее и медиана')\n",
    "plt.xlabel('Номер заказа')\n",
    "plt.ylabel('Сумма заказа, ₽')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dcc4926e-fae1-48f2-af3d-557dd94f2165",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 3))\n",
    "\n",
    "sns.boxplot(\n",
    "    x=orders['order_amount'],\n",
    "    color='#72B7B2'\n",
    ")\n",
    "\n",
    "plt.xscale('log')\n",
    "\n",
    "plt.title('Распределение суммы заказа')\n",
    "plt.xlabel('Сумма заказа, ₽ (логарифмическая шкала)')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bada080a-610b-410c-acee-04fb1186c8b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Средний чек без выброса: 1 338.89 ₽\n",
      "Медианный чек без выброса: 1 300.00 ₽\n"
     ]
    }
   ],
   "source": [
    "orders_without_outlier = orders[\n",
    "    orders['order_amount'] < 10000\n",
    "].copy()\n",
    "\n",
    "mean_without_outlier = (\n",
    "    orders_without_outlier['order_amount'].mean()\n",
    ")\n",
    "\n",
    "median_without_outlier = (\n",
    "    orders_without_outlier['order_amount'].median()\n",
    ")\n",
    "\n",
    "print(f'Средний чек без выброса: {format_currency(mean_without_outlier)} ₽')\n",
    "print(f'Медианный чек без выброса: {format_currency(median_without_outlier)} ₽')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "50595c9c-667a-40ef-b42d-4d7bb757d260",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       10.000000\n",
       "mean      6205.000000\n",
       "std      15390.120388\n",
       "min        900.000000\n",
       "25%       1212.500000\n",
       "50%       1350.000000\n",
       "75%       1575.000000\n",
       "90%       6620.000000\n",
       "95%      28310.000000\n",
       "max      50000.000000\n",
       "Name: order_amount, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders['order_amount'].describe(\n",
    "    percentiles=[0.25, 0.5, 0.75, 0.9, 0.95]\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:base] *",
   "language": "python",
   "name": "conda-base-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}