{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Мини-проект: магазин из трёх таблиц\n",
    "\n",
    "Собрать данные магазина из заказов, справочника товаров и справочника клиентов, проверить соединение, сверить с готовой таблицей и посчитать то, чего раньше не было, — прибыль и маржу по городам и товарам, доставку по каналам.\n",
    "\n",
    "Курс pandas · урок `pd-project-merge` · [урок на сайте](https://ds-edu-trainer.vercel.app/courses/pandas/project-merge).\n",
    "\n",
    "Выполняйте ячейки по порядку (Shift+Enter), начиная с первой — она готовит урок. После каждого упражнения идёт\n",
    "ячейка проверки: она печатает ✓ / ✗ и итог."
   ],
   "id": "intro"
  },
  {
   "cell_type": "code",
   "metadata": {
    "cellView": "form",
    "jupyter": {
     "source_hidden": true
    }
   },
   "source": [
    "# @title Подготовка урока — выполните эту ячейку первой\n",
    "# Проверяет версии пакетов, записывает файлы данных в папку data/ и определяет _check — проверку упражнений.\n",
    "import base64, os, zlib\n",
    "\n",
    "for _name, _min in {\"numpy\":\"2.0\",\"pandas\":\"3.0\"}.items():\n",
    "    try:\n",
    "        _have = __import__(_name).__version__\n",
    "    except ImportError:\n",
    "        print(f\"Нет пакета {_name}: выполните %pip install {_name} и перезапустите ядро\")\n",
    "        continue\n",
    "    if tuple(int(x) for x in _have.split(\".\")[:2]) < tuple(int(x) for x in _min.split(\".\")):\n",
    "        print(f\"Урок написан для {_name} {_min} и новее, у вас {_have}: выполните %pip install -U {_name} и перезапустите ядро\")\n",
    "\n",
    "_DATA = {\n",
    "    \"orders.csv\": \"\"\"\n",
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    "N4shIXRq9WY7MHXgg+zdlsoCDhQQ5G2oD9sDLe16ABHqTwnRyB3Kzsp5ZySe+SGFwhE2yFLamQT6Z5T4arYMNpSVgMNpUjLEgJal\n",
    "9++A/Fo+1BCo92MU7z8OJNAPCVFicRnFENLujpGMamv4xjkKUTYjfzT3Hdu4SkwpsZF1Q1nvLriroTKGA23TFuWUbKfWfmREUOXm\n",
    "RK7n03NXkJMy0HZ6asWXBEDPksSA5uoZO61yypYytrsvSImtI6diKRW8wINB7EHVfWJ6LpMQ5G/S5tRsj86DcDDuMKFEhG4JA8z+\n",
    "OQ1XKvphKdkPOtJuBsEemchRxu56QSZrY3ysVu8fCAn1Xa/OpAz0epYzSB+aKRvCnTnL9E6xpQo/T2sY5Dz4maqt7kps99o+ROAf\n",
    "EiJvRaZmCGvLN/zHdftxnJ7+mkzK0saZUHZEk2fmZCjEm9zvmBCkR89MtlGUdolIMrOhrOhvdz8BgtxL6JRZbOV9XTd4//U4W0Iq\n",
    "8UYqc7GEsQnrwYQcEW6DmlGZGexDMcgdCTI3C6rg2bzM3YBOASL4gQEhCpjLPGwzjsybQRL0LMnWQT2+GwYJ4e8oZAjnkSBa8xAl\n",
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    "WqQ35JxsU6SDzRMCeR9DzmQrJwhS9sXHnZHYhZezGMp508+1MNty29isJYgQLsa5GIKKUH5vebV19PMFa9NyBPJmn5sFjQGmWAh6\n",
    "/ADdgo64saCjlE3JeYCIpr+SDGE+7PeYZwrZUvNWj8yFbbm0W7yKWEJLsS8nl+xa3o5gjVdCeRLCzkSE6EJBLtX2k/RnJBYGNauw\n",
    "59Js1XUTYZ+LspR8xu37AwYCPfaGZRgQHcEaRtzPNVlQkzhOJFeyVZd2LgVvBDYETiCPEwa5wK9cBYDGN0A+6BWX5KLDshKMZtfU\n",
    "FsvqWelCK/eZoKqrA4GkoRjkJx6l/szw3rJ5JCEr7ecKGo5iriEhelU2K91nEnLedRkiROanZKIZ60pAqMxK6ZmgVp7vSmGQH75K\n",
    "3Vmv46HjvpJqVptAHAoE+UQwWSk4K5J3I1Flpd9Qu2+uvf9CSr+Z+eRSAXtCpdisN+I2rwlDQrj3VtrNemaPNzOyEmWuN/N8rEhW\n",
    "Ugqd7xU/jAOC/C5Q6S3rFTqgA2PQd0ryGn1Wys1sk5TtiVnpOPOX5wp8TEq7WeHZAtYxBHrsHJSks0DgzhkE8aNN1YDODWv4rc1S\n",
    "Ej2TIGaly0wQ8+ZQqhSUGed8HBjdLwNAPtEyLsllRMlKTJmB2x0tcUoLoSuvs7c7pX6s6mjr2VYyx3oTb6OuZyVIrJjutJmJECGK\n",
    "Gs9KweB0v3P6/nspOYLTfTE/OsQgyubco+SImdeO8u66YlZyxKQIeOAlKzniStz3OKdBkBtARakU87es9ZmwqyiVYoKOGLjXnwMT\n",
    "KCKw+/3qM3a0KPViRqzLJu8SJpSIkA3hfo7HNAOAnv2KSqq+X4ttX0/PwyUGuZiQogSNWV0d6GdstqQMXqPBoOy/rhvQ2heHdg0p\n",
    "sShUlPIxm5I6ai8CuT1IUfLIrJx4l9O2KHlk3kqQ/HRlFaVvrFyLtJtFMCXHFDGUVIDfFYL8wluUWjIr7/W5p8EgPwiUWjKrayAX\n",
    "Fga5XXFRgsesLhHqaADy4kxRSsisroKVsyglZIFanIATEsK5TMkofEY1+RvLEOR3IUWJJeuFUInjODAhaqtSV1YGTIlDXzAhOGMV\n",
    "pbPwGVgVB20Wpbqs9Jk1dgRCQpTJrSiZZn5FrbGQCQlxxxZLSOW4fvQTQqR8FiXZTEIHGwUI8huFonSd6zeNDlyQEMVsF6XxzB9I\n",
    "+i6QpyiVZ9lN20WwQkp866MonYevu/thQASmcFyLGjTz2nbZJU2ClPhggymbWthQzidErXkoqWhlGG27aMqidKD7qVp3wMMgd0W+\n",
    "KLGILzdYZGqIEG4SlVQ0r+tc0njwec1Sjth/123dgOgIZQi7DVA22xQlF3E9ZWy/8VPazwRxfqZ8xSC/BVcC0WxhBVePMcjPTEpF\n",
    "Wt/aY/9hURrS9RitdxpAkPcPF6UbrWyzjEpCIPIfAUCPDbxSnbi/ZEIsSkKaoLSNvi9KK5qUlmL5CxLCnYwSmviKGolNWElN82JQ\n",
    "6qDblLx0XwyKFhxIiGYBpU2th2rRr4dA3qFVlGQ1bw8xkACKkqlWPlze3fiClPjNjKK0rXkv6ZAE3n8/JVVNAoGon6KUpwUaOwdQ\n",
    "UToUnz6jxwkBgvxZQ2lPkvQT5VuQ3ysrgWpdOhq7tJJFyVWS7qcOc0QhW0uXZ6a2ovSnCWore+H7dhQRwg2YkpxWQuQG+lZJTuup\n",
    "4LRzPhUlOq07YPUZSVCUniTXYw0j7jZAIY5/HFRLGGFelIY1P1gKMEsE8vetcEmPflBmSS/BOBjk3RNKuJqJkBMQhooSq4TMvvPF\n",
    "SJQmtdIr01PlKEqTkuuOWjSzKulpEkaNUz5hQuBKL0q3ErpP6LbLlFY1QaXtLsxiShivUJTANdtewk3K/weV3t2F\n",
    "\"\"\",\n",
    "    \"products.csv\": \"\"\"\n",
    "eNp9k01qG0EQhfc6RR+gDNU9P5q5hW9gjCyCF4mM4iyym7EMCligeBUwgRDH4GwCI1tjj35mfIVXNwrV4/hPIsvu+V6/qlc1R8PB\n",
    "wafe8d7hAX3Yf9+n3v5x/91g+JmOhoe9PvUGH487u2wJfyTHg2QoJZcczY7kWPvDxFiDJW4JF2jkFCXZMGJKutzZZedvsZIR1pih\n",
    "kqlxEZuXdJwyBYnCgbqcokKDh61oN2UKA0VDwrlkKHCPCisPb1ThUqaufzh6ujWYocS9ov6MCjWKTSPHFFqVxoRrUatazrAwMkaB\n",
    "hbHc8td6oiBispHSXcI3lFjJ+X/5lMn6eBLCLx9O/QaJQiYXK5ISLn2nNzJFjUYfzTFDI5mJXkpczGSZO7uWCb/RaItYocDc4FZx\n",
    "mWCJCgvCz/YejeRygopswpSq0hJ+oJQxStQy0bga3Ej+6FxuUUZM2ol1hO8+zDkKrGRicGPky8sqtskD95iEDXxyOn3JNjln//UW\n",
    "Ei4kkxHusERhAo1gjZXWrfBI/SlKmZwOxEaEK11a1DLeed7f17hNUqbU87GmPZLMP64LjrkuiExfK1zITDbwFXUfB4AaFZYG9zor\n",
    "FJKhkXG7Bm/sdF/iULXJ08ph7XNSW21Pla3tVywlb6uWkT4rZxobk029fUq40n9AJnKiHw1m/mcrcOfNy+1PuLAduWPCJUrJfAHL\n",
    "52i3y6wOPIq58xfQUEqA\n",
    "\"\"\",\n",
    "    \"customers.csv\": \"\"\"\n",
    "eNqlW0tuHEcS3fsUPECVkJGfqqz1HMQwbMLQQrJhyQvvJM1yDJikukWC7OafoDYk29IQQ5EifYXsGw2yWmNlxqcqpdkSzOjIyIgX\n",
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    "IbYKG+E+/Hv5YqXJ5FGU+rB8GW7Du7CIB02tuhocOuiqMO9/ahHulr8v/1gL0+Hft7XytbaiTZoq7MSfDPcrUVQLXesGadFG9e/C\n",
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    "ZGuA/q0EEUDsFb1mHs3QC7oJf4ab5Yso8vPTG1GcrsJBWIR34TrcrYUZ1cfFV1PId6LA5NhkWINoE/lCtgqT8DHcLF/19vywMjKx\n",
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    "1vW1QpGpFUKn8wIl+neKSvAXi/b7GyKuWCPbOgadcFwnx/dGTexqA1V4CH9FQZ/vZXqUCNfh/QojhmLHDjx5RMJ5uAm30XejZY+L\n",
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    "mQxYZmWh29WAFbHoba44TwAYiBLnCPWdl9QXCIxcgzTZKS6gkJO6tk8f8Xejb28ORVoPRoBQ2/kkUAvVcD0lESzURRHv+pN3vajF\n",
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    "03NkQeWWJPKZQLc0Sm6NR5Y/YlNbM0CymxxKd+nrO47PtIoUYpes2Vx/8+wobl1tCwcxVrY5Vs5Hk0fXGww/WZsX+2/LqhxNcKql\n",
    "lf5MiB3sbq0j+auA3LR9IifXaVDZts9mY+i7f+Rwm3QJ3rK53w10b1rsfwesAXStRe9vcUtqUmYMM9DN9IptwnEVEKaLPs/rrwVi\n",
    "INNyjzupm2wsa5KsfO6Wh4LChilbPfXE41KvxkHmaRLfKysrVs1QwSq0ctoXumKYmPi2hJgoGPIHj0Jkr7RQkv3Wdwj4T0r9VrZS\n",
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    "LdgTPBZQttIdqtq2S/mEbA6DVwHelNa0mta0YHBT6m3xRAJhb5xjZS8tzXpQIQgGt/xPR4Pa4N+2Y/PLyMS56zsUbtPS1iPapgHT\n",
    "ZBhwJnT3xXIADM8HCiJv9Rbkaj4rL84L2KeWna5jGo/DsGTlySBY2lY9Ld0R0Exs27yXME7t/MBd4xwrwZuzUWGN3CWFOM/6u905\n",
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    "4jgLmWHjS0bdmRIGbccUpXvM250ltcjhKErAAA9xjl1soGZthoSku4W7BbHMsRhHdw0vSmiIRRbywhSGnQOL6OlyX90c2aHT8g4d\n",
    "NAqtNHGxKPZ9oMEDhrNS9sAl4yYH3j1WHUPQKc62EF1lqR2KvIa667bUS0DVS4NJ7Wy0jrIDmT9OtpLsxaPAwGonNOmKwFzq9orY\n",
    "F2dbX8E8+va8Qi4eh1yoRj0Vhh1oxAatQhzjTHqOBh3EXngkrJ3I6N3qpDjYKru8IZdvDTMkHF5vFZdyoP3KJUE1dE03buJVp5YB\n",
    "wRZvsBwUT8LFZN7Sxu2WoBJOWm2+sz0r9VjUqYW2S57+mJ0F6YHg9fl+C7t5p4A0muL0K8G6HWltGCFPnHkllx7dZAU9kBo915md\n",
    "FA9pUfh6vNZyXvwiYgx4vNq6XTogk8HS52h7KgwN0ecGsBqOIWOdS+tQyFe9Z5pO3I6tOLGCOAX78qYMWUCEjpZeZyP9Kk+8t+Nm\n",
    "CXvC9Enc4IUuX309HNHDyet10KV11uZ43xc1ajqLGDCdbAx8igAdbiPMBO6iZRENSZwXgh5yRMc5GPnqhF3AlN/Eo/7wl8Fhx/do\n",
    "2a0rSYhWKoHkTal/CeLxdJ11m2UfjRxpevWFFnLuy9ISU3wcHVlk4u0HEi2SBdC2wLbAFZV8O0wBLsoqecKCtUq/e9kXVgVQ1taq\n",
    "FaaQRSoYLM1nJj0vXV8kWtFp7amQkdFAUgNmrZvS92stOphW/hPWfG2Pndkh/RUcbAUaDZKEJ12HZcbTfWGKuKFmvsWaSgsRUh2i\n",
    "Y85G/j0TtoIUNmaT8aJ99reB2+LQkM4Fxrukhv62Z/Y6R7sMWAhezjopGM3JptT08wDOHmJq1hpYrBmb5YqNUK01mr9f/T+fjGpN\n",
    "P3qZF3/qhoJKp9ODi4Gvn7DrxGyecLLLEdrh5Rpc63x8MBMeDBExTb7B2hzI2NlBfnGAJtp2IJfoDmHIlbRNi1KHUeQrUG68L6fC\n",
    "mAgQWxIXsDz6bV20dygO4LTJ8/heWfLqSAKIQ6ss5A+L99rEsGU+ubqQPEIMVZOD6bR4/iurlX8ecCVsLQyo5EmkjX0zL4OjwVh7\n",
    "MbA4youwiqExZ9I32UIn978sij3j\n",
    "\"\"\",\n",
    "    \"shop_orders.csv\": \"\"\"\n",
    "eNrNvcuudclxHjjvp6gHOAQy8p5jPoghUIRbQLfkZlMDzapIAzJgArIGDQOCG4YvgHtioESpJKrIKr3CPm9krFzn3ztiX1bGbR31\n",
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    "/s0v/uyP//xnv3z7N7/4k5/9/O3/+vM/+tNf/skv/+J/gxACvMUQy08C/CTA20/DKG+X/3T58f2by/eX316+fbv84fLt+9eX7y/f\n",
    "vf/q8s+X31++u/zT+zdvl/9w+f79m8t379+8f/P+6+1P3v/92+VvLj++/9vLd5c/XH68/P7y/eXbr96/fv/1+19efrh8+/5XbymG\n",
    "8BYtqP/j8u3ln94u//X968u3l9++/9Xlh8uPl3/66v2by99efnz/+qsSvrr83Vus4Q0mTKQwZdzB/PP715ffXX5/+fHyD5fvLj9c\n",
    "fnf57u3yXy6/v3x7+fvt795/dfnd2+X/u/x4+X4O6dvL3391+bsN6v03l+8vv7v80xt0E9jHiP7H+1+/f3354f3fb6P5y+2ffQVh\n",
    "DiaVL/YTsR9D2ib828s/Xr69/PD+m7f3b7b/7P1X12V4u/zP9397+d3lx8s/X373/ldfxX122hAb/PiR/+3y+/dfX3748stK/rKa\n",
    "+WYovv005KKa5v94+W77ve9fv0X4YrlQy+nuJ560OytBhRS2X/vt5YfL9++/+snlP0+0796/vvzt+6/fv7783eNMMTZonUiNIrW6\n",
    "QHo+ldcF/5ttaO+/vvzh8rd4yesIb2ni9Rteevvp9h1e/p9tKj5gfnf5YQH1sGr/af5Hf3/59vL79998dfntV+//Dn8ul++2if1A\n",
    "HxS9FdVoP+b4P16+u/z+/a+ffzTjY4K3jYQhe+A5mv88B/jrbVzbav56/s23X13+cPnu8vcfGybm8DEwAIoyIm+bLj6B3XaktmN6\n",
    "u/y/lx8vv52j+N1c6K+38TD2xvY/vrr87eW7yz9+tS3R/By25fv26hzih3Nwg/2f79/Mv5gf4+XHn7x/s03i+zfvv/kKth/xd2+Q\n",
    "y8eH5wb61/P7+8f5F9t38AUpfvkQAPm+/PZTgPESibt2vA9hH2cm6JvDYThM9lcOhQ6uMTc9B2D//chBluN1Oj7IV9/wvhUbRUud\n",
    "7Y5ZG2IH6RSkJzpnktO1yQyyrgB52kQOtL79NMYmujY9Ob/nfomBmA3j/pR1cixRD8aao2S2z71YRqAgtfMWgnPn0dpe78u5h2K8\n",
    "mx9QXda2r+4vt3///ptt6X+8/Pb9m49p++4NrouhApOeHfuSJArVMuN6fIZ51lbdVzrffc4HtzGNT9PZX1z2YyFGIebVJY77LfCe\n",
    "WRGdPW0blPfhFhsBePz+LJdSMCNwXKDKPmtf7TOEjrb+9lMY4dXxL9y1auvM2d8d4CD2t0P1xcHMfgSAzK7y5EyBTkzNfgBJBsCP\n",
    "WiQQ2lyt4T4Vkc718kpovIcmV1DOsy+h82ZsYJJvbPVASPnOeFK+VwVvHjfM5TFhGiL55v9mCxBd/mF+9Wmbwz9cfv9WrpuwEAgo\n",
    "ZbG1GbfL/bdX+ttzsp3bYDbK+dAbnY3alFfi5c3SBCe86KfbYQRhe8VVEQzPmdkwOGszCEJM7ZwH3gTL4Q4ss5/r5OObw5u/5fuv\n",
    "5i/97ZyGH7eA7TbYN7iOzwAp3A8Z6Frdb7ubYYuv5KMsPvociSGA7rp9TRjKW1BOFO719dCacMlZuKtcQgu+qIxXaC50QssQ3DRe\n",
    "xhrsppfP23w70WDLX8Xq/BLUAgi2WCMID9+Og2tkI3DXtFODBfQGi9ogK3CTZeZNHntQlBwFtyvWs9WE8CryWwIxGlrn3ZKZGWat\n",
    "efbDe859gbvPVHvz5L0s3OAE26tEipmKMI76YpX2ASW6Sq9vG8ojmw8gfwzsAOjMjIfB2aeFAf91fkBbWcD3twffG9zMF2q+D+n1\n",
    "knEMG0DkG3l6lFIJIoz6atZMW5eNwjyASrszqLsAsp5mBR0eacMqziE3BcDC5Q5qMLQTrsdzbmqgSLUc1JL4vMYrEMjNsWjuxov4\n",
    "W3aEWiRTnFDYgb8a6ZrFLE+lv9x/1YggrJ0yIElPgX3zoVNsK9roTZBYZr2ka6YIJbDPyScR5FqotddFGsbjUQPE2bH7nFRiHO6D\n",
    "Y+LL9G610Z+srERjvnBqJ2iPN0arJ0Y+v2yeuLL3DS/nygd4uoH++za499+8/2r7v19tc7qdB5d/mKP6brvJ7zAtEJitrI6b+eOe\n",
    "IA053C17XYNv6rVFav/1c3vxMuMbYoczWiImH67h+rNln9pMzd9fwp3uAFoYbQClFYrXg3c5QKsE4cE96AMg+7I3upOiWz2RyTzn\n",
    "CGv97iMYyjpDxuaKPoCq4qKG/Gt7kp/RHXI9EKuQoqL2+WUKdC5QBwoBnxIid0K1vGp7JD+BeRlkvuF0xiVl8T3dbY3uW3mms79I\n",
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    "\"\"\",\n",
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    "ATyZAM6HsCcnN0/bTedm7M0NoSRCeRhfuT6uKUJe1L/4gvFio/HNYK/YXUJUfMh7nb2wFhz5mXUp7BqT4BAifoiG7/TsZwY4Pxul\n",
    "5/Ai27C3fEOwxnlzxPJ+I5Y3I4gJ7J17hvD0+V7YNJUPmfgMIfMQ3U1bypeKYRsy2ofUXmUGhPSYG+AbL3Z8DjBGA/MnBrGfswK/\n",
    "70GXoAmBMSFwpgwHWOJCOUqEYqmVoEYmOhiFBgmOuHGfwXqptUE3C4JpHDeN3xI/14TNqBW2Jth4ITaNw2rgYhqH9Xz9iG3GsR+0\n",
    "whmlwYdw+PuajvDYU8mlGonONhY+j9TYHI4AlyaHI5gCEZxVImIpziqxr+AIlokIDruRQmUTu/amrch1QPJbUCQLaSS/pUTW7hKh\n",
    "naaj+MU1CkvpmHz/Jt2b5XRKbZy+r1n/B5CuGbA=\n",
    "\"\"\",\n",
    "}\n",
    "os.makedirs(\"data\", exist_ok=True)\n",
    "for _name, _packed in _DATA.items():\n",
    "    with open(os.path.join(\"data\", _name), \"wb\") as _f:\n",
    "        _f.write(zlib.decompress(base64.b64decode(_packed)))\n",
    "print(\"Файлы данных:\", \", \".join(sorted(_DATA)))\n",
    "\n",
    "# ─── Проверка упражнений: тот же раннер, что у задач на сайте ───\n",
    "# ─── Запуск тестов ───────────────────────────────────────────────────────────\n",
    "\n",
    "_TIMEOUT = 2.0  # секунд на тест; отдельный тест может задать свой: test_x.timeout = 10\n",
    "\n",
    "\n",
    "def _call_with_timeout(target, seconds: float) -> bool:\n",
    "    \"\"\"Запускает target() в отдельном потоке. Возвращает False, если за seconds он не завершился.\"\"\"\n",
    "    import sys\n",
    "    import threading\n",
    "\n",
    "    if sys.platform == \"emscripten\":  # Python в браузере: потоков нет, таймаут обеспечивает страница\n",
    "        target()\n",
    "        return True\n",
    "    # Большой стек нужен, чтобы глубокая рекурсия давала RecursionError, а не аварийное завершение Python.\n",
    "    for megabytes in (256, 64, 0):\n",
    "        try:\n",
    "            threading.stack_size(megabytes * 1024 * 1024)\n",
    "            worker = threading.Thread(target=target, daemon=True)\n",
    "            worker.start()\n",
    "        except (ValueError, RuntimeError, MemoryError):\n",
    "            continue\n",
    "        worker.join(seconds)\n",
    "        return not worker.is_alive()\n",
    "    target()  # потоки недоступны — запускаем без таймаута\n",
    "    return True\n",
    "\n",
    "\n",
    "def _test_info(fn) -> dict:\n",
    "    \"\"\"Описание теста, его лимит времени и сообщение на случай зависания.\"\"\"\n",
    "    limit = getattr(fn, \"timeout\", _TIMEOUT)\n",
    "    hint = getattr(fn, \"timeout_hint\", \"\")\n",
    "    message = (\n",
    "        f\"тест не завершился за {limit:g} с — возможно, бесконечный цикл или рекурсия: \"\n",
    "        \"проверьте, что границы/аргументы изменяются на каждом шаге\" + (f\". {hint}\" if hint else \"\")\n",
    "    )\n",
    "    return {\"title\": (fn.__doc__ or fn.__name__).strip(), \"timeout\": limit, \"timeout_message\": message}\n",
    "\n",
    "\n",
    "def _error_result(title: str, error: BaseException, user_file: str | None) -> dict:\n",
    "    \"\"\"Результат упавшего теста: статус, сообщение, тип исключения и строка в коде решения.\"\"\"\n",
    "    line = None\n",
    "    if user_file:  # самый глубокий кадр в коде решения; ошибка внутри самих тестов — без номера строки\n",
    "        tb = error.__traceback__\n",
    "        while tb is not None:\n",
    "            if tb.tb_frame.f_code.co_filename == user_file:\n",
    "                line = tb.tb_lineno\n",
    "            tb = tb.tb_next\n",
    "    status, message = \"error\", f\"{type(error).__name__}: {error}\"\n",
    "    if isinstance(error, NotImplementedError):\n",
    "        status, message, line = \"not_written\", \"функция ещё не написана\", None\n",
    "    elif isinstance(error, AssertionError):\n",
    "        status, message = \"failed\", str(error) or \"проверка не прошла\"\n",
    "    elif isinstance(error, RecursionError):\n",
    "        message = \"RecursionError: слишком глубокая рекурсия — проверьте базовый случай и то, что задача уменьшается\"\n",
    "    elif isinstance(error, MemoryError) or (isinstance(error, OverflowError) and \"index-sized\" in str(error)):\n",
    "        message = f\"{type(error).__name__}: не хватило памяти — возможно, создаётся слишком большая структура данных\"\n",
    "    return {\"title\": title, \"status\": status, \"message\": message, \"error_type\": type(error).__name__, \"line\": line}\n",
    "\n",
    "\n",
    "def _run_tests(verbose: bool = True, on_start=None, on_result=None, user_file: str | None = None) -> list[dict]:\n",
    "    \"\"\"Запускает тесты задачи (_TESTS — функции из tests.py) по порядку, каждый — с таймаутом.\n",
    "\n",
    "    Возвращает список результатов: {\"title\", \"status\", \"message\", \"error_type\", \"line\"}, где status —\n",
    "    passed · failed · error · not_written · timeout · not_run. Если тест завис, при verbose=True печатает\n",
    "    итог и завершает процесс: зависший поток остановить нельзя. on_start(i) и on_result(i, результат)\n",
    "    вызываются по мере выполнения; user_file — имя файла с кодом решения для номеров строк в ошибках.\n",
    "    \"\"\"\n",
    "    import os\n",
    "    import sys\n",
    "\n",
    "    if verbose:\n",
    "        try:  # ✓ и кириллица в консоли Windows со старой кодировкой\n",
    "            sys.stdout.reconfigure(encoding=\"utf-8\")\n",
    "        except Exception:  # noqa: BLE001 — reconfigure может не быть (перенаправленный вывод, браузер)\n",
    "            pass\n",
    "\n",
    "    tests = list(_TESTS)\n",
    "    results: list[dict] = []\n",
    "    not_written = hung = False\n",
    "    for index, fn in enumerate(tests):\n",
    "        info = _test_info(fn)\n",
    "        outcome: dict = {}\n",
    "\n",
    "        def target(fn=fn, outcome=outcome):\n",
    "            try:\n",
    "                fn()\n",
    "            except BaseException as e:\n",
    "                outcome[\"error\"] = e\n",
    "\n",
    "        if on_start:\n",
    "            on_start(index)\n",
    "        if not _call_with_timeout(target, info[\"timeout\"]):\n",
    "            results.append({\"title\": info[\"title\"], \"status\": \"timeout\", \"message\": info[\"timeout_message\"], \"error_type\": None, \"line\": None})\n",
    "            for other in tests[index + 1:]:\n",
    "                results.append({\"title\": _test_info(other)[\"title\"], \"status\": \"not_run\", \"message\": \"не запускался: предыдущий тест завис\", \"error_type\": None, \"line\": None})\n",
    "            hung = True\n",
    "            break\n",
    "        error = outcome.get(\"error\")\n",
    "        if error is None:\n",
    "            result = {\"title\": info[\"title\"], \"status\": \"passed\", \"message\": \"\", \"error_type\": None, \"line\": None}\n",
    "        else:\n",
    "            result = _error_result(info[\"title\"], error, user_file)\n",
    "            not_written = not_written or result[\"status\"] == \"not_written\"\n",
    "        results.append(result)\n",
    "        if on_result:\n",
    "            on_result(index, result)\n",
    "\n",
    "    if not_written:  # пока функция не написана, «даром» пройденные тесты не засчитываются\n",
    "        for r in results:\n",
    "            if r[\"status\"] == \"passed\":\n",
    "                r.update(status=\"not_written\", message=\"не засчитан, пока функция не написана\")\n",
    "    for r in results:\n",
    "        if len(r[\"message\"]) > 300:\n",
    "            r[\"message\"] = r[\"message\"][:300] + \"…\"\n",
    "\n",
    "    if verbose:\n",
    "        if not_written:\n",
    "            print(\"Функция ещё не написана — замените raise NotImplementedError своим кодом.\\n\")\n",
    "        for r in results:\n",
    "            mark = {\"passed\": \"✓\", \"not_run\": \"–\"}.get(r[\"status\"], \"✗\")\n",
    "            print(f\"{mark} {r['title']}\")\n",
    "            if r[\"message\"]:\n",
    "                print(f\"    {r['message']}\")\n",
    "        passed = sum(r[\"status\"] == \"passed\" for r in results)\n",
    "        print(\"─\" * 40)\n",
    "        print(f\"Прошло {passed} из {len(results)}\" + (\" — всё верно!\" if passed == len(results) else \"\"))\n",
    "        if hung:\n",
    "            print(\"Выполнение остановлено: один из тестов завис.\")\n",
    "            sys.stdout.flush()\n",
    "            os._exit(1)\n",
    "    return results\n",
    "\n",
    "\n",
    "def _check(*tests, names=()):\n",
    "    \"\"\"Запускает проверки упражнения и печатает ✓ / ✗ и итог — как на сайте.\"\"\"\n",
    "    for name in names:\n",
    "        if \".\" in name:  # «Класс.метод» из заготовки: класс должен быть объявлен\n",
    "            if not isinstance(globals().get(name.split(\".\")[0]), type):\n",
    "                print(f\"✗ класс {name.split('.')[0]} не объявлен — выполните ячейку упражнения\")\n",
    "                return\n",
    "            continue\n",
    "        if name not in globals():\n",
    "            print(f\"✗ переменная {name} не создана — выполните ячейку упражнения\")\n",
    "            return\n",
    "        if globals()[name] is Ellipsis:\n",
    "            print(f\"✗ {name} пока равна ... — замените многоточие своим кодом\")\n",
    "            return\n",
    "    globals()[\"_TESTS\"] = list(tests)\n",
    "    _run_tests()"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "setup"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Весь курс мы пользовались готовой таблицей `shop_orders.csv`. Теперь соберём её сами — из трёх таблиц,\n",
    "как они лежат в базе магазина: заказы, справочник товаров, справочник клиентов. А заодно получим то, чего\n",
    "в готовой таблице не было: себестоимость, а значит, и прибыль. Новых методов в проекте нет."
   ],
   "id": "text-10"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Три таблицы\n",
    "import pandas as pd\n",
    "\n",
    "orders = pd.read_csv(\"data/orders.csv\")\n",
    "products = pd.read_csv(\"data/products.csv\")\n",
    "customers = pd.read_csv(\"data/customers.csv\")\n",
    "print(orders.shape, products.shape, customers.shape)\n",
    "orders.head(3)"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "load"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Шаг 1. Проверить ключи\n",
    "\n",
    "Прежде чем соединять, убедимся, что в справочниках ключ не повторяется: иначе строки размножатся."
   ],
   "id": "text-16"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Упражнение 1. Уникальны ли ключи\n",
    "\n",
    "Сохраните в `product_key_ok`, уникальны ли значения `product_id` в таблице `products`; в `customer_key_ok` —\n",
    "уникальны ли `customer_id` в `customers`; в `order_key_ok` — уникальны ли `order_id` в `orders`.\n",
    "\n",
    "<details><summary>Подсказка 1</summary>\n",
    "\n",
    "Атрибут столбца `is_unique` возвращает `True` или `False`: `products[\"product_id\"].is_unique`.\n",
    "\n",
    "</details>"
   ],
   "id": "keys-task"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "product_key_ok = ...\n",
    "customer_key_ok = ...\n",
    "order_key_ok = ..."
   ],
   "execution_count": null,
   "outputs": [],
   "id": "keys"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Проверка упражнения 1 — выполните после своего решения\n",
    "def test_keys():\n",
    "    \"уникальны ли ключи трёх таблиц\"\n",
    "    assert product_key_ok is True or product_key_ok == True, \"product_key_ok — это не ответ на вопрос, уникален ли product_id в products\"\n",
    "    assert customer_key_ok is True or customer_key_ok == True, \"customer_key_ok — это не ответ на вопрос, уникален ли customer_id в customers\"\n",
    "    assert not isinstance(order_key_ok, pd.Series), \"order_key_ok — Series, а нужно одно значение True или False\"\n",
    "    assert order_key_ok is False or order_key_ok == False, \"order_key_ok — это не ответ на вопрос, уникален ли order_id в orders\"\n",
    "\n",
    "_check(test_keys, names=[\"product_key_ok\",\"customer_key_ok\",\"order_key_ok\"])"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "keys-check"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Решение</summary>\n",
    "\n",
    "```python\n",
    "product_key_ok = products[\"product_id\"].is_unique\n",
    "customer_key_ok = customers[\"customer_id\"].is_unique\n",
    "order_key_ok = orders[\"order_id\"].is_unique\n",
    "```\n",
    "\n",
    "</details>"
   ],
   "id": "keys-solution"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В справочниках ключи уникальны — так и должно быть. В заказах `order_id` повторяется, и это тоже нормально:\n",
    "заказ состоит из нескольких строк. Значит, оба соединения будут «многие к одному».\n",
    "\n",
    "## Шаг 2. Заказы и товары"
   ],
   "id": "text-27"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Упражнение 2. Выручка и прибыль строки\n",
    "\n",
    "Соедините `orders` с `products` по `product_id` и сохраните в `lines`; попросите pandas проверить, что\n",
    "соединение «многие к одному». Добавьте столбцы `revenue` (цена × количество) и `profit` — прибыль:\n",
    "(цена − себестоимость) × количество.\n",
    "\n",
    "<details><summary>Подсказка 1</summary>\n",
    "\n",
    "`orders.merge(products, on=\"product_id\", validate=\"many_to_one\")`.\n",
    "\n",
    "</details>\n",
    "<details><summary>Подсказка 2</summary>\n",
    "\n",
    "`(lines[\"price\"] - lines[\"cost\"]) * lines[\"quantity\"]`.\n",
    "\n",
    "</details>"
   ],
   "id": "lines-task"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "lines = ...\n",
    "# добавьте в lines столбцы revenue и profit"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "lines"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Проверка упражнения 2 — выполните после своего решения\n",
    "def test_lines():\n",
    "    \"lines — заказы с данными товара\"\n",
    "    assert isinstance(lines, pd.DataFrame), f\"lines — это {type(lines).__name__}, а нужна таблица\"\n",
    "    assert len(lines) == 2448, f\"в lines {len(lines)} строк, а должно остаться 2448\"\n",
    "    assert \"price\" in lines.columns and \"cost\" in lines.columns and \"order_id\" in lines.columns, \"в lines должны быть столбцы и заказов, и товаров\"\n",
    "\n",
    "\n",
    "def test_money():\n",
    "    \"revenue и profit — выручка и прибыль строки\"\n",
    "    assert \"revenue\" in lines.columns and \"profit\" in lines.columns, \"в lines нужны столбцы revenue и profit\"\n",
    "    assert lines[\"revenue\"].sum() == 3301420, \"revenue не тот: выручка строки — цена × количество\"\n",
    "    assert lines[\"profit\"].sum() != 3301420 - 9670, \"себестоимость нужно умножить на количество\"\n",
    "    assert lines[\"profit\"].sum() == 1542150, \"profit не тот: проверьте формулу из условия\"\n",
    "\n",
    "_check(test_lines, test_money, names=[\"lines\"])"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "lines-check"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Решение</summary>\n",
    "\n",
    "```python\n",
    "lines = orders.merge(products, on=\"product_id\", validate=\"many_to_one\")\n",
    "lines[\"revenue\"] = lines[\"price\"] * lines[\"quantity\"]\n",
    "lines[\"profit\"] = (lines[\"price\"] - lines[\"cost\"]) * lines[\"quantity\"]\n",
    "```\n",
    "\n",
    "</details>"
   ],
   "id": "lines-solution"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Шаг 3. Добавить покупателей"
   ],
   "id": "text-41"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Упражнение 3. Город и сегмент\n",
    "\n",
    "Добавьте к `lines` город и сегмент покупателя и сохраните в `full`. Из справочника `customers` возьмите только\n",
    "три столбца: `customer_id`, `city`, `segment`.\n",
    "\n",
    "<details><summary>Подсказка 1</summary>\n",
    "\n",
    "`lines.merge(customers[[\"customer_id\", \"city\", \"segment\"]], on=\"customer_id\", validate=\"many_to_one\")`.\n",
    "\n",
    "</details>"
   ],
   "id": "full-task"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "full = ..."
   ],
   "execution_count": null,
   "outputs": [],
   "id": "full"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Проверка упражнения 3 — выполните после своего решения\n",
    "def test_full():\n",
    "    \"full — к строкам заказов добавлены город и сегмент покупателя\"\n",
    "    assert isinstance(full, pd.DataFrame), f\"full — это {type(full).__name__}, а нужна таблица\"\n",
    "    assert len(full) == 2448, f\"в full {len(full)} строк, а должно остаться 2448\"\n",
    "    assert \"city\" in full.columns and \"segment\" in full.columns, \"в full нужны столбцы city и segment из справочника клиентов\"\n",
    "    assert \"name_x\" not in full.columns and \"name_y\" not in full.columns, \"задвоился столбец name: берите из customers только customer_id, city и segment\"\n",
    "    assert \"signup_date\" not in full.columns, \"лишние столбцы: берите из customers только customer_id, city и segment\"\n",
    "    assert \"name\" in full.columns and \"profit\" in full.columns, \"в full должны остаться столбцы таблицы lines\"\n",
    "\n",
    "_check(test_full, names=[\"full\"])"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "full-check"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Решение</summary>\n",
    "\n",
    "```python\n",
    "full = lines.merge(customers[[\"customer_id\", \"city\", \"segment\"]], on=\"customer_id\", validate=\"many_to_one\")\n",
    "```\n",
    "\n",
    "</details>"
   ],
   "id": "full-solution"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Шаг 4. Сверка\n",
    "\n",
    "Собранную таблицу стоит сверить с чем-нибудь известным. У нас есть готовая `shop_orders.csv` — сравним\n",
    "выручку по городам:"
   ],
   "id": "text-50"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Наша сборка против готовой таблицы\n",
    "shop = pd.read_csv(\"data/shop_orders.csv\")\n",
    "shop[\"revenue\"] = shop[\"price\"] * shop[\"quantity\"]\n",
    "ours = full.groupby(\"city\")[\"revenue\"].sum()\n",
    "theirs = shop.groupby(\"city\")[\"revenue\"].sum()\n",
    "print((ours == theirs).all())\n",
    "print(ours.sum(), theirs.sum())"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "check"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выручка каждого города совпала, общий итог тоже. У обоих Series одинаковые метки в одинаковом порядке\n",
    "(группировка сортирует города по алфавиту), поэтому их можно сравнить через `==`; `all()` подтверждает, что\n",
    "совпало всё.\n",
    "\n",
    "## Шаг 5. Прибыль по городам\n",
    "\n",
    "**Маржа** — доля прибыли в выручке: сколько копеек с каждого рубля остаётся магазину."
   ],
   "id": "text-57"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Упражнение 4. Города\n",
    "\n",
    "Сохраните в `city_table` таблицу по городам (город в индексе): `revenue` — выручка, `profit` — прибыль,\n",
    "`margin` — маржа, округлённая до трёх знаков. Отсортируйте по убыванию прибыли.\n",
    "\n",
    "<details><summary>Подсказка 1</summary>\n",
    "\n",
    "`full.groupby(\"city\").agg(revenue=(\"revenue\", \"sum\"), profit=(\"profit\", \"sum\"))`.\n",
    "\n",
    "</details>\n",
    "<details><summary>Подсказка 2</summary>\n",
    "\n",
    "Маржа — `profit / revenue`; затем `sort_values(\"profit\", ascending=False)`.\n",
    "\n",
    "</details>"
   ],
   "id": "cities-task"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "city_table = ..."
   ],
   "execution_count": null,
   "outputs": [],
   "id": "cities"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Проверка упражнения 4 — выполните после своего решения\n",
    "def test_city_table():\n",
    "    \"city_table — выручка, прибыль и маржа по городам, по убыванию прибыли\"\n",
    "    assert isinstance(city_table, pd.DataFrame), f\"city_table — это {type(city_table).__name__}, а нужна таблица\"\n",
    "    assert list(city_table.columns) == [\"revenue\", \"profit\", \"margin\"], f\"столбцы сейчас {list(city_table.columns)}, а нужны revenue, profit, margin\"\n",
    "    assert len(city_table) == 5 and \"Казань\" in city_table.index, \"в индексе должны быть пять городов\"\n",
    "    assert city_table.loc[\"Москва\", \"profit\"] == 612070, \"profit не тот: нужна прибыль города\"\n",
    "    assert abs(city_table.loc[\"Москва\", \"margin\"] - 0.467) < 1e-9, \"margin не тот: нужна доля прибыли в выручке с округлением до трёх знаков\"\n",
    "    assert city_table[\"profit\"].tolist() == sorted(city_table[\"profit\"].tolist(), reverse=True), \"отсортируйте city_table по убыванию profit\"\n",
    "\n",
    "_check(test_city_table, names=[\"city_table\"])"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "cities-check"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Решение</summary>\n",
    "\n",
    "```python\n",
    "city_table = full.groupby(\"city\").agg(revenue=(\"revenue\", \"sum\"), profit=(\"profit\", \"sum\"))\n",
    "city_table[\"margin\"] = (city_table[\"profit\"] / city_table[\"revenue\"]).round(3)\n",
    "city_table = city_table.sort_values(\"profit\", ascending=False)\n",
    "```\n",
    "\n",
    "</details>"
   ],
   "id": "cities-solution"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Шаг 6. Прибыль по товарам"
   ],
   "id": "text-73"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Упражнение 5. Что приносит деньги\n",
    "\n",
    "Сохраните в `product_table` плоскую таблицу по товарам со столбцами `category`, `name`, `revenue`, `profit`,\n",
    "`margin` (три знака). В `top_profit` сохраните три товара с наибольшей прибылью, в `low_margin` — три товара\n",
    "с наименьшей маржой.\n",
    "\n",
    "<details><summary>Подсказка 1</summary>\n",
    "\n",
    "Группировка по `[\"category\", \"name\"]` с `as_index=False`.\n",
    "\n",
    "</details>\n",
    "<details><summary>Подсказка 2</summary>\n",
    "\n",
    "`product_table.nlargest(3, \"profit\")` и `product_table.nsmallest(3, \"margin\")`.\n",
    "\n",
    "</details>"
   ],
   "id": "products-task"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "product_table = ...\n",
    "top_profit = ...\n",
    "low_margin = ..."
   ],
   "execution_count": null,
   "outputs": [],
   "id": "products"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Проверка упражнения 5 — выполните после своего решения\n",
    "def test_product_table():\n",
    "    \"product_table — выручка, прибыль и маржа по товарам\"\n",
    "    assert isinstance(product_table, pd.DataFrame), f\"product_table — это {type(product_table).__name__}, а нужна таблица\"\n",
    "    assert list(product_table.columns) == [\"category\", \"name\", \"revenue\", \"profit\", \"margin\"], f\"столбцы сейчас {list(product_table.columns)}, а нужны category, name, revenue, profit, margin\"\n",
    "    assert len(product_table) == 20 and product_table[\"profit\"].sum() == 1542150, \"в product_table 20 товаров\"\n",
    "    assert abs(product_table[\"margin\"].max() - 0.625) < 1e-9, \"margin не тот: нужна доля прибыли в выручке с округлением до трёх знаков\"\n",
    "\n",
    "\n",
    "def test_tops():\n",
    "    \"top_profit — три самых прибыльных товара, low_margin — три товара с наименьшей маржой\"\n",
    "    assert isinstance(top_profit, pd.DataFrame) and len(top_profit) == 3, \"top_profit — три строки\"\n",
    "    assert top_profit[\"name\"].tolist() == [\"Эфиопия 250 г\", \"Колумбия 250 г\", \"Кофе без кофеина 250 г\"], f\"в top_profit сейчас {top_profit['name'].tolist()}: нужны три товара с наибольшей прибылью\"\n",
    "    assert isinstance(low_margin, pd.DataFrame) and len(low_margin) == 3, \"low_margin — три строки\"\n",
    "    assert low_margin[\"name\"].tolist() == [\"Бразилия 1 кг\", \"Эспрессо-смесь 1 кг\", \"Кофемолка ручная\"], f\"в low_margin сейчас {low_margin['name'].tolist()}: нужны три товара с наименьшей маржой\"\n",
    "\n",
    "_check(test_product_table, test_tops, names=[\"product_table\",\"top_profit\",\"low_margin\"])"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "products-check"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Решение</summary>\n",
    "\n",
    "```python\n",
    "product_table = full.groupby([\"category\", \"name\"], as_index=False).agg(revenue=(\"revenue\", \"sum\"), profit=(\"profit\", \"sum\"))\n",
    "product_table[\"margin\"] = (product_table[\"profit\"] / product_table[\"revenue\"]).round(3)\n",
    "top_profit = product_table.nlargest(3, \"profit\")\n",
    "low_margin = product_table.nsmallest(3, \"margin\")\n",
    "```\n",
    "\n",
    "</details>"
   ],
   "id": "products-solution"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Шаг 7. Доставка по каналам\n",
    "\n",
    "Данные о доставке — по строке на **заказ**, а наша таблица — по строке на **позицию**. Если соединить их\n",
    "напрямую, срок и оценка заказа повторятся в каждой его позиции, и заказы из трёх позиций будут весить втрое\n",
    "больше. Сначала свернём таблицу до заказов."
   ],
   "id": "text-84"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Упражнение 6. Срок и оценка\n",
    "\n",
    "Склейте файлы `data/delivery_h1.csv` и `data/delivery_h2.csv` в таблицу `delivery`. Соберите `order_table`:\n",
    "сверните `full` до заказов — плоская таблица `order_id`, `channel`, `total` (сумма `revenue`), — и присоедините\n",
    "к ней `delivery` по `order_id` с проверкой «один к одному». В `channel_delivery` сохраните средний срок (`days`)\n",
    "и среднюю оценку (`rating`) по каналам, округлённые до двух знаков.\n",
    "\n",
    "<details><summary>Подсказка 1</summary>\n",
    "\n",
    "`pd.concat([pd.read_csv(...), pd.read_csv(...)], ignore_index=True)`.\n",
    "\n",
    "</details>\n",
    "<details><summary>Подсказка 2</summary>\n",
    "\n",
    "`full.groupby([\"order_id\", \"channel\"], as_index=False).agg(total=(\"revenue\", \"sum\"))`, затем `.merge(delivery, on=\"order_id\", validate=\"one_to_one\")`.\n",
    "\n",
    "</details>\n",
    "<details><summary>Подсказка 3</summary>\n",
    "\n",
    "`order_table.groupby(\"channel\").agg(days=(\"days\", \"mean\"), rating=(\"rating\", \"mean\")).round(2)`.\n",
    "\n",
    "</details>"
   ],
   "id": "delivery-task"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "delivery = ...\n",
    "order_table = ...\n",
    "channel_delivery = ..."
   ],
   "execution_count": null,
   "outputs": [],
   "id": "delivery"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Проверка упражнения 6 — выполните после своего решения\n",
    "def test_delivery():\n",
    "    \"delivery — доставка за год\"\n",
    "    assert isinstance(delivery, pd.DataFrame) and len(delivery) == 1576, \"delivery — обе выгрузки одной таблицей\"\n",
    "    assert delivery.index.is_unique, \"метки delivery повторяются\"\n",
    "\n",
    "\n",
    "def test_order_table():\n",
    "    \"order_table — один заказ в строке: канал, сумма, срок, оценка\"\n",
    "    assert isinstance(order_table, pd.DataFrame), f\"order_table — это {type(order_table).__name__}, а нужна таблица\"\n",
    "    assert len(order_table) != 2448, \"в order_table 2448 строк — это позиции, а не заказы: сначала сгруппируйте full по order_id и channel\"\n",
    "    assert len(order_table) == 1576, f\"в order_table {len(order_table)} строк: нужна одна строка на заказ\"\n",
    "    assert list(order_table.columns) == [\"order_id\", \"channel\", \"total\", \"days\", \"rating\"], f\"столбцы сейчас {list(order_table.columns)}, а нужны order_id, channel, total, days, rating\"\n",
    "    assert order_table[\"total\"].sum() == 3301420, \"total не тот: нужна сумма revenue заказа\"\n",
    "\n",
    "\n",
    "def test_channels():\n",
    "    \"channel_delivery — средний срок и оценка по каналам\"\n",
    "    assert isinstance(channel_delivery, pd.DataFrame) and list(channel_delivery.columns) == [\"days\", \"rating\"], \"channel_delivery — таблица со столбцами days и rating\"\n",
    "    assert channel_delivery.loc[\"маркетплейс\", \"days\"] == 5.25 and channel_delivery.loc[\"сайт\", \"rating\"] == 3.76, \"средние должны быть округлены до двух знаков\"\n",
    "\n",
    "_check(test_delivery, test_order_table, test_channels, names=[\"delivery\",\"order_table\",\"channel_delivery\"])"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "delivery-check"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Решение</summary>\n",
    "\n",
    "```python\n",
    "delivery = pd.concat([pd.read_csv(\"data/delivery_h1.csv\"), pd.read_csv(\"data/delivery_h2.csv\")], ignore_index=True)\n",
    "order_table = full.groupby([\"order_id\", \"channel\"], as_index=False).agg(total=(\"revenue\", \"sum\"))\n",
    "order_table = order_table.merge(delivery, on=\"order_id\", validate=\"one_to_one\")\n",
    "channel_delivery = order_table.groupby(\"channel\").agg(days=(\"days\", \"mean\"), rating=(\"rating\", \"mean\")).round(2)\n",
    "```\n",
    "\n",
    "</details>"
   ],
   "id": "delivery-solution"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Типичная ошибка: разная «единица» у двух таблиц.**\n",
    ">\n",
    "> Перед соединением спросите, что значит одна строка в каждой таблице. Если слева позиции, а справа заказы,\n",
    "> значения заказа размножатся по позициям — и средние, посчитанные после этого, сместятся в сторону больших\n",
    "> заказов. Сведите таблицы к одной единице, потом соединяйте."
   ],
   "id": "component-102"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Итог"
   ],
   "id": "text-107"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Сводка\n",
    "print(\"Выручка:\", full[\"revenue\"].sum(), \"· прибыль:\", full[\"profit\"].sum(), \"· маржа:\", round(full[\"profit\"].sum() / full[\"revenue\"].sum(), 3))\n",
    "print(city_table)\n",
    "print(channel_delivery)"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "summary"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Выводы"
   ],
   "id": "text-111"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*После решения всех упражнений урока:*\n",
    "\n",
    "- За год магазин заработал 1.54 млн ₽ прибыли с 3.3 млн ₽ выручки — маржа 47 %.\n",
    "- Маржа по городам одинакова: 46.5–46.9 %. Города отличаются объёмом, а не выгодностью.\n",
    "- Больше всего прибыли приносят кофе в пачках по 250 г. Килограммовые пачки продаются на те же суммы,\n",
    "  но маржа у них самая низкая — около 40 %.\n",
    "- Маркетплейс везёт заказы на день с лишним дольше (5.3 дня против 4.0), и оценки у него ниже: 3.3\n",
    "  против 3.8. Возможно, поэтому на маркетплейс реже возвращаются."
   ],
   "id": "component-114"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Три таблицы, два соединения, одна склейка — и каждое действие проверено: ключи уникальны, число строк\n",
    "не изменилось, итог сошёлся с известным."
   ],
   "id": "text-122"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Подробнее в справочнике:** [pandas/merge](https://ds-edu-trainer.vercel.app/reference/pandas/merge) · [pandas/merge-keys](https://ds-edu-trainer.vercel.app/reference/pandas/merge-keys) · [pandas/concat](https://ds-edu-trainer.vercel.app/reference/pandas/concat) · [pandas/groupby-agg](https://ds-edu-trainer.vercel.app/reference/pandas/groupby-agg)"
   ],
   "id": "more"
  }
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