{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Интервалы\n",
    "\n",
    "Превратить числа в группы: pd.cut делит по заданным границам и подписывает интервалы, pd.qcut — на равные по числу строк части. Как читать запись (0, 15], что происходит на границах и за ними.\n",
    "\n",
    "Курс pandas · урок `pd-cut` · [урок на сайте](https://ds-edu-trainer.vercel.app/courses/pandas/cut).\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",
    "    \"weather.csv\": \"\"\"\n",
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    "QdhBfX1fsFq0yIIee0lJVt4iw+d3QJOkb/puoNiwt9r4ScN9lMabmeKdDNI6k04RFBnU2/p+bXNVmFoRlPq61dtIDkRAy6oHvoHQ\n",
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    "PximTRO0s11r8axVsTFftreACtlbtZqoQ+a8pyM2BQ47bIqmIzY11lApRj+4IzZJ6OtZ+IBNB0UZw6GO2ATxY9HPOvELZFKJHbGp\n",
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    "s4oo4gr4SLfK4Ecrt6rzkoAIHGf1LNr8e3LjpaFpruu40ssdmMw2CshMFATqsrKbsqyraib5sACQD7J1AYIyWv1OItEokXCDPFYe\n",
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    "N5nD4lJkjEugNTAQlzp/vVVsGhaXBu9iClznN3CXFnhlk0kyDcAl1t/ZClNy0KXRwuZVwzpWOAQ04Wp9bThOWiNwdm12Mn8Y8hvQ\n",
    "dM83VnrwTdBUtWwZARlXQU7QYELAv8VAuroTvWGBaWhRrQ4sW8wWEBPFExC+I0asRUsdDqDTwrBSPwpjOlBobQJpGLGRYYOmsX1Y\n",
    "MdfbDMQmqZw4aTVsGsNfvzgQmyRdyERjoFvSVQQDHFY8p8mcUMe64WAP6ctKrzZNoc3wXVlS+EFLnQmj1R8YWG4ewE7Q1QPA2oUD\n",
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    "jsKBdrS3uglbFM2+zOqAoW5FD4g5Yl7kWYTh4lA/GxYofLrI4cNXacRp+wkmkWEfRzT6sdd71VVIIeRObFUmYbApSksAwBnMXhFB\n",
    "yGQonOvkMXzU7ZAFSJsYJ+y1IpqebfBmrCHVEx2H+azsbh0OCDfEp0d9598g+xKWYj1WdQa2JVQlHDUASbMe8dCePsdlqFWTMBMM\n",
    "hALxqnhL5hxtLMWorp9V4Fn9yK8Q3JEeqom+ZnjCZaANeJHqlWUd3UErLKtIC581vdCgUmZva8lZFM5i2qSv0SMsY94HvuOlj4R1\n",
    "zHvsmBydjpIfMFMCRljIvNciRcPbxUrm3aI5Khz6u1wcyjtMWrqGRPYorXOswBSfCrRDMIppYGTYazRMXc8SsmnAK1xYEpeoUtbs\n",
    "PrLVEOWY6wbEQVOQNO8qV2RyLGguLOludgmEFc1SoZjdIMSCFba+DJewpFl2XNmTF8EVEKNEndWoHVh9pl49oToI2aLmusr/NdM0\n",
    "gSOQ0/pZs6ruPw9kKviSrWonLFiW+uhk+JxYrnwojgRDUNEwI+f5nYGyKsKMhpmq6yaTpvxVU2bMdxMZMmHD8tIml+CRflDHmslu\n",
    "9c4oGpZE5ToENMxoSBJ1xQOa1EeEdZVDriGL6m3IkSD2ufaTsFw5SFUsAQGTsu8+rkwsVg7CD4CUOmGl8s5JwcgKC5W5krfihpuw\n",
    "TpmL8aWqLRrGmalvZOHPonoWrXGHI/9dgU+mCQLj2Es0YI+t7geunCpM/wCymGIA1eNmlKHfpvkRUeg/UBxPWKeceVtjYAnLlHlm\n",
    "TWEVYZVynl0zyTPCIuWdaWRnXyNJmhpJA4/GhbQFtdXp+OgBHK31oP7ph4nEv/l/UASf5A==\n",
    "\"\"\",\n",
    "    \"delivery_h1.csv\": \"\"\"\n",
    "eNpFWTkOZTcOzH0WBeJOncYw0Magkxmgx4lvbzyJVY4+IelRFMni9v/368efv37/+WP9+OPv/69ff/z187//+U323rJ8XUKXL7+U\n",
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    "n1lEBmFC+IqME4nY3C3Qj0hgCS4qQK9Ic+mAl26sKeVXyK8GeS6ELxFrDiUO1bxDe5xQlOxtg4MNBMR0zpvx1ChfDMoxSm/UDiEs\n",
    "Ru07zCsuw9bB3w1KcfD34FKufqbx4tpYVxzRUwLaD2r/QvhRAzEJqj8CbCMhY0BB0eRxBk6Se0RLGSsltJ8IQZLQfn7Wvewz8R31\n",
    "kxOAJCcASUH6kjldXwC9PIvaL2ingptUfxVYUPgL32vTnggqLXDvVm5CO+3DouGbTfbErjSk7zPinA0NHkQfOTosDuK/HGRGOTEs\n",
    "DnV/yP/0Enm757mwblygW7A0zqMXunEpJxUTEXTnvFeRenX3k0w31KOyvyvlkjJCquhIpDIKUhkFqSA9quQLriqFm6QhrcA9VSf4\n",
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    "6IZI5MSxm3MNBnAbB3JDXeEGELid1W/ttsb3BgcI3AEz9xkauE/h4g4NeUIHXliCgzqTsl8U35uCFgiAzNkbO2HsAQvH9B0etfoR\n",
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    "/0wdAyc641SwZ2OcCoQlB9OpUFBqcbfnJg620lChJErptOma0iY/JhGchhorjfLb5Pj8F8FpY+J0lOrpVJBPgE7H2DLZF6fzBU4L\n",
    "OIrQdFqAs60MeFHGpIAMPCFQrGdMmZgxCMtAO5NR+K7xHaJ05iYFBGfiATkZJtNHszkpIBM1aHI0nYnRcTL/5p1t3V3k3ywdx887\n",
    "3LofsIjOooIKZVxWcQ0pMlFGJ4fT2TMeylbsoQ/Inn4sO3g8cWrSTDZCUDYVdDakPYjReZAjk5Pp5HwrDzDA0XSyjM6DOjTPPKD2\n",
    "BoEiqDCarm0gxgK1p4arjdlQ7cLSRIgCfotJuGT8vwQJpmQSTInzFMYGxW64pPBlv4eVTCdZCniVjm8WptJF/JY6jkN2TX5XPNWk\n",
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    "TXGiVUBuRfMUlJNAViVifwG5hdRbidhfidhWCeXc7vctAVeVSC7F0rlq0mOx/a1CgVUF69bEhiJwq4qn4DsF10TyrZ7AUE31NHJj\n",
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    "/J3UMnmxBTOmFnAnbFvg+D3/J/0DpZ50FA==\n",
    "\"\"\",\n",
    "    \"shop_orders.csv\": \"\"\"\n",
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    "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",
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    "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",
    "ZvV8Ny2sYkJBQl8HwF3UQqdk4wicVzPdK0VbE0vcsnS++MrC045Opa5gEqy2YifmH5IK+jN1/zwHMR/BpWhqBDonRXZBe3VTHUDN\n",
    "Zq8yGL5hZqB7IL+61YxE+JTT3RF2dcUfiUBt/DmvIp99BjO1n+GcErjoCrbawIWuz5Lu50Ub3UdZ6ShLWz96WbOnsMvfZ+3OeHa8\n",
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    "/dGzfI4g3vfEGlUQ+331ANcZ5XGqCuKWz30qaEXE+xAQo3wCHAhduMVcow+wpV1AQdz2uWpbc23Fq+Q+PPi/AOwhIAI=\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": [
    "Точная температура — 17.3 или 18.1 градуса — нужна редко. Обычно хватает «прохладно», «тепло», «жарко».\n",
    "Так же с ценами («дешёвые», «средние», «дорогие»), возрастом, сроками. Превращение чисел в группы по\n",
    "интервалам — частая операция, и для неё есть готовая функция.\n",
    "\n",
    "## pd.cut: свои границы\n",
    "\n",
    "`pd.cut` получает столбец и список границ `bins`. Четыре интервала задают пять границ:"
   ],
   "id": "text-10"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Дневной максимум — по интервалам\n",
    "import pandas as pd\n",
    "\n",
    "weather = pd.read_csv(\"data/weather.csv\", parse_dates=[\"date\"])\n",
    "moscow = weather[weather[\"city\"] == \"Москва\"].copy()\n",
    "pd.cut(moscow[\"temp_max\"], bins=[-50, 0, 15, 25, 50]).head(3)"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "intervals"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вместо температуры в каждой строке теперь **интервал**. Запись `(-50, 0]` читается как в математике: круглая\n",
    "скобка — граница не входит, квадратная — входит. То есть «больше −50 и не больше 0».\n",
    "\n",
    "Тип результата — `category`, **категория**: значение из заранее известного набора. Строка `Categories` внизу\n",
    "перечисляет набор, а знаки `<` показывают, что у категорий есть порядок."
   ],
   "id": "text-20"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Сколько дней в каждом интервале\n",
    "pd.cut(moscow[\"temp_max\"], bins=[-50, 0, 15, 25, 50]).value_counts().sort_index()"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "interval-counts"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Порядок пригодился: `sort_index` расставил интервалы по шкале — от холодных к тёплым.\n",
    "\n",
    "Интервалам можно дать названия — параметром `labels`. Названий должно быть на одно меньше, чем границ:"
   ],
   "id": "text-28"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Подписи вместо интервалов\n",
    "names = [\"ниже нуля\", \"прохладно\", \"тепло\", \"жарко\"]\n",
    "moscow[\"kind\"] = pd.cut(moscow[\"temp_max\"], bins=[-50, 0, 15, 25, 50], labels=names)\n",
    "moscow[[\"date\", \"temp_max\", \"kind\"]].head(3)"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "labels"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Дни по типам\n",
    "moscow[\"kind\"].value_counts().sort_index()"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "label-counts"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Упражнение 1. Климат Сочи\n",
    "\n",
    "Добавьте в таблицу `sochi` столбец `kind` — тип дня по `temp_max` с теми же границами и подписями, что для\n",
    "Москвы: до 0 — «ниже нуля», до 15 — «прохладно», до 25 — «тепло», выше — «жарко». В `kinds` сохраните число\n",
    "дней каждого типа по порядку шкалы.\n",
    "\n",
    "<details><summary>Подсказка 1</summary>\n",
    "\n",
    "`pd.cut(sochi[\"temp_max\"], bins=[-50, 0, 15, 25, 50], labels=[...])` — четыре подписи.\n",
    "\n",
    "</details>\n",
    "<details><summary>Подсказка 2</summary>\n",
    "\n",
    "`sochi[\"kind\"].value_counts().sort_index()`.\n",
    "\n",
    "</details>"
   ],
   "id": "sochi-task"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "sochi = weather[weather[\"city\"] == \"Сочи\"].copy()\n",
    "# добавьте в sochi столбец kind\n",
    "kinds = ..."
   ],
   "execution_count": null,
   "outputs": [],
   "id": "sochi"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Проверка упражнения 1 — выполните после своего решения\n",
    "def test_kind():\n",
    "    \"kind — тип дня по дневному максимуму\"\n",
    "    assert \"kind\" in sochi.columns, \"в sochi нет столбца kind\"\n",
    "    assert sochi[\"kind\"].isna().sum() == 0, \"в kind есть пропуски: границы должны покрывать все температуры\"\n",
    "    found = sorted(map(str, sochi[\"kind\"].unique()))\n",
    "    assert set(found) <= {\"ниже нуля\", \"прохладно\", \"тепло\", \"жарко\"}, f\"в kind значения {found}, а нужны подписи типов дней: вспомните параметр labels\"\n",
    "    assert (sochi[\"kind\"] == \"жарко\").sum() == 50, \"границы не те: сверьте их с условием\"\n",
    "\n",
    "\n",
    "def test_kinds():\n",
    "    \"kinds — сколько дней каждого типа, по порядку шкалы\"\n",
    "    assert isinstance(kinds, pd.Series), f\"kinds — это {type(kinds).__name__}, а нужен результат value_counts()\"\n",
    "    assert list(kinds.index) != [\"тепло\", \"прохладно\", \"жарко\", \"ниже нуля\"], \"типы идут по убыванию числа дней, а нужны по порядку шкалы: отсортируйте по индексу\"\n",
    "    assert list(kinds.index) == [\"ниже нуля\", \"прохладно\", \"тепло\", \"жарко\"], f\"порядок сейчас {list(kinds.index)}, а нужен от «ниже нуля» к «жарко»\"\n",
    "    assert kinds.tolist() == [0, 137, 178, 50], f\"значения сейчас {kinds.tolist()}\"\n",
    "\n",
    "_check(test_kind, test_kinds, names=[\"kinds\"])"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "sochi-check"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Решение</summary>\n",
    "\n",
    "```python\n",
    "sochi = weather[weather[\"city\"] == \"Сочи\"].copy()\n",
    "sochi[\"kind\"] = pd.cut(sochi[\"temp_max\"], bins=[-50, 0, 15, 25, 50], labels=[\"ниже нуля\", \"прохладно\", \"тепло\", \"жарко\"])\n",
    "kinds = sochi[\"kind\"].value_counts().sort_index()\n",
    "```\n",
    "\n",
    "</details>"
   ],
   "id": "sochi-solution"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Дней «ниже нуля» в Сочи нет, но строка с нулём в ответе осталась: категория помнит весь набор значений,\n",
    "даже те, что не встретились.\n",
    "\n",
    "## Границы\n",
    "\n",
    "Куда попадёт значение, равное границе? По умолчанию — в интервал **слева** от неё: правая граница входит."
   ],
   "id": "text-45"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Правая граница входит\n",
    "marks = pd.Series([0, 15, 15.1, 25])\n",
    "print(pd.cut(marks, bins=[0, 15, 25]).tolist())"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "edges"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В списке интервалы показаны подробной записью: `closed='right'` значит «закрыт справа», то есть `(0, 15]`.\n",
    "Ровно 15 — это ещё `(0, 15]`, а 15.1 — уже `(15, 25]`. Ноль не попал никуда: левая граница первого интервала\n",
    "не входит, и вместо интервала стоит `nan`.\n",
    "\n",
    "Параметр `right=False` переворачивает правило: входит левая граница, правая — нет."
   ],
   "id": "text-54"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Левая граница входит\n",
    "print(pd.cut(marks, bins=[0, 15, 25], right=False).tolist())"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "edges-left"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Значения **за пределами** границ тоже становятся пропусками — без ошибки и предупреждения:"
   ],
   "id": "text-62"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# 60 лет — за последней границей\n",
    "ages = pd.Series([5, 15, 30, 60])\n",
    "pd.cut(ages, bins=[0, 15, 30], labels=[\"дети\", \"молодые\"])"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "outside"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Типичная ошибка: значения за границами молча пропадают.**\n",
    ">\n",
    "> Если крайние границы не покрывают все данные, часть строк получит `NaN` и выпадет из подсчётов. Берите\n",
    "> границы с запасом (в примере с температурой — −50 и 50) и проверяйте `isna().sum()` результата."
   ],
   "id": "component-67"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Вопрос.** Какую подпись получит значение 10:\n",
    "`pd.cut(pd.Series([10]), bins=[0, 10, 20], labels=[\"низкий\", \"высокий\"])`?\n",
    "\n",
    "- `низкий`\n",
    "- `высокий`\n",
    "- `nan`\n",
    "\n",
    "<details><summary>Ответ</summary>\n",
    "\n",
    "`низкий`\n",
    "\n",
    "Интервалы — `(0, 10]` и `(10, 20]`. Правая граница входит, поэтому 10 относится к первому — «низкий». С `right=False` интервалы стали бы `[0, 10)` и `[10, 20)`, и 10 попало бы в «высокий».\n",
    "\n",
    "</details>"
   ],
   "id": "quiz-72"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Упражнение 2. Скорость доставки\n",
    "\n",
    "Добавьте в `delivery` столбец `speed` по сроку `days`: до 3 дней включительно — «быстро», 4–5 дней —\n",
    "«нормально», 6 дней и больше — «долго». Самый долгий срок в данных — 13 дней. В `speed_share` сохраните\n",
    "доли каждой группы по порядку шкалы.\n",
    "\n",
    "<details><summary>Подсказка 1</summary>\n",
    "\n",
    "Границы `[0, 3, 5, 14]`: интервалы `(0, 3]`, `(3, 5]`, `(5, 14]`.\n",
    "\n",
    "</details>\n",
    "<details><summary>Подсказка 2</summary>\n",
    "\n",
    "`delivery[\"speed\"].value_counts(normalize=True).sort_index()`.\n",
    "\n",
    "</details>"
   ],
   "id": "speed-task"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "delivery = pd.read_csv(\"data/delivery_h1.csv\")\n",
    "# добавьте в delivery столбец speed\n",
    "speed_share = ..."
   ],
   "execution_count": null,
   "outputs": [],
   "id": "speed"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Проверка упражнения 2 — выполните после своего решения\n",
    "def test_speed():\n",
    "    \"speed — быстро (до 3 дней), нормально (4–5), долго (6 и больше)\"\n",
    "    assert \"speed\" in delivery.columns, \"в delivery нет столбца speed\"\n",
    "    assert (delivery[\"speed\"] == \"быстро\").sum() != 119, \"3 дня должны считаться «быстро»: интервал (0, 3] включает правую границу\"\n",
    "    assert (delivery[\"speed\"] == \"быстро\").sum() == 309, \"границы не те: сверьте их с условием\"\n",
    "    assert delivery[\"speed\"].isna().sum() != 43, \"заказы, которые везли дольше 10 дней, стали пропусками: последняя граница должна покрывать самый долгий срок\"\n",
    "    assert delivery[\"speed\"].isna().sum() == 36, f\"в speed {delivery['speed'].isna().sum()} пропусков: пропуски должны остаться только там, где срок неизвестен\"\n",
    "\n",
    "\n",
    "def test_share():\n",
    "    \"speed_share — доли по порядку шкалы\"\n",
    "    assert isinstance(speed_share, pd.Series), f\"speed_share — это {type(speed_share).__name__}, а нужен Series\"\n",
    "    assert list(speed_share.index) == [\"быстро\", \"нормально\", \"долго\"], f\"порядок сейчас {list(speed_share.index)}, а нужен быстро, нормально, долго\"\n",
    "    assert abs(speed_share[\"быстро\"] - 309) > 1, \"в speed_share числа заказов, а нужны доли\"\n",
    "    assert abs(speed_share[\"быстро\"] - 309 / 780) < 1e-9 and abs(speed_share[\"долго\"] - 150 / 780) < 1e-9, \"доли не те: нужны доли заказов в каждой группе speed\"\n",
    "\n",
    "_check(test_speed, test_share, names=[\"speed_share\"])"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "speed-check"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Решение</summary>\n",
    "\n",
    "```python\n",
    "delivery = pd.read_csv(\"data/delivery_h1.csv\")\n",
    "delivery[\"speed\"] = pd.cut(delivery[\"days\"], bins=[0, 3, 5, 14], labels=[\"быстро\", \"нормально\", \"долго\"])\n",
    "speed_share = delivery[\"speed\"].value_counts(normalize=True).sort_index()\n",
    "```\n",
    "\n",
    "</details>"
   ],
   "id": "speed-solution"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*После решения упражнения выше:*\n",
    "\n",
    "Пропуски в `days` остались пропусками в `speed` — их 36, и в доли они не вошли."
   ],
   "id": "component-92"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Равные интервалы и равные группы\n",
    "\n",
    "Если вместо списка границ передать число, `pd.cut` сам разделит диапазон на столько интервалов **равной\n",
    "ширины**:"
   ],
   "id": "text-95"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Ветер: три интервала одинаковой ширины\n",
    "pd.cut(moscow[\"wind_ms\"], bins=3).value_counts().sort_index()"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "equal-width"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ширина одинаковая, а строк в интервалах — очень разное число: сильный ветер бывает редко. Левая граница\n",
    "первого интервала (0.487) чуть меньше наименьшего значения (0.5) — pandas сдвигает её, чтобы минимум тоже\n",
    "попал внутрь: ведь левая граница не входит.\n",
    "\n",
    "Функция `pd.qcut` делит иначе — по квантилям: так, чтобы в каждой группе оказалось **примерно поровну строк**.\n",
    "Границы она подбирает сама:"
   ],
   "id": "text-102"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Выручка строки: четыре группы по четверти строк\n",
    "orders = pd.read_csv(\"data/shop_orders.csv\")\n",
    "revenue = orders[\"price\"] * orders[\"quantity\"]\n",
    "pd.qcut(revenue, 4).value_counts().sort_index()"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "qcut"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Границы — знакомые квартили: 535, 1080 и 1580 ₽. Группы получились не совсем равными, потому что многие\n",
    "строки имеют одинаковую выручку, а делить одинаковые значения между группами нельзя.\n",
    "\n",
    "Когда что выбирать: `pd.cut` — когда границы имеют смысл сами по себе (ноль градусов, 1000 рублей,\n",
    "совершеннолетие); `pd.qcut` — когда нужны группы «нижняя четверть, верхняя четверть» относительно самих\n",
    "данных."
   ],
   "id": "text-111"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Упражнение 3. Размер покупки\n",
    "\n",
    "Добавьте в `orders` столбец `size` — четверть, в которую попадает выручка строки `revenue`: «малая», «средняя»,\n",
    "«крупная», «очень крупная». В `big` сохраните строки верхней четверти, а в `big_min` — наименьшую выручку среди них.\n",
    "\n",
    "<details><summary>Подсказка 1</summary>\n",
    "\n",
    "`pd.qcut(revenue, 4, labels=[\"малая\", \"средняя\", \"крупная\", \"очень крупная\"])`.\n",
    "\n",
    "</details>\n",
    "<details><summary>Подсказка 2</summary>\n",
    "\n",
    "`big = orders[orders[\"size\"] == \"очень крупная\"]`; выручка строк — `big[\"price\"] * big[\"quantity\"]`.\n",
    "\n",
    "</details>"
   ],
   "id": "size-task"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# добавьте в orders столбец size\n",
    "big = ...\n",
    "big_min = ..."
   ],
   "execution_count": null,
   "outputs": [],
   "id": "size"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Проверка упражнения 3 — выполните после своего решения\n",
    "def test_size():\n",
    "    \"size — четверть, в которую попала выручка строки\"\n",
    "    assert \"size\" in orders.columns, \"в orders нет столбца size\"\n",
    "    found = set(map(str, orders[\"size\"].unique()))\n",
    "    assert found == {\"малая\", \"средняя\", \"крупная\", \"очень крупная\"}, f\"в size значения {sorted(found)}, а нужны четыре подписи: малая, средняя, крупная, очень крупная\"\n",
    "    assert (orders[\"size\"] == \"малая\").sum() < 2000, \"использован pd.cut — он делит на интервалы равной ширины, и почти все строки попали в первый. Равные по числу строк группы делает pd.qcut\"\n",
    "    assert (orders[\"size\"] == \"малая\").sum() == 612 and (orders[\"size\"] == \"очень крупная\").sum() == 573, \"группы не те: нужны четыре равные по числу строк группы по выручке, подписи — от малой к очень крупной\"\n",
    "\n",
    "\n",
    "def test_big():\n",
    "    \"big — строки верхней четверти, big_min — с какой выручки она начинается\"\n",
    "    assert isinstance(big, pd.DataFrame), f\"big — это {type(big).__name__}, а нужна таблица\"\n",
    "    assert len(big) == 573, f\"в big {len(big)} строк: нужны все строки группы «очень крупная»\"\n",
    "    assert big_min > 1580 and big_min <= 1600, f\"big_min = {big_min!r} — это не наименьшая выручка в верхней четверти\"\n",
    "\n",
    "_check(test_size, test_big, names=[\"big\",\"big_min\"])"
   ],
   "execution_count": null,
   "outputs": [],
   "id": "size-check"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Решение</summary>\n",
    "\n",
    "```python\n",
    "orders[\"size\"] = pd.qcut(revenue, 4, labels=[\"малая\", \"средняя\", \"крупная\", \"очень крупная\"])\n",
    "big = orders[orders[\"size\"] == \"очень крупная\"]\n",
    "big_min = (big[\"price\"] * big[\"quantity\"]).min()\n",
    "```\n",
    "\n",
    "</details>"
   ],
   "id": "size-solution"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Итоги\n",
    "\n",
    "- `pd.cut(столбец, bins=[...], labels=[...])` — группы по своим границам; подписей на одну меньше, чем границ.\n",
    "- `(a, b]` — правая граница входит; `right=False` — входит левая.\n",
    "- Значения за границами становятся `NaN`: берите границы с запасом.\n",
    "- `pd.cut(столбец, bins=n)` — `n` интервалов равной ширины; `pd.qcut(столбец, n)` — `n` групп с примерно\n",
    "  равным числом строк."
   ],
   "id": "text-126"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Подробнее в справочнике:** [pandas/cut-qcut](https://ds-edu-trainer.vercel.app/reference/pandas/cut-qcut) · [pandas/category](https://ds-edu-trainer.vercel.app/reference/pandas/category)"
   ],
   "id": "more"
  }
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