Statistics1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ab5f3a99",
   "metadata": {},
   "source": [
    "<h1 align=center style=\"line-height:200%;font-family:vazir;color:#0099cc\">\n",
    "<font face=\"vazir\" color=\"#0099cc\" size=6>\n",
    "بررسی تاثیر تخفیف روی تعداد فروش\n",
    "</font>\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb104da2",
   "metadata": {},
   "source": [
    "<p dir=rtl style=\"direction: rtl;text-align: justify;line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "<span style=\"color:purple\"><b>مرحله اول:  </b></span>\n",
    "    لایبرری‌های لازم را\n",
    "    <code>import</code>\n",
    "    می‌کنیم.\n",
    "</font>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e6dd98eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as st\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b15be6e",
   "metadata": {},
   "source": [
    "<p dir=rtl style=\"direction: rtl;text-align: justify;line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "<span style=\"color:purple\"><b>مرحله دوم:  </b></span>\n",
    "    داده‌های لازم را به دو دسته تخفیف‌دار و غیر تخفیف‌دار تقسیم می‌کنیم.\n",
    "</font>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "475dda4b",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Order Priority</th>\n",
       "      <th>Row ID</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Shipping Cost</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>returned</th>\n",
       "      <th>Ship_Date_ID</th>\n",
       "      <th>Order_Date_ID</th>\n",
       "      <th>Geography_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA-2012-124891</td>\n",
       "      <td>RH-19495</td>\n",
       "      <td>Critical</td>\n",
       "      <td>32298</td>\n",
       "      <td>TEC-AC-10003033</td>\n",
       "      <td>2309.650</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>762.1850</td>\n",
       "      <td>933.570</td>\n",
       "      <td>Same Day</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20120731</td>\n",
       "      <td>20120731</td>\n",
       "      <td>NNUE-21043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA-2012-124891</td>\n",
       "      <td>RH-19495</td>\n",
       "      <td>Critical</td>\n",
       "      <td>32299</td>\n",
       "      <td>FUR-TA-10002903</td>\n",
       "      <td>1090.780</td>\n",
       "      <td>7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>-290.8750</td>\n",
       "      <td>348.180</td>\n",
       "      <td>Same Day</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20120731</td>\n",
       "      <td>20120731</td>\n",
       "      <td>NNUE-21043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA-2012-124891</td>\n",
       "      <td>RH-19495</td>\n",
       "      <td>Critical</td>\n",
       "      <td>32300</td>\n",
       "      <td>OFF-PA-10004621</td>\n",
       "      <td>19.440</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.3312</td>\n",
       "      <td>7.240</td>\n",
       "      <td>Same Day</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20120731</td>\n",
       "      <td>20120731</td>\n",
       "      <td>NNUE-21043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IN-2013-77878</td>\n",
       "      <td>JR-16210</td>\n",
       "      <td>Critical</td>\n",
       "      <td>26339</td>\n",
       "      <td>TEC-CO-10000660</td>\n",
       "      <td>344.682</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>34.4220</td>\n",
       "      <td>65.350</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20130207</td>\n",
       "      <td>20130205</td>\n",
       "      <td>WNAO-21044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IN-2013-77878</td>\n",
       "      <td>JR-16210</td>\n",
       "      <td>Critical</td>\n",
       "      <td>26340</td>\n",
       "      <td>OFF-ST-10001366</td>\n",
       "      <td>133.920</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-6.0300</td>\n",
       "      <td>41.640</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20130207</td>\n",
       "      <td>20130205</td>\n",
       "      <td>WNAO-21044</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49665</th>\n",
       "      <td>ZI-2011-4350</td>\n",
       "      <td>AS-285</td>\n",
       "      <td>Medium</td>\n",
       "      <td>46231</td>\n",
       "      <td>OFF-SAN-10001862</td>\n",
       "      <td>9.612</td>\n",
       "      <td>2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>-21.1680</td>\n",
       "      <td>0.020</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20110326</td>\n",
       "      <td>20110321</td>\n",
       "      <td>HHZA-46071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49666</th>\n",
       "      <td>MX-2014-169530</td>\n",
       "      <td>HG-15025</td>\n",
       "      <td>High</td>\n",
       "      <td>6039</td>\n",
       "      <td>OFF-PA-10002418</td>\n",
       "      <td>84.000</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.2000</td>\n",
       "      <td>0.019</td>\n",
       "      <td>First Class</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20140611</td>\n",
       "      <td>20140609</td>\n",
       "      <td>BSBS-46072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49667</th>\n",
       "      <td>IN-2014-72327</td>\n",
       "      <td>KH-16330</td>\n",
       "      <td>High</td>\n",
       "      <td>24105</td>\n",
       "      <td>OFF-PA-10000215</td>\n",
       "      <td>26.940</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8600</td>\n",
       "      <td>0.010</td>\n",
       "      <td>Same Day</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20140530</td>\n",
       "      <td>20140530</td>\n",
       "      <td>LUIC-46073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49668</th>\n",
       "      <td>IN-2014-57662</td>\n",
       "      <td>DB-13270</td>\n",
       "      <td>Medium</td>\n",
       "      <td>24175</td>\n",
       "      <td>OFF-BI-10002424</td>\n",
       "      <td>58.050</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>19.9500</td>\n",
       "      <td>0.010</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20140810</td>\n",
       "      <td>20140805</td>\n",
       "      <td>TQAO-46074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49669</th>\n",
       "      <td>MX-2012-134460</td>\n",
       "      <td>MC-18100</td>\n",
       "      <td>High</td>\n",
       "      <td>6147</td>\n",
       "      <td>OFF-PA-10004155</td>\n",
       "      <td>61.380</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20120526</td>\n",
       "      <td>20120522</td>\n",
       "      <td>TMNC-46075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49670 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Order ID Customer ID Order Priority  Row ID        Product ID  \\\n",
       "0      CA-2012-124891    RH-19495       Critical   32298   TEC-AC-10003033   \n",
       "1      CA-2012-124891    RH-19495       Critical   32299   FUR-TA-10002903   \n",
       "2      CA-2012-124891    RH-19495       Critical   32300   OFF-PA-10004621   \n",
       "3       IN-2013-77878    JR-16210       Critical   26339   TEC-CO-10000660   \n",
       "4       IN-2013-77878    JR-16210       Critical   26340   OFF-ST-10001366   \n",
       "...               ...         ...            ...     ...               ...   \n",
       "49665    ZI-2011-4350      AS-285         Medium   46231  OFF-SAN-10001862   \n",
       "49666  MX-2014-169530    HG-15025           High    6039   OFF-PA-10002418   \n",
       "49667   IN-2014-72327    KH-16330           High   24105   OFF-PA-10000215   \n",
       "49668   IN-2014-57662    DB-13270         Medium   24175   OFF-BI-10002424   \n",
       "49669  MX-2012-134460    MC-18100           High    6147   OFF-PA-10004155   \n",
       "\n",
       "          Sales  Quantity  Discount    Profit  Shipping Cost       Ship Mode  \\\n",
       "0      2309.650         7       0.0  762.1850        933.570        Same Day   \n",
       "1      1090.780         7       0.4 -290.8750        348.180        Same Day   \n",
       "2        19.440         3       0.0    9.3312          7.240        Same Day   \n",
       "3       344.682         2       0.1   34.4220         65.350    Second Class   \n",
       "4       133.920         5       0.1   -6.0300         41.640    Second Class   \n",
       "...         ...       ...       ...       ...            ...             ...   \n",
       "49665     9.612         2       0.7  -21.1680          0.020  Standard Class   \n",
       "49666    84.000         5       0.0    9.2000          0.019     First Class   \n",
       "49667    26.940         2       0.0    1.8600          0.010        Same Day   \n",
       "49668    58.050         5       0.1   19.9500          0.010  Standard Class   \n",
       "49669    61.380         3       0.0    1.8000          0.002    Second Class   \n",
       "\n",
       "       returned  Ship_Date_ID  Order_Date_ID Geography_ID  \n",
       "0           0.0      20120731       20120731   NNUE-21043  \n",
       "1           0.0      20120731       20120731   NNUE-21043  \n",
       "2           0.0      20120731       20120731   NNUE-21043  \n",
       "3           1.0      20130207       20130205   WNAO-21044  \n",
       "4           1.0      20130207       20130205   WNAO-21044  \n",
       "...         ...           ...            ...          ...  \n",
       "49665       0.0      20110326       20110321   HHZA-46071  \n",
       "49666       0.0      20140611       20140609   BSBS-46072  \n",
       "49667       0.0      20140530       20140530   LUIC-46073  \n",
       "49668       0.0      20140810       20140805   TQAO-46074  \n",
       "49669       0.0      20120526       20120522   TMNC-46075  \n",
       "\n",
       "[49670 rows x 15 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"./facts.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "669aa2c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = list(df[df.Discount == 0][\"Quantity\"])\n",
    "y = list(df[df.Discount != 0][\"Quantity\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b95a49f0",
   "metadata": {},
   "source": [
    "<p dir=rtl style=\"direction: rtl;text-align: justify;line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "<span style=\"color:purple\"><b>مرحله سوم:  </b></span>\n",
    "    نمودار مربوط به توزیع هر دسته را رسم می‌کنیم و سپس میانگین هر دسته را بررسی می‌کنیم.\n",
    "</font>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0e8d8945",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "OptionError",
     "evalue": "\"No such keys(s): 'mode.use_inf_as_null'\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOptionError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[4], line 6\u001b[0m\n\u001b[1;32m      4\u001b[0m ax[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mset_xlabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mQuantity\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      5\u001b[0m ax[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mset_ylabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFrequency\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 6\u001b[0m \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhistplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m14\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkde\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43max\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      8\u001b[0m ax[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mset_title(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHistogram of Quantity Where Discount != 0\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      9\u001b[0m ax[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mset_xlabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mQuantity\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/seaborn/distributions.py:1432\u001b[0m, in \u001b[0;36mhistplot\u001b[0;34m(data, x, y, hue, weights, stat, bins, binwidth, binrange, discrete, cumulative, common_bins, common_norm, multiple, element, fill, shrink, kde, kde_kws, line_kws, thresh, pthresh, pmax, cbar, cbar_ax, cbar_kws, palette, hue_order, hue_norm, color, log_scale, legend, ax, **kwargs)\u001b[0m\n\u001b[1;32m   1421\u001b[0m estimate_kws \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\n\u001b[1;32m   1422\u001b[0m     stat\u001b[38;5;241m=\u001b[39mstat,\n\u001b[1;32m   1423\u001b[0m     bins\u001b[38;5;241m=\u001b[39mbins,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1427\u001b[0m     cumulative\u001b[38;5;241m=\u001b[39mcumulative,\n\u001b[1;32m   1428\u001b[0m )\n\u001b[1;32m   1430\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m p\u001b[38;5;241m.\u001b[39munivariate:\n\u001b[0;32m-> 1432\u001b[0m     \u001b[43mp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot_univariate_histogram\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1433\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmultiple\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmultiple\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1434\u001b[0m \u001b[43m        \u001b[49m\u001b[43melement\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43melement\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1435\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfill\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfill\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1436\u001b[0m \u001b[43m        \u001b[49m\u001b[43mshrink\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshrink\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1437\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcommon_norm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcommon_norm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1438\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcommon_bins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcommon_bins\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1439\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkde\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkde\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1440\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkde_kws\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkde_kws\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1441\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1442\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlegend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlegend\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1443\u001b[0m \u001b[43m        \u001b[49m\u001b[43mestimate_kws\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimate_kws\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1444\u001b[0m \u001b[43m        \u001b[49m\u001b[43mline_kws\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mline_kws\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1445\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1446\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1448\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1450\u001b[0m     p\u001b[38;5;241m.\u001b[39mplot_bivariate_histogram(\n\u001b[1;32m   1451\u001b[0m         common_bins\u001b[38;5;241m=\u001b[39mcommon_bins,\n\u001b[1;32m   1452\u001b[0m         common_norm\u001b[38;5;241m=\u001b[39mcommon_norm,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1462\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m   1463\u001b[0m     )\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/seaborn/distributions.py:430\u001b[0m, in \u001b[0;36m_DistributionPlotter.plot_univariate_histogram\u001b[0;34m(self, multiple, element, fill, common_norm, common_bins, shrink, kde, kde_kws, color, legend, line_kws, estimate_kws, **plot_kws)\u001b[0m\n\u001b[1;32m    427\u001b[0m histograms \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m    429\u001b[0m \u001b[38;5;66;03m# Do pre-compute housekeeping related to multiple groups\u001b[39;00m\n\u001b[0;32m--> 430\u001b[0m all_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcomp_data\u001b[49m\u001b[38;5;241m.\u001b[39mdropna()\n\u001b[1;32m    431\u001b[0m all_weights \u001b[38;5;241m=\u001b[39m all_data\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mweights\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    433\u001b[0m multiple_histograms \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvariables) \u001b[38;5;241m-\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/seaborn/_oldcore.py:1119\u001b[0m, in \u001b[0;36mVectorPlotter.comp_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1117\u001b[0m grouped \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mplot_data[var]\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverters[var], sort\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m   1118\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m converter, orig \u001b[38;5;129;01min\u001b[39;00m grouped:\n\u001b[0;32m-> 1119\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m pd\u001b[38;5;241m.\u001b[39moption_context(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmode.use_inf_as_null\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m   1120\u001b[0m         orig \u001b[38;5;241m=\u001b[39m orig\u001b[38;5;241m.\u001b[39mdropna()\n\u001b[1;32m   1121\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m var \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvar_levels:\n\u001b[1;32m   1122\u001b[0m             \u001b[38;5;66;03m# TODO this should happen in some centralized location\u001b[39;00m\n\u001b[1;32m   1123\u001b[0m             \u001b[38;5;66;03m# it is similar to GH2419, but more complicated because\u001b[39;00m\n\u001b[1;32m   1124\u001b[0m             \u001b[38;5;66;03m# supporting `order` in categorical plots is tricky\u001b[39;00m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/_config/config.py:441\u001b[0m, in \u001b[0;36moption_context.__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    440\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__enter__\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 441\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mundo \u001b[38;5;241m=\u001b[39m [(pat, _get_option(pat, silent\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)) \u001b[38;5;28;01mfor\u001b[39;00m pat, val \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mops]\n\u001b[1;32m    443\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m pat, val \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mops:\n\u001b[1;32m    444\u001b[0m         _set_option(pat, val, silent\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/_config/config.py:441\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    440\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__enter__\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 441\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mundo \u001b[38;5;241m=\u001b[39m [(pat, \u001b[43m_get_option\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpat\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msilent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m) \u001b[38;5;28;01mfor\u001b[39;00m pat, val \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mops]\n\u001b[1;32m    443\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m pat, val \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mops:\n\u001b[1;32m    444\u001b[0m         _set_option(pat, val, silent\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/_config/config.py:135\u001b[0m, in \u001b[0;36m_get_option\u001b[0;34m(pat, silent)\u001b[0m\n\u001b[1;32m    134\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_option\u001b[39m(pat: \u001b[38;5;28mstr\u001b[39m, silent: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[0;32m--> 135\u001b[0m     key \u001b[38;5;241m=\u001b[39m \u001b[43m_get_single_key\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpat\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msilent\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    137\u001b[0m     \u001b[38;5;66;03m# walk the nested dict\u001b[39;00m\n\u001b[1;32m    138\u001b[0m     root, k \u001b[38;5;241m=\u001b[39m _get_root(key)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/_config/config.py:121\u001b[0m, in \u001b[0;36m_get_single_key\u001b[0;34m(pat, silent)\u001b[0m\n\u001b[1;32m    119\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m silent:\n\u001b[1;32m    120\u001b[0m         _warn_if_deprecated(pat)\n\u001b[0;32m--> 121\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m OptionError(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo such keys(s): \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mrepr\u001b[39m(pat)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    122\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(keys) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m    123\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m OptionError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPattern matched multiple keys\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mOptionError\u001b[0m: \"No such keys(s): 'mode.use_inf_as_null'\""
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 4), nrows=1, ncols=2, facecolor='white')\n",
    "\n",
    "ax[0].set_title(\"Histogram of Quantity Where Discount = 0\")\n",
    "ax[0].set_xlabel(\"Quantity\")\n",
    "ax[0].set_ylabel(\"Frequency\")\n",
    "sns.histplot(x, bins= 14, kde=True, ax=ax[0])\n",
    "\n",
    "ax[1].set_title(\"Histogram of Quantity Where Discount != 0\")\n",
    "ax[1].set_xlabel(\"Quantity\")\n",
    "ax[1].set_ylabel(\"Frequency\")\n",
    "sns.histplot(y, bins= 14, kde=True, ax=ax[1])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dbe44cc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 4), nrows=1, ncols=2, facecolor='white')\n",
    "ax[0].hist(x, bins=14, edgecolor='black')\n",
    "ax[0].set_title(\"Histogram of Quantity Where Discount = 0\")\n",
    "ax[0].set_xlabel(\"Quantity\")\n",
    "ax[0].set_ylabel(\"Frequency\")\n",
    "    \n",
    "ax[1].hist(y, bins= 14, edgecolor='black')\n",
    "ax[1].set_title(\"Histogram of Quantity Where Discount != 0\")\n",
    "ax[1].set_xlabel(\"Quantity\")\n",
    "ax[1].set_ylabel(\"Frequency\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "048d0f6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.3957940433405684\n",
      "3.5685074419251634\n"
     ]
    }
   ],
   "source": [
    "x = np.array(x)\n",
    "y = np.array(y)\n",
    "print(x.mean())\n",
    "print(y.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aad8f7f2",
   "metadata": {},
   "source": [
    "<p dir=rtl style=\"direction: rtl;text-align: justify;line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "<span style=\"color:purple\"><b>مرحله چهارم:  </b></span>\n",
    "    حال با بررسی نمودار و میانگین‌ها می‌بینیم که در دسته تخفیف‌دارها به صورت میانگین تعداد فروش ببیشتر است. حال با استفاده از آزمون ttest بررسی می‌کنیم چقدر امکان وجود دارد که توزیع دو دسته یکسان باشد ولی همچین داده‌هایی به وجود بیاید.\n",
    "</font>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9186195d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-8.39266716365362\n"
     ]
    }
   ],
   "source": [
    "x_n = len(x)\n",
    "y_n = len(y)\n",
    "x_m = x.mean()\n",
    "y_m = y.mean()\n",
    "std_x = math.sqrt(((x-x_m)**2).sum()/(x_n-1))\n",
    "std_y = math.sqrt(((y-y_m)**2).sum()/(y_n-1))\n",
    "sp = math.sqrt(((x_n-1)*(std_x**2)+(y_n-1)*(std_y**2))/(x_n+y_n-2))\n",
    "t_value = (x_m-y_m)/(sp*math.sqrt((1/x_n)+(1/y_n)))\n",
    "print(t_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b9755d3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-8.029601411475701, 8.044058509206634)\n"
     ]
    }
   ],
   "source": [
    "print(st.t.interval(0.999999999999999, df=x_n+y_n-2, loc=0, scale=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deba996d",
   "metadata": {},
   "source": [
    "<p dir=rtl style=\"direction: rtl;text-align: justify;line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "<span style=\"color:purple\"><b>نتیجه:  </b></span>\n",
    "    با توجه به آزمون ttest انجام شده، متوجه شدیم که با احتمال تقریبا نزدیک به یک می‌توانیم بگوییم در دسته کالا‌های تخفیف‌دار میزان تعداد فروش بیشتر است.\n",
    "</font>\n",
    "</p>"
   ]
  }
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