sem05_pandas_adv/sem05_midyukin_1110.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import scipy.stats as sts\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_theme(style=\"whitegrid\", palette=\"muted\")\n",
    "plt.rcParams['figure.figsize'] = (8, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Семинар №5: Решаем аналитические кейсы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://raw.githubusercontent.com/hse-econ-data-science/andan_2023/main/sem05_pandas_adv/data/sales_data.csv'\n",
    "df = pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Смотрим на данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(187205, 7)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Пропуски"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order ID            545\n",
       "Product             545\n",
       "Quantity Ordered    545\n",
       "Price Each          545\n",
       "Order Date          545\n",
       "Purchase Address    545\n",
       "User ID             545\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Формат колонок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 186660 entries, 0 to 186659\n",
      "Data columns (total 7 columns):\n",
      " #   Column            Non-Null Count   Dtype \n",
      "---  ------            --------------   ----- \n",
      " 0   Order ID          186660 non-null  object\n",
      " 1   Product           186660 non-null  object\n",
      " 2   Quantity Ordered  186660 non-null  object\n",
      " 3   Price Each        186660 non-null  object\n",
      " 4   Order Date        186660 non-null  object\n",
      " 5   Purchase Address  186660 non-null  object\n",
      " 6   User ID           186660 non-null  object\n",
      "dtypes: object(7)\n",
      "memory usage: 11.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "invalid literal for int() with base 10: 'Order ID'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-2d0277f4e586>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Order ID'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'int'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   5544\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5545\u001b[0m             \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5546\u001b[0;31m             \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5547\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5548\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    593\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    594\u001b[0m     ) -> \"BlockManager\":\n\u001b[0;32m--> 595\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    596\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m     def convert(\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m    404\u001b[0m                 \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    405\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 406\u001b[0;31m                 \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    407\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    593\u001b[0m             \u001b[0mvals1d\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    594\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 595\u001b[0;31m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvals1d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    596\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m                 \u001b[0;31m# e.g. astype_nansafe can fail on object-dtype of strings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m    970\u001b[0m         \u001b[0;31m# work around NumPy brokenness, #1987\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    971\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0missubdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minteger\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 972\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype_intsafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    973\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    974\u001b[0m         \u001b[0;31m# if we have a datetime/timedelta array of objects\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.astype_intsafe\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'Order ID'"
     ]
    }
   ],
   "source": [
    "df['Order ID'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Quantity Ordered</td>\n",
       "      <td>Price Each</td>\n",
       "      <td>Order Date</td>\n",
       "      <td>Purchase Address</td>\n",
       "      <td>User ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186155</th>\n",
       "      <td>Order ID</td>\n",
       "      <td>Product</td>\n",
       "      <td>Quantity Ordered</td>\n",
       "      <td>Price Each</td>\n",
       "      <td>Order Date</td>\n",
       "      <td>Purchase Address</td>\n",
       "      <td>User ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186565</th>\n",
       "      <td>Order ID</td>\n",
       "      <td>Product</td>\n",
       "      <td>Quantity Ordered</td>\n",
       "      <td>Price Each</td>\n",
       "      <td>Order Date</td>\n",
       "      <td>Purchase Address</td>\n",
       "      <td>User ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186617</th>\n",
       "      <td>Order ID</td>\n",
       "      <td>Product</td>\n",
       "      <td>Quantity Ordered</td>\n",
       "      <td>Price Each</td>\n",
       "      <td>Order Date</td>\n",
       "      <td>Purchase Address</td>\n",
       "      <td>User ID</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>710 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Order ID  Product  Quantity Ordered  Price Each  Order Date  \\\n",
       "157     Order ID  Product  Quantity Ordered  Price Each  Order Date   \n",
       "679     Order ID  Product  Quantity Ordered  Price Each  Order Date   \n",
       "814     Order ID  Product  Quantity Ordered  Price Each  Order Date   \n",
       "897     Order ID  Product  Quantity Ordered  Price Each  Order Date   \n",
       "1071    Order ID  Product  Quantity Ordered  Price Each  Order Date   \n",
       "...          ...      ...               ...         ...         ...   \n",
       "184639  Order ID  Product  Quantity Ordered  Price Each  Order Date   \n",
       "185261  Order ID  Product  Quantity Ordered  Price Each  Order Date   \n",
       "186155  Order ID  Product  Quantity Ordered  Price Each  Order Date   \n",
       "186565  Order ID  Product  Quantity Ordered  Price Each  Order Date   \n",
       "186617  Order ID  Product  Quantity Ordered  Price Each  Order Date   \n",
       "\n",
       "        Purchase Address  User ID  \n",
       "157     Purchase Address  User ID  \n",
       "679     Purchase Address  User ID  \n",
       "814     Purchase Address  User ID  \n",
       "897     Purchase Address  User ID  \n",
       "1071    Purchase Address  User ID  \n",
       "...                  ...      ...  \n",
       "184639  Purchase Address  User ID  \n",
       "185261  Purchase Address  User ID  \n",
       "186155  Purchase Address  User ID  \n",
       "186565  Purchase Address  User ID  \n",
       "186617  Purchase Address  User ID  \n",
       "\n",
       "[710 rows x 7 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[~df['Order ID'].str.isdigit()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['Order ID'].str.isdigit()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Order ID'] = df['Order ID'].astype('int')\n",
    "df['Quantity Ordered'] = df['Quantity Ordered'].astype('int')\n",
    "df['Price Each'] = df['Price Each'].astype('float')\n",
    "df['User ID'] = df['User ID'].astype('float').astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Order Date'] = pd.to_datetime(df['Order Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 185950 entries, 0 to 186659\n",
      "Data columns (total 7 columns):\n",
      " #   Column            Non-Null Count   Dtype         \n",
      "---  ------            --------------   -----         \n",
      " 0   Order ID          185950 non-null  int64         \n",
      " 1   Product           185950 non-null  object        \n",
      " 2   Quantity Ordered  185950 non-null  int64         \n",
      " 3   Price Each        185950 non-null  float64       \n",
      " 4   Order Date        185950 non-null  datetime64[ns]\n",
      " 5   Purchase Address  185950 non-null  object        \n",
      " 6   User ID           185950 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(1), int64(3), object(2)\n",
      "memory usage: 11.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотреть на распределения"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Quantity Ordered'].hist(bins=30, log=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USB-C Charging Cable          21903\n",
       "Lightning Charging Cable      21658\n",
       "AAA Batteries (4-pack)        20641\n",
       "AA Batteries (4-pack)         20577\n",
       "Wired Headphones              18882\n",
       "Apple Airpods Headphones      15549\n",
       "Bose SoundSport Headphones    13325\n",
       "27in FHD Monitor               7507\n",
       "iPhone                         6842\n",
       "27in 4K Gaming Monitor         6230\n",
       "34in Ultrawide Monitor         6181\n",
       "Google Phone                   5525\n",
       "Flatscreen TV                  4800\n",
       "Macbook Pro Laptop             4724\n",
       "ThinkPad Laptop                4128\n",
       "20in Monitor                   4101\n",
       "Vareebadd Phone                2065\n",
       "LG Washing Machine              666\n",
       "LG Dryer                        646\n",
       "Name: Product, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Product'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Кейс №1: Реклама и пиар\n",
    "\n",
    "Предположим, что перед нами стоит задача привлечения новых пользователей и удержание существующих. Для этого мы собираемся, во-первых, устраивать различные промоакции в периоды падения активности, чтобы стимулировать пользователей покупать товары, а во-вторых, запускать крупные рекламные кампании, наоборот, в период высокой активности пользователей, чтобы пользователи покупали товары именно в нашем интернет-магазине.\n",
    "\n",
    "**Как определить период, когда следует запускать разного рода акции?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Идея №1: В какой месяц у нас больше всего заказов?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Month'] = df['Order Date'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "bymonths = df.groupby('Month')['Order ID'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(bymonths.index, bymonths.values)\n",
    "\n",
    "plt.xticks(bymonths.index, size=9)\n",
    "plt.ylabel('Кол-во заказов, шт', fontsize=12)\n",
    "plt.xlabel('Месяц');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Идея №2: Посмотреть активность по часам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Hour'] = df['Order Date'].dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "byhours = df.groupby('Hour')['Order ID'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(byhours.index, byhours.values)\n",
    "\n",
    "plt.xticks(byhours.index, size=9)\n",
    "plt.ylabel('Кол-во заказов, шт', fontsize=12)\n",
    "plt.xlabel('Время суток');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Кейс №2: Доверительный интервал для среднего чека\n",
    "\n",
    "Предположим, что вы хотите понять, а сколько денег вы в среднем получаете с одного сделанного заказа. При этом вы хотите не только получить точечную оценку, посчитав среднее по выборке, но и построить для этого среднего доверительный интервал."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['GMV'] = df['Quantity Ordered'] * df['Price Each']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "byorders = df.groupby('Order ID')['GMV'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "193.300918362983"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byorders.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "byorders.hist(bins=30, log=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Среднее по выборке: 193.3\n",
      "Доверительный интервал [191.7, 194.9] ширины 3.167\n"
     ]
    }
   ],
   "source": [
    "mean_hat, std_hat, n = byorders.mean(), byorders.std(), byorders.shape[0]\n",
    "alpha = 0.05\n",
    "\n",
    "norm_gen = sts.norm(0, 1)\n",
    "left_cr = norm_gen.ppf(alpha/2)\n",
    "right_cr = norm_gen.ppf(1-alpha/2)\n",
    "\n",
    "left = mean_hat + left_cr * std_hat/np.sqrt(n)\n",
    "right = mean_hat + right_cr * std_hat/np.sqrt(n)\n",
    "\n",
    "delta = right - left\n",
    "print(f\"Среднее по выборке: {mean_hat:.4}\")\n",
    "print(f\"Доверительный интервал [{left:.4}, {right:.4}] ширины {delta:.4}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Что тут не так?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "byusers = df.groupby(['User ID', 'Order ID'])['GMV'].sum()\n",
    "byorders_c = byusers.groupby('User ID').mean() # средний чек на одного пользователя"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Среднее по выборке: 193.4\n",
      "Доверительный интервал [191.6, 195.3] ширины 3.685\n"
     ]
    }
   ],
   "source": [
    "mean_hat, std_hat, n = byorders_c.mean(), byorders_c.std(), byorders_c.shape[0]\n",
    "alpha = 0.05\n",
    "\n",
    "norm_gen = sts.norm(0, 1)\n",
    "left_cr = norm_gen.ppf(alpha/2)\n",
    "right_cr = norm_gen.ppf(1-alpha/2)\n",
    "\n",
    "left = mean_hat + left_cr * std_hat/np.sqrt(n)\n",
    "right = mean_hat + right_cr * std_hat/np.sqrt(n)\n",
    "\n",
    "delta = right - left\n",
    "print(f\"Среднее по выборке: {mean_hat:.4}\")\n",
    "print(f\"Доверительный интервал [{left:.4}, {right:.4}] ширины {delta:.4}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>User ID</th>\n",
       "      <th>Month</th>\n",
       "      <th>Hour</th>\n",
       "      <th>GMV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165079</td>\n",
       "      <td>AA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2019-03-24 19:17:00</td>\n",
       "      <td>953 Johnson St, Boston, MA 02215</td>\n",
       "      <td>14152</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>3.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>289147</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>2019-11-29 07:25:00</td>\n",
       "      <td>826 13th St, Los Angeles, CA 90001</td>\n",
       "      <td>49793</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>1700.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>293940</td>\n",
       "      <td>AA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2019-11-28 12:00:00</td>\n",
       "      <td>543 Main St, San Francisco, CA 94016</td>\n",
       "      <td>4748</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>3.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>288851</td>\n",
       "      <td>Lightning Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>2019-11-02 18:32:00</td>\n",
       "      <td>601 Maple St, Los Angeles, CA 90001</td>\n",
       "      <td>73924</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>14.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>239927</td>\n",
       "      <td>Apple Airpods Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>150.00</td>\n",
       "      <td>2019-08-09 20:35:00</td>\n",
       "      <td>88 Maple St, Los Angeles, CA 90001</td>\n",
       "      <td>23344</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>150.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186655</th>\n",
       "      <td>194862</td>\n",
       "      <td>Vareebadd Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>400.00</td>\n",
       "      <td>2019-05-19 09:01:00</td>\n",
       "      <td>466 Dogwood St, San Francisco, CA 94016</td>\n",
       "      <td>69908</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>400.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186656</th>\n",
       "      <td>206915</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>2019-05-07 09:16:00</td>\n",
       "      <td>464 Johnson St, New York City, NY 10001</td>\n",
       "      <td>40697</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>99.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186657</th>\n",
       "      <td>253450</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>2</td>\n",
       "      <td>11.95</td>\n",
       "      <td>2019-09-13 21:04:00</td>\n",
       "      <td>501 Chestnut St, New York City, NY 10001</td>\n",
       "      <td>28413</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>23.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186658</th>\n",
       "      <td>193390</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>2</td>\n",
       "      <td>2.99</td>\n",
       "      <td>2019-04-12 13:43:00</td>\n",
       "      <td>722 Lake St, New York City, NY 10001</td>\n",
       "      <td>46472</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>5.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186659</th>\n",
       "      <td>144382</td>\n",
       "      <td>27in 4K Gaming Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>389.99</td>\n",
       "      <td>2019-01-19 21:58:00</td>\n",
       "      <td>501 Park St, Seattle, WA 98101</td>\n",
       "      <td>49654</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>389.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>185950 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Order ID                     Product  Quantity Ordered  Price Each  \\\n",
       "0         165079       AA Batteries (4-pack)                 1        3.84   \n",
       "1         289147          Macbook Pro Laptop                 1     1700.00   \n",
       "2         293940       AA Batteries (4-pack)                 1        3.84   \n",
       "3         288851    Lightning Charging Cable                 1       14.95   \n",
       "4         239927    Apple Airpods Headphones                 1      150.00   \n",
       "...          ...                         ...               ...         ...   \n",
       "186655    194862             Vareebadd Phone                 1      400.00   \n",
       "186656    206915  Bose SoundSport Headphones                 1       99.99   \n",
       "186657    253450        USB-C Charging Cable                 2       11.95   \n",
       "186658    193390      AAA Batteries (4-pack)                 2        2.99   \n",
       "186659    144382      27in 4K Gaming Monitor                 1      389.99   \n",
       "\n",
       "                Order Date                          Purchase Address  User ID  \\\n",
       "0      2019-03-24 19:17:00          953 Johnson St, Boston, MA 02215    14152   \n",
       "1      2019-11-29 07:25:00        826 13th St, Los Angeles, CA 90001    49793   \n",
       "2      2019-11-28 12:00:00      543 Main St, San Francisco, CA 94016     4748   \n",
       "3      2019-11-02 18:32:00       601 Maple St, Los Angeles, CA 90001    73924   \n",
       "4      2019-08-09 20:35:00        88 Maple St, Los Angeles, CA 90001    23344   \n",
       "...                    ...                                       ...      ...   \n",
       "186655 2019-05-19 09:01:00   466 Dogwood St, San Francisco, CA 94016    69908   \n",
       "186656 2019-05-07 09:16:00   464 Johnson St, New York City, NY 10001    40697   \n",
       "186657 2019-09-13 21:04:00  501 Chestnut St, New York City, NY 10001    28413   \n",
       "186658 2019-04-12 13:43:00      722 Lake St, New York City, NY 10001    46472   \n",
       "186659 2019-01-19 21:58:00            501 Park St, Seattle, WA 98101    49654   \n",
       "\n",
       "        Month  Hour      GMV  \n",
       "0           3    19     3.84  \n",
       "1          11     7  1700.00  \n",
       "2          11    12     3.84  \n",
       "3          11    18    14.95  \n",
       "4           8    20   150.00  \n",
       "...       ...   ...      ...  \n",
       "186655      5     9   400.00  \n",
       "186656      5     9    99.99  \n",
       "186657      9    21    23.90  \n",
       "186658      4    13     5.98  \n",
       "186659      1    21   389.99  \n",
       "\n",
       "[185950 rows x 10 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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