{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Итоговый проект: данные\n",
    "\n",
    "Собрать рабочую таблицу магазина из четырёх файлов: заказы, справочник товаров, грязная выгрузка клиентов и журнал возвратов. Почистить, убрать дубли, соединить с проверками, посчитать выручку с учётом возвратов и прибыль, сверить итог.\n",
    "\n",
    "Курс pandas · урок `pd-final-data` · [урок на сайте](https://ds-edu-trainer.vercel.app/courses/pandas/final-data).\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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    "\"\"\",\n",
    "    \"products.csv\": \"\"\"\n",
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    "\"\"\",\n",
    "    \"customers_raw.csv\": \"\"\"\n",
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    "Er3IvKdljskP1xmEymGsTLlIGqYJFbccjS4juVeRoTuvnWa6GS4x4PHI28YcbArZH3oT6Bc4cB6FaA8kico5fmQD14tT7/pwJBTr\n",
    "XZMDGXbLkkAx8WQA57l3Nzj3IhX0EuLTH/oqY/FxRM7xWztHKz9DFgHJDhdxPhauPCapgVZMnnS9hu9XUWXxJEu9exbSTEJEjrRi\n",
    "y4NF9GhFGk5cWHQBEr1UtVVAaCgz8oaC8pwb/FBi4KuiBKOHcJ4jlzi3CZ92mXnzjfRhTtz+5YKhNwYPic7KBLgR5G1gLgDh9ewd\n",
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    "\"\"\",\n",
    "    \"returns_raw.csv\": \"\"\"\n",
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    "\"\"\",\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": [
    "Итоговый проект курса — в двух частях. В этой части вы соберёте данные, в следующей — напишете отчёт.\n",
    "\n",
    "Год закончился, и владелец «Кофейной лавки» хочет знать, сколько магазин **на самом деле** заработал:\n",
    "с учётом себестоимости и возвратов. Готовой таблицы для этого нет. Есть четыре файла, как они лежат\n",
    "в разных системах магазина:\n",
    "\n",
    "| Файл | Что в нём | Состояние |\n",
    "| --- | --- | --- |\n",
    "| `orders.csv` | строки заказов: заказ, дата, покупатель, товар, количество, канал | чистый |\n",
    "| `products.csv` | справочник товаров: название, категория, цена, себестоимость | чистый |\n",
    "| `customers_raw.csv` | выгрузка клиентов из CRM | грязный: пробелы, регистр, дубли |\n",
    "| `returns_raw.csv` | журнал возвратов | грязный: дубли |\n",
    "\n",
    "Новых методов нет — только то, что вы уже умеете. Подсказок меньше, чем обычно: это проверка себя.\n",
    "\n",
    "## Шаг 1. Загрузка"
   ],
   "id": "text-10"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Упражнение 1. Четыре файла\n",
    "\n",
    "Загрузите файлы в таблицы `orders`, `products`, `customers_raw`, `returns_raw`. Столбец `date` в `orders`\n",
    "должен быть настоящей датой.\n",
    "\n",
    "<details><summary>Подсказка 1</summary>\n",
    "\n",
    "Все файлы лежат в папке `data/`. Даты при чтении — параметр `parse_dates=[\"date\"]`.\n",
    "\n",
    "</details>"
   ],
   "id": "load-task"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "import pandas as pd\n",
    "\n",
    "orders = ...\n",
    "products = ...\n",
    "customers_raw = ...\n",
    "returns_raw = ..."
   ],
   "execution_count": null,
   "outputs": [],
   "id": "load"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Проверка упражнения 1 — выполните после своего решения\n",
    "def test_orders():\n",
    "    \"orders — заказы, дата — настоящая дата\"\n",
    "    assert isinstance(orders, pd.DataFrame) and orders.shape == (2448, 6), \"orders — таблица из файла data/orders.csv: 2448 строк, 6 столбцов\"\n",
    "    assert str(orders[\"date\"].dtype).startswith(\"datetime64\"), \"столбец date в orders должен быть датой: вспомните параметр parse_dates\"\n",
    "\n",
    "\n",
    "def test_rest():\n",
    "    \"products, customers_raw, returns_raw — остальные три файла\"\n",
    "    assert isinstance(products, pd.DataFrame) and products.shape == (20, 5), \"products — таблица из data/products.csv: 20 строк, 5 столбцов\"\n",
    "    assert isinstance(customers_raw, pd.DataFrame) and customers_raw.shape == (257, 7), \"customers_raw — таблица из data/customers_raw.csv: 257 строк, 7 столбцов\"\n",
    "    assert isinstance(returns_raw, pd.DataFrame) and returns_raw.shape == (163, 6), \"returns_raw — таблица из data/returns_raw.csv: 163 строки, 6 столбцов\"\n",
    "\n",
    "_check(test_orders, test_rest, names=[\"orders\",\"products\",\"customers_raw\",\"returns_raw\"])"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "load-check"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Решение</summary>\n",
    "\n",
    "```python\n",
    "import pandas as pd\n",
    "\n",
    "orders = pd.read_csv(\"data/orders.csv\", parse_dates=[\"date\"])\n",
    "products = pd.read_csv(\"data/products.csv\")\n",
    "customers_raw = pd.read_csv(\"data/customers_raw.csv\")\n",
    "returns_raw = pd.read_csv(\"data/returns_raw.csv\")\n",
    "```\n",
    "\n",
    "</details>"
   ],
   "id": "load-solution"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Грязные выгрузки\n",
    "print(customers_raw.head(4))\n",
    "print(returns_raw.head(4))"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "look"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Шаг 2. Клиенты\n",
    "\n",
    "Из выгрузки клиентов для отчёта нужны три столбца: код, город и сегмент. Все три нужно почистить — вы уже\n",
    "делали это в модуле об очистке."
   ],
   "id": "text-36"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Упражнение 2. Чистый справочник клиентов\n",
    "\n",
    "Соберите таблицу `customers` из трёх столбцов — `customer_id`, `city`, `segment`: коды без пробелов\n",
    "и с заглавной буквой; города — пять названий без сокращений (словарь `short` уже есть); сегменты — три значения\n",
    "строчными буквами. Один клиент — одна строка; индекс с нуля.\n",
    "\n",
    "<details><summary>Подсказка 1</summary>\n",
    "\n",
    "Текст: `.str.strip()`, затем `.str.upper()` для кода, `.str.title().replace(short)` для города, `.str.lower()` для сегмента.\n",
    "\n",
    "</details>\n",
    "<details><summary>Подсказка 2</summary>\n",
    "\n",
    "Дубликаты — после очистки: выберите три столбца, затем `.drop_duplicates().reset_index(drop=True)`.\n",
    "\n",
    "</details>"
   ],
   "id": "customers-task"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "short = {\"Спб\": \"Санкт-Петербург\", \"С.-Петербург\": \"Санкт-Петербург\", \"Екб\": \"Екатеринбург\"}\n",
    "customers = ..."
   ],
   "execution_count": null,
   "outputs": [],
   "id": "customers"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Проверка упражнения 2 — выполните после своего решения\n",
    "def test_clean():\n",
    "    \"customers — три чистых столбца\"\n",
    "    assert isinstance(customers, pd.DataFrame), f\"customers — это {type(customers).__name__}, а нужна таблица\"\n",
    "    assert list(customers.columns) == [\"customer_id\", \"city\", \"segment\"], f\"столбцы сейчас {list(customers.columns)}, а нужны customer_id, city, segment\"\n",
    "    ids = customers[\"customer_id\"].tolist()\n",
    "    assert all(i == i.strip().upper() for i in ids), \"в customer_id остались пробелы или строчные буквы\"\n",
    "    cities = sorted(customers[\"city\"].unique())\n",
    "    assert cities == [\"Екатеринбург\", \"Казань\", \"Москва\", \"Новосибирск\", \"Санкт-Петербург\"], f\"в city сейчас {cities}, а должно быть пять городов: уберите пробелы, приведите регистр и замените сокращения\"\n",
    "    assert sorted(customers[\"segment\"].unique()) == [\"новый\", \"оптовый\", \"постоянный\"], \"в segment должно быть три значения: уберите пробелы и приведите к строчным\"\n",
    "\n",
    "\n",
    "def test_unique():\n",
    "    \"один клиент — одна строка\"\n",
    "    assert isinstance(customers, pd.DataFrame) and \"customer_id\" in customers.columns, \"сначала исправьте то, о чём говорит проверка выше\"\n",
    "    assert len(customers) != 257, \"в customers все 257 строк: дубликаты не удалены — удаляйте их после очистки текста\"\n",
    "    assert len(customers) == 240 and customers[\"customer_id\"].is_unique, f\"в customers {len(customers)} строк: коды клиентов не должны повторяться\"\n",
    "    assert list(customers.index) == list(range(240)), \"индекс customers должен идти с нуля подряд\"\n",
    "\n",
    "_check(test_clean, test_unique, names=[\"customers\"])"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "customers-check"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Решение</summary>\n",
    "\n",
    "```python\n",
    "short = {\"Спб\": \"Санкт-Петербург\", \"С.-Петербург\": \"Санкт-Петербург\", \"Екб\": \"Екатеринбург\"}\n",
    "customers = customers_raw.assign(\n",
    "    customer_id=lambda d: d[\"customer_id\"].str.strip().str.upper(),\n",
    "    city=lambda d: d[\"city\"].str.strip().str.title().replace(short),\n",
    "    segment=lambda d: d[\"segment\"].str.strip().str.lower(),\n",
    ")\n",
    "customers = customers[[\"customer_id\", \"city\", \"segment\"]].drop_duplicates().reset_index(drop=True)\n",
    "```\n",
    "\n",
    "</details>"
   ],
   "id": "customers-solution"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Шаг 3. Возвраты\n",
    "\n",
    "В журнале возвратов два вида дублей: записи, сохранённые дважды, и возвраты, оформленные повторно под новым\n",
    "номером. Возврат — это позиция заказа: пара «заказ, товар»."
   ],
   "id": "text-50"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Упражнение 3. Чистый журнал возвратов\n",
    "\n",
    "Соберите таблицу `returns`: по одной строке на возвращённую позицию, столбцы `order_id`, `product_id`\n",
    "и `returned` (число возвращённых штук — переименованный столбец `quantity`); индекс с нуля.\n",
    "\n",
    "<details><summary>Подсказка 1</summary>\n",
    "\n",
    "Два шага удаления: `.drop_duplicates()` и `.drop_duplicates(subset=[\"order_id\", \"product_id\"])`.\n",
    "\n",
    "</details>\n",
    "<details><summary>Подсказка 2</summary>\n",
    "\n",
    "Затем выбор трёх столбцов, `.rename(columns={\"quantity\": \"returned\"})` и `.reset_index(drop=True)`.\n",
    "\n",
    "</details>"
   ],
   "id": "returns-task"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "returns = (\n",
    "    returns_raw\n",
    "    # допишите шаги цепочки\n",
    ")"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "returns"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Проверка упражнения 3 — выполните после своего решения\n",
    "def test_returns():\n",
    "    \"returns — по строке на возвращённую позицию: order_id, product_id, returned\"\n",
    "    assert isinstance(returns, pd.DataFrame), f\"returns — это {type(returns).__name__}, а нужна таблица\"\n",
    "    assert list(returns.columns) == [\"order_id\", \"product_id\", \"returned\"], f\"столбцы сейчас {list(returns.columns)}, а нужны order_id, product_id, returned: выберите три столбца и переименуйте quantity\"\n",
    "    assert len(returns) != 163, \"в returns все 163 строки: дубликаты не удалены\"\n",
    "    assert len(returns) != 156, \"удалены только полные повторы (осталось 156): ещё шесть возвратов оформлены дважды под разными номерами — ищите повторы по паре заказ и товар\"\n",
    "    assert len(returns) == 150, f\"в returns {len(returns)} строк — проверьте удаление повторов\"\n",
    "    assert returns[\"returned\"].sum() == 214, \"сумма returned не та: проверьте удаление повторов\"\n",
    "    assert not returns.duplicated(subset=[\"order_id\", \"product_id\"]).any(), \"пара order_id и product_id не должна повторяться\"\n",
    "\n",
    "_check(test_returns)"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "returns-check"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Решение</summary>\n",
    "\n",
    "```python\n",
    "returns = (\n",
    "    returns_raw\n",
    "    .drop_duplicates()\n",
    "    .drop_duplicates(subset=[\"order_id\", \"product_id\"])\n",
    "    [[\"order_id\", \"product_id\", \"quantity\"]]\n",
    "    .rename(columns={\"quantity\": \"returned\"})\n",
    "    .reset_index(drop=True)\n",
    ")\n",
    "```\n",
    "\n",
    "</details>"
   ],
   "id": "returns-solution"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Шаг 4. Соединение\n",
    "\n",
    "Три соединения — и каждое стоит проверить параметром `validate`: заказы с товарами и с клиентами — «многие\n",
    "к одному», с возвратами — «один к одному» по двум ключам. Возвраты есть не у всех позиций."
   ],
   "id": "text-63"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Упражнение 4. Одна таблица\n",
    "\n",
    "Соедините `orders` с `products`, `customers` и `returns` и сохраните в `data`. Все 2448 строк заказов должны\n",
    "остаться.\n",
    "\n",
    "<details><summary>Подсказка 1</summary>\n",
    "\n",
    "`.merge(products, on=\"product_id\", validate=\"many_to_one\")`, так же `customers` по `customer_id`.\n",
    "\n",
    "</details>\n",
    "<details><summary>Подсказка 2</summary>\n",
    "\n",
    "Возвраты: `.merge(returns, on=[\"order_id\", \"product_id\"], how=\"left\", validate=\"one_to_one\")`.\n",
    "\n",
    "</details>"
   ],
   "id": "join-task"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "data = (\n",
    "    orders\n",
    "    # допишите три соединения\n",
    ")"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "join"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Проверка упражнения 4 — выполните после своего решения\n",
    "def test_shape():\n",
    "    \"data — заказы с товарами, покупателями и возвратами\"\n",
    "    assert isinstance(data, pd.DataFrame), f\"data — это {type(data).__name__}, а нужна таблица\"\n",
    "    assert len(data) != 150, \"осталось 150 строк — только позиции с возвратом: нужны все строки заказов — вспомните параметр how\"\n",
    "    assert len(data) == 2448, f\"в data {len(data)} строк, а должно остаться 2448 — столько же, сколько в orders\"\n",
    "\n",
    "\n",
    "def test_columns():\n",
    "    \"в data есть столбцы всех четырёх таблиц\"\n",
    "    assert isinstance(data, pd.DataFrame), \"сначала исправьте то, о чём говорит проверка выше\"\n",
    "    for column, source in [(\"price\", \"products\"), (\"cost\", \"products\"), (\"city\", \"customers\"), (\"segment\", \"customers\"), (\"returned\", \"returns\")]:\n",
    "        assert column in data.columns, f\"в data нет столбца {column} — не присоединена таблица {source}\"\n",
    "    assert data[\"returned\"].notna().sum() == 150, \"возвраты стоят не у тех позиций: соединяйте по двум ключам сразу\"\n",
    "\n",
    "_check(test_shape, test_columns)"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "join-check"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Решение</summary>\n",
    "\n",
    "```python\n",
    "data = (\n",
    "    orders\n",
    "    .merge(products, on=\"product_id\", validate=\"many_to_one\")\n",
    "    .merge(customers, on=\"customer_id\", validate=\"many_to_one\")\n",
    "    .merge(returns, on=[\"order_id\", \"product_id\"], how=\"left\", validate=\"one_to_one\")\n",
    ")\n",
    "```\n",
    "\n",
    "</details>"
   ],
   "id": "join-solution"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если бы в справочнике клиентов остались дубли, `validate` остановил бы соединение ошибкой — и это лучше, чем\n",
    "размноженные строки и завышенная выручка.\n",
    "\n",
    "## Шаг 5. Деньги"
   ],
   "id": "text-76"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Упражнение 5. Выручка, возвраты, прибыль\n",
    "\n",
    "В столбце `returned` замените пропуски нулями и сделайте его целым. Добавьте в `data` три столбца:\n",
    "\n",
    "- `revenue` — выручка: цена × количество;\n",
    "- `net` — выручка за вычетом возвратов: цена × (количество − возвращено);\n",
    "- `profit` — прибыль с проданного и не возвращённого: (цена − себестоимость) × (количество − возвращено).\n",
    "\n",
    "<details><summary>Подсказка 1</summary>\n",
    "\n",
    "`data[\"returned\"] = data[\"returned\"].fillna(0).astype(\"int64\")`.\n",
    "\n",
    "</details>\n",
    "<details><summary>Подсказка 2</summary>\n",
    "\n",
    "`data[\"price\"] * (data[\"quantity\"] - data[\"returned\"])`.\n",
    "\n",
    "</details>"
   ],
   "id": "money-task"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# заполните пропуски в data[\"returned\"] и добавьте столбцы revenue, net, profit"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "money"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Проверка упражнения 5 — выполните после своего решения\n",
    "def test_returned():\n",
    "    \"returned — целое число, без пропусков\"\n",
    "    assert data[\"returned\"].isna().sum() == 0, \"в returned остались пропуски: позиция без возврата — ноль возвращённых штук. Результат запишите обратно в столбец\"\n",
    "    assert str(data[\"returned\"].dtype) in (\"int64\", \"int32\"), \"returned должен быть целым\"\n",
    "    assert data[\"returned\"].sum() == 214, \"сумма returned не та: пропуски — нули, остальное не меняется\"\n",
    "\n",
    "\n",
    "def test_money():\n",
    "    \"revenue — выручка, net — выручка за вычетом возвратов, profit — прибыль с проданного\"\n",
    "    for column in [\"revenue\", \"net\", \"profit\"]:\n",
    "        assert column in data.columns, f\"в data нет столбца {column}\"\n",
    "    assert data[\"revenue\"].sum() == 3301420, \"revenue не тот: выручка — цена × количество\"\n",
    "    assert data[\"net\"].sum() != 3301420, \"net равна revenue: из количества нужно вычесть возвращённые штуки\"\n",
    "    assert data[\"net\"].sum() == 3159140, \"net не тот: проверьте формулу из условия\"\n",
    "    assert data[\"profit\"].sum() != 1542150, \"в profit не учтены возвраты\"\n",
    "    assert data[\"profit\"].sum() == 1476130, \"profit не тот: проверьте формулу из условия\"\n",
    "\n",
    "_check(test_returned, test_money)"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "money-check"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Решение</summary>\n",
    "\n",
    "```python\n",
    "data[\"returned\"] = data[\"returned\"].fillna(0).astype(\"int64\")\n",
    "data[\"revenue\"] = data[\"price\"] * data[\"quantity\"]\n",
    "data[\"net\"] = data[\"price\"] * (data[\"quantity\"] - data[\"returned\"])\n",
    "data[\"profit\"] = (data[\"price\"] - data[\"cost\"]) * (data[\"quantity\"] - data[\"returned\"])\n",
    "```\n",
    "\n",
    "</details>"
   ],
   "id": "money-solution"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Шаг 6. Проверка\n",
    "\n",
    "Собранной таблице нельзя верить на слово. Три проверки: сверка с известным итогом, отсутствие пропусков\n",
    "и здравый смысл."
   ],
   "id": "text-92"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Упражнение 6. Сходится ли\n",
    "\n",
    "У нас есть готовая таблица `shop` — сверим с ней выручку по городам. Сохраните в `ours` выручку (`revenue`)\n",
    "по городам в `data`, в `theirs` — в `shop`. В `cities_match` — одно значение `True` или `False`: совпали ли\n",
    "все города. В `no_gaps` — `True`, если во всей таблице `data` нет ни одного пропуска. В `lost` — сколько\n",
    "рублей выручки ушло в возвраты.\n",
    "\n",
    "<details><summary>Подсказка 1</summary>\n",
    "\n",
    "`(ours == theirs).all()` — одно значение вместо пяти.\n",
    "\n",
    "</details>\n",
    "<details><summary>Подсказка 2</summary>\n",
    "\n",
    "Все пропуски таблицы: `data.isna().sum().sum()` — сумма по столбцам и ещё раз сумма.\n",
    "\n",
    "</details>"
   ],
   "id": "check-task"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "shop = pd.read_csv(\"data/shop_orders.csv\")\n",
    "shop[\"revenue\"] = shop[\"price\"] * shop[\"quantity\"]\n",
    "ours = ...\n",
    "theirs = ...\n",
    "cities_match = ...\n",
    "no_gaps = ...\n",
    "lost = ..."
   ],
   "execution_count": null,
   "outputs": [],
   "id": "check"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Проверка упражнения 6 — выполните после своего решения\n",
    "def test_match():\n",
    "    \"ours и theirs — выручка по городам в нашей таблице и в готовой; cities_match — совпали ли\"\n",
    "    assert isinstance(ours, pd.Series) and len(ours) == 5 and ours.sum() == 3301420, \"ours — выручка (revenue) по городам в таблице data\"\n",
    "    assert isinstance(theirs, pd.Series) and len(theirs) == 5 and theirs.sum() == 3301420, \"theirs — выручка по городам в таблице shop\"\n",
    "    assert not isinstance(cities_match, pd.Series), \"cities_match — Series из пяти значений, а нужен один ответ\"\n",
    "    assert cities_match == True, \"cities_match — это не ответ на вопрос, совпала ли выручка по всем городам\"\n",
    "\n",
    "\n",
    "def test_rest():\n",
    "    \"no_gaps — нет ли пропусков; lost — сколько выручки ушло в возвраты\"\n",
    "    assert not isinstance(no_gaps, (pd.Series, pd.DataFrame)), \"no_gaps — не одно значение: нужен один ответ по всей таблице\"\n",
    "    assert no_gaps == True, \"no_gaps — это не ответ на вопрос, нет ли в data пропусков\"\n",
    "    assert lost == 142280, f\"lost = {lost!r} — это не разница сумм revenue и net\"\n",
    "\n",
    "_check(test_match, test_rest, names=[\"ours\",\"theirs\",\"cities_match\",\"no_gaps\",\"lost\"])"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "check-check"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Решение</summary>\n",
    "\n",
    "```python\n",
    "shop = pd.read_csv(\"data/shop_orders.csv\")\n",
    "shop[\"revenue\"] = shop[\"price\"] * shop[\"quantity\"]\n",
    "ours = data.groupby(\"city\")[\"revenue\"].sum()\n",
    "theirs = shop.groupby(\"city\")[\"revenue\"].sum()\n",
    "cities_match = (ours == theirs).all()\n",
    "no_gaps = data.isna().sum().sum() == 0\n",
    "lost = data[\"revenue\"].sum() - data[\"net\"].sum()\n",
    "```\n",
    "\n",
    "</details>"
   ],
   "id": "check-solution"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Рабочая таблица\n",
    "print(data.shape)\n",
    "print(list(data.columns))\n",
    "data[[\"date\", \"name\", \"city\", \"segment\", \"quantity\", \"returned\", \"revenue\", \"net\", \"profit\"]].head(4)"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "result"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Что получилось"
   ],
   "id": "text-109"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*После решения всех упражнений урока:*\n",
    "\n",
    "Из четырёх файлов — одна таблица: 2448 строк, 16 столбцов, ни одного пропуска. Выручка по городам совпала\n",
    "с известной. Возвраты уменьшили выручку на 142 тысячи рублей — 4.3 %."
   ],
   "id": "component-112"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "По дороге были сделаны все шаги, которые отличают аккуратную работу от «как-нибудь»:\n",
    "\n",
    "- грязные таблицы почищены **до** соединения;\n",
    "- дубли удалены по правильному ключу;\n",
    "- каждое соединение проверено: тип связи и число строк;\n",
    "- пропуски после соединения заполнены осмысленно;\n",
    "- итог сверен с независимым источником.\n",
    "\n",
    "В следующем уроке эта таблица станет отчётом."
   ],
   "id": "text-116"
  },
  {
   "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/duplicates](https://ds-edu-trainer.vercel.app/reference/pandas/duplicates) · [pandas/strings](https://ds-edu-trainer.vercel.app/reference/pandas/strings) · [pandas/method-chaining](https://ds-edu-trainer.vercel.app/reference/pandas/method-chaining)"
   ],
   "id": "more"
  }
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