{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "948d7cae-39ae-4cab-8eee-8726af623727",
   "metadata": {
    "tags": []
   },
   "source": [
    "# **Datetime**\n",
    "## **1 Datetime Object**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1f6ee828-a2bb-46ba-a376-d005328b18cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2002-08-02'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "_date = \"\".join(re.findall(r'[\\d]{8}', '20020802'))\n",
    "_date = f\"{_date[:4]}-{_date[4:6]}-{_date[6:]}\"\n",
    "_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "91d7b965-9353-4a8f-b3ae-c0ec6764a0a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import datetime\n",
    "def date_to_string(_date:any=None, datetime_obj:bool=False):\n",
    "    r\"\"\"date 객체를 string 으로 자동변환\n",
    "    _date    : 날짜객체\n",
    "    datetime : datetime 객체로 출력\n",
    "    return :: '2000-01-01'\"\"\"\n",
    "    _return = None\n",
    "    if type(_date) == datetime.date:\n",
    "        _return = _date.isoformat()\n",
    "    elif type(_date) == datetime.datetime:\n",
    "        _return = _date.date()\n",
    "    elif type(_date) == str:\n",
    "        # '20020802'\n",
    "        _check = \"\".join(re.findall(r'[\\d]{8}', _date))\n",
    "        if len(_check) == 8:\n",
    "            _return = f\"{_check[:4]}-{_check[4:6]}-{_check[6:]}\"\n",
    "            \n",
    "        for punct_string in ['-','/',',', '.']:\n",
    "            if _date.find(punct_string) != -1:\n",
    "                _return = \"-\".join(list(map(lambda x : (f'{x:0>2}'), _date.split(punct_string))))\n",
    "            else:\n",
    "                pass\n",
    "\n",
    "    assert _return is not None, f'TypeError : {_date} 를 분석할 수 없습니다'\n",
    "    if datetime_obj:\n",
    "        # datetime.datetime.strptime('09/19/22 13:55:26', '%m/%d/%y %H:%M:%S')\n",
    "        return datetime.datetime.strptime(_return, '%Y-%m-%d')\n",
    "    return _return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2eb50909-b5a5-4d48-ad67-5ea5b7a53c71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2022-01-01'"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_ = datetime.date.today()\n",
    "_ = '20220101'\n",
    "date_to_string(_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cf4596c-d59c-407d-8a91-fa202a6d3449",
   "metadata": {
    "tags": []
   },
   "source": [
    "# **Django ORM Datetime**\n",
    "- 일반적 ORM 객체등은 원칙적 **datetime.dateime** 객체이다 \n",
    "- **datetime.date** 객체로만 관리되면 문제가 없지만\n",
    "- **datetime.datetime** 에서 **date** 만 활용하는 경우에도 시간대가 다르면 날짜도 다르게 출력\n",
    "- **Pandas Datetime Index 객체** 는 추가적 편리한 메소드들을 제공\n",
    "\n",
    "## **1 tzinfo Convert : datetime**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "11357004-d020-4d08-9626-b9e8e3402a91",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Django ORM 데이터 tz_info 변경 및 확인\n",
    "# Django DateTime => datetime.datetime 객체를 출력\n",
    "import pytz\n",
    "seoul = pytz.timezone(\"Asia/Seoul\")\n",
    "# time_check = PriceKRX.objects.all().last().datetime\n",
    "# time_check.replace(tzinfo=seoul)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fb239b5-104c-46ff-ad52-7df189f32d75",
   "metadata": {
    "tags": []
   },
   "source": [
    "## **2 tzinfo Convert : pd.DataFrame**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4b4203ab-5826-4253-8ac1-827861bbd117",
   "metadata": {},
   "outputs": [],
   "source": [
    "# DataFrame 의 tz_info 변경\n",
    "import pytz\n",
    "import pandas as pd\n",
    "seoul = pytz.timezone(\"Asia/Seoul\")\n",
    "# df.datetime = list(map(\n",
    "#     lambda x : seoul.localize(x), \n",
    "#     df.datetime.tolist())\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ec1d402e-7a82-4905-a1dc-14b996694168",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/buffet/Coding/venv/Nlp/lib/python3.10/site-packages/krxstock/shinhan/data.py:51: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:,'수량'] = list(map(lambda x : int(x)*(-1), df.loc[:,'수량']))\n",
      "/home/buffet/Coding/venv/Nlp/lib/python3.10/site-packages/krxstock/shinhan/data.py:51: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:,'수량'] = list(map(lambda x : int(x)*(-1), df.loc[:,'수량']))\n",
      "/home/buffet/Coding/venv/Nlp/lib/python3.10/site-packages/krxstock/shinhan/data.py:78: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:,'비중'] = list(map(lambda x : float(x.replace('%',''))/100, df.loc[:,'비중']))\n",
      "/home/buffet/Coding/venv/Nlp/lib/python3.10/site-packages/krxstock/shinhan/data.py:80: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  df.loc[:,'시간'] = pd.DatetimeIndex(df.loc[:,'시간'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Timestamp('2023-03-30 15:30:00+0900', tz='Asia/Seoul')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pytz\n",
    "from krxstock.shinhan import get_invest\n",
    "seoul = pytz.timezone(\"Asia/Seoul\")\n",
    "df = get_invest(\"005930\")\n",
    "df[\"시간\"][0].replace(tzinfo=seoul)\n",
    "\n",
    "# from krx.tasks import get_trading_invest\n",
    "# get_trading_invest()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fa65479c-8b5e-434e-a282-c4e9a164416a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from glob import glob\n",
    "# from krx.tasks import get_trader_price\n",
    "# # files = glob(\"../jupyter/data/trade/csv/*.*\")\n",
    "# # get_trader_price(files[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd61cee2-5c9f-455a-abd7-085e04e42170",
   "metadata": {
    "tags": []
   },
   "source": [
    "# **Python Datetime**\n",
    "## **1 tzinfo Convert : datetime**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8a6cad02-6516-4532-a17c-8e3fb0a99af7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "06/12/2018, 18:55:22\n",
      "12 Jun, 2018\n",
      "12 June, 2018\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "timestamp = 1528797322\n",
    "date_time = datetime.datetime.fromtimestamp(timestamp)\n",
    "print( date_time.strftime(\"%m/%d/%Y, %H:%M:%S\") )\n",
    "print( date_time.strftime(\"%d %b, %Y\") )\n",
    "print( date_time.strftime(\"%d %B, %Y\") )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c2b0a144-6548-4bdd-a250-6c192b8786a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-03-30\n",
      "2000-01-01 00:00:00\n",
      "2018-09-19 13:55:26\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2023, 3, 23, 9, 54, 38, 820579)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# datetime 모듈의 활용\n",
    "import datetime\n",
    "print( datetime.datetime.today().strftime('%Y-%m-%d') )\n",
    "print( datetime.datetime(year=2000, month=1, day=1) )\n",
    "print( datetime.datetime.strptime('09/19/18 13:55:26', '%m/%d/%y %H:%M:%S') )\n",
    "datetime.datetime.today() - datetime.timedelta(days=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8a8b9321-75c2-4c36-9710-2843325bf893",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2018, 5, 1, 0, 0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "dateString = \"7-May-2018\"\n",
    "dateFormatter = \"%u-%b-%Y\"\n",
    "datetime.strptime(dateString, dateFormatter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "81de2e7e-8afb-4253-abac-faff5bb3e6a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2019-12-13 00:00'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_text = '2019.12.13.AM 00:00'\n",
    "import pandas as pd\n",
    "pd.to_datetime(time_text, format='%Y.%m.%d.%p %H:%M').strftime('%Y-%m-%d %H:%M')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab376e6a-146f-44c1-8a03-7a2fa2809b15",
   "metadata": {},
   "source": [
    "## **2 Lambda**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d684a414-7f7a-42eb-b84b-b307411df798",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n",
      "False\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['ok', None, None]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if given numbers are in range using lambda function\n",
    "test = lambda x : True if (x > 10 and x < 20) else False\n",
    "print(test(12))\n",
    "print(test(3))\n",
    "print(test(24))\n",
    "\n",
    "# https://thispointer.com/python-how-to-use-if-else-elif-in-lambda-functions/\n",
    "# https://stackoverflow.com/questions/60261960/how-to-use-lambda-if-else-in-map-on-list-in-python\n",
    "list(map(lambda x : 'ok' if x == \"apple\" else None, ['apple', 'banana', 'cherry']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0a3d37ae-9ed5-4a73-b874-72ffb1586792",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = {\n",
    "    \"A\":1,\n",
    "    \"B\":2,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ddc194f3-7d57-4a3d-ab61-47dcc0b242ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.get(\"d\", 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5c6763eb-0dc8-4348-9bbc-2e6733c13b19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': 1, 'B': 2}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "331cbb4e-e9b8-47b7-9a5a-4c667c63ad1e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
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   "file_extension": ".py",
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