{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2a077087",
   "metadata": {},
   "source": [
    "# Coffee sales 2025: analysis in Python\n",
    "Data: `coffee_sales_2025.csv` (one row per order)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f3814f6c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-13T15:37:39.925720Z",
     "iopub.status.busy": "2026-11-13T15:37:39.925560Z",
     "iopub.status.idle": "2026-11-13T15:37:40.514486Z",
     "shell.execute_reply": "2026-11-13T15:37:40.513053Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>date</th>\n",
       "      <th>branch</th>\n",
       "      <th>product</th>\n",
       "      <th>category</th>\n",
       "      <th>quantity</th>\n",
       "      <th>unit_price</th>\n",
       "      <th>customer_type</th>\n",
       "      <th>payment</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10001</td>\n",
       "      <td>2025-01-01</td>\n",
       "      <td>Maadi</td>\n",
       "      <td>Iced Latte</td>\n",
       "      <td>Cold drinks</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>Member</td>\n",
       "      <td>Cash</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002</td>\n",
       "      <td>2025-01-01</td>\n",
       "      <td>Maadi</td>\n",
       "      <td>Iced Latte</td>\n",
       "      <td>Cold drinks</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>Guest</td>\n",
       "      <td>Cash</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10003</td>\n",
       "      <td>2025-01-01</td>\n",
       "      <td>Maadi</td>\n",
       "      <td>Green Tea</td>\n",
       "      <td>Tea</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>Member</td>\n",
       "      <td>Cash</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10004</td>\n",
       "      <td>2025-01-01</td>\n",
       "      <td>Maadi</td>\n",
       "      <td>Cappuccino</td>\n",
       "      <td>Coffee</td>\n",
       "      <td>2</td>\n",
       "      <td>70</td>\n",
       "      <td>Guest</td>\n",
       "      <td>Wallet</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10005</td>\n",
       "      <td>2025-01-01</td>\n",
       "      <td>Maadi</td>\n",
       "      <td>Lemon Mint</td>\n",
       "      <td>Cold drinks</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>Member</td>\n",
       "      <td>Wallet</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id       date branch     product     category  quantity  unit_price  \\\n",
       "0     10001 2025-01-01  Maadi  Iced Latte  Cold drinks         1          80   \n",
       "1     10002 2025-01-01  Maadi  Iced Latte  Cold drinks         1          80   \n",
       "2     10003 2025-01-01  Maadi   Green Tea          Tea         1          35   \n",
       "3     10004 2025-01-01  Maadi  Cappuccino       Coffee         2          70   \n",
       "4     10005 2025-01-01  Maadi  Lemon Mint  Cold drinks         1          55   \n",
       "\n",
       "  customer_type payment  sales  \n",
       "0        Member    Cash     80  \n",
       "1         Guest    Cash     80  \n",
       "2        Member    Cash     35  \n",
       "3         Guest  Wallet    140  \n",
       "4        Member  Wallet     55  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv(\"coffee_sales_2025.csv\", parse_dates=[\"date\"])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "382e230d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-13T15:37:40.516386Z",
     "iopub.status.busy": "2026-11-13T15:37:40.516122Z",
     "iopub.status.idle": "2026-11-13T15:37:40.522569Z",
     "shell.execute_reply": "2026-11-13T15:37:40.521178Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20838, 10)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "52ded354",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-13T15:37:40.525111Z",
     "iopub.status.busy": "2026-11-13T15:37:40.524837Z",
     "iopub.status.idle": "2026-11-13T15:37:40.541166Z",
     "shell.execute_reply": "2026-11-13T15:37:40.539895Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 20838 entries, 0 to 20837\n",
      "Data columns (total 10 columns):\n",
      " #   Column         Non-Null Count  Dtype         \n",
      "---  ------         --------------  -----         \n",
      " 0   order_id       20838 non-null  int64         \n",
      " 1   date           20838 non-null  datetime64[us]\n",
      " 2   branch         20838 non-null  str           \n",
      " 3   product        20838 non-null  str           \n",
      " 4   category       20838 non-null  str           \n",
      " 5   quantity       20838 non-null  int64         \n",
      " 6   unit_price     20838 non-null  int64         \n",
      " 7   customer_type  20838 non-null  str           \n",
      " 8   payment        20838 non-null  str           \n",
      " 9   sales          20838 non-null  int64         \n",
      "dtypes: datetime64[us](1), int64(4), str(5)\n",
      "memory usage: 1.6 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "60e5af58",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-13T15:37:40.543545Z",
     "iopub.status.busy": "2026-11-13T15:37:40.543304Z",
     "iopub.status.idle": "2026-11-13T15:37:40.562902Z",
     "shell.execute_reply": "2026-11-13T15:37:40.561310Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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>date</th>\n",
       "      <th>quantity</th>\n",
       "      <th>unit_price</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20838.000000</td>\n",
       "      <td>20838</td>\n",
       "      <td>20838.000000</td>\n",
       "      <td>20838.000000</td>\n",
       "      <td>20838.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>20419.500000</td>\n",
       "      <td>2025-07-06 17:01:26.035128</td>\n",
       "      <td>1.431231</td>\n",
       "      <td>65.245273</td>\n",
       "      <td>93.550149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10001.000000</td>\n",
       "      <td>2025-01-01 00:00:00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>15210.250000</td>\n",
       "      <td>2025-04-16 00:00:00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>55.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>20419.500000</td>\n",
       "      <td>2025-07-09 00:00:00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>77.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>25628.750000</td>\n",
       "      <td>2025-09-27 00:00:00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>30838.000000</td>\n",
       "      <td>2025-12-31 00:00:00</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>420.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6015.556791</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.753898</td>\n",
       "      <td>20.559811</td>\n",
       "      <td>59.936388</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           order_id                        date      quantity    unit_price  \\\n",
       "count  20838.000000                       20838  20838.000000  20838.000000   \n",
       "mean   20419.500000  2025-07-06 17:01:26.035128      1.431231     65.245273   \n",
       "min    10001.000000         2025-01-01 00:00:00      1.000000     30.000000   \n",
       "25%    15210.250000         2025-04-16 00:00:00      1.000000     45.000000   \n",
       "50%    20419.500000         2025-07-09 00:00:00      1.000000     70.000000   \n",
       "75%    25628.750000         2025-09-27 00:00:00      2.000000     82.000000   \n",
       "max    30838.000000         2025-12-31 00:00:00      4.000000    105.000000   \n",
       "std     6015.556791                         NaN      0.753898     20.559811   \n",
       "\n",
       "              sales  \n",
       "count  20838.000000  \n",
       "mean      93.550149  \n",
       "min       30.000000  \n",
       "25%       55.000000  \n",
       "50%       77.000000  \n",
       "75%      100.000000  \n",
       "max      420.000000  \n",
       "std       59.936388  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cfafa2c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-13T15:37:40.565138Z",
     "iopub.status.busy": "2026-11-13T15:37:40.564944Z",
     "iopub.status.idle": "2026-11-13T15:37:40.576397Z",
     "shell.execute_reply": "2026-11-13T15:37:40.575382Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "branch\n",
       "Zamalek          435\n",
       "Maadi            368\n",
       "Nasr City        329\n",
       "Heliopolis       285\n",
       "  nasr city       19\n",
       "ZAMALEK           18\n",
       "  maadi           17\n",
       "  zamalek         17\n",
       "MAADI             16\n",
       "  heliopolis      11\n",
       "NASR CITY          9\n",
       "HELIOPOLIS         6\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The messy copy: extra spaces, MIXED case, blanks, duplicates, two date styles\n",
    "messy = pd.read_csv(\"coffee_sales_messy.csv\")\n",
    "messy[\"branch\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a60a0370",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-13T15:37:40.578227Z",
     "iopub.status.busy": "2026-11-13T15:37:40.578050Z",
     "iopub.status.idle": "2026-11-13T15:37:40.593871Z",
     "shell.execute_reply": "2026-11-13T15:37:40.592802Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "duplicates: 30\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "branch\n",
       "Zamalek       464\n",
       "Maadi         393\n",
       "Nasr City     349\n",
       "Heliopolis    294\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean = messy.copy()\n",
    "clean[\"branch\"] = clean[\"branch\"].str.strip().str.title()\n",
    "clean[\"product\"] = clean[\"product\"].str.strip()\n",
    "clean[\"date\"] = pd.to_datetime(clean[\"date\"], format=\"mixed\", dayfirst=True)\n",
    "clean[\"payment\"] = clean[\"payment\"].fillna(\"Unknown\")\n",
    "print(\"duplicates:\", clean.duplicated().sum())\n",
    "clean = clean.drop_duplicates()\n",
    "clean[\"branch\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6b8c35e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-13T15:37:40.595587Z",
     "iopub.status.busy": "2026-11-13T15:37:40.595420Z",
     "iopub.status.idle": "2026-11-13T15:37:40.609308Z",
     "shell.execute_reply": "2026-11-13T15:37:40.608296Z"
    }
   },
   "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>sales</th>\n",
       "      <th>orders</th>\n",
       "      <th>avg_order</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>branch</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Zamalek</th>\n",
       "      <td>584642</td>\n",
       "      <td>6207</td>\n",
       "      <td>94.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maadi</th>\n",
       "      <td>522355</td>\n",
       "      <td>5586</td>\n",
       "      <td>93.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nasr City</th>\n",
       "      <td>454465</td>\n",
       "      <td>4837</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heliopolis</th>\n",
       "      <td>387936</td>\n",
       "      <td>4208</td>\n",
       "      <td>92.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             sales  orders  avg_order\n",
       "branch                               \n",
       "Zamalek     584642    6207       94.2\n",
       "Maadi       522355    5586       93.5\n",
       "Nasr City   454465    4837       94.0\n",
       "Heliopolis  387936    4208       92.2"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_branch = (df.groupby(\"branch\")\n",
    "               .agg(sales=(\"sales\", \"sum\"), orders=(\"order_id\", \"count\"), avg_order=(\"sales\", \"mean\"))\n",
    "               .sort_values(\"sales\", ascending=False))\n",
    "by_branch.round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9911696e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-13T15:37:40.610958Z",
     "iopub.status.busy": "2026-11-13T15:37:40.610789Z",
     "iopub.status.idle": "2026-11-13T15:37:40.760102Z",
     "shell.execute_reply": "2026-11-13T15:37:40.758473Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "monthly = df.groupby(df[\"date\"].dt.to_period(\"M\"))[\"sales\"].sum()\n",
    "ax = monthly.plot(marker=\"o\", figsize=(9, 4), title=\"Sales by month, 2025 (EGP)\")\n",
    "ax.set_xlabel(\"\")\n",
    "ax.set_ylabel(\"EGP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fb9eb3f7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-13T15:37:40.762504Z",
     "iopub.status.busy": "2026-11-13T15:37:40.762271Z",
     "iopub.status.idle": "2026-11-13T15:37:40.895944Z",
     "shell.execute_reply": "2026-11-13T15:37:40.895277Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rQLPbbrutMUo7WRvdR2RntPEXvvCFsn5GUnbP6soo53nmmad8nkyu7hlT2ZeM9G7OTJNRwUBWZ1RMN910jd3805/+1DgH8nl3PWVU5Jyus53yuz7nVLOco3XG1YwzzjhW5kGcd95542QfNDvppJMan+cep1mdUZHzd6mllmqZEdkXq6yySmnjYx/7WF8W71WrjIpaT/citWS35VilT7vuumvb7S2zzDJlmdwTwaSqOWvr/PPP77Gba665ZvmZnmOOObpuueWWcZZNFkR9vTnyyCPH+bzOqEj2wTHHHDPW5zn31llnndJ+rn/Jwn7ooYfGWiaZW8nYyDK5LjTL89CSSy5ZPkuG6H333TfO9vNsVt9PJFOrWW8ZFZ/+9KfLuh//+MfHeYbJdfSAAw5otN09s61dRkWejVZcccWy3qyzzlruqQAY2AQqgLaaAxV5QZfSJr191S/UckPZPS04abt1yaMELNppDhp0LwE1PoGKU089tXFjfOGFF7bdbl2OKi/0n3322bGWaw5U1C82W9lzzz0bL/0ffvjhsRbZcMMNy2cp35KH+FbefPPNcqyzXEoxNBvfQMUGG2xQ2ssDRCt5KfPHP/6x64UXXnA2MNlpDlTUpVU+9alPlZ/5xRZbrOX+pPxa/SB+9913l2U22mijtoGKBA9SUiYvGNpJgCNtbr/99uMskpekhx12WNfrr7/ecvWUlar70/3FRvP1sF0JvpRtm3/++RttCFQwkNWBigTXm9W/p/MSMOdUs95KPz366KPlHG33u/n6669vnF8p69Js3333LZ9lIEQ7F1xwQdfjjz8+zsd1oCIvJe+///7x/m+rSzu2GuAxPj5ooCK+8pWvNF7Ytiprl1I69XXvd7/73fh0Ez50CWROM8005Wd166237nV7uY9P8LIumdZK/SzTqjxTHajIy/lWUqqyPm++//3vt1wm368HP3SXwQ8JQFx77bVt9yXPell/jz32GGuZngIVKU+ZdXItaxWgqdWBnJTpbNYqUJG+pqRu2s3zY8rYAjDwTTWZJoIAE1nKBqW0SW9fKbXSzpVXXln95z//KenFe++9d9vlUv6glnU+qEwWFym1khTqdr797W+Xj1LG4cILL2y73Le+9a22n2W/Urohk+Y1T4KZdPFMwBuZjHuWWWZp2UZSrHfbbbfyWSah6+l49lXS1COTdKY0RndJz87Ee/VyMLmrS5lk8smUZOuuLsP0qU99qpRe601KNqQ8QsovtFOXb0jJhe4+9rGPVd/5zneqmWaaqeXqzaUfuq9fXwNTViX9bSWTeGfCW5iSHXPMMaU0Wkqa7LLLLmVC177KpNc5R9v9bu7pHK1/d77yyivVtdde23KTKf+y0EILte3O4osvXibBHV8ptRKtSk52Sl3KKWVbUmayu5Ssi1wXv/SlL3Wqm9BW7t0zcXXKOKVc61FHHdXr0cp9fMpDrrfeem2X7el+obbaaqu1XD/Xqlq7ialTpi0yYXae35rNPPPMpTzcpz/96ZbtR1/6111KyEbKXqU0Zju5NkfK3T388MNtl0t52kzqndKbuUZceumlpYQWAAOfOSqAPsncCnnZ1psEMvbff/+Wiz344IPl+wsuuGCp09qTPPiPHDmysc4HUW83dVMTRGgn9U6HDh1aXhRmfozM2dBd6t2n/+3kRUReFOQ45CVp7dFHHy317SN1VnuS+tqpAx2pF5u61h/EtttuW2rijh49ulp22WWrHXbYodSVzkNQu5cyMDnLPBUJGiYw+rvf/W6sB/IE6/7+97+X3etrbfbu0u4DDzxQzvPUck4N9ptuuqksliBlb7JeHtBHjBhR/kzt+1r39evrSLsa77WVV165t83CgJbfvZnbKb+783s/80EddNBB47XP9Tla/y4/77zz2p6jm266afXjH/+4vFjL7/fMb5Ha7JmnYq655urT9hdYYIE+LdfOrLPOWo0aNar0d1Kx1lprVQsvvHC55p5xxhnlmNRSVz/zU0SCwO5FmBTl+pFBPpF5IdrNJ9GTDH7K/UJ+3+dcyL1CPZCpp/uFdvNrZe6bWgZRtJLBC83nWk8S5GzuXwZ31Pvcl/uZWv2slYDODTfc0OM2m9fJfDzdZT6ODCxL4Dfz/mTAWU+BFQAGFoEKoE8yqqXdaN5mmUi2t0BFXtrnAb4vuo8E6q8EBx555JGyWk+jGWtZJoGKBEla+ehHP9prFxLIqF9y1PIQ0LyNnjT3s10/+iOjwH/zm9+UCUfzgvW3v/1t+cokfxmNlZGMmWwvDxcwEGT0XV4W5sVCXjAeeeSR5UVeZGLbjLjOqMIvf/nLfd7dnMOnnHJKGTWYlw1po6/yUu7iiy8uAcNMepugYV9kNOTzzz9fFu0pQBp9uTbBQJeMzEwGmxdlv/jFL0rQcskll+x1t3OO5mVYJuVOILOv52jkZXwmrs3I62RVpI18RTIlMro6E2zXo5xbaX75OD7mnHPOcm/V0wjliS37lGNywAEHVJdcckk5pvV1OBP6ZuLfyOAJmNRkYugEPiOTVveUkd1dzsPcL+T+I+dlf+4Xah/0mtCTe++9t/TvggsuKJkNE0I9qCIByDoI2Zt2z3jf+MY3qrfffrusnmyW/gRMAJj8CVQAE01905mXiHmw74sP+vI8gYo6k6F5hFE79TIf5Ka4friotxtJH6/11o/mzz9IP5qlXNVGG21USi3kwSQPKenfP//5z/L1s5/9rLzA3WSTTfry3wKTvJRYS6Aio5zPPffc8u+8LDj99NNL3/MCM8GKvsgLzAT06lIyOcfzAjJZZvkz2UknnnhiKdfWXR6yk9XUnDWR7ebladZP6allllmmBIO7y8vTWm8vLXr7HKYEOQ9OOumkarnlliu/d3PeX3/99T1mU+b3bF5ENr9cy31KsixzjubPFVZYoRo+fHjbQ5iXmAlmJkjyxz/+sbrttttKu/neIYccUh122GHVoYceWu2zzz4fyn9D+pdM0KeeeqpcpzIKeVKQQMWBBx5Y+pSXtl//+tdLt5JhETm+Rkozqcm1I9eEnMPzzz9/GezTVynzmiyrZAXUEqRM+dlPfOITpXxRfv4TvOuE3A9lcFLuTSLXzJSdq+9nkr15+OGHl+tmX+Xeqn7Gy3NbAqd90a4cZtpaYoklymCtZIqlVFUCR30ZcAbA5E+gAphocoMew4YNq+65556Jst1pp5223NhmRFNfRg098cQTpV/tbrLzEqA3dRvN6cz1vkf60dPNdr1+T/0YH3kISUmpfGXUZ0acZpR3Rn6++uqrZQR6AhjN9W9hcpUXd/m64447ygvIvLBMiab6OrDjjjv2addyPmZEdF5YpDxLMsYS9OteCz7ZEq3kJV0dpMjLi/Tjs5/9bLk21Z555pmW66befkrHZNRhb9ev3j6HKUVeuv3gBz8ov+tyzufc7Gl+mZSIqoMUCdanhnrO0eaX/X2ZLyolK1NyLl8JkKa2euZmSOAk2QSZBytByd7KP46P1IU/7rjjygCEDEjo6/Ut2WbJBkmGZV6w9jV421fJ9ErZzauuuqoc4wQqErTIgImQTcGkKNeOZIhHzt/ZZputT91MllBKnCXQkXuE3C+kfGuee5olmNkJ999/f7nXzyCIPBN8//vfL/cz3ctM9ScwEwl2JBiTklHJVO3v+t2llGWunXfddVeZ6yPz3OT+Kc8tmQMEgIHNZNrARFO/rE9KdEqaTCwZ8Rx1Dfl2Uj+2ru+84oortlwsLyvavVSMrF+XM2gOVOQGvh7x3Fs/mj9v148PavbZZy8PJ3kAqycbz+ivniYRh8lNggKRmssZmVdP9phshr6Usos8LNeZTXm5ljZbTVjbrkxbPWoyD94ZUZyH7uYgRfRU4q2+jmS0dE8SkAH+v7yAqyenzt97Kr1Sn6MphZhrRDInumck9LcMY2rH5wV9MimSXTH99NOXfiXj4sOQl6H1AIt6voze5LqW8lj1ZLUTOkhRqyfVTr35BGxSWitlKDNnWEpiwaQkc8P98pe/LF3KPFbrr79+n7t3xRVXNDKok7mQEkbdgxS9/c7/MGVC6jpT889//nMJHLaaC6O/17uon/F6m4OwLxLUHTJkSAkwJ5AcaTel/QAY+AQqgIkm6f2DBw8uN8lHHXVU2+1mYrfUhU1JoubSJ+OrHkmZtOG8sGwn8zZke+lj86SP3WUEYjv5LG0kKNE8+W1eWqQ8TBx99NGNlOvuMhoy/YgEKT5omnNGdV199dXlxUU90V13mbw7+xxJsYaBIiMHU1og52ReRtZBuf5Mol2XM8hLtUxG30oCmP/6179aflavn3XblWdqVTKqVpdFydwW7V4A5LpxzDHHDJT/NvjAEgxMID7nXM7Pk08+uW2bzedozvNWejpHM7ggL/wz70IrCTam7MuH+Ts2o4z32muvso3Ms5W/91Y6MmWo6oEVKU83Pvpyj5aR0BmRnv7kRW4dGFp33XVbvsSFTsm1IOXK8js1g5z6mxlQX0sigc9WXnrppfI80gl1/3K9qAdxdZfM8eZ59foqgdlIyag8v7WTQFBKZWaur7743ve+1yhLm9Kd7nUABj6BCmCiycN6RhfFQQcdVF6gd5eH3pRNyAjIjECeECNzdtttt0Y5ozyAtJpsMqUP6uBJlm/OhugugYbLLrtsnO9fdNFFjTaS+pza881+9atflX8mIyP1YZvnrYjUi00ZhMcee6z8+4gjjvjA/zd5MZCyNTneKYXR6qVCyjHULzRSOxcGillmmaXMRRGpuZwScBkp3Z+XcvXLhpw7ya7oLiVecl2p56/orl7/mmuuqd54441xPs8D/U9/+tO2h/y73/1uYwLajD6uXyzW8kJljz32GKtkHFCVwQL1CNy8cEx5o1bqczQj/ltleyZbKaVg2vnmN79Z7bfffmUUcLtz/N///ndZ/cP8HZsXequsskrZToI0n/nMZ1qWq0zmZ7Ic6nueDFZIDfi+ynGs5w/LNbU3ySapr8MJUtSBCmWfmNRkUE/O1dwnnHPOOWW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      "text/plain": [
       "<Figure size 1600x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table = df.pivot_table(index=\"branch\", columns=\"category\", values=\"sales\", aggfunc=\"sum\")\n",
    "table.plot(kind=\"bar\", stacked=True, figsize=(8, 3.6), title=\"Sales by branch and category\", rot=0)\n",
    "plt.ylabel(\"EGP\")\n",
    "plt.legend(title=\"category\", bbox_to_anchor=(1.01, 1), loc=\"upper left\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "762f54ec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-13T15:37:40.898627Z",
     "iopub.status.busy": "2026-11-13T15:37:40.898427Z",
     "iopub.status.idle": "2026-11-13T15:37:40.912831Z",
     "shell.execute_reply": "2026-11-13T15:37:40.911772Z"
    }
   },
   "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>branch</th>\n",
       "      <th>sales</th>\n",
       "      <th>orders</th>\n",
       "      <th>avg_order</th>\n",
       "      <th>monthly_target_egp</th>\n",
       "      <th>monthly_avg</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Zamalek</td>\n",
       "      <td>584642</td>\n",
       "      <td>6207</td>\n",
       "      <td>94.0</td>\n",
       "      <td>50000</td>\n",
       "      <td>48720.0</td>\n",
       "      <td>Below target</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Maadi</td>\n",
       "      <td>522355</td>\n",
       "      <td>5586</td>\n",
       "      <td>94.0</td>\n",
       "      <td>42000</td>\n",
       "      <td>43530.0</td>\n",
       "      <td>On target</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nasr City</td>\n",
       "      <td>454465</td>\n",
       "      <td>4837</td>\n",
       "      <td>94.0</td>\n",
       "      <td>36000</td>\n",
       "      <td>37872.0</td>\n",
       "      <td>On target</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Heliopolis</td>\n",
       "      <td>387936</td>\n",
       "      <td>4208</td>\n",
       "      <td>92.0</td>\n",
       "      <td>35000</td>\n",
       "      <td>32328.0</td>\n",
       "      <td>Below target</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       branch   sales  orders  avg_order  monthly_target_egp  monthly_avg  \\\n",
       "0     Zamalek  584642    6207       94.0               50000      48720.0   \n",
       "1       Maadi  522355    5586       94.0               42000      43530.0   \n",
       "2   Nasr City  454465    4837       94.0               36000      37872.0   \n",
       "3  Heliopolis  387936    4208       92.0               35000      32328.0   \n",
       "\n",
       "         status  \n",
       "0  Below target  \n",
       "1     On target  \n",
       "2     On target  \n",
       "3  Below target  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targets = pd.read_csv(\"branch_targets.csv\")\n",
    "report = by_branch.reset_index().merge(targets, on=\"branch\")\n",
    "report[\"monthly_avg\"] = report[\"sales\"] / 12\n",
    "report[\"status\"] = report.apply(lambda r: \"On target\" if r.monthly_avg >= r.monthly_target_egp else \"Below target\", axis=1)\n",
    "report.round(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d732e0a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-13T15:37:40.914659Z",
     "iopub.status.busy": "2026-11-13T15:37:40.914482Z",
     "iopub.status.idle": "2026-11-13T15:37:40.950445Z",
     "shell.execute_reply": "2026-11-13T15:37:40.949585Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved branch_report.xlsx\n"
     ]
    }
   ],
   "source": [
    "report.to_excel(\"branch_report.xlsx\", index=False)\n",
    "print(\"Saved branch_report.xlsx\")"
   ]
  }
 ],
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